diff --git a/404.html b/404.html index 8bc9f4bf6ac482169cabe2d32f89f3283cae939a..f9eae2395cc2c0357943441f408106a6ebdefa0f 100755 --- a/404.html +++ b/404.html @@ -15,7 +15,6 @@ .norsTitle {font-size: 22px; font-family: Microsoft Yahei; font-weight: normal; color: #333; margin: 35px 0 25px 0; } -
@@ -32,4 +31,4 @@
- + \ No newline at end of file diff --git a/develop/doc/_sources/design/cluster_train/master_server.md.txt b/develop/doc/_sources/design/cluster_train/master_server.md.txt index bb8307652587b4dc56cd668a3a5e64722734d194..4bf3c506f101361875043f8bfd97972b8c981a22 100644 --- a/develop/doc/_sources/design/cluster_train/master_server.md.txt +++ b/develop/doc/_sources/design/cluster_train/master_server.md.txt @@ -10,7 +10,7 @@ A dataset is a list of files in *RecordIO* format. A RecordIO file consists of c ## Task Queue -As mentioned in [distributed training design doc](./README.md), a *task* is a data shard that the master server assigns to the trainer process to train on. A task consists of one or multiple *blocks* from one or multiple files. The master server maintains *task queues* to track the training progress. +As mentioned in [distributed training design doc](./README.md), a *task* is a data shard that the master server assigns to the trainer process to train on. A task consists of one or multiple *chunks* from one or multiple files. The master server maintains *task queues* to track the training progress. ### Task Queue Creation @@ -21,23 +21,23 @@ As mentioned in [distributed training design doc](./README.md), a *task* is a da func (m *RPCServer) ReportDataset(Paths []string, dummy *int) error { } ``` -1. The master server will scan through each RecordIO file to generate the *block index* and know how many blocks does each file have. A block can be referenced by the file path and the index of the block within the file. The block index is in memory data structure that enables fast access to each block, and the index of the block with the file is an integer start from 0, representing the n-th block within the file. +1. The master server will scan through each RecordIO file to generate the *chunk index* and know how many chunks does each file have. A chunk can be referenced by the file path and the index of the chunk within the file. The chunk index is in memory data structure that enables fast access to each chunk, and the index of the chunk with the file is an integer start from 0, representing the n-th chunk within the file. - The definition of the block is: + The definition of the chunk is: ```go - type Block struct { - Idx int // index of the block within the file + type Chunk struct { + Idx int // index of the chunk within the file Path string - Index recordio.Index // block index + Index recordio.Index // chunk index } ``` -1. Blocks are grouped into tasks, and tasks are filled into the todo queue. The pending queue and the done queue are initialized with no element. +1. Chunks are grouped into tasks, and tasks are filled into the todo queue. The pending queue and the done queue are initialized with no element. The definition of the task is: ```go type Task struct { Index int - Blocks []Block + Chunks []Chunk } ``` diff --git a/develop/doc/_sources/design/cluster_train/pserver_client.md.txt b/develop/doc/_sources/design/cluster_train/pserver_client.md.txt index 392bab25e9de6bf5aa7cc1b0ad345ef12f1d9e5d..b3e4079010490b69db1de28157f0cab80cad2381 100644 --- a/develop/doc/_sources/design/cluster_train/pserver_client.md.txt +++ b/develop/doc/_sources/design/cluster_train/pserver_client.md.txt @@ -55,7 +55,7 @@ The trainer select process is encapsulated in the C API function: ```c int paddle_begin_init_params(paddle_pserver_client* client, const char* config_proto); ``` -The selected trainer's call to `paddle_begin_init_params` will return with 1, and the other trainers' call to `paddle_begin_init_params` will block until initialization is done, and return 0. As illustrated below: +The selected trainer's call to `paddle_begin_init_params` will return with 1, and the other trainers' call to `paddle_begin_init_params` will return 0. `paddle_get_params` will be blocked until initialization is completed. As illustrated below: @@ -89,16 +89,13 @@ void paddle_pserver_client_release(paddle_pserver_client* client); * * paddle_begin_init_params will be called from multiple trainers, * only one trainer will be selected to initialize the parameters on - * parameter servers. Other trainers will be blocked until the - * initialization is done, and they need to get the initialized + * parameter servers. Other trainers need to get the initialized * parameters from parameter servers using @paddle_get_params. * - * @param pserver_config_proto serialized parameter server configuration in - * Protocol Buffers format. * @return 1 if the trainer is selected to initialize parameter * servers, otherwise 0. */ -int paddle_begin_init_params(paddle_pserver_client* client, const char* pserver_config_proto); +int paddle_begin_init_params(paddle_pserver_client* client); /** * @brief paddle_init_param initializes the parameter on parameter @@ -106,12 +103,13 @@ int paddle_begin_init_params(paddle_pserver_client* client, const char* pserver_ * * @param param the parameter to initialize. * @param param_config_proto the configuration for the parameter. + * @param config_len the length of param_config_proto * @return 0 if successful, otherwise -1. On failure, the trainer * needs to restart the entire initialization process (starting from * @paddle_begin_init_param). Or simply exit the program and wait for * the cluster management system to restart the trainer. */ -int paddle_init_param(paddle_pserver_client* client, paddle_parameter params, const char* param_config_proto); +int paddle_init_param(paddle_pserver_client* client, paddle_parameter param, const unsigned char* param_config_proto, int config_len); /** * @brief paddle_finish_init_params tells parameter servers client has @@ -138,6 +136,9 @@ int paddle_send_grads(paddle_pserver_client* client, const paddle_gradient* grad /** * @brief paddle_get_params gets parameters from parameter servers. * + * paddle_get_params will block until parameters are initialized on + * the parameter servers. + * * @param names the array of names of the parameters to get. * @param dst the destination array of parameters to save to. * @param len the length of the names array and the paddle_parameter diff --git a/develop/doc/_sources/design/parameters_in_cpp.md.txt b/develop/doc/_sources/design/parameters_in_cpp.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..b6f99bc7d9d6fafacb0a4bcff806b65d9aef98cc --- /dev/null +++ b/develop/doc/_sources/design/parameters_in_cpp.md.txt @@ -0,0 +1,41 @@ +# Design Doc: The C++ Class `Parameters` + +`Parameters` is a concept we designed in Paddle V2 API. `Parameters` is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of `Parameter` in [api.md](./api.md). + +We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation: +* We just use `memcpy` to share Parameters between topologies, but this is very inefficient. +* We did not implement share Parameters while training. We just trigger `memcpy` when start training. + +It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with `Parameters`: + +1. `paddle::Parameter`. A `Parameters` is a container for `paddle::Parameter`. +It is evident that we should use `paddle::Parameter` when developing `Parameters`. +However, the `Parameter` class contains many functions and does not have a clear interface. +It contains `create/store Parameter`, `serialize/deserialize`, `optimize(i.e SGD)`, `randomize/zero`. +When we developing `Parameters`, we only use `create/store Parameter` functionality. +We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation. + +2. `paddle::GradientMachine` and its sub-classes, e.g., `paddle::MultiGradientMachine`, `paddle::NeuralNetwork`. +We should pass `Parameters` to `paddle::GradientMachine` when `forward/backward` to avoid `memcpy` between topologies. +Also, we should handle multi-GPU/CPU training, because `forward` and `backward` would perform on multi-GPUs and multi-CPUs. +`Parameters` should dispatch the parameter value to each device, and gather the parameter gradient from each device. + +3. `paddle::ParameterUpdater`. The ParameterUpdater is used to update parameters in Paddle. +So `Parameters` should be used by `paddle::ParameterUpdater`, and `paddle::ParameterUpdater` should optimize `Parameters` (by SGD). + + +The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one. + +1. Clean `paddle::Parameter` interface. Extract the functionalities of `paddle::Parameter` to prepare for the implementation of Parameters. + +2. Implementation a `Parameters` class. It just stores the `paddle::Parameter` inside. Make `GradientMachine` uses `Parameters` as a class member. + +3. Make `Parameters` support Multi-CPU and Multi-GPU training to prepare for sharing `Parameter` between topologies. +Because we need share `Parameters` between topologies, it is `Parameters`'s response to exchange Parameters between GPUs. +`GradientMachine` should not handle how to exchange Parameters because `GradientMachine` only used to train one topology and we need to support train many topologies in Paddle, i.e., there could be many GradientMachines use one `Parameters`. + * We should use a global function to exchange Parameters between GPUs, not a member function in `Parameters`. The `MultiGradientMachine` invoke this function, which uses `Parameters` as this function inputs. + * The MultiGradientMachine contains many functionalities. Extracting the Parameters exchanging logic could make MultiGradientMachine clearer and simpler. + +4. Make `Parameters` as an argument for `forward/backward` function, not a data member for `GradientMachine`. For example, `forward` could be `forward(const Parameters& params, ...)` and `backward` could be `backward(Parameters* params, ...)`. After this step, Paddle could share `Parameters` between topologies. + +5. `ParameterUpdater` is invoked by `GradientMachine` and `Trainer`, but it updates `Parameters`. In the end of this code refactoring, we could change `ParameterUpdater` directly uses `Parameters` to make `ParameterUpdater`'s implementation clear. diff --git a/develop/doc/api/v2/config/evaluators.html b/develop/doc/api/v2/config/evaluators.html index 7e084cd1d29b9b36101513078af3360b10a216f0..1403ac262c9c840a756b7fadeb5459cac9bb09e2 100644 --- a/develop/doc/api/v2/config/evaluators.html +++ b/develop/doc/api/v2/config/evaluators.html @@ -189,15 +189,15 @@

Classification

classification_error

-
+
-class paddle.v2.evaluator.classification_error(*args, **kwargs)
+paddle.v2.evaluator.classification_error(*args, **xargs)

Classification Error Evaluator. It will print error rate for classification.

The classification error is:

\[classification\_error = \frac{NumOfWrongPredicts}{NumOfAllSamples}\]

The simple usage is:

-
eval =  classification_error_evaluator(input=prob,label=lbl)
+
eval =  classification_evaluator.error(input=prob,label=lbl)
 

 

 
@@ -228,12 +228,12 @@ important this sample is.

 
auc

-

+

 

-class paddle.v2.evaluator.auc(*args, **kwargs)

+paddle.v2.evaluator.auc(*args, **xargs)
 
Auc Evaluator which adapts to binary classification.

 
The simple usage:

-
eval = auc_evaluator(input, label)
+
eval = evaluator.auc(input, label)
 

 

 

 
 

@@ -256,12 +256,12 @@ important this sample is.

 
ctc_error

-

+

 

-class paddle.v2.evaluator.ctc_error(*args, **kwargs)

+paddle.v2.evaluator.ctc_error(*args, **xargs)
 
This evaluator is to calculate sequence-to-sequence edit distance.

 
The simple usage is :

-
eval = ctc_error_evaluator(input=input, label=lbl)
+
eval = ctc_evaluator.error(input=input, label=lbl)
 

 

 

 
 

@@ -283,32 +283,68 @@ label for ctc

 
chunk

-

+

 

-class paddle.v2.evaluator.chunk(*args, **kwargs)

+paddle.v2.evaluator.chunk(*args, **xargs)
 
Chunk evaluator is used to evaluate segment labelling accuracy for a
-sequence. It calculates the chunk detection F1 score.

-
A chunk is correctly detected if its beginning, end and type are correct.
-Other chunk type is ignored.

-
For each label in the label sequence, we have:

-
tagType = label % numTagType
-chunkType = label / numTagType
-otherChunkType = numChunkTypes
+sequence. It calculates precision, recall and F1 scores for the chunk detection.

+
To use chunk evaluator, several concepts need to be clarified firstly.

+
    
+
  • Chunk type is the type of the whole chunk and a chunk consists of one or several words.  (For example in NER, ORG for organization name, PER for person name etc.)
    • 
+
  • Tag type indicates the position of a word in a chunk. (B for begin, I for inside, E for end, S for single)
    • 
+

+
We can name a label by combining tag type and chunk type. (ie. B-ORG for begining of an organization name)

+
The construction of label dictionary should obey the following rules:

+
    
+
  • Use one of the listed labelling schemes. These schemes differ in ways indicating chunk boundry.
    • 
+

+
Scheme    Description
+plain    Use the same label for the whole chunk.
+IOB      Two labels for chunk type X, B-X for chunk begining and I-X for chunk inside.
+IOE      Two labels for chunk type X, E-X for chunk ending and I-X for chunk inside.
+IOBES    Four labels for chunk type X, B-X for chunk begining, I-X for chunk inside, E-X for chunk end and S-X for single word chunk.
+

+

+
To make it clear, let’s illustrate by an NER example.
+Assuming that there are three named entity types including ORG, PER and LOC which are called ‘chunk type’ here,
+if ‘IOB’ scheme were used, the label set will be extended to a set including B-ORG, I-ORG, B-PER, I-PER, B-LOC, I-LOC and O,
+in which B-ORG for begining of ORG and I-ORG for inside of ORG.
+Prefixes which are called ‘tag type’ here are added to chunk types and there are two tag types including B and I.
+Of course, the training data should be labeled accordingly.

+
    
+
  • Mapping is done correctly by the listed equations and assigning protocol.
    • 
+

+
The following table are equations to extract tag type and chunk type from a label.

+
tagType = label % numTagType
+chunkType = label / numTagType
+otherChunkType = numChunkTypes
+

+

+
The following table shows the mapping rule between tagType and tag type in each scheme.

+
Scheme Begin Inside End   Single
+plain  0     -      -     -
+IOB    0     1      -     -
+IOE    -     0      1     -
+IOBES  0     1      2     3
 

 

-
The total number of different labels is numTagType*numChunkTypes+1.
-We support 4 labelling scheme.
-The tag type for each of the scheme is shown as follows:

-
Scheme Begin Inside End   Single
-plain  0     -      -     -
-IOB    0     1      -     -
-IOE    -     0      1     -
-IOBES  0     1      2     3
+
Continue the NER example, and the label dict should look like this to satify above equations:

+
B-ORG  0
+I-ORG  1
+B-PER  2
+I-PER  3
+B-LOC  4
+I-LOC  5
+O      6
 

 

-
‘plain’ means the whole chunk must contain exactly the same chunk label.

+
In this example, chunkType has three values: 0 for ORG, 1 for PER, 2 for LOC, because the scheme is
+“IOB” so tagType has two values: 0 for B and 1 for I.
+Here we will use I-LOC to explain the above mapping rules in detail.
+For I-LOC, the label id is 5, so we can get tagType=1 and chunkType=2, which means I-LOC is a part of NER chunk LOC
+and the tag is I.

 
The simple usage is:

-
eval = chunk_evaluator(input, label, chunk_scheme, num_chunk_types)
+
eval = evaluator.chunk(input, label, chunk_scheme, num_chunk_types)
 

 

 

 
 

@@ -333,9 +369,9 @@ The tag type for each of the scheme is shown as follows:

 
precision_recall

-

+

 

-class paddle.v2.evaluator.precision_recall(*args, **kwargs)

+paddle.v2.evaluator.precision_recall(*args, **xargs)
 
An Evaluator to calculate precision and recall, F1-score.
 It is adapt to the task with multiple labels.

 
    
@@ -345,7 +381,7 @@ F1-score of all labels.
 F1-score of this label.
 

 
The simple usage:

-
eval = precision_recall_evaluator(input, label)
+
eval = precision_evaluator.recall(input, label)
 

 

 

 
 

@@ -372,13 +408,13 @@ F1-score of this label.
Rank

 
pnpair

-

+

 

-class paddle.v2.evaluator.pnpair(*args, **kwargs)

+paddle.v2.evaluator.pnpair(*args, **xargs)
 
Positive-negative pair rate Evaluator which adapts to rank task like
 learning to rank. This evaluator must contain at least three layers.

 
The simple usage:

-
eval = pnpair_evaluator(input, info, label)
+
eval = evaluator.pnpair(input, info, label)
 

 

 

 
 

@@ -405,12 +441,12 @@ learning to rank. This evaluator must contain at least three layers.
Utils

 
sum

-

+

 

-class paddle.v2.evaluator.sum(*args, **kwargs)

+paddle.v2.evaluator.sum(*args, **xargs)
 
An Evaluator to sum the result of input.

 
The simple usage:

-
eval = sum_evaluator(input)
+
eval = evaluator.sum(input)
 

 

 

 
 

@@ -432,12 +468,12 @@ learning to rank. This evaluator must contain at least three layers.

 
column_sum

-

+

 

-class paddle.v2.evaluator.column_sum(*args, **kwargs)

+paddle.v2.evaluator.column_sum(*args, **xargs)
 
This Evaluator is used to sum the last column of input.

 
The simple usage is:

-
eval = column_sum_evaluator(input, label)
+
eval = column_evaluator.sum(input, label)
 

 

 

 
 

@@ -460,12 +496,12 @@ learning to rank. This evaluator must contain at least three layers.
Print

 
classification_error_printer

-

+

 

-class paddle.v2.evaluator.classification_error_printer(*args, **kwargs)

+paddle.v2.evaluator.classification_error_printer(*args, **xargs)
 
This Evaluator is used to print the classification error of each sample.

 
The simple usage is:

-
eval = classification_error_printer_evaluator(input)
+
eval = classification_error_evaluator.printer(input)
 

 

 

 
 

@@ -486,13 +522,13 @@ learning to rank. This evaluator must contain at least three layers.

 
gradient_printer

-

+

 

-class paddle.v2.evaluator.gradient_printer(*args, **kwargs)

+paddle.v2.evaluator.gradient_printer(*args, **xargs)
 
This Evaluator is used to print the gradient of input layers. It contains
 one or more input layers.

 
The simple usage is:

-
eval = gradient_printer_evaluator(input)
+
eval = gradient_evaluator.printer(input)
 

 

 

 
 

@@ -512,14 +548,14 @@ one or more input layers.

 
maxid_printer

-

+

 

-class paddle.v2.evaluator.maxid_printer(*args, **kwargs)

+paddle.v2.evaluator.maxid_printer(*args, **xargs)
 
This Evaluator is used to print maximum top k values and their indexes
 of each row of input layers. It contains one or more input layers.
 k is specified by num_results.

 
The simple usage is:

-
eval = maxid_printer_evaluator(input)
+
eval = maxid_evaluator.printer(input)
 

 

 

 
 

@@ -541,9 +577,9 @@ It is 1 by default.

 
maxframe_printer

-

+

 

-class paddle.v2.evaluator.maxframe_printer(*args, **kwargs)

+paddle.v2.evaluator.maxframe_printer(*args, **xargs)
 
This Evaluator is used to print the top k frames of each input layers.
 The input layers should contain sequences info or sequences type.
 k is specified by num_results.
@@ -553,7 +589,7 @@ It contains one or more input layers.

 
The width of each frame is 1.

 
The simple usage is:

-
eval = maxframe_printer_evaluator(input)
+
eval = maxframe_evaluator.printer(input)
 

 

 

 
 
 

@@ -573,9 +609,9 @@ It contains one or more input layers.

 
seqtext_printer

-

+

 

-class paddle.v2.evaluator.seqtext_printer(*args, **kwargs)

+paddle.v2.evaluator.seqtext_printer(*args, **xargs)
 
Sequence text printer will print text according to index matrix and a
 dictionary. There can be multiple input to this layer:

 
1. If there is no id_input, the input must be a matrix containing
@@ -607,7 +643,7 @@ the sequence of indices;

 
Typically SequenceTextPrinter layer takes output of maxid or RecurrentGroup
 with maxid (when generating) as an input.

 
The simple usage is:

-
eval = seqtext_printer_evaluator(input=maxid,
+
eval = seqtext_evaluator.printer(input=maxid,
                                  id_input=sample_id,
                                  dict_file=dict_file,
                                  result_file=result_file)
@@ -647,13 +683,13 @@ Default is True. No space is added if set to False.
 

 

 
value_printer

-

+

 

-class paddle.v2.evaluator.value_printer(*args, **kwargs)

+paddle.v2.evaluator.value_printer(*args, **xargs)
 
This Evaluator is used to print the values of input layers. It contains
 one or more input layers.

 
The simple usage is:

-
eval = value_printer_evaluator(input)
+
eval = value_evaluator.printer(input)
 

 

 

 
 

diff --git a/develop/doc/api/v2/config/layer.html b/develop/doc/api/v2/config/layer.html
index cc431b0f0573523bebac84138c869f47099791dd..5f78f5798f0f417a8134aebe82c481dfd48ce87a 100644
--- a/develop/doc/api/v2/config/layer.html
+++ b/develop/doc/api/v2/config/layer.html
@@ -189,35 +189,10 @@
 
Data layer

 
data

-

+

 

-class paddle.v2.layer.data(name, type, **kwargs)

-
Define DataLayer For NeuralNetwork.

-
The example usage is:

-
data = paddle.layer.data(name="input", type=paddle.data_type.dense_vector(1000))
-

-

-

 

---
-
-
-
-
-
-
-
-



Parameters:
    
-
  • name (basestring) – Name of this data layer.
    • 
-
  • type – Data type of this data layer
    • 
-
  • height (int|None) – Height of this data layer, used for image
    • 
-
  • width (int|None) – Width of this data layer, used for image
    • 
-
  • layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
    • 
-

-
Returns:
paddle.v2.config_base.Layer object.

-
Return type:
paddle.v2.config_base.Layer

-

+paddle.v2.layer.data
+
alias of name
@@ -228,12 +203,12 @@

fc

-class paddle.v2.layer.fc(*args, **kwargs)
+class paddle.v2.layer.fc

Helper for declare fully connected layer.

The example usage is:

fc = fc(input=layer,
               size=1024,
-              act=paddle.v2.Activation.Linear(),
+              act=paddle.v2.activation.Linear(),
               bias_attr=False)
@@ -250,7 +225,7 @@
  • name (basestring) – The Layer Name.
  • input (paddle.v2.config_base.Layer|list|tuple) – The input layer. Could be a list/tuple of input layer.
  • size (int) – The layer dimension.
    • -
  • act (paddle.v2.Activation.Base) – Activation Type. Default is tanh.
    • +
  • act (paddle.v2.activation.Base) – Activation Type. Default is tanh.
  • param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute|list.
  • bias_attr (paddle.v2.attr.ParameterAttribute|None|Any) – The Bias Attribute. If no bias, then pass False or something not type of paddle.v2.attr.ParameterAttribute. None will get a @@ -274,13 +249,13 @@ default Bias.

    • selective_fc

    -class paddle.v2.layer.selective_fc(*args, **kwargs)
    +class paddle.v2.layer.selective_fc

    Selectived fully connected layer. Different from fc, the output of this layer maybe sparse. It requires an additional input to indicate several selected columns for output. If the selected columns is not specified, selective_fc acts exactly like fc.

    The simple usage is:

    -
    sel_fc = selective_fc(input=input, size=128, act=paddle.v2.Activation.Tanh())
+
    sel_fc = selective_fc(input=input, size=128, act=paddle.v2.activation.Tanh())
 
    
 
    
 
@@ -294,7 +269,7 @@ specified, selective_fc acts exactly like fc.
    
 sparse binary matrix, and treat as the mask of selective fc.
 If is None, acts exactly like fc.
  • size (int) – The layer dimension.
    • 
-
  • act (paddle.v2.Activation.Base) – Activation Type. Default is tanh.
    • 
+
  • act (paddle.v2.activation.Base) – Activation Type. Default is tanh.
  • param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute.
  • bias_attr (paddle.v2.attr.ParameterAttribute|None|Any) – The Bias Attribute. If no bias, then pass False or
 something not type of paddle.v2.attr.ParameterAttribute. None will get a
@@ -321,7 +296,7 @@ default Bias.
    • conv_operator
    
 
    
-class paddle.v2.layer.conv_operator(**kwargs)
    
+class paddle.v2.layer.conv_operator
 
    Different from img_conv, conv_op is an Operator, which can be used
 in mixed. And conv_op takes two inputs to perform convolution.
 The first input is the image and the second is filter kernel. It only
@@ -369,7 +344,7 @@ the filter’s shape can be (filter_size, filter_size_y).
 
    conv_projection
    
 
    
 
    
-class paddle.v2.layer.conv_projection(**kwargs)
    
+class paddle.v2.layer.conv_projection
 
    Different from img_conv and conv_op, conv_projection is an Projection,
 which can be used in mixed and conat. It use cudnn to implement
 conv and only support GPU mode.
    
@@ -417,7 +392,7 @@ the filter’s shape can be (filter_size, filter_size_y).
 
    conv_shift
    
 
    
 
    
-class paddle.v2.layer.conv_shift(*args, **kwargs)
    
+class paddle.v2.layer.conv_shift
 
    
 
    This layer performs cyclic convolution for two input. For example:
    
 
      
@@ -470,7 +445,7 @@ the right size (which is the end of array) to the left.
 
      img_conv
      
 
      
 
      
-class paddle.v2.layer.img_conv(*args, **kwargs)
      
+class paddle.v2.layer.img_conv
 
      Convolution layer for image. Paddle can support both square and non-square
 input currently.
      
 
      The details of convolution layer, please refer UFLDL’s convolution .
      
@@ -494,7 +469,7 @@ rest channels will be processed by rest group of filters.
      
                       num_channels=8,
                       num_filters=16, stride=1,
                       bias_attr=False,
-                      act=paddle.v2.Activation.Relu())
+                      act=paddle.v2.activation.Relu())
 
 
 
    
 
 
 
 
 
    
@@ -510,7 +485,7 @@ two image dimension.
 currently supports rectangular filters, the filter’s
 shape will be (filter_size, filter_size_y).
  • num_filters – Each filter group’s number of filter
    • 
-
  • act (paddle.v2.Activation.Base) – Activation type. Default is tanh
    • 
+
  • act (paddle.v2.activation.Base) – Activation type. Default is tanh
  • groups (int) – Group size of filters.
  • stride (int|tuple|list) – The x dimension of the stride. Or input a tuple for two image
 dimension.
    • 
@@ -548,7 +523,7 @@ otherwise layer_type has to be either “exconv” or
 
    context_projection
    
 
    
-class paddle.v2.layer.context_projection(**kwargs)
    
+class paddle.v2.layer.context_projection
 
    Context Projection.
    
 
    It just simply reorganizes input sequence, combines “context_len” sequence
 to one context from context_start. “context_start” will be set to
@@ -591,7 +566,7 @@ parameter attribute is set by this parameter.
 
    img_pool
    
 
    
 
    
-class paddle.v2.layer.img_pool(*args, **kwargs)
    
+class paddle.v2.layer.img_pool
 
    Image pooling Layer.
    
 
    The details of pooling layer, please refer ufldl’s pooling .
    
 
      
@@ -655,7 +630,7 @@ Defalut is True. If set false, Otherwise use floor.
 
      spp
      
 
      
 
      
-class paddle.v2.layer.spp(*args, **kwargs)
      
+class paddle.v2.layer.spp
 
      Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
 The details please refer to
 Kaiming He’s paper.
      
@@ -695,7 +670,7 @@ The details please refer to
 
      maxout
      
 
      
 
      
-class paddle.v2.layer.maxout(*args, **kwargs)
      
+class paddle.v2.layer.maxout
 
      
 
      A layer to do max out on conv layer output.
      
 
        
@@ -752,7 +727,7 @@ automatically from previous output.
 
        img_cmrnorm
        
 
        
 
        
-class paddle.v2.layer.img_cmrnorm(*args, **kwargs)
        
+class paddle.v2.layer.img_cmrnorm
 
        Response normalization across feature maps.
 The details please refer to
 Alex’s paper.
        
@@ -791,7 +766,7 @@ num_channels is None, it will be set automatically.
 
        batch_norm
        
 
        
 
        
-class paddle.v2.layer.batch_norm(*args, **kwargs)
        
+class paddle.v2.layer.batch_norm
 
        Batch Normalization Layer. The notation of this layer as follow.
        
 
        \(x\) is the input features over a mini-batch.
        
 
        
@@ -805,7 +780,7 @@ y_i &\gets \gamma \hat{x_i} + \beta \qquad &//\ scale\ and\ shift\end{sp
 
        The details of batch normalization please refer to this
 paper.
        
 
        The example usage is:
        
-
        norm = batch_norm(input=net, act=paddle.v2.Activation.Relu())
+
        norm = batch_norm(input=net, act=paddle.v2.activation.Relu())
 
        
 
        
 
    
 
 
 
 
    
@@ -825,7 +800,7 @@ automaticly select cudnn_batch_norm for GPU and
 batch_norm for CPU. Otherwise, select batch norm
 type based on the specified type. If you use cudnn_batch_norm,
 we suggested you use latest version, such as v5.1.
-
  • act (paddle.v2.Activation.Base) – Activation Type. Better be relu. Because batch
+
  • act (paddle.v2.activation.Base) – Activation Type. Better be relu. Because batch
 normalization will normalize input near zero.
  • num_channels (int) – num of image channels or previous layer’s number of
 filters. None will automatically get from layer’s
@@ -863,7 +838,7 @@ computation, referred to as facotr,
 
    sum_to_one_norm
    
 
    
 
    
-class paddle.v2.layer.sum_to_one_norm(*args, **kwargs)
    
+class paddle.v2.layer.sum_to_one_norm
 
    A layer for sum-to-one normalization,
 which is used in NEURAL TURING MACHINE.
    
 
    
@@ -900,7 +875,7 @@ and \(out\) is a (batchSize x dataDim) output vector.<
 
    cross_channel_norm
    
 
    
 
    
-class paddle.v2.layer.cross_channel_norm(*args, **kwargs)
    
+class paddle.v2.layer.cross_channel_norm
 
    Normalize a layer’s output. This layer is necessary for ssd.
 This layer applys normalize across the channels of each sample to
 a conv layer’s output and scale the output by a group of trainable
@@ -931,7 +906,7 @@ factors which dimensions equal to the channel’s number.
    
 
    recurrent
    
 
    
 
    
-class paddle.v2.layer.recurrent(*args, **kwargs)
    
+class paddle.v2.layer.recurrent
 
    Simple recurrent unit layer. It is just a fully connect layer through both
 time and neural network.
    
 
    For each sequence [start, end] it performs the following computation:
    
@@ -948,7 +923,7 @@ out_{i} = act(in_{i} + out_{i+1} * W) \ \ \text{for} \ start <= i < end\en
 
    • 
-
-
@@ -1153,9 +1128,9 @@ sequence input. This is extremely usefull for attention based model, or
 Neural Turning Machine like models.
    The basic usage (time steps) is:
    def step(input):
-    output = fc_layer(input=layer,
+    output = fc(input=layer,
                       size=1024,
-                      act=LinearActivation(),
+                      act=paddle.v2.activation.Linear(),
                       bias_attr=False)
     return output
 
@@ -1165,8 +1140,8 @@ Neural Turning Machine like models.
    
 
    
 
    You can see following configs for further usages:
    
 
      
-
    • time steps: lstmemory_group, paddle/gserver/tests/sequence_layer_group.conf,                   demo/seqToseq/seqToseq_net.py
      • 
-
    • sequence steps: paddle/gserver/tests/sequence_nest_layer_group.conf
      • 
+
    • time steps: lstmemory_group, paddle/gserver/tests/sequence_group.conf,                   demo/seqToseq/seqToseq_net.py
      • 
+
    • sequence steps: paddle/gserver/tests/sequence_nest_group.conf
      • 
 
    
 
    
 
 
    Parameters:
      
 
    • input (paddle.v2.config_base.Layer) – Input Layer
      • 
-
    • act (paddle.v2.Activation.Base) – activation.
      • 
+
    • act (paddle.v2.activation.Base) – activation.
      • 
 
    • bias_attr (paddle.v2.attr.ParameterAttribute) – bias attribute.
      • 
 
    • param_attr (paddle.v2.attr.ParameterAttribute) – parameter attribute.
      • 
 
    • name (basestring) – name of the layer
      • 
@@ -971,7 +946,7 @@ out_{i} = act(in_{i} + out_{i+1} * W) \ \ \text{for} \ start <= i < end\en
 
      lstmemory
      
 
      
 
      
-class paddle.v2.layer.lstmemory(*args, **kwargs)
      
+class paddle.v2.layer.lstmemory
 
      Long Short-term Memory Cell.
      
 
      The memory cell was implemented as follow equations.
      
 
      
@@ -995,9 +970,9 @@ more details about LSTM.
      
 
    • name (basestring) – The lstmemory layer name.
      • 
 
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
 
    • reverse (bool) – is sequence process reversed or not.
      • 
-
    • act (paddle.v2.Activation.Base) – activation type, paddle.v2.Activation.Tanh by default. \(h_t\)
      • 
-
    • gate_act (paddle.v2.Activation.Base) – gate activation type, paddle.v2.Activation.Sigmoid by default.
      • 
-
    • state_act (paddle.v2.Activation.Base) – state activation type, paddle.v2.Activation.Tanh by default.
      • 
+
    • act (paddle.v2.activation.Base) – activation type, paddle.v2.activation.Tanh by default. \(h_t\)
      • 
+
    • gate_act (paddle.v2.activation.Base) – gate activation type, paddle.v2.activation.Sigmoid by default.
      • 
+
    • state_act (paddle.v2.activation.Base) – state activation type, paddle.v2.activation.Tanh by default.
      • 
 
    • bias_attr (paddle.v2.attr.ParameterAttribute|None|False) – Bias attribute. None means default bias. False means no
 bias.
      • 
 
    • param_attr (paddle.v2.attr.ParameterAttribute|None|False) – Parameter Attribute.
      • 
@@ -1020,7 +995,7 @@ bias.
 
      grumemory
      
 
      
 
      
-class paddle.v2.layer.grumemory(*args, **kwargs)
      
+class paddle.v2.layer.grumemory
 
      Gate Recurrent Unit Layer.
      
 
      The memory cell was implemented as follow equations.
      
 
      1. update gate \(z\): defines how much of the previous memory to
@@ -1060,9 +1035,9 @@ Recurrent Neural Networks on Sequence Modeling.
      
 
    • name (None|basestring) – The gru layer name.
      • 
 
    • input (paddle.v2.config_base.Layer.) – input layer.
      • 
 
    • reverse (bool) – Whether sequence process is reversed or not.
      • 
-
    • act (paddle.v2.Activation.Base) – activation type, paddle.v2.Activation.Tanh by default. This activation
+
    • act (paddle.v2.activation.Base) – activation type, paddle.v2.activation.Tanh by default. This activation
 affects the \({\tilde{h_t}}\).
      • 
-
    • gate_act (paddle.v2.Activation.Base) – gate activation type, paddle.v2.Activation.Sigmoid by default.
+
    • gate_act (paddle.v2.activation.Base) – gate activation type, paddle.v2.activation.Sigmoid by default.
 This activation affects the \(z_t\) and \(r_t\). It is the
 \(\sigma\) in the above formula.
      • 
 
    • bias_attr (paddle.v2.attr.ParameterAttribute|None|False) – Bias attribute. None means default bias. False means no
@@ -1092,7 +1067,7 @@ will get a warning.
      • 
 
      memory
      
 
      
 
      
-class paddle.v2.layer.memory(name, extra_input=None, **kwargs)
      
+class paddle.v2.layer.memory
 
      The memory layers is a layer cross each time step. Reference this output
 as previous time step layer name ‘s output.
      
 
      The default memory is zero in first time step, previous time step’s
@@ -1101,12 +1076,12 @@ output in the rest time steps.
      
 with activation.
      
 
      If boot_with_const_id, then the first time stop is a IndexSlot, the
 Arguments.ids()[0] is this cost_id.
      
-
      If boot_layer is not null, the memory is just the boot_layer’s output.
+
      If boot is not null, the memory is just the boot’s output.
 Set is_seq is true boot layer is sequence.
      
 
      The same name layer in recurrent group will set memory on each time
 step.
      
 
      mem = memory(size=256, name='state')
-state = fc_layer(input=mem, size=256, name='state')
+state = fc(input=mem, size=256, name='state')
 
      
 
      
 
      If you do not want to specify the name, you can equivalently use set_input()
@@ -1122,18 +1097,18 @@ name of the layer which this memory remembers.
 
    • size (int) – size of memory.
      • 
 
    • memory_name (basestring) – the name of the memory.
 It is ignored when name is provided.
      • 
-
    • is_seq (bool) – is sequence for boot_layer
      • 
-
    • boot_layer (LayerOutput|None) – boot layer of memory.
      • 
-
    • boot_bias (ParameterAttribute|None) – boot layer’s bias
      • 
-
    • boot_bias_active_type (BaseActivation) – boot layer’s active type.
      • 
+
    • is_seq (bool) – is sequence for boot
      • 
+
    • boot (paddle.v2.config_base.Layer|None) – boot layer of memory.
      • 
+
    • boot_bias (paddle.v2.attr.ParameterAttribute|None) – boot layer’s bias
      • 
+
    • boot_bias_active_type (paddle.v2.activation.Base) – boot layer’s active type.
      • 
 
    • boot_with_const_id (int) – boot layer’s id.
      • 
 
    
     		
 
    Returns:
    LayerOutput object which is a memory.
    
+
    Returns:
    paddle.v2.config_base.Layer object which is a memory.
    
     		
 
    Return type:
    LayerOutput
    
+
    Return type:
    paddle.v2.config_base.Layer
    
     		
 
    
     
 
 
    @@ -1182,24 +1157,24 @@ a time step result. Then gather each time step of output into
 layer group’s output.
  • name (basestring) – recurrent_group’s name.
    • 
-
  • input (LayerOutput|StaticInput|SubsequenceInput|list|tuple) – 
    Input links array.
    
-
    LayerOutput will be scattered into time steps.
+
  • input (paddle.v2.config_base.Layer|StaticInput|SubsequenceInput|list|tuple) – 
    Input links array.
    
+
    paddle.v2.config_base.Layer will be scattered into time steps.
 SubsequenceInput will be scattered into sequence steps.
 StaticInput will be imported to each time step, and doesn’t change
 through time. It’s a mechanism to access layer outside step function.
    
 
  • reverse (bool) – If reverse is set true, the recurrent unit will process the
 input sequence in a reverse order.
    • 
-
  • targetInlink (LayerOutput|SubsequenceInput) – 
    the input layer which share info with layer group’s output
    
+
  • targetInlink (paddle.v2.config_base.Layer|SubsequenceInput) – 
    the input layer which share info with layer group’s output
    
 
    Param input specifies multiple input layers. For
 SubsequenceInput inputs, config should assign one input
 layer that share info(the number of sentences and the number
 of words in each sentence) with all layer group’s outputs.
 targetInlink should be one of the layer group’s input.
    
 
    • 
-
  • is_generating – If is generating, none of input type should be LayerOutput;
+
  • is_generating – If is generating, none of input type should be paddle.v2.config_base.Layer;
 else, for training or testing, one of the input type must
-be LayerOutput.
    • 
+be paddle.v2.config_base.Layer.
@@ -1210,9 +1185,9 @@ be LayerOutput.
-
+
-
+
    
 

 
 
 
 
 
 
 
 
 
    Returns:LayerOutput object.
    Returns:paddle.v2.config_base.Layer object.
 
    Return type:LayerOutput
    Return type:paddle.v2.config_base.Layer
 
    
     
 
    
@@ -1223,7 +1198,7 @@ be LayerOutput.
 
    lstm_step
    
 
    
 
    
-class paddle.v2.layer.lstm_step(*args, **kwargs)
    
+class paddle.v2.layer.lstm_step
 
    LSTM Step Layer. It used in recurrent_group. The lstm equations are shown
 as follow.
    
 
    
@@ -1248,10 +1223,10 @@ output is \(o_t\), which name is ‘state’ a
 state.size.
 
  • input (paddle.v2.config_base.Layer) – input layer. \(Wx_t + Wh_{t-1}\)
    • 
 
  • state (paddle.v2.config_base.Layer) – State Layer. \(c_{t-1}\)
    • 
-
  • act (paddle.v2.Activation.Base) – Activation type. Default is tanh
    • 
-
  • gate_act (paddle.v2.Activation.Base) – Gate Activation Type. Default is sigmoid, and should
+
  • act (paddle.v2.activation.Base) – Activation type. Default is tanh
    • 
+
  • gate_act (paddle.v2.activation.Base) – Gate Activation Type. Default is sigmoid, and should
 be sigmoid only.
    • 
-
  • state_act (paddle.v2.Activation.Base) – State Activation Type. Default is sigmoid, and should
+
  • state_act (paddle.v2.activation.Base) – State Activation Type. Default is sigmoid, and should
 be sigmoid only.
    • 
 
  • bias_attr (paddle.v2.attr.ParameterAttribute) – Bias Attribute.
    • 
 
  • layer_attr (paddle.v2.attr.ExtraAttribute) – layer’s extra attribute.
    • 
@@ -1273,7 +1248,7 @@ be sigmoid only.
 
    gru_step
    
 
    
 
    
-class paddle.v2.layer.gru_step(*args, **kwargs)
    
+class paddle.v2.layer.gru_step
 
    @@ -1314,7 +1289,7 @@ to maintain tractability.
    The example usage is:
    def rnn_step(input):
     last_time_step_output = memory(name='rnn', size=512)
-    with mixed_layer(size=512, name='rnn') as simple_rnn:
+    with mixed(size=512, name='rnn') as simple_rnn:
         simple_rnn += full_matrix_projection(input)
         simple_rnn += last_time_step_output
     return simple_rnn
@@ -1376,7 +1351,7 @@ beam size.
 
    
-
@@ -1388,7 +1363,7 @@ beam size.
    get_output
    
 
    
-class paddle.v2.layer.get_output(*args, **kwargs)
    
+class paddle.v2.layer.get_output
 
    Get layer’s output by name. In PaddlePaddle, a layer might return multiple
 values, but returns one layer’s output. If the user wants to use another
 output besides the default one, please use get_output first to get
@@ -1429,17 +1404,17 @@ multiple outputs.
 Each inputs is a projection or operator.
    
 
    There are two styles of usages.
    
 
      
-
    1. When not set inputs parameter, use mixed_layer like this:
      2. 
+
    2. When not set inputs parameter, use mixed like this:
      3. 
 
    
-
    with mixed_layer(size=256) as m:
+
    with mixed(size=256) as m:
     m += full_matrix_projection(input=layer1)
     m += identity_projection(input=layer2)
 
    
 
    
 
      
-
    1. You can also set all inputs when invoke mixed_layer as follows:
      2. 
+
    2. You can also set all inputs when invoke mixed as follows:
      3. 
 
    
-
    m = mixed_layer(size=256,
+
    m = mixed(size=256,
                 input=[full_matrix_projection(input=layer1),
                        full_matrix_projection(input=layer2)])
 
    
@@ -1453,11 +1428,11 @@ Each inputs is a projection or operator.
    
 
  • size (int) – layer size.
    • 
 
  • input – inputs layer. It is an optional parameter. If set,
 then this function will just return layer’s name.
    • 
-
  • act (BaseActivation) – Activation Type.
    • 
-
  • bias_attr (ParameterAttribute or None or bool) – The Bias Attribute. If no bias, then pass False or
-something not type of ParameterAttribute. None will get a
+
  • act (paddle.v2.activation.Base) – Activation Type.
    • 
+
  • bias_attr (paddle.v2.attr.ParameterAttribute or None or bool) – The Bias Attribute. If no bias, then pass False or
+something not type of paddle.v2.attr.ParameterAttribute. None will get a
 default Bias.
    • 
-
  • layer_attr (ExtraLayerAttribute) – The extra layer config. Default is None.
    • 
+
  • layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer config. Default is None.
    • 
 
 
 
@@ -1476,7 +1451,7 @@ default Bias.
 
    embedding
    
 
    
 
    
-class paddle.v2.layer.embedding(*args, **kwargs)
    
+class paddle.v2.layer.embedding
 
    Define a embedding Layer.
    
 
    
 
 

 
 
    Returns:
    The generated word index.
    
     		
 
    Return type:
    LayerOutput
    
+
    Return type:
    paddle.v2.config_base.Layer
    
     		
 
    
     
 
 
    @@ -1507,7 +1482,7 @@ for details.
    scaling_projection
    
 
    
-class paddle.v2.layer.scaling_projection(**kwargs)
    
+class paddle.v2.layer.scaling_projection
 
    scaling_projection multiplies the input with a scalar parameter and add to
 the output.
    
 
    
@@ -1541,7 +1516,7 @@ the output.
    
 
    dotmul_projection
    
 
    
 
    
-class paddle.v2.layer.dotmul_projection(**kwargs)
    
+class paddle.v2.layer.dotmul_projection
 
    DotMulProjection with a layer as input.
 It performs element-wise multiplication with weight.
    
 
    
@@ -1576,7 +1551,7 @@ It performs element-wise multiplication with weight.
    
 
    dotmul_operator
    
 
    
 
    
-class paddle.v2.layer.dotmul_operator(**kwargs)
    
+class paddle.v2.layer.dotmul_operator
 
    DotMulOperator takes two inputs and performs element-wise multiplication:
    
 
    
 \[out.row[i] += scale * (a.row[i] .* b.row[i])\]
    
@@ -1612,7 +1587,7 @@ scale is a config scalar, its default value is one.
    
 
    full_matrix_projection
    
 
    
 
    
-class paddle.v2.layer.full_matrix_projection(**kwargs)
    
+class paddle.v2.layer.full_matrix_projection
 
    Full Matrix Projection. It performs full matrix multiplication.
    
 
    
 \[out.row[i] += in.row[i] * weight\]
    
@@ -1658,7 +1633,7 @@ scale is a config scalar, its default value is one.
    
 
    identity_projection
    
 
    
 
    
-class paddle.v2.layer.identity_projection(**kwargs)
    
+class paddle.v2.layer.identity_projection
 
      
 
    1. IdentityProjection if offset=None. It performs:
      2. 
 
    
@@ -1704,7 +1679,7 @@ It select dimesions [offset, offset+layer_size) from input:
    
 
    table_projection
    
 
    
 
    
-class paddle.v2.layer.table_projection(**kwargs)
    
+class paddle.v2.layer.table_projection
 
    Table Projection. It selects rows from parameter where row_id
 is in input_ids.
    
 
    
@@ -1753,7 +1728,7 @@ and \(i\) is row_id.
    
 
    trans_full_matrix_projection
    
 
    
 
    
-class paddle.v2.layer.trans_full_matrix_projection(**kwargs)
    
+class paddle.v2.layer.trans_full_matrix_projection
 
    Different from full_matrix_projection, this projection performs matrix
 multiplication, using transpose of weight.
    
 
    
@@ -1821,7 +1796,7 @@ sequence of a nested sequence, 
    pooling
    
 
    
 
    
-class paddle.v2.layer.pooling(*args, **kwargs)
    
+class paddle.v2.layer.pooling
 
    Pooling layer for sequence inputs, not used for Image.
    
 
    The example usage is:
    
 
    seq_pool = pooling(input=layer,
@@ -1860,7 +1835,7 @@ SumPooling, SquareRootNPooling.
 
    last_seq
    
 
    
 
    
-class paddle.v2.layer.last_seq(*args, **kwargs)
    
+class paddle.v2.layer.last_seq
 
    Get Last Timestamp Activation of a sequence.
    
 
    If stride > 0, this layer slides a window whose size is determined by stride,
 and return the last value of the window as the output. Thus, a long sequence
@@ -1898,7 +1873,7 @@ of stride is -1.
    
 
    first_seq
    
 
    
 
    
-class paddle.v2.layer.first_seq(*args, **kwargs)
    
+class paddle.v2.layer.first_seq
 
    Get First Timestamp Activation of a sequence.
    
 
    If stride > 0, this layer slides a window whose size is determined by stride,
 and return the first value of the window as the output. Thus, a long sequence
@@ -1936,7 +1911,7 @@ of stride is -1.
    
 
    concat
    
 
    
 
    
-class paddle.v2.layer.concat(*args, **kwargs)
    
+class paddle.v2.layer.concat
 
    Concat all input vector into one huge vector.
 Inputs can be list of paddle.v2.config_base.Layer or list of projection.
    
 
    The example usage is:
    
@@ -1950,7 +1925,7 @@ Inputs can be list of paddle.v2.config_base.Layer or list of projection.
    
 
    
@@ -1970,7 +1945,7 @@ Inputs can be list of paddle.v2.config_base.Layer or list of projection.
    
 
    seq_concat
    
 
    
 
    
-class paddle.v2.layer.seq_concat(*args, **kwargs)
    
+class paddle.v2.layer.seq_concat
 
    Concat sequence a with sequence b.
    
 
    
 
    Inputs:
    
@@ -1994,7 +1969,7 @@ Inputs can be list of paddle.v2.config_base.Layer or list of projection.
    
 
  • name (basestring) – Layer name.
    • 
 
  • a (paddle.v2.config_base.Layer) – input sequence layer
    • 
 
  • b (paddle.v2.config_base.Layer) – input sequence layer
    • 
-
  • act (paddle.v2.Activation.Base) – Activation type.
    • 
+
  • act (paddle.v2.activation.Base) – Activation type.
    • 
 
  • layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
    • 
 
  • bias_attr (paddle.v2.attr.ParameterAttribute or None or bool) – The Bias Attribute. If no bias, then pass False or
 something not type of paddle.v2.attr.ParameterAttribute. None will get a
@@ -2020,7 +1995,7 @@ default Bias.
    • 
 
    block_expand
    
 
    
 
    
-class paddle.v2.layer.block_expand(*args, **kwargs)
    
+class paddle.v2.layer.block_expand
 
    
 
    Expand feature map to minibatch matrix.
    
 
      
@@ -2096,7 +2071,7 @@ sequence of a nested sequence, 
      
 
      
-class paddle.v2.layer.expand(*args, **kwargs)
      
+class paddle.v2.layer.expand
 
      A layer for “Expand Dense data or (sequence data where the length of each
 sequence is one) to sequence data.”
      
 
      The example usage is:
      
@@ -2135,7 +2110,7 @@ bias.
 
      repeat
      
 
      
 
      
-class paddle.v2.layer.repeat(*args, **kwargs)
      
+class paddle.v2.layer.repeat
 
      A layer for repeating the input for num_repeats times. This is equivalent
 to apply concat() with num_repeats same input.
      
 
      
@@ -2171,7 +2146,7 @@ to apply concat() with num_repeats same input.
      
 
      rotate
      
 
      
 
      
-class paddle.v2.layer.rotate(*args, **kwargs)
      
+class paddle.v2.layer.rotate
 
      A layer for rotating 90 degrees (clock-wise) for each feature channel,
 usually used when the input sample is some image or feature map.
      
 
      
@@ -2210,7 +2185,7 @@ usually used when the input sample is some image or feature map.
      
 
      seq_reshape
      
 
      
 
      
-class paddle.v2.layer.seq_reshape(*args, **kwargs)
      
+class paddle.v2.layer.seq_reshape
 
      A layer for reshaping the sequence. Assume the input sequence has T instances,
 the dimension of each instance is M, and the input reshape_size is N, then the
 output sequence has T*M/N instances, the dimension of each instance is N.
      
@@ -2227,7 +2202,7 @@ output sequence has T*M/N instances, the dimension of each instance is N.
      
 
    • input (paddle.v2.config_base.Layer) – Input layer.
      • 
 
    • reshape_size (int) – the size of reshaped sequence.
      • 
 
    • name (basestring) – Layer name.
      • 
-
    • act (paddle.v2.Activation.Base) – Activation type.
      • 
+
    • act (paddle.v2.activation.Base) – Activation type.
      • 
 
    • layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
      • 
 
    • bias_attr (paddle.v2.attr.ParameterAttribute or None or bool) – The Bias Attribute. If no bias, then pass False or
 something not type of paddle.v2.attr.ParameterAttribute. None will get a
@@ -2253,7 +2228,7 @@ default Bias.
      • 
 
      addto
      
 
      
 
      
-class paddle.v2.layer.addto(*args, **kwargs)
      
+class paddle.v2.layer.addto
 
      AddtoLayer.
      
 
      
 \[y = f(\sum_{i} x_i + b)\]
      
@@ -2261,7 +2236,7 @@ default Bias.
 and \(f\) is activation function.
      
 
      The example usage is:
      
 
      addto = addto(input=[layer1, layer2],
-                    act=paddle.v2.Activation.Relu(),
+                    act=paddle.v2.activation.Relu(),
                     bias_attr=False)
 
      
 
      
@@ -2283,7 +2258,7 @@ Please refer to dropout for details.
      
 
    • name (basestring) – Layer name.
      • 
 
    • input (paddle.v2.config_base.Layer|list|tuple) – Input layers. It could be a paddle.v2.config_base.Layer or list/tuple of
 paddle.v2.config_base.Layer.
      • 
-
    • act (paddle.v2.Activation.Base) – Activation Type, default is tanh.
      • 
+
    • act (paddle.v2.activation.Base) – Activation Type, default is tanh.
      • 
 
    • bias_attr (paddle.v2.attr.ParameterAttribute|bool) – Bias attribute. If False, means no bias. None is default
 bias.
      • 
 
    • layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer attribute.
      • 
@@ -2305,7 +2280,7 @@ bias.
 
      linear_comb
      
 
      
 
      
-class paddle.v2.layer.linear_comb(*args, **kwargs)
      
+class paddle.v2.layer.linear_comb
 
      
 
      A layer for weighted sum of vectors takes two inputs.
      
 
        
@@ -2368,7 +2343,7 @@ processed in one batch.
        
 
        interpolation
        
 
        
 
        
-class paddle.v2.layer.interpolation(*args, **kwargs)
        
+class paddle.v2.layer.interpolation
 
        This layer is for linear interpolation with two inputs,
 which is used in NEURAL TURING MACHINE.
        
 
        
@@ -2407,7 +2382,7 @@ which is used in NEURAL TURING MACHINE.
        
 
        bilinear_interp
        
 
        
 
        
-class paddle.v2.layer.bilinear_interp(*args, **kwargs)
        
+class paddle.v2.layer.bilinear_interp
 
        This layer is to implement bilinear interpolation on conv layer output.
        
 
        Please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation
        
 
        The simple usage is:
        
@@ -2442,7 +2417,7 @@ which is used in NEURAL TURING MACHINE.
        
 
        power
        
 
        
 
        
-class paddle.v2.layer.power(*args, **kwargs)
        
+class paddle.v2.layer.power
 
        This layer applies a power function to a vector element-wise,
 which is used in NEURAL TURING MACHINE.
        
 
        
@@ -2480,7 +2455,7 @@ and \(y\) is a output vector.
        
 
        scaling
        
 
        
 
        
-class paddle.v2.layer.scaling(*args, **kwargs)
        
+class paddle.v2.layer.scaling
 
        A layer for multiplying input vector by weight scalar.
        
 
        
 \[y  = w x\]
        
@@ -2519,7 +2494,7 @@ processed in one batch.
        
 
        slope_intercept
        
 
        
 
        
-class paddle.v2.layer.slope_intercept(*args, **kwargs)
        
+class paddle.v2.layer.slope_intercept
 
        This layer for applying a slope and an intercept to the input
 element-wise. There is no activation and weight.
        
 
        
@@ -2556,7 +2531,7 @@ element-wise. There is no activation and weight.
        
 
        tensor
        
 
        
 
        
-class paddle.v2.layer.tensor(*args, **kwargs)
        
+class paddle.v2.layer.tensor
 
        This layer performs tensor operation for two input.
 For example, each sample:
        
 
        
@@ -2585,7 +2560,7 @@ For example, each sample:
        
 
      • a (paddle.v2.config_base.Layer) – Input layer a.
        • 
 
      • b (paddle.v2.config_base.Layer) – input layer b.
        • 
 
      • size (int.) – the layer dimension.
        • 
-
      • act (paddle.v2.Activation.Base) – Activation Type. Default is tanh.
        • 
+
      • act (paddle.v2.activation.Base) – Activation Type. Default is tanh.
        • 
 
      • param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute.
        • 
 
      • bias_attr (paddle.v2.attr.ParameterAttribute|None|Any) – The Bias Attribute. If no bias, then pass False or
 something not type of paddle.v2.attr.ParameterAttribute. None will get a
@@ -2609,7 +2584,7 @@ default Bias.
        • 
 
        cos_sim
        
 
        
 
        
-class paddle.v2.layer.cos_sim(*args, **kwargs)
        
+class paddle.v2.layer.cos_sim
 
        Cosine Similarity Layer. The cosine similarity equation is here.
        
 
        
 \[similarity = cos(\theta) = {\mathbf{a} \cdot \mathbf{b}
@@ -2652,7 +2627,7 @@ processed in one batch.
        
 
        trans
        
 
        
 
        
-class paddle.v2.layer.trans(*args, **kwargs)
        
+class paddle.v2.layer.trans
 
        A layer for transposing a minibatch matrix.
        
 
        
 \[y = x^\mathrm{T}\]
        
@@ -2690,7 +2665,7 @@ processed in one batch.
        
 
        maxid
        
 
        
 
        
-class paddle.v2.layer.max_id(*args, **kwargs)
        
+class paddle.v2.layer.max_id
 
        A layer for finding the id which has the maximal value for each sample.
 The result is stored in output.ids.
        
 
        The example usage is:
        
@@ -2723,7 +2698,7 @@ The result is stored in output.ids.
        
 
        sampling_id
        
 
        
 
        
-class paddle.v2.layer.sampling_id(*args, **kwargs)
        
+class paddle.v2.layer.sampling_id
 
        A layer for sampling id from multinomial distribution from the input layer.
 Sampling one id for one sample.
        
 
        The simple usage is:
        
@@ -2759,7 +2734,7 @@ Sampling one id for one sample.
        
 
        pad
        
 
        
 
        
-class paddle.v2.layer.pad(*args, **kwargs)
        
+class paddle.v2.layer.pad
 
        This operation pads zeros to the input data according to pad_c,pad_h
 and pad_w. pad_c, pad_h, pad_w specifies the which dimension and size
 of padding. And the input data shape is NCHW.
        
@@ -2828,7 +2803,7 @@ in width dimension.
        
 
        cross_entropy_cost
        
 
        
 
        
-class paddle.v2.layer.cross_entropy_cost(*args, **kwargs)
        
+class paddle.v2.layer.cross_entropy_cost
 
        A loss layer for multi class entropy.
        
 
        cost = cross_entropy(input=input,
                      label=label)
@@ -2866,7 +2841,7 @@ will not be calculated for weight.
 
        cross_entropy_with_selfnorm_cost
        
 
        
 
        
-class paddle.v2.layer.cross_entropy_with_selfnorm_cost(*args, **kwargs)
        
+class paddle.v2.layer.cross_entropy_with_selfnorm_cost
 
        A loss layer for multi class entropy with selfnorm.
 Input should be a vector of positive numbers, without normalization.
        
 
        cost = cross_entropy_with_selfnorm(input=input,
@@ -2902,7 +2877,7 @@ Input should be a vector of positive numbers, without normalization.
        
 
        multi_binary_label_cross_entropy_cost
        
 
        
 
        
-class paddle.v2.layer.multi_binary_label_cross_entropy_cost(*args, **kwargs)
        
+class paddle.v2.layer.multi_binary_label_cross_entropy_cost
 
        A loss layer for multi binary label cross entropy.
        
 
        cost = multi_binary_label_cross_entropy(input=input,
                                         label=label)
@@ -2936,7 +2911,7 @@ Input should be a vector of positive numbers, without normalization.
        
 
        huber_cost
        
 
        
 
        
-class paddle.v2.layer.huber_cost(*args, **kwargs)
        
+class paddle.v2.layer.huber_cost
 
        A loss layer for huber loss.
        
 
        cost = huber_cost(input=input,
                   label=label)
@@ -2970,7 +2945,7 @@ Input should be a vector of positive numbers, without normalization.
        
 
        lambda_cost
        
 
        
 
        
-class paddle.v2.layer.lambda_cost(*args, **kwargs)
        
+class paddle.v2.layer.lambda_cost
 
        lambdaCost for lambdaRank LTR approach.
        
 
        The simple usage:
        
 
        cost = lambda_cost(input=input,
@@ -3016,7 +2991,7 @@ entire list of get gradient.
 
        mse_cost
        
 
        
 
        
-class paddle.v2.layer.mse_cost(*args, **kwargs)
        
+class paddle.v2.layer.mse_cost
 
        
 
        mean squared error cost:
        
 
        
@@ -3069,7 +3044,7 @@ It is an optional argument.
 
        rank_cost
        
 
        
 
        
-class paddle.v2.layer.rank_cost(*args, **kwargs)
        
+class paddle.v2.layer.rank_cost
 
        A cost Layer for learning to rank using gradient descent. Details can refer
 to papers.
 This layer contains at least three inputs. The weight is an optional
@@ -3124,7 +3099,7 @@ It is an optional argument.
 
        sum_cost
        
 
        
 
        
-class paddle.v2.layer.sum_cost(*args, **kwargs)
        
+class paddle.v2.layer.sum_cost
 
        A loss layer which calculate the sum of the input as loss
        
 
        cost = sum_cost(input=input)
 
        
@@ -3155,7 +3130,7 @@ It is an optional argument.
 
        crf
        
 
        
 
        
-class paddle.v2.layer.crf(*args, **kwargs)
        
+class paddle.v2.layer.crf
 
        A layer for calculating the cost of sequential conditional random
 field model.
        
 
        The simple usage:
        
@@ -3196,7 +3171,7 @@ optional argument.
 
        crf_decoding
        
 
        
 
        
-class paddle.v2.layer.crf_decoding(*args, **kwargs)
        
+class paddle.v2.layer.crf_decoding
 
        A layer for calculating the decoding sequence of sequential conditional
 random field model. The decoding sequence is stored in output.ids.
 If a second input is provided, it is treated as the ground-truth label, and
@@ -3236,7 +3211,7 @@ decoding or 0 for correct decoding.
        
 
        ctc
        
 
        
 
        
-class paddle.v2.layer.ctc(*args, **kwargs)
        
+class paddle.v2.layer.ctc
 
        Connectionist Temporal Classification (CTC) is designed for temporal
 classication task. That is, for sequence labeling problems where the
 alignment between the inputs and the target labels is unknown.
        
@@ -3287,7 +3262,7 @@ should also be num_classes + 1.
        
 
        warp_ctc
        
 
        
 
        
-class paddle.v2.layer.warp_ctc(*args, **kwargs)
        
+class paddle.v2.layer.warp_ctc
 
        A layer intergrating the open-source warp-ctc
 <https://github.com/baidu-research/warp-ctc> library, which is used in
 Deep Speech 2: End-toEnd Speech Recognition in English and Mandarin
@@ -3347,7 +3322,7 @@ should be consistent as that used in your labels.
 
        nce
        
 
        
 
        
-class paddle.v2.layer.nce(*args, **kwargs)
        
+class paddle.v2.layer.nce
 
        Noise-contrastive estimation.
 Implements the method in the following paper:
 A fast and simple algorithm for training neural probabilistic language models.
        
@@ -3367,7 +3342,7 @@ A fast and simple algorithm for training neural probabilistic language models.label (paddle.v2.config_base.Layer) – label layer
 
      • weight (paddle.v2.config_base.Layer) – weight layer, can be None(default)
        • 
 
      • num_classes (int) – number of classes.
        • 
-
      • act (paddle.v2.Activation.Base) – Activation, default is Sigmoid.
        • 
+
      • act (paddle.v2.activation.Base) – Activation, default is Sigmoid.
        • 
 
      • param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute|list.
        • 
 
      • num_neg_samples (int) – number of negative samples. Default is 10.
        • 
 
      • neg_distribution (list|tuple|collections.Sequence|None) – The distribution for generating the random negative labels.
@@ -3393,7 +3368,7 @@ If not None, its length must be equal to num_classes.
        • 
 
        hsigmoid
        
 
        
 
        
-class paddle.v2.layer.hsigmoid(*args, **kwargs)
        
+class paddle.v2.layer.hsigmoid
 
        Organize the classes into a binary tree. At each node, a sigmoid function
 is used to calculate the probability of belonging to the right branch.
 This idea is from “F. Morin, Y. Bengio (AISTATS 05):
@@ -3435,7 +3410,7 @@ False means no bias.
 
        smooth_l1_cost
        
 
        
 
        
-class paddle.v2.layer.smooth_l1_cost(*args, **kwargs)
        
+class paddle.v2.layer.smooth_l1_cost
 
        This is a L1 loss but more smooth. It requires that the
 size of input and label are equal. The formula is as follows,
        
 
        
@@ -3479,7 +3454,7 @@ size of input and label are equal. The formula is as follows,
        
 
        eos
        
 
        
 
        
-class paddle.v2.layer.eos(*args, **kwargs)
        
+class paddle.v2.layer.eos
 
        A layer for checking EOS for each sample:
 - output_id = (input_id == conf.eos_id)
        
 
        The result is stored in output_.ids.
diff --git a/develop/doc/api/v2/config/networks.html b/develop/doc/api/v2/config/networks.html
index 69e2b9c5c491c8fe8a40395fd609829e68e33d2e..0ba183af51ad53403b39c63f7c2314ea5d286f02 100644
--- a/develop/doc/api/v2/config/networks.html
+++ b/develop/doc/api/v2/config/networks.html
@@ -190,9 +190,9 @@
 
        NLP
        
 
        
 
        sequence_conv_pool
        
-
        
+
        
 
        
-class paddle.v2.networks.sequence_conv_pool(*args, **kwargs)
        
+paddle.v2.networks.sequence_conv_pool(*args, **kwargs)
 
        Text convolution pooling layers helper.
        
 
        Text input => Context Projection => FC Layer => Pooling => Output.
        
 
    
 

 
 
    Parameters:
      
 
    • name (basestring) – Layer name.
      • 
 
    • input (list|tuple|collections.Sequence) – input layers or projections
      • 
-
    • act (paddle.v2.Activation.Base) – Activation type.
      • 
+
    • act (paddle.v2.activation.Base) – Activation type.
      • 
 
    • layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
      • 
 
    
 
    
@@ -201,34 +201,34 @@
 
-
@@ -238,9 +238,9 @@ False if no bias.
 
 
    
 
    text_conv_pool
    
-
    
+
    
 
    
-class paddle.v2.networks.text_conv_pool(*args, **kwargs)
    
+paddle.v2.networks.text_conv_pool(*args, **kwargs)
 
    Text convolution pooling layers helper.
    
 
    Text input => Context Projection => FC Layer => Pooling => Output.
    
 
    
 
    Parameters:
      
 
    • name (basestring) – name of output layer(pooling layer name)
      • 
-
    • input (paddle.v2.config_base.Layer) – name of input layer
      • 
+
    • input (LayerOutput) – name of input layer
      • 
 
    • context_len (int) – context projection length. See
 context_projection’s document.
      • 
 
    • hidden_size (int) – FC Layer size.
      • 
 
    • context_start (int or None) – context projection length. See
 context_projection’s context_start.
      • 
-
    • pool_type (BasePoolingType.) – pooling layer type. See pooling’s document.
      • 
-
    • context_proj_name (basestring) – context projection layer name.
+
    • pool_type (BasePoolingType.) – pooling layer type. See pooling_layer’s document.
      • 
+
    • context_proj_layer_name (basestring) – context projection layer name.
 None if user don’t care.
      • 
-
    • context_proj_param_attr (paddle.v2.attr.ParameterAttribute or None.) – context projection parameter attribute.
+
    • context_proj_param_attr (ParameterAttribute or None.) – context projection parameter attribute.
 None if user don’t care.
      • 
-
    • fc_name (basestring) – fc layer name. None if user don’t care.
      • 
-
    • fc_param_attr (paddle.v2.attr.ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
      • 
-
    • fc_bias_attr (paddle.v2.attr.ParameterAttribute or None) – fc bias parameter attribute. False if no bias,
+
    • fc_layer_name (basestring) – fc layer name. None if user don’t care.
      • 
+
    • fc_param_attr (ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
      • 
+
    • fc_bias_attr (ParameterAttribute or None) – fc bias parameter attribute. False if no bias,
 None if user don’t care.
      • 
-
    • fc_act (paddle.v2.Activation.Base) – fc layer activation type. None means tanh
      • 
-
    • pool_bias_attr (paddle.v2.attr.ParameterAttribute or None.) – pooling layer bias attr. None if don’t care.
+
    • fc_act (BaseActivation) – fc layer activation type. None means tanh
      • 
+
    • pool_bias_attr (ParameterAttribute or None.) – pooling layer bias attr. None if don’t care.
 False if no bias.
      • 
-
    • fc_attr (paddle.v2.attr.ExtraAttribute) – fc layer extra attribute.
      • 
-
    • context_attr (paddle.v2.attr.ExtraAttribute) – context projection layer extra attribute.
      • 
-
    • pool_attr (paddle.v2.attr.ExtraAttribute) – pooling layer extra attribute.
      • 
+
    • fc_attr (ExtraLayerAttribute) – fc layer extra attribute.
      • 
+
    • context_attr (ExtraLayerAttribute) – context projection layer extra attribute.
      • 
+
    • pool_attr (ExtraLayerAttribute) – pooling layer extra attribute.
      • 
 
    
     		
 
    
 
    Returns:
    output layer name.
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
 
    
@@ -249,34 +249,34 @@ False if no bias.
 
-
@@ -289,9 +289,9 @@ False if no bias.
 
    Images
    
 
    
 
    img_conv_bn_pool
    
-
    
+
    
 
    
-class paddle.v2.networks.img_conv_bn_pool(*args, **kwargs)
    
+paddle.v2.networks.img_conv_bn_pool(*args, **kwargs)
 
    Convolution, batch normalization, pooling group.
    
 
    
 
    Parameters:
      
 
    • name (basestring) – name of output layer(pooling layer name)
      • 
-
    • input (paddle.v2.config_base.Layer) – name of input layer
      • 
+
    • input (LayerOutput) – name of input layer
      • 
 
    • context_len (int) – context projection length. See
 context_projection’s document.
      • 
 
    • hidden_size (int) – FC Layer size.
      • 
 
    • context_start (int or None) – context projection length. See
 context_projection’s context_start.
      • 
-
    • pool_type (BasePoolingType.) – pooling layer type. See pooling’s document.
      • 
-
    • context_proj_name (basestring) – context projection layer name.
+
    • pool_type (BasePoolingType.) – pooling layer type. See pooling_layer’s document.
      • 
+
    • context_proj_layer_name (basestring) – context projection layer name.
 None if user don’t care.
      • 
-
    • context_proj_param_attr (paddle.v2.attr.ParameterAttribute or None.) – context projection parameter attribute.
+
    • context_proj_param_attr (ParameterAttribute or None.) – context projection parameter attribute.
 None if user don’t care.
      • 
-
    • fc_name (basestring) – fc layer name. None if user don’t care.
      • 
-
    • fc_param_attr (paddle.v2.attr.ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
      • 
-
    • fc_bias_attr (paddle.v2.attr.ParameterAttribute or None) – fc bias parameter attribute. False if no bias,
+
    • fc_layer_name (basestring) – fc layer name. None if user don’t care.
      • 
+
    • fc_param_attr (ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
      • 
+
    • fc_bias_attr (ParameterAttribute or None) – fc bias parameter attribute. False if no bias,
 None if user don’t care.
      • 
-
    • fc_act (paddle.v2.Activation.Base) – fc layer activation type. None means tanh
      • 
-
    • pool_bias_attr (paddle.v2.attr.ParameterAttribute or None.) – pooling layer bias attr. None if don’t care.
+
    • fc_act (BaseActivation) – fc layer activation type. None means tanh
      • 
+
    • pool_bias_attr (ParameterAttribute or None.) – pooling layer bias attr. None if don’t care.
 False if no bias.
      • 
-
    • fc_attr (paddle.v2.attr.ExtraAttribute) – fc layer extra attribute.
      • 
-
    • context_attr (paddle.v2.attr.ExtraAttribute) – context projection layer extra attribute.
      • 
-
    • pool_attr (paddle.v2.attr.ExtraAttribute) – pooling layer extra attribute.
      • 
+
    • fc_attr (ExtraLayerAttribute) – fc layer extra attribute.
      • 
+
    • context_attr (ExtraLayerAttribute) – context projection layer extra attribute.
      • 
+
    • pool_attr (ExtraLayerAttribute) – pooling layer extra attribute.
      • 
 
    
     		
 
    
 
    Returns:
    output layer name.
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
 
    @@ -299,33 +299,33 @@ False if no bias.
 
-
@@ -335,9 +335,9 @@ False if no bias.
 
 
    
 
    img_conv_group
    
-
    
+
    
 
    
-class paddle.v2.networks.img_conv_group(**kwargs)
    
+paddle.v2.networks.img_conv_group(*args, **kwargs)
 
    Image Convolution Group, Used for vgg net.
    
 
    TODO(yuyang18): Complete docs
    
 
    
 

 
    Parameters:
      
 
    • name (basestring) – group name
      • 
-
    • input (paddle.v2.config_base.Layer) – layer’s input
      • 
-
    • filter_size (int) – see img_conv’s document
      • 
-
    • num_filters (int) – see img_conv’s document
      • 
-
    • pool_size (int) – see img_pool’s document.
      • 
-
    • pool_type (BasePoolingType) – see img_pool’s document.
      • 
-
    • act (paddle.v2.Activation.Base) – see batch_norm’s document.
      • 
-
    • groups (int) – see img_conv’s document
      • 
-
    • conv_stride (int) – see img_conv’s document.
      • 
-
    • conv_padding (int) – see img_conv’s document.
      • 
-
    • conv_bias_attr (paddle.v2.attr.ParameterAttribute) – see img_conv’s document.
      • 
-
    • num_channel (int) – see img_conv’s document.
      • 
-
    • conv_param_attr (paddle.v2.attr.ParameterAttribute) – see img_conv’s document.
      • 
-
    • shared_bias (bool) – see img_conv’s document.
      • 
-
    • conv_attr (Extrapaddle.v2.config_base.Layer) – see img_conv’s document.
      • 
-
    • bn_param_attr (paddle.v2.attr.ParameterAttribute.) – see batch_norm’s document.
      • 
-
    • bn_bias_attr – see batch_norm’s document.
      • 
-
    • bn_attr – paddle.v2.attr.ParameterAttribute.
      • 
-
    • pool_stride (int) – see img_pool’s document.
      • 
-
    • pool_padding (int) – see img_pool’s document.
      • 
-
    • pool_attr (paddle.v2.attr.ExtraAttribute) – see img_pool’s document.
      • 
+
    • input (LayerOutput) – layer’s input
      • 
+
    • filter_size (int) – see img_conv_layer’s document
      • 
+
    • num_filters (int) – see img_conv_layer’s document
      • 
+
    • pool_size (int) – see img_pool_layer’s document.
      • 
+
    • pool_type (BasePoolingType) – see img_pool_layer’s document.
      • 
+
    • act (BaseActivation) – see batch_norm_layer’s document.
      • 
+
    • groups (int) – see img_conv_layer’s document
      • 
+
    • conv_stride (int) – see img_conv_layer’s document.
      • 
+
    • conv_padding (int) – see img_conv_layer’s document.
      • 
+
    • conv_bias_attr (ParameterAttribute) – see img_conv_layer’s document.
      • 
+
    • num_channel (int) – see img_conv_layer’s document.
      • 
+
    • conv_param_attr (ParameterAttribute) – see img_conv_layer’s document.
      • 
+
    • shared_bias (bool) – see img_conv_layer’s document.
      • 
+
    • conv_layer_attr (ExtraLayerOutput) – see img_conv_layer’s document.
      • 
+
    • bn_param_attr (ParameterAttribute.) – see batch_norm_layer’s document.
      • 
+
    • bn_bias_attr – see batch_norm_layer’s document.
      • 
+
    • bn_layer_attr – ParameterAttribute.
      • 
+
    • pool_stride (int) – see img_pool_layer’s document.
      • 
+
    • pool_padding (int) – see img_pool_layer’s document.
      • 
+
    • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer’s document.
      • 
 
    
     		
 
    
 
    Returns:
    Layer groups output
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
 
    
@@ -369,9 +369,9 @@ False if no bias.
 
 
    
 
    simple_img_conv_pool
    
-
    
+
    
 
    
-class paddle.v2.networks.simple_img_conv_pool(*args, **kwargs)
    
+paddle.v2.networks.simple_img_conv_pool(*args, **kwargs)
 
    Simple image convolution and pooling group.
    
 
    Input => conv => pooling
    
 
    
@@ -380,30 +380,30 @@ False if no bias.
 
-
@@ -416,9 +416,9 @@ False if no bias.
 
 
    
 
    vgg_16_network
    
-
    
+
    
 
    
-class paddle.v2.networks.vgg_16_network(**kwargs)
    
+paddle.v2.networks.vgg_16_network(input_image, num_channels, num_classes=1000)
 
    Same model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8
    
 
    
 
    Parameters:
      
 
    • name (basestring) – group name
      • 
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
-
    • filter_size (int) – see img_conv for details
      • 
-
    • num_filters (int) – see img_conv for details
      • 
-
    • pool_size (int) – see img_pool for details
      • 
-
    • pool_type (BasePoolingType) – see img_pool for details
      • 
-
    • act (paddle.v2.Activation.Base) – see img_conv for details
      • 
-
    • groups (int) – see img_conv for details
      • 
-
    • conv_stride (int) – see img_conv for details
      • 
-
    • conv_padding (int) – see img_conv for details
      • 
-
    • bias_attr (paddle.v2.attr.ParameterAttribute) – see img_conv for details
      • 
-
    • num_channel (int) – see img_conv for details
      • 
-
    • param_attr (paddle.v2.attr.ParameterAttribute) – see img_conv for details
      • 
-
    • shared_bias (bool) – see img_conv for details
      • 
-
    • conv_attr (paddle.v2.attr.ExtraAttribute) – see img_conv for details
      • 
-
    • pool_stride (int) – see img_pool for details
      • 
-
    • pool_padding (int) – see img_pool for details
      • 
-
    • pool_attr (paddle.v2.attr.ExtraAttribute) – see img_pool for details
      • 
+
    • input (LayerOutput) – input layer name.
      • 
+
    • filter_size (int) – see img_conv_layer for details
      • 
+
    • num_filters (int) – see img_conv_layer for details
      • 
+
    • pool_size (int) – see img_pool_layer for details
      • 
+
    • pool_type (BasePoolingType) – see img_pool_layer for details
      • 
+
    • act (BaseActivation) – see img_conv_layer for details
      • 
+
    • groups (int) – see img_conv_layer for details
      • 
+
    • conv_stride (int) – see img_conv_layer for details
      • 
+
    • conv_padding (int) – see img_conv_layer for details
      • 
+
    • bias_attr (ParameterAttribute) – see img_conv_layer for details
      • 
+
    • num_channel (int) – see img_conv_layer for details
      • 
+
    • param_attr (ParameterAttribute) – see img_conv_layer for details
      • 
+
    • shared_bias (bool) – see img_conv_layer for details
      • 
+
    • conv_layer_attr (ExtraLayerAttribute) – see img_conv_layer for details
      • 
+
    • pool_stride (int) – see img_pool_layer for details
      • 
+
    • pool_padding (int) – see img_pool_layer for details
      • 
+
    • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer for details
      • 
 
    
     		
 
    
 
    Returns:
    Layer’s output
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
 
    @@ -426,7 +426,7 @@ False if no bias.
 
@@ -446,9 +446,9 @@ False if no bias.
 
    LSTM
    
 
    
 
    lstmemory_unit
    
-
    
+
    
 
    
-class paddle.v2.networks.lstmemory_unit(*args, **kwargs)
    
+paddle.v2.networks.lstmemory_unit(*args, **kwargs)
 
    Define calculations that a LSTM unit performs in a single time step.
 This function itself is not a recurrent layer, so that it can not be
 directly applied to sequence input. This function is always used in
@@ -462,9 +462,9 @@ for more details about LSTM. The link goes as follows:
 
    The example usage is:
    
 
    lstm_step = lstmemory_unit(input=[layer1],
                            size=256,
-                           act=paddle.v2.Activation.Tanh(),
-                           gate_act=paddle.v2.Activation.Sigmoid(),
-                           state_act=paddle.v2.Activation.Tanh())
+                           act=TanhActivation(),
+                           gate_act=SigmoidActivation(),
+                           state_act=TanhActivation())
 
    
 
    
 
    
 

 
    Parameters:
      
 
    • num_classes – 
      • 
-
    • input_image (paddle.v2.config_base.Layer) – 
      • 
+
    • input_image (LayerOutput) – 
      • 
 
    • num_channels (int) – 
      • 
 
    
 
    
@@ -472,27 +472,27 @@ for more details about LSTM. The link goes as follows:
 
-
@@ -502,9 +502,9 @@ False means no bias, None means default bias.
 
 
    
 
    lstmemory_group
    
-
    
+
    
 
    
-class paddle.v2.networks.lstmemory_group(*args, **kwargs)
    
+paddle.v2.networks.lstmemory_group(*args, **kwargs)
 
    lstm_group is a recurrent layer group version of Long Short Term Memory. It
 does exactly the same calculation as the lstmemory layer (see lstmemory in
 layers.py for the maths) does. A promising benefit is that LSTM memory
@@ -517,14 +517,14 @@ lstmemory_group.
    
 multiplications:
 \(W_{xi}x_{t}\) , \(W_{xf}x_{t}\),
 \(W_{xc}x_t\), \(W_{xo}x_{t}\) are not done in lstmemory_unit to
-speed up the calculations. Consequently, an additional mixed with
+speed up the calculations. Consequently, an additional mixed_layer with
 full_matrix_projection must be included before lstmemory_unit is called.
    
 
    The example usage is:
    
 
    lstm_step = lstmemory_group(input=[layer1],
                             size=256,
-                            act=paddle.v2.Activation.Tanh(),
-                            gate_act=paddle.v2.Activation.Sigmoid(),
-                            state_act=paddle.v2.Activation.Tanh())
+                            act=TanhActivation(),
+                            gate_act=SigmoidActivation(),
+                            state_act=TanhActivation())
 
    
 
    
 
    
 
 
    Parameters:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – lstmemory unit name.
      • 
 
    • size (int) – lstmemory unit size.
      • 
-
    • param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default.
      • 
-
    • act (paddle.v2.Activation.Base) – lstm final activiation type
      • 
-
    • gate_act (paddle.v2.Activation.Base) – lstm gate activiation type
      • 
-
    • state_act (paddle.v2.Activation.Base) – lstm state activiation type.
      • 
-
    • mixed_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias parameter attribute of mixed layer.
+
    • param_attr (ParameterAttribute) – Parameter config, None if use default.
      • 
+
    • act (BaseActivation) – lstm final activiation type
      • 
+
    • gate_act (BaseActivation) – lstm gate activiation type
      • 
+
    • state_act (BaseActivation) – lstm state activiation type.
      • 
+
    • mixed_bias_attr (ParameterAttribute|False) – bias parameter attribute of mixed layer.
 False means no bias, None means default bias.
      • 
-
    • lstm_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias parameter attribute of lstm layer.
+
    • lstm_bias_attr (ParameterAttribute|False) – bias parameter attribute of lstm layer.
 False means no bias, None means default bias.
      • 
-
    • mixed_attr (paddle.v2.attr.ExtraAttribute) – mixed layer’s extra attribute.
      • 
-
    • lstm_attr (paddle.v2.attr.ExtraAttribute) – lstm layer’s extra attribute.
      • 
-
    • get_output_attr (paddle.v2.attr.ExtraAttribute) – get output layer’s extra attribute.
      • 
+
    • mixed_layer_attr (ExtraLayerAttribute) – mixed layer’s extra attribute.
      • 
+
    • lstm_layer_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
      • 
+
    • get_output_layer_attr (ExtraLayerAttribute) – get output layer’s extra attribute.
      • 
 
    
     		
 
    
 
    Returns:
    lstmemory unit name.
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
 
    
@@ -532,28 +532,28 @@ full_matrix_projection must be included before lstmemory_unit is called.
    
     
-
@@ -563,9 +563,9 @@ False means no bias, None means default bias.
 
 
    
 
    simple_lstm
    
-
    
+
    
 
    
-class paddle.v2.networks.simple_lstm(*args, **kwargs)
    
+paddle.v2.networks.simple_lstm(*args, **kwargs)
 
    Simple LSTM Cell.
    
 
    It just combine a mixed layer with fully_matrix_projection and a lstmemory
 layer. The simple lstm cell was implemented as follow equations.
    
@@ -579,25 +579,25 @@ want to know what lstm is. 
 
    
-
@@ -607,9 +607,9 @@ means default bias.
 
 
    
 
    bidirectional_lstm
    
-
    
+
    
 
    
-class paddle.v2.networks.bidirectional_lstm(*args, **kwargs)
    
+paddle.v2.networks.bidirectional_lstm(*args, **kwargs)
 
    A bidirectional_lstm is a recurrent unit that iterates over the input
 sequence both in forward and bardward orders, and then concatenate two
 outputs form a final output. However, concatenation of two outputs
@@ -629,7 +629,7 @@ The link goes as follows:
 
    
-
-
@@ -655,9 +655,9 @@ concatenated and returned.
 
    GRU
    
 
    
 
    gru_unit
    
-
    
+
    
 
    
-class paddle.v2.networks.gru_unit(*args, **kwargs)
    
+paddle.v2.networks.gru_unit(*args, **kwargs)
 
    Define calculations that a gated recurrent unit performs in a single time
 step. This function itself is not a recurrent layer, so that it can not be
 directly applied to sequence input. This function is almost always used in
@@ -669,19 +669,19 @@ mechanism.
    
 
    
-
@@ -691,9 +691,9 @@ mechanism.
    
 
 
    
 
    gru_group
    
-
    
+
    
 
    
-class paddle.v2.networks.gru_group(*args, **kwargs)
    
+paddle.v2.networks.gru_group(*args, **kwargs)
 
    gru_group is a recurrent layer group version of Gated Recurrent Unit. It
 does exactly the same calculation as the grumemory layer does. A promising
 benefit is that gru hidden states are accessible to the user. This is
@@ -704,8 +704,8 @@ to use the grumemory, which is relatively faster.
    
 
    The example usage is:
    
 
    gru = gur_group(input=[layer1],
                 size=256,
-                act=paddle.v2.Activation.Tanh(),
-                gate_act=paddle.v2.Activation.Sigmoid())
+                act=TanhActivation(),
+                gate_act=SigmoidActivation())
 
    
 
    
 
    
 
 
    Parameters:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – lstmemory group name.
      • 
 
    • size (int) – lstmemory group size.
      • 
 
    • reverse (bool) – is lstm reversed
      • 
-
    • param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default.
      • 
-
    • act (paddle.v2.Activation.Base) – lstm final activiation type
      • 
-
    • gate_act (paddle.v2.Activation.Base) – lstm gate activiation type
      • 
-
    • state_act (paddle.v2.Activation.Base) – lstm state activiation type.
      • 
-
    • mixed_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias parameter attribute of mixed layer.
+
    • param_attr (ParameterAttribute) – Parameter config, None if use default.
      • 
+
    • act (BaseActivation) – lstm final activiation type
      • 
+
    • gate_act (BaseActivation) – lstm gate activiation type
      • 
+
    • state_act (BaseActivation) – lstm state activiation type.
      • 
+
    • mixed_bias_attr (ParameterAttribute|False) – bias parameter attribute of mixed layer.
 False means no bias, None means default bias.
      • 
-
    • lstm_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias parameter attribute of lstm layer.
+
    • lstm_bias_attr (ParameterAttribute|False) – bias parameter attribute of lstm layer.
 False means no bias, None means default bias.
      • 
-
    • mixed_attr (paddle.v2.attr.ExtraAttribute) – mixed layer’s extra attribute.
      • 
-
    • lstm_attr (paddle.v2.attr.ExtraAttribute) – lstm layer’s extra attribute.
      • 
-
    • get_output_attr (paddle.v2.attr.ExtraAttribute) – get output layer’s extra attribute.
      • 
+
    • mixed_layer_attr (ExtraLayerAttribute) – mixed layer’s extra attribute.
      • 
+
    • lstm_layer_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
      • 
+
    • get_output_layer_attr (ExtraLayerAttribute) – get output layer’s extra attribute.
      • 
 
    
     		
 
    
 
    Returns:
    the lstmemory group.
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
 
    Parameters:
      
 
    • name (basestring) – lstm layer name.
      • 
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • size (int) – lstm layer size.
      • 
 
    • reverse (bool) – whether to process the input data in a reverse order
      • 
-
    • mat_param_attr (paddle.v2.attr.ParameterAttribute) – mixed layer’s matrix projection parameter attribute.
      • 
-
    • bias_param_attr (paddle.v2.attr.ParameterAttribute|False) – bias parameter attribute. False means no bias, None
+
    • mat_param_attr (ParameterAttribute) – mixed layer’s matrix projection parameter attribute.
      • 
+
    • bias_param_attr (ParameterAttribute|False) – bias parameter attribute. False means no bias, None
 means default bias.
      • 
-
    • inner_param_attr (paddle.v2.attr.ParameterAttribute) – lstm cell parameter attribute.
      • 
-
    • act (paddle.v2.Activation.Base) – lstm final activiation type
      • 
-
    • gate_act (paddle.v2.Activation.Base) – lstm gate activiation type
      • 
-
    • state_act (paddle.v2.Activation.Base) – lstm state activiation type.
      • 
-
    • mixed_attr (paddle.v2.attr.ExtraAttribute) – mixed layer’s extra attribute.
      • 
-
    • lstm_cell_attr (paddle.v2.attr.ExtraAttribute) – lstm layer’s extra attribute.
      • 
+
    • inner_param_attr (ParameterAttribute) – lstm cell parameter attribute.
      • 
+
    • act (BaseActivation) – lstm final activiation type
      • 
+
    • gate_act (BaseActivation) – lstm gate activiation type
      • 
+
    • state_act (BaseActivation) – lstm state activiation type.
      • 
+
    • mixed_layer_attr (ExtraLayerAttribute) – mixed layer’s extra attribute.
      • 
+
    • lstm_cell_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
      • 
 
    
     		
 
    
 
    Returns:
    lstm layer name.
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
     
 
    Parameters:
      
 
    • name (basestring) – bidirectional lstm layer name.
      • 
-
    • input (paddle.v2.config_base.Layer) – input layer.
      • 
+
    • input (LayerOutput) – input layer.
      • 
 
    • size (int) – lstm layer size.
      • 
 
    • return_seq (bool) – If set False, outputs of the last time step are
 concatenated and returned.
@@ -639,10 +639,10 @@ concatenated and returned.
      • 
 
    
     		
 
    Returns:
    paddle.v2.config_base.Layer object accroding to the return_seq.
    
+
    Returns:
    LayerOutput object accroding to the return_seq.
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
     
 
 
    Parameters:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – name of the gru group.
      • 
 
    • size (int) – hidden size of the gru.
      • 
-
    • act (paddle.v2.Activation.Base) – type of the activation
      • 
-
    • gate_act (paddle.v2.Activation.Base) – type of the gate activation
      • 
-
    • gru_attr (paddle.v2.attr.ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
+
    • act (BaseActivation) – type of the activation
      • 
+
    • gate_act (BaseActivation) – type of the gate activation
      • 
+
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
 
    
     		
 
    
 
    Returns:
    the gru output layer.
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
 
    
@@ -713,21 +713,21 @@ to use the grumemory, which is relatively faster.
    
     
-
@@ -737,23 +737,23 @@ to use the grumemory, which is relatively faster.
    
 
 
    
 
    simple_gru
    
-
    
+
    
 
    
-class paddle.v2.networks.simple_gru(*args, **kwargs)
    
-
    You maybe see gru_step, grumemory in layers.py, gru_unit, gru_group,
+paddle.v2.networks.simple_gru(*args, **kwargs)
+
    You maybe see gru_step_layer, grumemory in layers.py, gru_unit, gru_group,
 simple_gru in network.py. The reason why there are so many interfaces is
 that we have two ways to implement recurrent neural network. One way is to
 use one complete layer to implement rnn (including simple rnn, gru and lstm)
-with multiple time steps, such as recurrent, lstmemory, grumemory. But,
+with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But,
 the multiplication operation \(W x_t\) is not computed in these layers.
 See details in their interfaces in layers.py.
 The other implementation is to use an recurrent group which can ensemble a
 series of layers to compute rnn step by step. This way is flexible for
 attenion mechanism or other complex connections.
    
 
      
-
    • gru_step: only compute rnn by one step. It needs an memory as input
+
    • gru_step_layer: only compute rnn by one step. It needs an memory as input
 and can be used in recurrent group.
      • 
-
    • gru_unit: a wrapper of gru_step with memory.
      • 
+
    • gru_unit: a wrapper of gru_step_layer with memory.
      • 
 
    • gru_group: a GRU cell implemented by a combination of multiple layers in
 recurrent group.
 But \(W x_t\) is not done in group.
      • 
@@ -774,21 +774,21 @@ gru_group, and gru_group is relatively better than simple_gru.
      
 
    
-
@@ -798,9 +798,9 @@ gru_group, and gru_group is relatively better than simple_gru.
    
 
 
    
 
    simple_gru2
    
-
    
+
    
 
    
-class paddle.v2.networks.simple_gru2(*args, **kwargs)
    
+paddle.v2.networks.simple_gru2(*args, **kwargs)
 
    simple_gru2 is the same with simple_gru, but using grumemory instead
 Please see grumemory in layers.py for more detail about the maths.
 simple_gru2 is faster than simple_gru.
    
@@ -813,21 +813,21 @@ simple_gru2 is faster than simple_gru.
    
 
    
-
@@ -837,9 +837,9 @@ simple_gru2 is faster than simple_gru.
    
 
 
    
 
    bidirectional_gru
    
-
    
+
    
 
    
-class paddle.v2.networks.bidirectional_gru(*args, **kwargs)
    
+paddle.v2.networks.bidirectional_gru(*args, **kwargs)
 
    A bidirectional_gru is a recurrent unit that iterates over the input
 sequence both in forward and bardward orders, and then concatenate two
 outputs to form a final output. However, concatenation of two outputs
@@ -855,7 +855,7 @@ just add them together.
    
 
    
-
-
@@ -879,9 +879,9 @@ concatenated and returned.
 
 
    
 
    simple_attention
    
-
    
+
    
 
    
-class paddle.v2.networks.simple_attention(*args, **kwargs)
    
+paddle.v2.networks.simple_attention(*args, **kwargs)
 
    Calculate and then return a context vector by attention machanism.
 Size of the context vector equals to size of the encoded_sequence.
    
 
    
@@ -905,18 +905,18 @@ Align and Translate for more details. The link is as follows:
 
    
 
 
    Parameters:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – name of the gru group.
      • 
 
    • size (int) – hidden size of the gru.
      • 
 
    • reverse (bool) – whether to process the input data in a reverse order
      • 
-
    • act (paddle.v2.Activation.Base) – type of the activiation
      • 
-
    • gate_act (paddle.v2.Activation.Base) – type of the gate activiation
      • 
-
    • gru_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
-
    • gru_attr (paddle.v2.attr.ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
+
    • act (BaseActivation) – type of the activiation
      • 
+
    • gate_act (BaseActivation) – type of the gate activiation
      • 
+
    • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
+
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
 
    
     		
 
    
 
    Returns:
    the gru group.
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
     
 
 
    Parameters:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – name of the gru group.
      • 
 
    • size (int) – hidden size of the gru.
      • 
 
    • reverse (bool) – whether to process the input data in a reverse order
      • 
-
    • act (paddle.v2.Activation.Base) – type of the activiation
      • 
-
    • gate_act (paddle.v2.Activation.Base) – type of the gate activiation
      • 
-
    • gru_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
-
    • gru_attr (paddle.v2.attr.ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
+
    • act (BaseActivation) – type of the activiation
      • 
+
    • gate_act (BaseActivation) – type of the gate activiation
      • 
+
    • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
+
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
 
    
     		
 
    
 
    Returns:
    the gru group.
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
     
 
 
    Parameters:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – name of the gru group.
      • 
 
    • size (int) – hidden size of the gru.
      • 
 
    • reverse (bool) – whether to process the input data in a reverse order
      • 
-
    • act (paddle.v2.Activation.Base) – type of the activiation
      • 
-
    • gate_act (paddle.v2.Activation.Base) – type of the gate activiation
      • 
-
    • gru_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
-
    • gru_attr (paddle.v2.attr.ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
+
    • act (BaseActivation) – type of the activiation
      • 
+
    • gate_act (BaseActivation) – type of the gate activiation
      • 
+
    • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
+
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
 
    
     		
 
    
 
    Returns:
    the gru group.
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
     
 
    Parameters:
      
 
    • name (basestring) – bidirectional gru layer name.
      • 
-
    • input (paddle.v2.config_base.Layer) – input layer.
      • 
+
    • input (LayerOutput) – input layer.
      • 
 
    • size (int) – gru layer size.
      • 
 
    • return_seq (bool) – If set False, outputs of the last time step are
 concatenated and returned.
@@ -865,10 +865,10 @@ concatenated and returned.
      • 
 
    
     		
 
    Returns:
    paddle.v2.config_base.Layer object.
    
+
    Returns:
    LayerOutput object.
    
     		
 
    Return type:
    paddle.v2.config_base.Layer
    
+
    Return type:
    LayerOutput
    
     		
 
    
     
 
    Parameters:
      
 
    • name (basestring) – name of the attention model.
      • 
-
    • softmax_param_attr (paddle.v2.attr.ParameterAttribute) – parameter attribute of sequence softmax
+
    • softmax_param_attr (ParameterAttribute) – parameter attribute of sequence softmax
 that is used to produce attention weight
      • 
 
    • weight_act (Activation) – activation of the attention model
      • 
-
    • encoded_sequence (paddle.v2.config_base.Layer) – output of the encoder
      • 
-
    • encoded_proj (paddle.v2.config_base.Layer) – attention weight is computed by a feed forward neural
+
    • encoded_sequence (LayerOutput) – output of the encoder
      • 
+
    • encoded_proj (LayerOutput) – attention weight is computed by a feed forward neural
 network which has two inputs : decoder’s hidden state
 of previous time step and encoder’s output.
 encoded_proj is output of the feed-forward network for
 encoder’s output. Here we pre-compute it outside
 simple_attention for speed consideration.
      • 
-
    • decoder_state (paddle.v2.config_base.Layer) – hidden state of decoder in previous time step
      • 
-
    • transform_param_attr (paddle.v2.attr.ParameterAttribute) – parameter attribute of the feed-forward
+
    • decoder_state (LayerOutput) – hidden state of decoder in previous time step
      • 
+
    • transform_param_attr (ParameterAttribute) – parameter attribute of the feed-forward
 network that takes decoder_state as inputs to
 compute attention weight.
      • 
 
    
@@ -935,9 +935,9 @@ compute attention weight.
 
    Miscs
    
 
    
 
    dropout_layer
    
-
    
+
    
 
    
-class paddle.v2.networks.dropout_layer(*args, **kwargs)
    
+paddle.v2.networks.dropout_layer(*args, **kwargs)
 
    @TODO(yuyang18): Add comments.
    
 diff --git a/develop/doc/api/v2/data.html b/develop/doc/api/v2/data.html
index 1dff7c81242bdf09d5c8d4b76a5627961167051c..697dfc899ec775c85b0514bc9f20ff946e72c4b8 100644
--- a/develop/doc/api/v2/data.html
+++ b/develop/doc/api/v2/data.html
@@ -185,12 +185,50 @@
 
    Data Reader Interface and DataSets
    
 
    DataTypes
    
+
    
+
    
+paddle.v2.data_type.dense_array(dim, seq_type=0)
    
+
    Dense Array. It means the input feature is dense array with float type.
+For example, if the input is an image with 28*28 pixels, the input of
+Paddle neural network could be a dense vector with dimension 784 or a
+numpy array with shape (28, 28).
    
+
    For the 2-D convolution operation, each sample in one mini-batch must have
+the similarly size in PaddlePaddle now. But, it supports variable-dimension
+feature across mini-batch. For the variable-dimension, the param dim is not
+used. While the data reader must yield numpy array and the data feeder will
+set the data shape correctly.
    
+
    
 

 
    
+++
+
+
+
+
+
+
+
+
    


    Parameters:
      
+
    • dim (int) – dimension of this vector.
      • 
+
    • seq_type (int) – sequence type of input.
      • 
+
    
+
    Returns:
    An input type object.
    
+
    Return type:
    InputType
    
+
    
+
    
+
 
    
 
    
 paddle.v2.data_type.dense_vector(dim, seq_type=0)
    
-
    Dense Vector. It means the input feature is dense float vector. For example,
-if the input is an image with 28*28 pixels, the input of Paddle neural
-network should be a dense vector with dimension 784.
    
+
    Dense Array. It means the input feature is dense array with float type.
+For example, if the input is an image with 28*28 pixels, the input of
+Paddle neural network could be a dense vector with dimension 784 or a
+numpy array with shape (28, 28).
    
+
    For the 2-D convolution operation, each sample in one mini-batch must have
+the similarly size in PaddlePaddle now. But, it supports variable-dimension
+feature across mini-batch. For the variable-dimension, the param dim is not
+used. While the data reader must yield numpy array and the data feeder will
+set the data shape correctly.
    
 diff --git a/develop/doc/design/cluster_train/master_server.html b/develop/doc/design/cluster_train/master_server.html
index 0967bf90a79ee567392dcd7a2924d6c2674a5b49..5eb3ab2c27e53a151dd6f6f71292c5b56e3e3db8 100644
--- a/develop/doc/design/cluster_train/master_server.html
+++ b/develop/doc/design/cluster_train/master_server.html
@@ -186,7 +186,7 @@
 
    
 
    Task Queue
    
-
    As mentioned in distributed training design doc, a task is a data shard that the master server assigns to the trainer process to train on. A task consists of one or multiple blocks from one or multiple files. The master server maintains task queues to track the training progress.
    
+
    As mentioned in distributed training design doc, a task is a data shard that the master server assigns to the trainer process to train on. A task consists of one or multiple chunks from one or multiple files. The master server maintains task queues to track the training progress.
    
 
    
 
    Task Queue Creation
    
 
      
@@ -197,21 +197,21 @@
 
    
 
    
-
  • The master server will scan through each RecordIO file to generate the block index and know how many blocks does each file have. A block can be referenced by the file path and the index of the block within the file. The block index is in memory data structure that enables fast access to each block, and the index of the block with the file is an integer start from 0, representing the n-th block within the file.
    
-
    The definition of the block is:
    
-
    type Block struct {
-    Idx   int // index of the block within the file
+
  • The master server will scan through each RecordIO file to generate the chunk index and know how many chunks does each file have. A chunk can be referenced by the file path and the index of the chunk within the file. The chunk index is in memory data structure that enables fast access to each chunk, and the index of the chunk with the file is an integer start from 0, representing the n-th chunk within the file.
    
+
    The definition of the chunk is:
    
+
    type Chunk struct {
+    Idx   int // index of the chunk within the file
     Path  string
-    Index recordio.Index // block index
+    Index recordio.Index // chunk index
 }
 
    
 
    
 
    • 
-
  • Blocks are grouped into tasks, and tasks are filled into the todo queue. The pending queue and the done queue are initialized with no element.
    
+
  • Chunks are grouped into tasks, and tasks are filled into the todo queue. The pending queue and the done queue are initialized with no element.
    
 
    The definition of the task is:
    
 
    type Task struct {
     Index  int
-    Blocks []Block
+    Chunks []Chunk
 }
 
    
 
    
diff --git a/develop/doc/design/cluster_train/pserver_client.html b/develop/doc/design/cluster_train/pserver_client.html
index 7147b93446c2af232d1d4dbd85b6f72dfe77214d..5962822f6e94033d4a9c9813b26093c79aab1824 100644
--- a/develop/doc/design/cluster_train/pserver_client.html
+++ b/develop/doc/design/cluster_train/pserver_client.html
@@ -226,7 +226,7 @@ name:sparse-n-1
 
    int paddle_begin_init_params(paddle_pserver_client* client, const char* config_proto);
 
    
 
    
-
    The selected trainer’s call to paddle_begin_init_params will return with 1, and the other trainers’ call to paddle_begin_init_params will block until initialization is done, and return 0. As illustrated below:
    
+
    The selected trainer’s call to paddle_begin_init_params will return with 1, and the other trainers’ call to paddle_begin_init_params will return 0. paddle_get_params will be blocked until initialization is completed. As illustrated below:
    
 
    
 
    • 
 
    
@@ -259,16 +259,13 @@ name:sparse-n-1
  *
  * paddle_begin_init_params will be called from multiple trainers,
  * only one trainer will be selected to initialize the parameters on
- * parameter servers. Other trainers will be blocked until the
- * initialization is done, and they need to get the initialized
+ * parameter servers. Other trainers need to get the initialized
  * parameters from parameter servers using @paddle_get_params.
  *
- * @param pserver_config_proto serialized parameter server configuration in
- * Protocol Buffers format.
  * @return 1 if the trainer is selected to initialize parameter
  * servers, otherwise 0.
  */
-int paddle_begin_init_params(paddle_pserver_client* client, const char* pserver_config_proto);
+int paddle_begin_init_params(paddle_pserver_client* client);
 
 /**
  * @brief paddle_init_param initializes the parameter on parameter
@@ -276,12 +273,13 @@ name:sparse-n-1
  *
  * @param param the parameter to initialize.
  * @param param_config_proto the configuration for the parameter.
+ * @param config_len the length of param_config_proto
  * @return 0 if successful, otherwise -1. On failure, the trainer
  * needs to restart the entire initialization process (starting from
  * @paddle_begin_init_param). Or simply exit the program and wait for
  * the cluster management system to restart the trainer.
  */
-int paddle_init_param(paddle_pserver_client* client, paddle_parameter params, const char* param_config_proto);
+int paddle_init_param(paddle_pserver_client* client, paddle_parameter param, const unsigned char* param_config_proto, int config_len);
 
 /**
  * @brief paddle_finish_init_params tells parameter servers client has
@@ -308,6 +306,9 @@ name:sparse-n-1
 /**
  * @brief paddle_get_params gets parameters from parameter servers.
  *
+ * paddle_get_params will block until parameters are initialized on
+ * the parameter servers.
+ *
  * @param names the array of names of the parameters to get.
  * @param dst the destination array of parameters to save to.
  * @param len the length of the names array and the paddle_parameter
diff --git a/develop/doc/design/parameters_in_cpp.html b/develop/doc/design/parameters_in_cpp.html
new file mode 100644
index 0000000000000000000000000000000000000000..437d9d8524fa3f37e0fabd4ab663ceeabc9c7c54
--- /dev/null
+++ b/develop/doc/design/parameters_in_cpp.html
@@ -0,0 +1,274 @@
+
+
+
+
+  
+
+  
+  
+  
+  
+  Design Doc: The C++ Class Parameters — PaddlePaddle  documentation
+  
+
+  
+  
+
+  
+
+  
+  
+    
+
+  
+
+  
+  
+    
+  
+
+  
+  
+        
+        
+     
+
+  
+  
+  
+
+  
+
+  
+  
+
+
+
+
+
+  
+  
    
+    
+    
    
+      
    
+        Folk me on Github
+        
    
+          
+            English
+            
+            
+          
+          
+        
    
+        
+      
    
+      
    
+        
+        
+
+        
+
    
+  
    
+    
+    
+    
+  
+
            
+      
    
+    
    
+  
    
+  
+  
    
+
+    
+    
+    
+    
    
+
+      
+
+ 
+
+
+
+
+
+
+
+
    
+  
      
+      
+    
    • Design Doc: The C++ Class Parameters
      • 
+  
    
+
    
+      
+      
    
+        
    
+          
    
+           
    
+            
+  
    
+
    Design Doc: The C++ Class Parameters
    
+
    Parameters is a concept we designed in Paddle V2 API. Parameters is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of Parameter in api.md.
    
+
    We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation:
    
+
      
+
    • We just use memcpy to share Parameters between topologies, but this is very inefficient.
      • 
+
    • We did not implement share Parameters while training. We just trigger memcpy when start training.
      • 
+
    
+
    It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with Parameters:
    
+
      
+
    1. paddle::Parameter. A Parameters is a container for paddle::Parameter.
+It is evident that we should use paddle::Parameter when developing Parameters.
+However, the Parameter class contains many functions and does not have a clear interface.
+It contains create/store Parameter, serialize/deserialize, optimize(i.e SGD), randomize/zero.
+When we developing Parameters, we only use create/store Parameter functionality.
+We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation.
      2. 
+
    2. paddle::GradientMachine and its sub-classes, e.g., paddle::MultiGradientMachine, paddle::NeuralNetwork.
+We should pass Parameters to paddle::GradientMachine when forward/backward to avoid memcpy between topologies.
+Also, we should handle multi-GPU/CPU training, because forward and backward would perform on multi-GPUs and multi-CPUs.
+Parameters should dispatch the parameter value to each device, and gather the parameter gradient from each device.
      3. 
+
    3. paddle::ParameterUpdater. The ParameterUpdater is used to update parameters in Paddle.
+So Parameters should be used by paddle::ParameterUpdater, and paddle::ParameterUpdater should optimize Parameters (by SGD).
      4. 
+
    
+
    The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one.
    
+
      
+
    1. Clean paddle::Parameter interface. Extract the functionalities of paddle::Parameter to prepare for the implementation of Parameters.
      2. 
+
    2. Implementation a Parameters class. It just stores the paddle::Parameter inside. Make GradientMachine uses Parameters as a class member.
      3. 
+
    3. Make Parameters support Multi-CPU and Multi-GPU training to prepare for sharing Parameter between topologies.
+Because we need share Parameters between topologies, it is Parameters‘s response to exchange Parameters between GPUs.
+GradientMachine should not handle how to exchange Parameters because GradientMachine only used to train one topology and we need to support train many topologies in Paddle, i.e., there could be many GradientMachines use one Parameters.
        
+
      • We should use a global function to exchange Parameters between GPUs, not a member function in Parameters. The MultiGradientMachine invoke this function, which uses Parameters as this function inputs.
        • 
+
      • The MultiGradientMachine contains many functionalities. Extracting the Parameters exchanging logic could make MultiGradientMachine clearer and simpler.
        • 
+
      
+
      4. 
+
    4. Make Parameters as an argument for forward/backward function, not a data member for GradientMachine. For example, forward could be forward(const Parameters& params, ...) and backward could be backward(Parameters* params, ...). After this step, Paddle could share Parameters between topologies.
      5. 
+
    5. ParameterUpdater is invoked by GradientMachine and Trainer, but it updates Parameters. In the end of this code refactoring, we could change ParameterUpdater directly uses Parameters to make ParameterUpdater‘s implementation clear.
      6. 
+
    
+
    
+
+
+           
    
+          
    
+          
    
+  
+
+  
    
+
+  
    
+    
    
+        © Copyright 2016, PaddlePaddle developers.
+
+    
    
+  
    
+  Built with Sphinx using a theme provided by Read the Docs. 
+
+
    
+
+        
    
+      
    
+
+    
    
+
+  
    
+  
+
+
+  
+
+    
+      
+      
+      
+      
+       
+  
+
+  
+  
+    
+  
+  
+  
+  
+   
+
+
+
\ No newline at end of file
diff --git a/develop/doc/objects.inv b/develop/doc/objects.inv
index fcd99a2a66f08ab215163b04c4beef27abd3499c..bef3f91ad5451c52b856210cf15d95255cc27472 100644
Binary files a/develop/doc/objects.inv and b/develop/doc/objects.inv differ
diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js
index dbfcd27214b7a1729c0b3ab0e3095e275ab5cdde..cd3e703049a70a12958113a9eb8ee2d5b9c35dd1 100644
--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
@@ -1 +1 @@
-Search.setIndex({docnames:["about/index_en","api/index_en","api/v1/data_provider/dataprovider_en","api/v1/data_provider/pydataprovider2_en","api/v1/index_en","api/v1/predict/swig_py_paddle_en","api/v2/config/activation","api/v2/config/attr","api/v2/config/evaluators","api/v2/config/layer","api/v2/config/networks","api/v2/config/optimizer","api/v2/config/pooling","api/v2/data","api/v2/model_configs","api/v2/run_logic","design/api","design/build_system/README","design/cluster_train/README","design/cluster_train/checkpointing","design/cluster_train/data_dispatch","design/cluster_train/master_server","design/cluster_train/pserver_client","design/cluster_train/submit-job","design/file_manager/README","design/file_manager/pfs/pfsclient","design/multi_language_interface/00.why_plain_c","design/multi_language_interface/01.inference_implementation","design/reader/README","design/releasing_process","getstarted/basic_usage/index_en","getstarted/build_and_install/build_from_source_en","getstarted/build_and_install/docker_install_en","getstarted/build_and_install/index_en","getstarted/build_and_install/ubuntu_install_en","getstarted/index_en","howto/deep_model/rnn/index_en","howto/deep_model/rnn/rnn_config_en","howto/dev/contribute_to_paddle_en","howto/dev/new_layer_en","howto/index_en","howto/optimization/gpu_profiling_en","howto/usage/cluster/cluster_train_en","howto/usage/cmd_parameter/arguments_en","howto/usage/cmd_parameter/detail_introduction_en","howto/usage/cmd_parameter/index_en","howto/usage/cmd_parameter/use_case_en","howto/usage/k8s/k8s_aws_en","howto/usage/k8s/k8s_en","howto/usage/k8s/src/k8s_data/README","howto/usage/k8s/src/k8s_train/README","index_en","tutorials/embedding_model/index_en","tutorials/gan/index_en","tutorials/image_classification/index_en","tutorials/imagenet_model/resnet_model_en","tutorials/index_en","tutorials/quick_start/index_en","tutorials/rec/ml_dataset_en","tutorials/rec/ml_regression_en","tutorials/semantic_role_labeling/index_en","tutorials/sentiment_analysis/index_en","tutorials/text_generation/index_en"],envversion:50,filenames:["about/index_en.rst","api/index_en.rst","api/v1/data_provider/dataprovider_en.rst","api/v1/data_provider/pydataprovider2_en.rst","api/v1/index_en.rst","api/v1/predict/swig_py_paddle_en.rst","api/v2/config/activation.rst","api/v2/config/attr.rst","api/v2/config/evaluators.rst","api/v2/config/layer.rst","api/v2/config/networks.rst","api/v2/config/optimizer.rst","api/v2/config/pooling.rst","api/v2/data.rst","api/v2/model_configs.rst","api/v2/run_logic.rst","design/api.md","design/build_system/README.md","design/cluster_train/README.md","design/cluster_train/checkpointing.md","design/cluster_train/data_dispatch.md","design/cluster_train/master_server.md","design/cluster_train/pserver_client.md","design/cluster_train/submit-job.md","design/file_manager/README.md","design/file_manager/pfs/pfsclient.md","design/multi_language_interface/00.why_plain_c.md","design/multi_language_interface/01.inference_implementation.md","design/reader/README.md","design/releasing_process.md","getstarted/basic_usage/index_en.rst","getstarted/build_and_install/build_from_source_en.md","getstarted/build_and_install/docker_install_en.rst","getstarted/build_and_install/index_en.rst","getstarted/build_and_install/ubuntu_install_en.rst","getstarted/index_en.rst","howto/deep_model/rnn/index_en.rst","howto/deep_model/rnn/rnn_config_en.rst","howto/dev/contribute_to_paddle_en.md","howto/dev/new_layer_en.rst","howto/index_en.rst","howto/optimization/gpu_profiling_en.rst","howto/usage/cluster/cluster_train_en.md","howto/usage/cmd_parameter/arguments_en.md","howto/usage/cmd_parameter/detail_introduction_en.md","howto/usage/cmd_parameter/index_en.rst","howto/usage/cmd_parameter/use_case_en.md","howto/usage/k8s/k8s_aws_en.md","howto/usage/k8s/k8s_en.md","howto/usage/k8s/src/k8s_data/README.md","howto/usage/k8s/src/k8s_train/README.md","index_en.rst","tutorials/embedding_model/index_en.md","tutorials/gan/index_en.md","tutorials/image_classification/index_en.md","tutorials/imagenet_model/resnet_model_en.md","tutorials/index_en.md","tutorials/quick_start/index_en.md","tutorials/rec/ml_dataset_en.md","tutorials/rec/ml_regression_en.rst","tutorials/semantic_role_labeling/index_en.md","tutorials/sentiment_analysis/index_en.md","tutorials/text_generation/index_en.md"],objects:{"paddle.trainer.PyDataProvider2":{provider:[3,0,1,""]}},objnames:{"0":["py","function","Python function"]},objtypes:{"0":"py:function"},terms:{"0000x":57,"00186201e":5,"00m":41,"02595v1":9,"03m":41,"0424m":41,"0473v3":10,"055ee37d":47,"05d":54,"0630u":41,"06u":41,"0810u":41,"08823112e":5,"0957m":41,"0ab":9,"0rc1":29,"0rc2":[29,32],"0th":62,"10007_10":61,"10014_7":61,"100gb":41,"100gi":47,"10g":23,"10m":41,"1150u":41,"11\u5b9e\u73b0\u4e86c":27,"11e6":48,"12194102e":5,"124n":41,"13m":48,"1490u":41,"15501715e":5,"1550u":41,"15mb":57,"1636k":62,"16mb":57,"16u":41,"173m":55,"173n":41,"1770u":41,"18ad":47,"18e457ce3d362ff5f3febf8e7f85ffec852f70f3b629add10aed84f930a68750":48,"197u":41,"1gb":41,"1st":[52,55,61,62],"202mb":62,"210u":41,"211839e770f7b538e2d8":10,"215n":41,"228u":41,"234m":55,"2520u":41,"252kb":57,"25639710e":5,"25k":57,"2680u":41,"27787406e":5,"279n":41,"27m":41,"285m":41,"2863m":41,"28m":41,"28x28":3,"2977m":41,"2cbf7385":47,"2nd":[9,61,62],"302n":41,"30u":41,"32777140e":5,"328n":41,"32u":41,"32x32":[13,54],"331n":41,"3320u":41,"36540484e":5,"365e":47,"36u":41,"3710m":41,"3768m":41,"387u":41,"38u":41,"3920u":41,"39u":41,"3rd":[59,61,62],"4035m":41,"4090u":41,"4096mb":44,"4279m":41,"43630644e":5,"43u":41,"448a5b355b84":48,"4560u":41,"4563m":41,"45u":41,"4650u":41,"4726m":41,"473m":48,"48565123e":5,"48684503e":5,"49316648e":5,"4gb":44,"50bd":47,"50gi":47,"51111044e":5,"514u":41,"525n":41,"526u":41,"53018653e":5,"536u":41,"5460u":41,"5470u":41,"54u":41,"55g":62,"5690m":41,"573u":41,"578n":41,"5798m":41,"586u":41,"58s":48,"5969m":41,"6080u":41,"6082v4":9,"6140u":41,"6305m":41,"639u":41,"655u":41,"6780u":41,"6810u":41,"682u":41,"6970u":41,"6ce9":47,"6node":42,"6th":62,"704u":41,"70634608e":5,"7090u":41,"72296313e":5,"72u":41,"73u":41,"75u":41,"760u":41,"767u":41,"783n":41,"784u":41,"78m":41,"7eamaa":13,"7kb":48,"8250u":41,"8300u":41,"830n":41,"849m":41,"85625684e":5,"861u":41,"864k":62,"8661m":41,"892m":41,"901n":41,"90u":41,"918u":41,"9247m":41,"924n":41,"9261m":41,"93137714e":5,"9330m":41,"94u":41,"9530m":41,"96644767e":5,"983m":41,"988u":41,"997u":41,"99982715e":5,"99m":55,"99u":41,"9f18":48,"\u4e00\u4e2a\u5178\u578b\u7684chunk\u5982\u4e0b\u6240\u793a":24,"\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc\u7684\u6a21\u578b\u7531\u5927\u91cf\u7684\u53c2\u6570\u7ec4\u6210":19,"\u4e00\u4e2achunk\u7531\u6240\u5728\u7684\u6587\u4ef6\u504f\u79fb":24,"\u4e00\u4e2aposix\u517c\u5bb9\u7684\u6587\u4ef6\u7cfb\u7edf":24,"\u4e00\u822c\u4e0d\u5141\u8bb8\u518d\u4ece":29,"\u4e0a\u4f20\u5230cloud\u6216\u8005\u4e0b\u8f7d\u5230\u672c\u5730\u7684\u65f6\u95f4\u53ef\u80fd\u6bd4\u8f83\u957f":24,"\u4e0a\u6ce8\u518c\u4e00\u4e0b":24,"\u4e0b\u5b58\u653e\u516c\u5171\u6570\u636e\u96c6\u5408":20,"\u4e0b\u8f7d":24,"\u4e0b\u8f7d\u5230\u672c\u5730":24,"\u4e0b\u9762\u5206\u522b\u4ecb\u7ecd\u67d0\u4e00\u7c7b\u6587\u4ef6\u7684\u5b9e\u73b0\u65b9\u5f0f":27,"\u4e0d\u4e00\u81f4\u7684\u7531pfsclient\u4e0b\u8f7d\u6216\u8005\u4f20\u8f93chunk\u5b8c\u6210":24,"\u4e0d\u4f7f\u7528\u9759\u6001\u5e93":26,"\u4e0d\u4f7f\u7528c":26,"\u4e0d\u4f7f\u7528swig":26,"\u4e0d\u540c\u7248\u672c\u7684\u7f16\u8bd1\u5668\u4e4b\u95f4":26,"\u4e0d\u540c\u8bed\u8a00\u7684\u63a5\u53e3\u9002\u5e94\u4e0d\u540c\u8bed\u8a00\u7684\u7279\u6027":26,"\u4e0d\u5728":27,"\u4e0d\u5bb9\u6613\u51fa\u9519":24,"\u4e0d\u5d4c\u5165\u5176\u4ed6\u8bed\u8a00\u89e3\u91ca\u5668":26,"\u4e0d\u5d4c\u5165python\u89e3\u91ca\u5668":26,"\u4e0d\u663e\u793a\u7684\u5199\u6bcf\u4e2a\u7c7b\u5177\u4f53\u5305\u542b\u4ec0\u4e48":26,"\u4e0d\u7528mount\u7684\u65b9\u5f0f\u6765\u8bbf\u95ee\u6570\u636e":20,"\u4e0e\u4e4b\u76f8\u5bf9\u7684\u662flocal":24,"\u4e0e\u529f\u80fd\u5206\u652f\u4e0d\u540c\u7684\u662f":29,"\u4e0e\u53ef\u80fd\u6709\u7684":29,"\u4e14\u589e\u52a0\u4e00\u4e2a\u7b2c\u4e09\u65b9\u8bed\u8a00":26,"\u4e14\u8c03\u7528\u65f6\u4e0d\u80fd\u629b\u51fa\u5f02\u5e38\u6216\u51fa\u73b0\u8fd0\u884c\u65f6\u9519\u8bef":27,"\u4e14c99\u652f\u6301bool\u7c7b\u578b\u548c\u5b9a\u957f\u6574\u6570":26,"\u4e14c99\u76f8\u5bf9\u4e8ec11\u4f7f\u7528\u66f4\u52a0\u5e7f\u6cdb":26,"\u4e2a\u6027\u5316\u63a8\u8350":29,"\u4e2d":[26,27],"\u4e2d\u5199\u5165json\u5185\u5bb9":19,"\u4e2d\u5b8c\u5168\u4e00\u81f4":26,"\u4e2d\u5b9e\u73b0\u7684\u7ed3\u6784\u4f53":27,"\u4e2d\u8fd0\u884c\u4efb\u52a1\u7684\u89d2\u5ea6":20,"\u4e3a\u4e86\u5e94\u5bf9\u4ee5\u4e0a\u7684\u95ee\u9898":24,"\u4e3a\u4e86\u66b4\u9732\u7684\u63a5\u53e3\u5c3d\u91cf\u7b80\u5355":27,"\u4e3b\u8981\u529f\u80fd\u5305\u62ec":24,"\u4e4b\u5916\u7684\u6240\u6709\u5934\u6587\u4ef6":27,"\u4e5f\u4e0d\u4f7f\u7528\u5176\u4ed6\u52a8\u6001\u5e93":26,"\u4e5f\u4e0d\u5e94\u8be5\u62a5\u9519":27,"\u4e5f\u4e0d\u751f\u6210":27,"\u4e66\u5199":26,"\u4eba\u8138\u8bc6\u522b":20,"\u4ec5\u4ec5\u4f7f\u7528":26,"\u4ece":29,"\u4ece\u78c1\u76d8\u6587\u4ef6\u4e2d\u52a0\u8f7duuid\u6587\u4ef6\u540d\u7684\u68c0\u67e5\u70b9\u5feb\u7167\u6587\u4ef6":19,"\u4eceetcd\u4e2d\u8bfb\u53d6\u8282\u70b9":19,"\u4ed6\u4e3b\u8981\u5305\u542b\u4e86\u5b9e\u9645\u66b4\u9732\u7684\u7c7b\u578b\u7ed3\u6784":27,"\u4ed6\u662f\u5c06":27,"\u4ed6\u7684\u76ee\u6807\u662f\u4f7f\u7528c":26,"\u4ee3\u7801\u751f\u6210\u7684\u7b26\u53f7\u53ef\u80fd\u4e0d\u4e00\u81f4":26,"\u4ee3\u8868\u8fd9\u4e2ashard\u7684\u6700\u5927index":20,"\u4ee3\u8868shard\u7684index":20,"\u4ee5\u4e0a\u4ee3\u7801\u7684reader\u8f93\u51fa\u7684data":20,"\u4ee5\u4e0a\u547d\u4ee4\u4f1a\u5728\u5f53\u524d\u76ee\u5f55\u4e0b\u751f\u6210100\u4e2a\u6587\u4ef6":20,"\u4ee5\u4e0b":20,"\u4ee5\u4fbf\u6211\u4eec\u53ef\u4ee5\u628a\u66f4\u591a\u7684\u7cbe\u529b\u653e\u5230\u903b\u8f91\u672c\u8eab\u4e0a":24,"\u4ee5\u53canumpi":20,"\u4efb\u610f\u65f6\u523b\u53ea\u53ef\u80fd\u540c\u65f6\u6709\u4e00\u53f0\u670d\u52a1\u5668\u6545\u969c":19,"\u4f1a\u5bfc\u81f4\u4e0d\u540c\u7248\u672cpython\u5728\u4e00\u4e2a\u8fdb\u7a0b\u91cc\u7684bug":26,"\u4f1a\u76f4\u63a5\u62a5\u9519\u9000\u51fa":26,"\u4f1a\u88abpickle\u5e8f\u5217\u5316\u6210\u5b57\u7b26\u4e32":20,"\u4f20\u5165":20,"\u4f46":27,"\u4f46\u4e0d\u66b4\u9732":27,"\u4f46\u5e76\u6ca1\u6709\u7ecf\u8fc7\u56de\u5f52\u6d4b\u8bd5":29,"\u4f46\u6240\u6709fork\u7684\u7248\u672c\u5e93\u7684\u6240\u6709\u5206\u652f\u90fd\u76f8\u5f53\u4e8e\u7279\u6027\u5206\u652f":29,"\u4f46\u662f\u53c8\u8fc7\u4e8e\u7410\u788e":27,"\u4f46\u662f\u89e3\u91ca\u6027\u8bed\u8a00":26,"\u4f5c\u4e3a\u5b58\u50a8\u7cfb\u7edf":20,"\u4f5c\u4e3a\u7c7b\u53e5\u67c4":26,"\u4f7f\u7528":[27,29],"\u4f7f\u7528\u4e0b\u9762\u547d\u4ee4":20,"\u4f7f\u7528\u52a8\u6001\u5e93":26,"\u4f7f\u7528\u540c\u6837\u7684\u8bad\u7ec3\u6570\u636eblock":19,"\u4f7f\u7528\u667a\u80fd\u6307\u9488\u7684\u539f\u56e0\u662f":27,"\u4f7f\u7528\u76f8\u5bf9\u8def\u5f84\u7684\u5f15\u7528\u65b9\u5f0f":27,"\u4f7f\u7528\u8fd9\u4e2a\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u5b8c\u6210\u5bf9\u65b0\u6570\u636e\u7684\u9884\u6d4b":19,"\u4f7f\u7528\u9759\u6001\u5e93\u548c\u52a8\u6001\u5e93\u96be\u5ea6\u5dee\u4e0d\u591a":26,"\u4f7f\u7528c":27,"\u4f7f\u7528c99\u505a\u63a5\u53e3":26,"\u4f7f\u7528c99\u800c\u4e0d\u4f7f\u7528c11\u7684\u539f\u56e0\u662f":26,"\u4f7f\u7528c99\u800c\u4e0d\u4f7f\u7528c89":26,"\u4f7f\u7528regress":29,"\u4f7f\u7528swig\u53ea\u652f\u6301cpython\u89e3\u91ca\u5668":26,"\u4f7f\u7528swig\u9700\u8981\u591a\u8bed\u8a00\u7ed1\u5b9a\u7684\u5f00\u53d1\u4eba\u5458\u719f\u7ec3\u638c\u63e1swig\u914d\u7f6e":26,"\u4f7f\u7528void":26,"\u4f8b\u5982":[20,26,27,29],"\u4f8b\u5982\u5bf9\u4e8ejava\u6216\u8005python":26,"\u4f8b\u5982\u5bf9\u4e8ejava\u6765\u8bf4":26,"\u4f8b\u5982\u5bf9\u4e8epython":26,"\u4f8b\u5982c":26,"\u4f8b\u5982java\u4e0epython\u7684\u9519\u8bef\u5904\u7406\u662f\u76f4\u63a5\u6254\u51fa\u6765except":26,"\u4f8b\u5982python\u53ef\u4ee5\u4f7f\u7528":26,"\u4f8b\u5982python\u7684":26,"\u4f9d\u6b21\u7c7b\u63a8":29,"\u4fbf\u662f\u5c06\u9759\u6001\u5e93\u52a0\u5165jvm\u4e2d":26,"\u4fee\u590d\u6240\u6709bug\u540e":29,"\u4fee\u590ddocker\u7f16\u8bd1\u955c\u50cf\u95ee\u9898":29,"\u4fee\u590dubuntu":29,"\u505a\u53ea\u8bfb\u6302\u8f7d":20,"\u505a\u5982\u4e0b\u51e0\u4e2a\u64cd\u4f5c":29,"\u505a\u63a5\u53e3":26,"\u505c\u6b62\u4fdd\u5b58\u68c0\u67e5\u70b9\u7684\u7ebf\u7a0b":19,"\u5148\u5b9e\u73b0\u6a21\u578b\u63a8\u65ad\u7684api":27,"\u5176\u4e2d":[26,29],"\u5176\u4ed6\u51fd\u6570\u5747\u8fd4\u56de":27,"\u5176\u4ed6\u7528\u6237\u7684fork\u7248\u672c\u5e93\u5e76\u4e0d\u9700\u8981\u4e25\u683c\u9075\u5b88":29,"\u5177\u4f53\u4f7f\u7528\u65b9\u6cd5\u4e3a":27,"\u5177\u4f53\u539f\u56e0\u53c2\u8003":27,"\u5177\u4f53\u8bf7\u53c2\u8003":27,"\u5185\u90e8\u9a71\u52a8python\u89e3\u91ca\u5668\u8fdb\u884c\u6a21\u578b\u914d\u7f6e\u89e3\u6790\u548c\u6570\u636e\u8bfb\u53d6":26,"\u518d\u5728\u6bcf\u4e00\u4e2aapi\u4e2d\u81ea\u5df1\u68c0\u67e5\u7c7b\u578b":26,"\u518d\u57fa\u4e8e":29,"\u5199\u4ee3\u7801":26,"\u5199\u5165\u5feb\u7167\u6570\u636e":19,"\u51fd\u6570\u5373\u53ef\u5b8c\u6210\u8f6c\u6362":20,"\u51fd\u6570\u540d\u4e3a":27,"\u51fd\u6570\u547d\u540d":26,"\u5206\u5e03\u5f0f\u5b58\u50a8\u670d\u52a1":19,"\u5206\u652f":29,"\u5206\u652f\u4e00\u65e6\u5efa\u7acb":29,"\u5206\u652f\u4e2d":29,"\u5206\u652f\u4e3a\u5f00\u53d1":29,"\u5206\u652f\u4e3a\u6bcf\u4e00\u6b21release\u65f6\u5efa\u7acb\u7684\u4e34\u65f6\u5206\u652f":29,"\u5206\u652f\u4e3a\u7a33\u5b9a":29,"\u5206\u652f\u529f\u80fd\u7684\u5c01\u95ed":29,"\u5206\u652f\u5408\u5165":29,"\u5206\u652f\u5408\u5165master\u5206\u652f":29,"\u5206\u652f\u540c\u6b65\u4e3b\u7248\u672c\u5e93\u7684":29,"\u5206\u652f\u540d\u4e3a":29,"\u5206\u652f\u5b58\u5728\u7684\u65f6\u5019":29,"\u5206\u652f\u6d3e\u751f\u51fa\u65b0\u7684\u5206\u652f":29,"\u5206\u652f\u7684\u7248\u672c\u90fd\u662f\u7ecf\u8fc7\u5355\u5143\u6d4b\u8bd5\u548c\u56de\u5f52\u6d4b\u8bd5\u7684\u7248\u672c":29,"\u5206\u652f\u7684\u7248\u672c\u90fd\u7ecf\u8fc7\u5355\u5143\u6d4b\u8bd5":29,"\u5206\u7247":19,"\u5219\u4f7f\u7528\u542f\u52a8\u53c2\u6570\u5b9a\u4e49\u7684\u521d\u59cb\u5316\u65b9\u6cd5\u521d\u59cb\u5316\u53c2\u6570":19,"\u5219\u5ffd\u7565":19,"\u5219\u628a\u53e6\u4e00\u4e2a\u6162\u901f\u7684kill\u6389":19,"\u5219\u76f4\u63a5\u5f15\u5165\u53e6\u4e00\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5219\u9700\u8981\u56de\u6eda\u5230\u4e0a\u4e00\u4e2a\u68c0\u67e5\u70b9":19,"\u5220\u9664\u78c1\u76d8\u76ee\u5f55\u4e2d\u4e0d\u662f\u5f53\u524duuid\u7684\u5feb\u7167\u6587\u4ef6":19,"\u5230":19,"\u529f\u80fd":24,"\u529f\u80fd\u7684\u6b63\u786e\u6027\u5305\u62ec\u9a8c\u8bc1paddle\u76ee\u524d\u7684":29,"\u52a8\u6001\u5e93":26,"\u5305\u542b\u4e86\u67d0\u79cd\u7c7b\u578b\u7684\u7c7b\u578b\u5b9a\u4e49\u548c\u66b4\u9732\u7684\u5168\u90e8\u51fd\u6570":27,"\u5305\u62ec":20,"\u5305\u62ec\u6743\u91cdw\u548c\u504f\u7f6eb":19,"\u534f\u540c\u5b8c\u6210releas":29,"\u5355\u4e2a\u503c":20,"\u5355\u70b9\u6545\u969c":19,"\u5373":27,"\u5373\u4f7f\u7528":27,"\u5373\u4f7f\u7528\u6237\u76f4\u63a5\u5f15\u7528\u67d0\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5373\u4f7fc":27,"\u5373\u4f8b\u5982":27,"\u5373\u4fbfpaddl":27,"\u5373\u5b8c\u6210\u67d0\u4e00\u4e2a\u4efb\u52a1\u7684\u6700\u5c11\u51fd\u6570":27,"\u5373\u66b4\u9732":27,"\u5373\u8fd9\u4e2a\u52a8\u6001\u5e93\u662f\u4e0d\u4f9d\u8d56\u4e8e\u5176\u4ed6\u4efb\u4f55\u6587\u4ef6\u7684":26,"\u53c2\u6570":26,"\u53c2\u8003":[24,26],"\u53cc\u5411\u9a8c\u8bc1":24,"\u53d1\u5e03\u5230dockerhub":29,"\u53d1\u5e03\u5230github":29,"\u53ea\u5bf9\u7279\u6b8a\u5728\u7ebf\u7cfb\u7edf\u8003\u8651\u4e24\u53f0\u4ee5\u4e0a\u540c\u65f6\u6545\u969c\u7684\u5bb9\u707e":19,"\u53ea\u66b4\u9732\u6982\u5ff5\u7684\u63a5\u53e3":27,"\u53ea\u80fd\u8c03\u7528paddle\u7684\u52a8\u6001\u5e93":26,"\u53ea\u9700\u8981\u6062\u590d\u8fd9\u53f0\u8282\u70b9":19,"\u53ef\u4ee5\u51cf\u5c0f\u7cfb\u7edf\u590d\u6742\u6027":19,"\u53ef\u4ee5\u5728\u4efb\u4f55\u673a\u5668\u4e0a\u6267\u884c\u7684":26,"\u53ef\u4ee5\u628a\u672c\u5730\u7684\u6570\u636e\u4e0a\u4f20\u5230\u5b58\u50a8\u96c6\u7fa4\u4e2d":20,"\u53ef\u4ee5\u6709\u6548\u7684\u907f\u514dparamet":19,"\u53ef\u4ee5\u7528":24,"\u53ef\u4ee5\u7528\u4ee5\u4e0b\u6307\u4ee4":20,"\u53ef\u4ee5\u7ee7\u7eed\u5728\u81ea\u5df1\u7684\u529f\u80fd\u5206\u652f\u63d0\u4ea4\u4ee3\u7801":29,"\u53ef\u4ee5\u901a\u8fc7\u9636\u6bb5\u6027\u7684\u4fdd\u5b58\u6bcf\u4e2aparamet":19,"\u53ef\u80fd\u4f1a\u9020\u6210\u7f51\u7edc\u62e5\u585e":19,"\u540c\u65f6\u518d\u5c06":29,"\u540c\u65f6\u63d0\u8d77":29,"\u540d\u5b57\u4fee\u9970":26,"\u5411\u6307\u5b9a\u7684\u76ee\u5f55\u4e2d\u4e00\u4e2a\u65b0\u7684\u6587\u4ef6":19,"\u5411paddle\u7684\u4e3b\u7248\u672c\u5e93\u63d0\u4ea4":29,"\u5426\u5219\u5f97\u628apaddle\u9759\u6001\u5e93\u94fe\u63a5\u5230\u89e3\u91ca\u5668\u91cc":26,"\u542f\u52a8\u4e00\u4e2a\u65b0\u7684\u7ebf\u7a0b\u5f00\u59cb\u4fdd\u5b58\u68c0\u67e5\u70b9":19,"\u548c":[20,26,27,29],"\u548c\u79bb\u7ebf\u6570\u636e\u7684\u65b9\u5f0f":20,"\u54ea\u4e2atrainer\u5148\u5b8c\u6210block\u7684\u8bad\u7ec3":19,"\u56e0\u4e3a\u8fd9\u6837\u505a\u4e5f\u6ca1\u6cd5\u4fdd\u8bc1\u6d88\u9664\u968f\u673a\u6027":19,"\u56e0\u4e3aswig\u5728\u7b2c\u4e09\u65b9\u8bed\u8a00\u4e2d\u66b4\u9732\u7684\u51fd\u6570\u540d":26,"\u56fe\u50cf\u5206\u7c7b":29,"\u5728":[27,29],"\u5728\u4e00\u4e2a\u4e0d\u53ef\u4e2d\u65ad\u5e76\u7f3a\u5c11\u5907\u4efd\u7684\u8bad\u7ec3\u4efb\u52a1\u4e2d":19,"\u5728\u4e0a\u56fe\u4e2d\u663e\u793a\u4e86\u5728\u4e00\u4e2a\u5b9e\u9645\u751f\u4ea7\u73af\u5883\u4e2d\u7684\u5e94\u7528":20,"\u5728\u51fa\u73b0\u5355\u70b9\u6545\u969c\u65f6":19,"\u5728\u5b9e\u73b0\u8fc7\u7a0b\u4e2d":27,"\u5728\u5f00\u59cb\u8bad\u7ec3\u4e4b\u524d":20,"\u5728\u5f02\u6784\u96c6\u7fa4\u4e2d":19,"\u5728\u5f15\u5165\u5176\u4ed6\u7c7b\u578b\u7684\u5934\u6587\u4ef6\u65f6":27,"\u5728\u5feb\u7167\u5199\u5165\u5b8c\u6210\u540e":19,"\u5728\u60a8\u7684\u5b9e\u9645\u73af\u5883\u4e2d":19,"\u5728\u672c\u6587\u6863\u4e2d":24,"\u5728\u673a\u7fa4\u4e0a\u8fd0\u884c\u8f6c\u6362\u7a0b\u5e8f":20,"\u5728\u6837\u4f8b\u4e2d":27,"\u5728\u7528\u6237\u4f7f\u7528c":27,"\u5728\u7ebf\u6a21\u578b\u9884\u6d4b\u670d\u52a1":20,"\u5728\u8bc4\u5ba1\u8fc7\u7a0b\u4e2d":29,"\u5728\u8fd9\u4e2a":29,"\u5728\u8fd9\u4e2a\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165\u4efb\u4f55\u5176\u4ed6\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u5728\u8fd9\u4e2a\u9636\u6bb5\u7684\u4ee3\u7801\u6b63\u5728\u7ecf\u5386\u56de\u5f52\u6d4b\u8bd5":29,"\u5728\u8fd9\u4e9b\u5934\u6587\u4ef6\u4e2d":27,"\u5728\u8fd9\u4e9b\u6587\u4ef6\u4e2d":27,"\u5728c":26,"\u5728c\u7684\u5934\u6587\u4ef6":26,"\u5728paddle\u4e4b\u4e0a\u8fd0\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u8f93\u51fa\u7684\u6a21\u578b\u4f1a\u63d0\u4f9b\u7ed9\u5728\u7ebf\u4eba\u8138\u8bc6\u522b\u7684\u5e94\u7528\u4f7f\u7528":20,"\u5728paramet":19,"\u5747\u4f1a\u88ab\u5b89\u88c5\u5230includ":27,"\u5747\u662f\u5728":27,"\u57fa\u4e8e\u7c98\u6027\u4f1a\u8bdd\u7684\u8d1f\u8f7d\u5747\u8861\u529f\u80fd":24,"\u591a\u4e2a\u503c":20,"\u591a\u4e2aparamet":19,"\u5927\u591a\u6570\u8bed\u8a00\u90fd\u652f\u6301\u4f7f\u7528c\u8bed\u8a00api":26,"\u5982\u56fe\u4e2dtrainer":19,"\u5982\u679c\u4e0a\u9762\u4e24\u6b65\u51fa\u73b0\u9519\u8bef":19,"\u5982\u679c\u4f7f\u7528swig\u6211\u4eec\u9700\u8981\u5c06\u5728interface\u6587\u4ef6\u91cc":26,"\u5982\u679c\u5931\u8d25":29,"\u5982\u679c\u5b58\u5728\u67d0\u4e9btrainer\u6267\u884c\u901f\u5ea6\u8fc7\u6162\u4f1a\u5f71\u54cd\u6574\u4f53\u96c6\u7fa4\u7684\u901f\u5ea6":19,"\u5982\u679c\u5df2\u7ecf\u6b63\u5728\u6267\u884c\u4fdd\u5b58\u68c0\u67e5\u70b9\u7684\u7ebf\u7a0b":19,"\u5982\u679c\u662f\u5176\u5b83\u7c7b\u578b":20,"\u5982\u679c\u6709bugfix\u7684\u884c\u4e3a":29,"\u5982\u679c\u67d0\u4e00\u4e2a\u7c7b\u578b\u9700\u8981\u5f15\u7528\u53e6\u4e00\u4e2a\u7c7b\u578b":27,"\u5982\u679c\u67d0\u4e00\u4e2apaddl":27,"\u5982\u679c\u67d0\u4e00\u4e2apaddle\u6982\u5ff5\u5fc5\u987b\u8981\u66b4\u9732":27,"\u5982\u679c\u6ee1\u8db3\u6761\u4ef6":19,"\u5982\u679c\u7528\u6237\u8981\u628apaddle\u7684\u9759\u6001\u5e93":26,"\u5982\u679c\u8c03\u7528\u9759\u6001\u5e93\u53ea\u80fd\u5c06\u9759\u6001\u5e93\u4e0e\u89e3\u91ca\u5668\u94fe\u63a5":26,"\u5982\u679cparamet":19,"\u5b57\u7b26\u4e32":20,"\u5b58\u50a8":20,"\u5b66\u4e60\u6210\u672c\u9ad8":26,"\u5b83\u4eec\u7684\u6587\u4ef6\u540d\u662f":20,"\u5b89\u88c5\u540e\u7684\u76ee\u5f55\u7ed3\u6784\u4e3a":27,"\u5b8c\u6210\u4e00\u4e2a\u4f20\u8f93\u52a8\u4f5c\u5b8c\u6210\u7684\u65f6\u95f4\u4e5f\u6bd4\u8f83\u77ed":24,"\u5b8c\u6210\u6570\u636e\u7684\u9884\u5904\u7406":20,"\u5b9e\u73b0\u7b80\u5355":26,"\u5bf9\u4e8e\u4e0d\u540c\u8bed\u8a00":26,"\u5bf9\u4e8e\u540c\u4e00\u6bb5c":26,"\u5bf9\u4e8e\u591a\u8bed\u8a00\u63a5\u53e3":26,"\u5bf9\u4e8e\u5927\u591a\u6570\u8bed\u8a00":26,"\u5bf9\u4e8e\u6bcf\u79cd\u7c7b\u578b":27,"\u5bf9\u4e8e\u6bcf\u79cdc":27,"\u5bf9\u6bd4":26,"\u5bf9\u8f93\u5165\u53c2\u6570\u7684\u5b89\u5168\u6027\u8fdb\u884c\u4e86\u5fc5\u8981\u7684\u5224\u65ad":27,"\u5bf9\u8fd9\u4e2a\u7248\u672c\u7684\u63d0\u4ea4":29,"\u5bfc\u51fa\u8fd9\u4e9b\u63a5\u53e3":27,"\u5c06":29,"\u5c06\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc\u53c2\u6570\u62c6\u5206\u6210\u591a\u4efd":19,"\u5c06\u5927\u91cf\u7684":26,"\u5c06\u65b0\u5206\u652f\u7684\u7248\u672c\u6253\u4e0atag":29,"\u5c06master\u5206\u652f\u7684\u5408\u5165commit\u6253\u4e0atag":29,"\u5c31\u9700\u8981\u5bf9\u8fd9\u4e2a\u7b2c\u4e09\u65b9\u8bed\u8a00\u589e\u52a0\u4e00\u4e9b\u5b9a\u4e49":26,"\u5e73\u5747\u6545\u969c\u4fee\u590d\u65f6\u95f4":19,"\u5e73\u5747\u6545\u969c\u7387":19,"\u5e76\u4e14\u4f7f\u7528":27,"\u5e76\u4e14\u5728\u5e38\u89c1\u7684\u5e73\u53f0\u4e0a":26,"\u5e76\u4e14\u628a\u7cfb\u7edf\u751f\u6210\u7684ca":24,"\u5e76\u4e14\u628a\u7ed3\u679c\u8fd4\u56depfsclient\u7aef":24,"\u5e76\u4e14\u8ba9\u63a5\u53e3\u8131\u79bb\u5b9e\u73b0\u7ec6\u8282":26,"\u5e76\u5220\u9664":29,"\u5e76\u5220\u9664\u66f4\u65e9\u7684\u5feb\u7167":19,"\u5e76\u52a0\u8f7d\u5176\u4e2d\u7684\u53c2\u6570":19,"\u5e76\u5728\u96c6\u7fa4\u4e2d\u8fd0\u884c\u591a\u4e2a\u5206\u5e03\u5f0f\u6570\u636e\u5904\u7406\u4efb\u52a1":20,"\u5e76\u5c06c":27,"\u5e76\u628a\u5feb\u7167\u4fdd\u5b58\u5230\u8fd9\u4e2a\u76ee\u5f55\u4e0b":19,"\u5e76\u6ca1\u6709paddle\u7279\u522b\u9700\u8981\u7684\u7279\u6027":26,"\u5e76\u88ab\u5b58\u50a8\u5728\u8bf8\u5982hadoop":20,"\u5e76\u9002\u5e94github\u7684\u7279\u6027\u505a\u4e86\u4e00\u4e9b\u533a\u522b":29,"\u5efa\u8bae":29,"\u5f00\u53d1\u4e86\u6a21\u578b\u9884\u6d4b\u7684\u6837\u4f8b\u4ee3\u7801":27,"\u5f00\u53d1\u8005\u4fee\u6539\u81ea\u5df1\u7684\u4ee3\u7801":29,"\u5f00\u53d1\u8005fork\u7684\u7248\u672c\u5e93\u4e2d":29,"\u5f00\u53d1\u8005fork\u7684\u7248\u672c\u5e93\u4f7f\u7528":29,"\u5f00\u59cb\u63d0\u4f9b\u670d\u52a1":19,"\u5f15\u5165\u4e86\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5f53\u529f\u80fd\u5206\u652f\u5f00\u53d1\u5b8c\u6bd5\u540e":29,"\u5f53\u7528\u6237\u4f7f\u7528\u5b8c\u8fd9\u4e2a\u53c2\u6570\u540e":27,"\u5f53destination\u6587\u4ef6\u4e0d\u5b58\u5728\u6216\u8005\u5927\u5c0f\u548csource\u6587\u4ef6\u4e0d\u4e00\u81f4\u65f6":24,"\u5f88\u96be\u4fdd\u8bc1\u591a\u8bed\u8a00\u4ee3\u7801\u98ce\u683c\u7684\u4e00\u81f4\u6027":26,"\u5f97\u4f7f\u7528":26,"\u5fc5\u8981":27,"\u60c5\u611f\u5206\u6790":29,"\u6211\u4eec\u4e5f\u53ef\u4ee5\u786e\u5b9a\u6bcf\u4e00\u4e2a\u53c2\u6570\u7684\u7c7b\u578b":27,"\u6211\u4eec\u4f7f\u7528\u52a8\u6001\u5e93\u6765\u5206\u53d1paddl":26,"\u6211\u4eec\u63d0\u4f9b\u4e24\u4e2a\u8f6c\u6362\u65b9\u5f0f":20,"\u6211\u4eec\u63d0\u51fa\u4e86chunk\u7684\u6982\u5ff5":24,"\u6211\u4eec\u6700\u7ec8\u7684\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165python\u6216\u8005\u5176\u4ed6\u4efb\u4f55\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u6211\u4eec\u8bbe\u8ba1\u8bf4\u660e\u4e86\u540d\u4e3afilemanager\u7cfb\u7edf":24,"\u6211\u4eec\u9009\u62e9":20,"\u6211\u4eec\u90fd\u63d0\u4f9bpython\u7684\u8f6c\u6362\u5e93":20,"\u6216\u8005":[26,27],"\u6216\u8005\u5c06\u8fd9\u53f0\u8282\u70b9\u8fc1\u79fb\u5230\u53e6\u4e00\u4e2a\u8282\u70b9\u5e76\u542f\u52a8\u5373\u53ef\u6062\u590d\u8bad\u7ec3\u4efb\u52a1":19,"\u6216\u8005\u7528tuple\u8868\u793a\u7684\u591a\u4e2a\u503c":20,"\u6216\u8005\u7531\u5b83\u4eec\u7ec4\u6210\u7684list":20,"\u6240\u4ee5\u5728\u5199\u5165\u5feb\u7167\u7684\u8fc7\u7a0b\u4e2d":19,"\u6240\u4ee5\u7528\u6237\u9700\u8981\u9996\u5148\u5728":24,"\u6240\u6709\u4e0e\u7c7b\u578b\u76f8\u5173\u7684\u51fd\u6570":27,"\u6240\u6709\u7684\u63a5\u53e3\u5747\u4e3ac\u63a5\u53e3":27,"\u6240\u6709\u7c7b\u578b\u540d\u4e3a":27,"\u624b\u5199\u591a\u8bed\u8a00\u7ed1\u5b9a":26,"\u6253\u5f00\u8fd9\u4e2a\u7f16\u8bd1\u9009\u9879":27,"\u628a":20,"\u628a\u4e4b\u524d\u793a\u4f8b\u4e2d\u8f6c\u6362\u5b8c\u6bd5\u7684random":20,"\u6307\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u4e4b\u540e\u5f97\u5230\u7684\u6240\u6709\u53c2\u6570":19,"\u63a5\u53e3":[26,27],"\u63a5\u53e3\u5c42\u505a\u8fc7\u591a\u5c01\u88c5":27,"\u63a5\u53e3\u662f":20,"\u63a5\u6536\u5904\u7406pfsclient\u7aef\u7684\u6587\u4ef6\u7ba1\u7406\u8bf7\u6c42":24,"\u63a7\u5236\u7528\u6237\u6743\u9650":20,"\u63d0\u4f9b\u4e03\u5c42\u534f\u8bae\u7684\u53cd\u5411\u4ee3\u7406":24,"\u63d0\u4f9b\u5e38\u7528\u7684\u547d\u4ee4\u884c\u7ba1\u7406\u547d\u4ee4\u7ba1\u7406\u6587\u4ef6\u548c\u76ee\u5f55":24,"\u63d0\u4f9b\u7528\u6237\u7ba1\u7406\u6587\u4ef6\u7684\u547d\u4ee4":24,"\u63d0\u4f9b\u7ed9paddle\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e":20,"\u652f\u6301\u5927\u6587\u4ef6\u7684\u65ad\u70b9\u4e0a\u4f20":24,"\u6570\u636e":24,"\u6570\u636e\u8bfb\u53d6\u5747\u4ea4\u7531\u5176\u4ed6\u8bed\u8a00\u5b8c\u6210":26,"\u6570\u636e\u957f\u5ea6\u53ca\u6821\u9a8c\u503c\u7ec4\u6210":24,"\u6570\u636e\u96c6\u9700\u8981\u9884\u5148\u88ab\u8f6c\u6362\u6210paddlepaddle\u5206\u5e03\u5f0f\u8bad\u7ec3\u4f7f\u7528\u7684\u5b58\u50a8\u683c":20,"\u6570\u636e\u9884\u5904\u7406\u4efb\u52a1":20,"\u6587\u4ef6":26,"\u6587\u4ef6\u4f20\u8f93\u7684\u7684\u5173\u952e\u5728\u4e8e\u9700\u8981pfsclient\u7aef\u5bf9\u6bd4source\u548cdestination\u7684\u6587\u4ef6chunks\u7684checksum\u662f\u5426\u4fdd\u6301\u4e00\u81f4":24,"\u6587\u4ef6\u5185\u5bb9\u4e3a":26,"\u6587\u4ef6\u540d\u4e3a\u6b64uuid":19,"\u6587\u4ef6\u5bf9\u5e94\u7684data":20,"\u6587\u4ef6\u7684\u4e0a\u4f20\u548c\u4e0b\u8f7d\u90fd\u662f\u901a\u8fc7\u5bf9chunk\u7684\u64cd\u4f5c\u6765\u5b9e\u73b0\u7684":24,"\u65b0\u624b\u5165\u95e8\u7ae0\u8282":29,"\u65b9\u4fbf\u6d4b\u8bd5\u4eba\u5458\u6d4b\u8bd5paddle\u7684\u884c\u4e3a":29,"\u65b9\u4fbf\u7528\u6237\u4e0a\u4f20\u81ea\u5df1\u7684\u8bad\u7ec3\u6570\u636e\u4ee5\u8fdb\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3":24,"\u65e0\u6cd5\u505a\u5230\u5bf9\u4e8e\u5404\u79cd\u8bed\u8a00\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u7684\u9002\u914d":26,"\u65e0\u8bba\u5728\u672c\u5730\u8fd8\u662f\u5728\u4e91\u7aef":20,"\u65e0\u8bba\u662f\u4ece":20,"\u65e0\u8bba\u662f\u5728\u672c\u5730\u6216\u662f\u4e91\u7aef\u8f6c\u6362":20,"\u65f6":19,"\u662f":24,"\u662f\u4e00\u4e2a\u591a\u8bed\u8a00\u63a5\u53e3\u7684\u4ee3\u7801\u751f\u6210\u5668":26,"\u662f\u4e00\u4e2a\u7c7b\u578b\u7684\u6807\u5fd7":27,"\u662f\u4e0d\u5e38\u89c1\u7684\u505a\u6cd5":26,"\u662f\u5404\u4e2a\u5b9e\u73b0\u4e2d\u5171\u4eab\u7684\u5934\u6587\u4ef6":27,"\u662f\u56e0\u4e3ac99\u652f\u6301":26,"\u662f\u5bf9\u7528\u6237\u6587\u4ef6\u5b58\u50a8\u7a7a\u95f4\u7684\u62bd\u8c61":24,"\u662f\u6307":27,"\u662f\u7528\u6237\u4f7f\u7528c":27,"\u662fc":27,"\u6682\u65f6\u4e0d\u8003\u8651\u591a\u4e2aparamet":19,"\u66b4\u9732\u8fd9\u4e2a\u6982\u5ff5\u5fc5\u8981\u51fd\u6570":27,"\u6700\u540e\u5220\u9664":29,"\u6700\u5e38\u89c1\u7684\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u662fexcept":26,"\u6709\u6807\u51c6\u7684":26,"\u6709\u7684\u65f6\u5019":26,"\u672c\u5217\u8868\u8bf4\u660epaddle\u53d1\u7248\u4e4b\u524d\u9700\u8981\u6d4b\u8bd5\u7684\u529f\u80fd\u70b9":29,"\u672c\u6587\u6863\u63cf\u8ff0paddl":27,"\u673a\u5668\u7ffb\u8bd1":29,"\u6765\u4fdd\u8bc1\u8bad\u7ec3\u8fc7\u7a0b\u53ef\u4ee5\u4ece\u4e2d\u95f4\u72b6\u6001\u91cd\u65b0\u542f\u52a8":19,"\u6765\u786e\u4fdd\u628a":26,"\u6765\u8868\u793apaddle\u5185\u90e8\u7c7b":26,"\u6765\u8bbf\u95ee\u7528\u6237\u81ea\u5df1\u7684\u6570\u636e":20,"\u6765\u8fdb\u884c\u8ba8\u8bba":27,"\u6807\u51c6\u8868\u793apaddle\u7248\u672c\u53f7":29,"\u68c0\u67e5\u70b9\u4fdd\u5b58\u7a0b\u5e8f\u6d41\u7a0b":19,"\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9\u901a\u8fc7\u5b9a\u671f\u5411\u78c1\u76d8\u4e0a\u4fdd\u5b58\u4e00\u4efd\u5b58\u50a8\u5728paramet":19,"\u6a21\u578b\u6570\u636e\u68c0\u67e5\u70b9\u7684\u5b9e\u73b0":19,"\u6a21\u578b\u914d\u7f6e\u89e3\u6790":26,"\u6b64\u65f6master\u5c06\u8d1f\u8d23\u542f\u52a8\u4e00\u4e2a\u65b0\u7684train":19,"\u6bcf\u4e00\u4e2a":29,"\u6bcf\u4e00\u4e2a\u6587\u4ef6\u662f\u6570\u636e\u96c6\u7684\u4e00\u4e2ashard":20,"\u6bcf\u4e2a\u503c\u7684\u7c7b\u578b\u53ef\u4ee5\u662f\u6574\u5f62":20,"\u6bcf\u4e2adata":20,"\u6bcf\u4e2aparamet":19,"\u6bcf\u4e2ashard\u5206\u522b\u5b58\u50a8\u5728\u5176\u4e2d\u4e00\u53f0paramet":19,"\u6bcf\u6b21\u8f93\u51fa\u4e00\u4e2adata":20,"\u6bcf\u969410\u5206\u949f":19,"\u6bd4\u5982":20,"\u6bd4\u5982\u6bcf\u969410\u5206\u949f\u6700\u65b0\u7684\u5feb\u7167":19,"\u6bd4\u5982\u6d41\u5f0f\u6570\u636e\u5904\u7406":20,"\u6bd4\u5982imagenet\u8fd9\u4e2a\u6570\u636e\u96c6\u53ef\u80fd\u88ab\u5206\u62101000\u4e2ashard":20,"\u6ce8":19,"\u6d4b\u8bd5docker\u955c\u50cf":29,"\u6d6e\u70b9\u578b\u6570\u636e":20,"\u7136\u540e\u5728etcd\u7684":19,"\u7136\u540e\u5c31\u53ef\u4ee5\u5e76\u53d1\u5199\u5165\u591a\u4e2achunk":24,"\u7136\u540e\u624d\u80fd\u4f7f\u7528pfsclient":24,"\u7248\u672c\u5206\u652f":29,"\u7248\u672c\u53f7":29,"\u7248\u672c\u53f7rc":29,"\u7248\u672cfork\u51fa\u81ea\u5df1\u7684\u529f\u80fd\u5206\u652f":29,"\u73b0\u9636\u6bb5paddle\u6709\u4e00\u4e2a\u95ee\u9898\u662f":26,"\u751f\u4ea7\u73af\u5883\u4e2d\u7684\u8bad\u7ec3\u6570\u636e\u96c6\u901a\u5e38\u4f53\u79ef\u5f88\u5927":20,"\u751f\u4ea7\u73af\u5883\u7684\u65e5\u5fd7\u6570\u636e\u4f1a\u901a\u8fc7\u5b9e\u65f6\u6d41\u7684\u65b9\u5f0f":20,"\u751f\u6210\u5404\u79cd\u8bed\u8a00\u7684\u7ed1\u5b9a\u4ee3\u7801":26,"\u751f\u6210\u6587\u6863":26,"\u751f\u6210\u7684":20,"\u751f\u6210\u7ed9\u5b9a":20,"\u751f\u6210api\u6587\u6863":26,"\u751f\u6210pfsclient\u548cpfsserver\u7684\u6846\u67b6\u90e8\u5206":24,"\u7528":24,"\u7528\u6237\u4e0a\u4f20\u6570\u636e\u540e":20,"\u7528\u6237\u4e5f\u53ef\u4ee5\u4e0a\u4f20label":20,"\u7528\u6237\u53ef\u4ee5\u5b89\u5168\u7684\u91ca\u653e\u67d0\u4e2ac":27,"\u7528\u6237\u53ef\u4ee5\u628a\u81ea\u5df1\u7684\u6570\u636e\u5206\u4eab\u7ed9\u522b\u4eba":20,"\u7528\u6237\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u8fd9\u4e2a\u52a8\u6001\u5e93\u6765\u5f15\u5165paddl":27,"\u7528\u6237\u5728\u672c\u5730\u8f6c\u6362\u597d\u518d\u4e0a\u4f20":20,"\u7528\u6237\u6587\u4ef6\u53ef\u80fd\u662f\u6bd4\u8f83\u5927\u7684":24,"\u7528\u6237\u901a\u8fc7c":27,"\u7531\u4e8e\u5bf9parameters\u7684\u66f4\u65b0\u9700\u8981\u83b7\u53d6parameters\u5185\u5b58\u7684":19,"\u7531\u4e8e\u96c6\u7fa4\u4e2d\u540c\u65f6\u5b58\u5728\u4e24\u53f0\u673a\u5668\u6545\u969c\u7684\u6982\u7387\u6781\u4f4e":19,"\u7531\u4e8ec":26,"\u7531\u4e8echunk\u6bd4\u8f83\u5c0f":24,"\u7533\u8bf7\u7528\u6237\u7a7a\u95f4":24,"\u7684\u547d\u540d\u98ce\u683c\u5e76\u4e0d\u80fd\u9002\u5e94\u5176\u4ed6\u7b2c\u4e09\u65b9\u8bed\u8a00":26,"\u7684\u5934\u6587\u4ef6":26,"\u7684\u63a5\u53e3\u6837\u5f0f":26,"\u7684\u6570\u636e\u6d41\u56fe":20,"\u7684\u6e90\u7801\u91cc\u4f7f\u7528\u4e86":26,"\u7684\u7f29\u5199":24,"\u7684\u89c4\u8303":26,"\u7684\u89d2\u5ea6":20,"\u7684\u914d\u7f6e\u5199\u5230\u914d\u7f6e\u6587\u4ef6\u4e2d":20,"\u76ee\u524d\u53ea\u8003\u8651\u52a8\u6001\u6269\u5bb9trainer\u6570\u91cf":19,"\u76ee\u524d\u5d4c\u5165python\u89e3\u91ca\u5668":26,"\u76ee\u524d\u6211\u4eec\u7528cephfs\u6765\u642d\u5efa":24,"\u76ee\u524dpaddle\u7684\u8fdb\u7a0b\u6a21\u578b\u662fc":26,"\u76ee\u5f55\u4e0b":27,"\u76f4\u63a5\u4f7f\u7528c\u8bed\u8a00\u7684":26,"\u76f4\u63a5\u5220\u9664\u8fd9\u4e2a\u53c2\u6570\u5373\u53ef":27,"\u76f4\u63a5\u5bfc\u51fa\u5230c\u7684\u63a5\u53e3\u6bd4\u8f83\u56f0\u96be":26,"\u793e\u533a\u53c2\u4e0e\u56f0\u96be":26,"\u793e\u533a\u8d21\u732e\u4ee3\u7801\u5b66\u4e60\u6210\u672c\u9ad8":26,"\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u53c2\u6570":19,"\u79bb\u7ebf\u6279\u5904\u7406":20,"\u7b2c\u4e00\u4e2atag\u4e3a":29,"\u7b2c\u4e09\u6b65\u5b8c\u6210\u540e":29,"\u7b2c\u4e8c\u4e2a\u4e3a":29,"\u7b49":27,"\u7b49\u5168\u90e8\u9759\u6001\u5e93\u4e2d\u7684\u76ee\u6807\u6587\u4ef6\u5168\u90e8\u6253\u5305\u540e\u4ea7\u751f\u7684\u6587\u4ef6":27,"\u7b49\u6587\u4ef6":27,"\u7c7b\u4f3c":27,"\u7c7b\u540d\u548cc":26,"\u7c7b\u578b":26,"\u7ea2\u697c\u68a6":52,"\u7ed3\u8bba":26,"\u7edf\u4e00\u7528":20,"\u7f16\u8bd1\u5668\u6ca1\u6709":26,"\u7f16\u8bd1\u578b\u8bed\u8a00":26,"\u7f16\u8bd1\u8fd9\u4e2a\u7248\u672c\u7684docker\u53d1\u884c\u955c\u50cf":29,"\u7f16\u8bd1\u8fd9\u4e2a\u7248\u672c\u7684ubuntu":29,"\u7f16\u8bd1c":27,"\u7f16\u8bd1master\u5206\u652f\u7684docker\u53d1\u884c\u955c\u50cf":29,"\u7f16\u8bd1ubuntu\u7684deb\u5305":29,"\u800c\u4e0d\u5fc5\u5728\u610fpaddl":27,"\u800c\u4e0d\u652f\u6301pypy\u89e3\u91ca\u5668":26,"\u800c\u4e0d\u66b4\u9732\u6982\u5ff5\u7684\u5b9e\u73b0":27,"\u800c\u4e14\u5728\u4f20\u8f93\u7684\u8fc7\u7a0b\u4e2d\u4e5f\u53ef\u80fd\u51fa\u73b0\u7f51\u7edc\u4e0d\u7a33\u5b9a\u7684\u60c5\u51b5":24,"\u800c\u51fa\u73b0\u9636\u6bb5\u6027\u7684\u8fd0\u884c\u505c\u6ede":19,"\u800c\u5728cpp\u91cc\u9762\u5b9e\u73b0\u8fd9\u4e2ac\u7684\u63a5\u53e3":26,"\u800c\u591a\u8bed\u8a00\u63a5\u53e3\u9700\u8981\u76f4\u63a5\u8bfb\u53d6\u751f\u6210\u7684\u4e8c\u8fdb\u5236":26,"\u800c\u5bf9\u4e8egolang":26,"\u800c\u5bf9\u4e8egolang\u9519\u8bef\u5904\u7406\u5e94\u8be5\u4f7f\u7528\u8fd4\u56de\u503c":26,"\u800c\u662f\u76f4\u63a5\u4fee\u6539paddl":27,"\u800c\u662f\u76f4\u63a5\u7528api\u7684\u63a5\u53e3\u8fdc\u7a0b\u8bbf\u95ee":20,"\u800cswig\u53ea\u80fd\u7b80\u5355\u7684\u66b4\u9732c":26,"\u81ea\u52a8\u6302\u8f7d\u5206\u5e03\u5f0f\u5b58\u50a8\u76ee\u5f55":19,"\u81f3\u4e8e\u4e3a\u4ec0\u4e48\u9700\u8981c":27,"\u826f\u597d\u7684\u6587\u6863":26,"\u83b7\u53d6\u6700\u65b0\u7684\u68c0\u67e5\u70b9\u7684\u6587\u4ef6uuid":19,"\u867d\u7136\u4e0d\u9f13\u52b1\u8fd9\u6837":27,"\u89e3\u91ca\u578b\u8bed\u8a00\u53ea\u80fd\u8c03\u7528\u52a8\u6001\u5e93":26,"\u89e3\u91ca\u6027\u8bed\u8a00\u5b9e\u9645\u8fd0\u884c\u7684\u4e8c\u8fdb\u5236\u662f\u89e3\u91ca\u5668\u672c\u8eab":26,"\u8ba1\u7b97\u8fd9\u4e2a\u6587\u4ef6\u7684md5":19,"\u8ba9paddle\u6838\u5fc3\u4e2d":27,"\u8bad\u7ec3\u4efb\u52a1\u7684\u8fd0\u884c\u53ef\u80fd\u4f1a\u5360\u6ee1trainer\u548cparamet":19,"\u8bad\u7ec3\u548c\u7eaf\u4f7f\u7528":29,"\u8bad\u7ec3\u6a21\u578b\u6b63\u786e\u6027":29,"\u8bb0\u5f55\u4e0b\u6240\u6709\u5931\u8d25\u7684\u4f8b\u5b50":29,"\u8bbe\u7f6e":27,"\u8bc6\u522b\u6570\u5b57":29,"\u8bcd\u5411\u91cf":29,"\u8be6\u7ec6\u8bbe\u8ba1":24,"\u8bed\u610f\u89d2\u8272\u6807\u6ce8":29,"\u8bf4\u660e":19,"\u8bf7\u53c2\u8003":27,"\u8f6c\u6362\u751f\u6210\u7684\u6587\u4ef6\u540d\u4f1a\u662f\u4ee5\u4e0b\u683c\u5f0f":20,"\u8fbe\u5230\u5bb9\u707e\u7684\u76ee\u7684":19,"\u8fd4\u56de\u7b2c\u4e8c\u6b65":29,"\u8fd8\u662f\u4ece":20,"\u8fd9\u4e00\u5c42\u8fdb\u884c\u5c01\u88c5":27,"\u8fd9\u4e00\u6982\u5ff5\u4e0d\u518d\u7410\u788e":27,"\u8fd9\u4e09\u4e2a\u5206\u652f":29,"\u8fd9\u4e2a\u52a8\u6001\u5e93\u7684\u8fde\u63a5\u53c2\u6570\u4e0epaddle\u7684\u5176\u4ed6\u4e8c\u8fdb\u5236":27,"\u8fd9\u4e2a\u53c2\u6570\u4e5f\u4e0d\u4f1a\u4e00\u5e76\u5220\u9664":27,"\u8fd9\u4e2a\u5934\u6587\u4ef6\u4e0d\u5047\u8bbe\u5176\u4ed6\u6587\u4ef6\u7684\u5f15\u7528\u987a\u5e8f":27,"\u8fd9\u4e2a\u63a5\u53e3\u9700\u8981\u505a\u5230":26,"\u8fd9\u4e2a\u6587\u4ef6\u5177\u6709\u72ec\u7279\u7684\u8bed\u6cd5":26,"\u8fd9\u4e2a\u76ee\u5f55\u4e2d\u9664\u4e86":27,"\u8fd9\u4e2a\u7ed3\u6784\u4f53\u4e2d\u7684\u53e6\u4e00\u4e2a\u9879\u76ee\u662f":27,"\u8fd9\u4e2a\u7ed3\u6784\u4f53\u5305\u542b\u4e24\u4e2a\u9879\u76ee":27,"\u8fd9\u4e2a\u9759\u6001\u5e93\u5305\u542b\u4e86paddle\u7684\u5168\u90e8\u7b26\u53f7":27,"\u8fd9\u4e2ainstance\u53ef\u4ee5\u662f\u5355\u4e2a\u503c":20,"\u8fd9\u4e9b\u5206\u5e03\u5f0f\u5b58\u50a8\u670d\u52a1\u901a\u5e38\u4f1a\u628a\u6570\u636e\u5207\u5272\u6210\u591a\u4e2a\u5206\u7247\u5206\u5e03\u5f0f\u7684\u5b58\u50a8\u5728\u591a\u4e2a\u8282\u70b9\u4e4b\u4e0a":20,"\u8fd9\u5bf9\u4e8e\u901a\u5e38\u7684java\u7684\u5f00\u53d1\u8005\u6765\u8bf4":26,"\u8fd9\u662f\u56e0\u4e3a":26,"\u8fd9\u6837":27,"\u8fd9\u6837\u4fdd\u8bc1":29,"\u8fd9\u6837\u5c31\u53ef\u4ee5\u5728\u4e91\u7aef\u6267\u884c\u591a\u79cd\u6570\u636e\u7c7b\u8ba1\u7b97\u4efb\u52a1":20,"\u8fd9\u6837\u5df2\u7ecf\u4f20\u8f93\u6210\u529f\u7684\u90e8\u5206\u5c31\u4e0d\u7528\u91cd\u65b0\u4f20\u8f93\u4e86":24,"\u8fd9\u90fd\u9700\u8981\u8fd9\u4e2a\u63a5\u53e3\u6309\u7167\u7ea6\u5b9a\u4fd7\u6210\u7684\u89c4\u5219\u6765\u6ce8\u91ca\u5b8c\u5907":26,"\u8fd9\u91cc\u9700\u8981\u7528\u6237\u989d\u5916\u6ce8\u610f":19,"\u8fdb\u800c\u8fdb\u884c\u4ee3\u7801\u8bc4\u5ba1":29,"\u900f\u4f20\u7528\u6237\u8eab\u4efd\u7684\u529e\u6cd5":24,"\u901a\u5e38":27,"\u901a\u5e38\u6307\u5c06\u4e00\u4e2a\u6574\u4f53\u62c6\u5206\u6210\u591a\u4efd\u7684\u5176\u4e2d\u7684\u4e00\u4efd":19,"\u901a\u8fc7\u6a21\u578b\u63a8\u65adapi\u7684\u5b9e\u73b0\u4f5c\u4e3a\u4e00\u4e2a\u6837\u4f8b":27,"\u903b\u8f91\u5212\u4e0a\u6587\u4ef6\u5206\u5757\u7684\u5355\u4f4d":24,"\u9075\u5faa\u4ee5\u4e0b\u6d41\u7a0b":29,"\u90a3\u4e48":27,"\u90fd\u662f\u4e94\u4f4d\u7684\u6570\u5b57":20,"\u90fd\u662fabi\u8c03\u7528\u6807\u51c6\u7684":26,"\u914d\u7f6e\u7684\u65b9\u6cd5\u53c2\u8003":24,"\u91ca\u653e\u5bf9paramters\u5185\u5b58\u7684\u9501\u5b9a":19,"\u91cc\u6240\u6709\u7684\u7b26\u53f7\u90fd\u5199\u5165\u81ea\u5df1\u7684\u7a0b\u5e8f\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u91cc":26,"\u91cd\u547d\u540d\u6210":26,"\u94fe\u63a5\u5230\u81ea\u5df1\u7684\u7a0b\u5e8f\u91cc":26,"\u9519\u8bef\u5904\u7406":26,"\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u662f\u8fd4\u56de\u503c":26,"\u9519\u8bef\u5904\u7406\u7684\u65b9\u5f0f\u4e5f\u4e0d\u5c3d\u76f8\u540c":26,"\u9664\u6784\u9020\u67d0\u79cd\u7c7b\u578b\u7684\u51fd\u6570":27,"\u9700\u8981":20,"\u9700\u8981\u53ef\u4ee5\u8de8\u5e73\u53f0\u6267\u884c":24,"\u9700\u8981\u5728cmake\u7684\u65f6\u5019":27,"\u9700\u8981\u5c06bugfix\u7684\u5206\u652f\u540c\u65f6merge\u5230":29,"\u9700\u8981\u5f15\u7528":27,"\u9700\u8981\u6709\u7a33\u5b9a\u7684\u5bfc\u51fa\u7b26\u53f7":26,"\u9700\u8981\u6ce8\u610f\u7684\u662f":29,"\u9700\u8981\u88ab\u66b4\u9732\u5230\u5176\u4ed6\u8bed\u8a00":27,"\u9ed8\u8ba4256k":24,"\ufb01xed":62,"abstract":[39,44],"api\u4e2d\u4f7f\u7528":26,"api\u5bfc\u51fa\u7684\u52a8\u6001\u5e93":27,"api\u5bfc\u51fa\u7684\u9759\u6001\u5e93":27,"api\u63a5\u53d7\u7684\u7c7b\u578b\u5168\u662f":27,"api\u63a5\u53e3":24,"api\u63a5\u53e3\u7684\u53c2\u6570\u8f6c\u53d1\u7ed9":27,"api\u65f6":27,"api\u65f6\u6240\u552f\u4e00\u9700\u8981\u5f15\u5165\u7684\u5934\u6587\u4ef6":27,"api\u662f\u591a\u8bed\u8a00api\u7684\u57fa\u7840\u90e8\u5206":27,"api\u66b4\u9732\u7684\u7c7b\u578b":27,"api\u751f\u6210\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u4f1a\u88ab\u5b89\u88c5\u5230":27,"api\u7684\u5b9e\u4f8b":27,"api\u7684\u5b9e\u73b0\u7ec6\u8282":27,"api\u7684\u63a5\u53e3":27,"api\u7684\u65f6\u5019\u63a8\u8350paddle\u4e0d\u5d4c\u5165python\u89e3\u91ca\u5668":27,"api\u7684\u7f16\u8bd1\u9009\u9879\u9ed8\u8ba4\u5173\u95ed":27,"api\u76ee\u5f55\u7ed3\u6784\u5982\u4e0a\u56fe\u8868\u6240\u793a":27,"api\u83b7\u5f97\u4e86\u795e\u7ecf\u7f51\u7edc\u7684\u53c2\u6570\u5b9e\u4f8b":27,"block\u6784\u6210\u4e00\u4e2amodel":19,"book\u4e2d\u6240\u6709\u7ae0\u8282\u529f\u80fd\u7684\u6b63\u786e\u6027":29,"boolean":[9,25,26],"break":57,"bugfix\u5206\u652f\u4e5f\u662f\u5728\u5f00\u53d1\u8005\u81ea\u5df1\u7684fork\u7248\u672c\u5e93\u7ef4\u62a4":29,"bugfix\u5206\u652f\u9700\u8981\u5206\u522b\u7ed9\u4e3b\u7248\u672c\u5e93\u7684":29,"byte":24,"c99\u662f\u76ee\u524dc\u6700\u5e7f\u6cdb\u7684\u4f7f\u7528\u6807\u51c6":26,"c\u6709\u6807\u51c6\u7684abi":26,"c\u8bed\u8a00\u662f\u6709\u5bfc\u51fa\u7b26\u53f7\u7684\u6807\u51c6\u7684":26,"case":[9,27,28,30,37,38,39,41,45,47,53,57],"char":[22,59],"class":[5,6,7,8,9,10,11,12,13,15,16,26,43,54,61],"const":[22,39],"core\u4e2d\u7684\u6a21\u578b\u8fd8\u5728\u4f7f\u7528\u8fd9\u4e2a\u53c2\u6570":27,"core\u4e2d\u8fd9\u4e00\u7c7b\u578b\u63a5\u53e3\u7684\u667a\u80fd\u6307\u9488":27,"core\u662f\u5426\u8fd8\u5728\u4f7f\u7528\u8fd9\u4e2a\u5b9e\u4f8b":27,"core\u6982\u5ff5":27,"data\u5230\u5206\u5e03\u5f0f\u5b58\u50a8\u8865\u5145\u8bad\u7ec3\u6570\u636e":20,"deb\u5305":29,"deb\u5305\u7f16\u8bd1\u95ee\u9898":29,"default":[3,7,8,9,10,12,13,15,16,17,32,42,44,46,47,48,57,59,61,62],"enum":22,"export":[31,54],"final":[10,30,31,39,59,61],"float":[3,7,8,9,11,13,30,39,41,46,52,55,59],"function":[3,5,9,10,11,13,16,18,21,22,23,28,30,37,39,41,42,44,53,54,57,60,61,62],"golang\u53ef\u4ee5\u4f7f\u7528":26,"golang\u7684":26,"h\u5e76\u4e0d\u56f0\u96be":26,"images\u6570\u636e\u96c6\u4e0a\u4f20\u5230\u4e91\u7aef\u7684":20,"import":[3,5,8,9,16,30,37,41,47,52,53,54,55,57,59,61,62],"ingress\u9700\u8981\u628apfsclient\u7684\u8eab\u4efd\u4fe1\u606f\u4f20\u7ed9pfsserv":24,"instance\u4e0e\u751f\u6210\u6570\u636e\u96c6\u65f6":20,"instance\u5305\u6db5\u4e24\u4e2a\u503c":20,"instance\u662f\u4e00\u6a21\u4e00\u6837\u7684":20,"int":[3,7,8,9,10,13,21,22,23,26,27,28,39,46,57,59,60],"interface\u6587\u4ef6\u7684\u5199\u6cd5\u975e\u5e38":26,"list\u4f5c\u4e3a\u68c0\u67e5\u5217\u8868":29,"long":[2,9,10,13,32,41,60,61],"model\u505a\u5206\u652f\u7ba1\u7406":29,"ndarray\u7c7b\u578b\u7684\u503c\u548c\u6574\u578b\u7684\u503c":20,"new":[3,9,13,17,18,21,22,28,38,40,47,48,53,57,60,61],"note\u7684\u4e66\u5199":29,"null":[9,39,44,59],"paddle\u4e00\u4e2a\u52a8\u6001\u5e93\u53ef\u4ee5\u5728\u4efb\u4f55linux\u7cfb\u7edf\u4e0a\u8fd0\u884c":26,"paddle\u4f7f\u7528git":29,"paddle\u5185\u5d4c\u7684python\u89e3\u91ca\u5668\u548c\u5916\u90e8\u4f7f\u7528\u7684python\u5982\u679c\u7248\u672c\u4e0d\u540c":26,"paddle\u5185\u90e8\u7684\u7c7b\u4e3ac":26,"paddle\u5f00\u53d1\u8fc7\u7a0b\u4f7f\u7528":29,"paddle\u6bcf\u6b21\u53d1\u65b0\u7684\u7248\u672c":29,"paddle\u6bcf\u6b21\u53d1\u7248\u672c\u9996\u5148\u8981\u4fdd\u8bc1paddl":29,"paddle\u7684\u4e3b\u7248\u672c\u5e93\u9075\u5faa":29,"paddle\u7684\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0\u5305\u62ec\u4e00\u4e0b\u51e0\u4e2a\u65b9\u9762":26,"paddle\u7684\u7c7b\u578b\u5168\u90e8\u9000\u5316\u6210":27,"paddle\u7684\u94fe\u63a5\u65b9\u5f0f\u6bd4\u8f83\u590d\u6742":26,"paddle\u7684c":27,"paddle\u8bad\u7ec3\u4efb\u52a1":20,"paddle\u8def\u5f84\u4e0b":27,"paddle\u9700\u8981\u4e00\u4e2a\u591a\u8bed\u8a00\u63a5\u53e3":26,"paddle\u9700\u8981\u66b4\u9732\u7684api\u5f88\u591a":27,"paddle\u9759\u6001\u5e93\u94fe\u63a5\u590d\u6742":26,"paddle_\u7c7b\u578b\u540d":27,"paddle_\u7c7b\u578b\u540d_\u51fd\u6570\u540d":27,"paddlepaddle\u63d0\u4f9b\u4e13\u7528\u7684":20,"patch\u53f7":29,"patch\u53f7\u52a0\u4e00":29,"pfsclient\u9700\u8981\u548cingress\u4e4b\u95f4\u505a\u53cc\u5411\u9a8c\u8bc1":24,"pfsclient\u9700\u8981\u5728\u4f20\u8f93\u5b8c\u6bd5\u6700\u540e\u4e00\u4e2achunk\u7684\u65f6\u5019\u68c0\u67e5destination\u6587\u4ef6\u7684md5\u503c\u662f\u5426\u548csource\u6587\u4ef6\u4e00\u81f4":24,"pfsserver\u63d0\u4f9brest":24,"public":[13,39,42,47,48,61],"reader\u7684\u4f7f\u7528\u65b9\u5f0f\u90fd\u662f\u4e00\u81f4\u7684":20,"reader\u8f93\u51fa\u7684data":20,"release\u9875\u9762":29,"return":[3,8,9,10,12,13,15,16,20,22,23,30,37,39,47,53,55,57,58,59,62],"s3\u4e4b\u7c7b\u7684\u5206\u5e03\u5f0f\u5b58\u50a8\u4e4b\u4e0a":20,"server\u4e4b\u4e0a":19,"server\u4e4b\u95f4\u7684\u7f51\u7edc\u5e26\u5bbd":19,"server\u4f1a\u6682\u505c\u53c2\u6570\u66f4\u65b0\u5e76\u7b49\u5f85":19,"server\u4f1a\u83b7\u53d6parameters\u5185\u5b58\u7684":19,"server\u5185\u5b58\u4e2d\u7684\u6a21\u578b\u6570\u636e\u7684\u5b8c\u6574\u955c\u50cf":19,"server\u540c\u6b65\u7684\u4fdd\u5b58\u4e00\u4e2a\u7279\u5b9a\u65f6\u95f4\u70b9\u7684\u5168\u5c40\u68c0\u67e5\u70b9":19,"server\u5728\u96c6\u7fa4\u4e2d\u542f\u52a8\u540e":19,"server\u6545\u969c\u540e\u88abkubernetes\u91cd\u65b0\u542f\u52a8":19,"server\u6b64\u65f6\u8fd8\u9700\u8981\u901a\u8fc7\u7f51\u7edc\u8bbf\u95ee\u5206\u5e03\u5f0f\u5b58\u50a8\u4ee5\u4fdd\u5b58\u5feb\u7167":19,"server\u751f\u6210\u4e00\u4e2auuid":19,"server\u7684\u5355\u70b9\u6216\u591a\u70b9\u540c\u65f6\u6545\u969c":19,"server\u7684\u6570\u636e\u5feb\u7167":19,"server\u7684\u68c0\u67e5\u70b9\u5404\u81ea\u72ec\u7acb\u4fdd\u5b58":19,"server\u7b2c\u4e00\u6b21\u542f\u52a8\u6216\u4efb\u610f\u65f6\u95f4paramet":19,"short":[9,10,30,59,60,61],"static":[22,27,47],"super":39,"swig\u652f\u6301\u7684\u8bed\u8a00\u6216\u8005\u89e3\u91ca\u5668\u6709\u5c40\u9650":26,"swig\u66b4\u9732\u7684\u63a5\u53e3\u4fdd\u7559\u4e86c":26,"swig\u751f\u6210\u7684\u4ee3\u7801\u4e0d\u80fd\u4fdd\u8bc1\u591a\u8bed\u8a00\u4ee3\u7801\u98ce\u683c\u7684\u4e00\u81f4\u6027":26,"swig\u76f4\u63a5\u8bfb\u53d6c":26,"swig\u9700\u8981\u5199\u4e00\u4e2ainterface\u6587\u4ef6":26,"switch":[27,47,61],"tag\u4e3a":29,"throw":47,"true":[3,7,8,9,10,12,13,15,16,28,30,37,39,44,46,47,55,59,60,61,62],"try":[11,17,18,21,22,28,41,53,59],"type\u5b57\u6bb5\u5747\u4e0d\u5c3d\u76f8\u540c":27,"ubuntu\u5b89\u88c5\u5305\u7684\u529f\u80fd\u6b63\u786e\u6027":29,"void":[22,26,27,39],"while":[2,3,7,13,28,32,37,44,53,57,61,62],AGE:[47,48],AND:59,ARE:59,AWS:[20,40,49,50],Age:58,And:[3,8,9,11,13,23,28,34,38,46,47,48,52,55,59,61,62],But:[3,9,10],EOS:9,For:[2,3,8,9,11,13,16,21,22,23,28,30,31,32,37,39,41,42,43,44,46,52,54,55,57,61,62],Going:61,Has:3,IDs:[13,57],Ids:57,Into:47,Its:[3,37,47,59],Not:[16,18,42],ONE:3,One:[8,10,15,37,39,44,53,57,61,62],PFS:24,QoS:48,THE:3,TLS:[16,24,47],That:[9,13,28,32,44,46],The:[2,3,5,6,7,8,9,10,11,13,15,16,17,18,21,23,25,27,28,30,31,32,33,34,37,38,39,41,42,44,46,47,48,52,53,54,55,57,58,59,60,61,62],Their:[3,9,18],Then:[5,9,31,32,37,38,39,41,47,48,52,54,59,60,61],There:[8,9,13,15,16,18,22,23,25,30,32,34,41,47,53,54,55,56,57,59,62],These:[42,46,54,60],USE:59,USING:59,Use:[3,16,25,28,32,39,41,44,45,47,59],Used:10,Useful:3,Using:[18,48,61],VPS:47,WITH:38,Will:[13,15],With:[3,9,10,30,53,60],YES:23,Yes:32,___fc_layer_0__:47,__file__:23,__init__:39,__list_to_map__:59,__main__:55,__meta__:59,__name__:55,__rnn_step__:37,_binari:17,_error:53,_librari:17,_link:10,_proj:9,_res2_1_branch1_bn:55,_source_language_embed:[37,52],_target_language_embed:[37,52],_test:17,aaaaa:20,aaaaaaaaaaaaa:47,abc:9,abl:[9,16,53,61],about:[5,9,10,23,25,30,41,43,44,47,51,60,61,62],abov:[3,5,9,16,17,18,21,30,32,41,47,48,53,55,57,60],abs:[10,53],absolut:[2,42],academ:58,acceler:[19,46],accept:[3,5,13,16,28,57,60],acceptor:60,access:[2,9,10,16,21,23,37,62],accessmod:47,accident:58,accomplish:32,accord:[2,3,8,9,22,37,38,42,43,44,46],accordingli:[5,9,39],accordingto:60,accrod:10,accumul:[18,22],accuraci:[8,39,57,58,61],achiev:[41,54],ack:44,acl:61,aclimdb:61,across:9,act:[9,10,30,37,57],act_typ:57,action:[47,58],activ:[0,5,9,10,14,30,31,39,44,57,61],activi:10,actual:[3,9,30],adadelta:57,adagrad:57,adam:[16,22,57,61,62],adamax:57,adamoptim:[52,57,61,62],adapt:[8,11,30,61,62],add:[3,9,10,13,17,30,31,38,39,41,46,57,59],add_depend:17,add_execut:17,add_input:39,add_test:[17,39],add_to:9,add_unittest_without_exec:39,addbia:39,added:[3,8,39],adding:55,addit:[9,10,32,57],address:[18,32,41,44],addrow:39,addtion:42,addtolay:9,adject:61,adjust:30,admin:58,adopt:60,advanc:[37,41,44],advantag:61,adventur:58,adverb:61,adversari:28,advic:41,affect:9,afford:21,afi:3,aforement:[17,42],after:[9,13,21,22,25,31,34,37,39,42,44,46,47,48,53,54,55,57,59,60,61,62],again:[16,18,41],against:47,age:[13,59],agg_level:9,aggreg:47,ago:17,aid:41,aim:[61,62],aircraft:62,airplan:54,aistat:9,alex:[9,61],alexnet_pass1:46,alexnet_pass2:46,algorithm:[9,11,21,30,37,52,54,61,62],alia:[6,7],align:[9,10,13,62],all:[0,3,7,8,9,11,16,18,22,23,25,27,30,32,37,38,39,41,42,43,44,46,47,48,52,53,55,57,58,59,60,61,62],alloc:[7,23,39,46],allow:[16,22,32,38,39,41,44,47,57],allow_only_one_model_on_one_gpu:[43,44,46],almost:[10,30,42,52],along:61,alreadi:[18,32,41,42,44,47,48,61],alreali:[43,62],also:[2,3,9,10,13,16,17,28,31,32,37,39,41,42,48,53,54,55,57,60,61],although:30,alwai:[5,9,10,15,28,30,44,47,62],amaz:54,amazon:[47,48,57,61],amazonaw:47,amazonec2fullaccess:47,amazonelasticfilesystemfullaccess:47,amazonroute53domainsfullaccess:47,amazonroute53fullaccess:47,amazons3fullaccess:47,amazonvpcfullaccess:47,ambigu:[28,60],amd64:47,amend:38,american:54,among:[47,61],amount:[41,61],analysi:[30,41,56,60],analyz:[57,61],andd:47,ani:[2,3,9,10,13,16,17,18,22,23,28,37,38,41,47,57,59,62],anim:58,annot:60,annual:60,anoth:[3,9,16,23,32,44,47,60,61],ans:47,answer:[30,47,60],anyth:[13,28,38,47,60],api:[13,15,16,17,22,23,24,29,31,39,41,47,51,53,57,59,61],api_shar:17,api_test:17,api_trainer_config_helpers_data_sourc:59,api_trainer_config_helpers_lay:37,api_trainer_config_helpers_layers_context_project:59,api_trainer_config_helpers_layers_cos_sim:59,api_trainer_config_helpers_layers_data_lay:59,api_trainer_config_helpers_layers_embedding_lay:59,api_trainer_config_helpers_layers_fc_lay:59,api_trainer_config_helpers_layers_pooling_lay:59,apiserv:47,apivers:[47,48],apo:62,appar:62,appear:60,append:[3,15,28,37,39,42,59],append_gradient_machin:15,appleclang:31,appleyard:41,appli:[0,9,10,37,39,54,57],applic:[32,41,47,48,61],appreci:[38,61],approach:9,apt:[31,34,54],arbitrari:9,architectur:[52,60,61,62],architecur:61,archiv:[13,26,27],arg:[3,8,9,10,13,30,43,53,54,55,57,59,60,61],arg_nam:9,argu:60,argument:[3,5,9,13,21,37,39,44,45,52,53,54,55,59,60,61,62],argv:55,arn:47,around:[3,9,47],arrai:[5,9,13,15,22,28,30,55],art:[30,60],articl:[42,48],artifact:47,artifici:53,artist:58,arxiv:[9,10,53,61],ask:18,aspect:61,assign:[9,21,44,47],associ:[60,61,62],assum:[9,37,46,52],assur:2,astyp:[28,53],asyc:18,async:[18,43],async_count:44,async_lagged_grad_discard_ratio:44,async_lagged_ratio_default:[43,44],async_lagged_ratio_min:[43,44],asynchron:[18,44],atla:31,atlas_root:31,attenion:10,attent:[9,10,32,62],attitud:61,attr1:9,attr2:9,attr:[7,9,10],attribut:[3,9,10,14,39,52,60],auc:43,auc_evalu:8,aucvalidationlay:44,authent:47,author:[24,47,55],authorized_kei:42,autmot:38,auto:[26,39,41,56,59],autom:[47,62],automak:31,automat:[9,16,22,31,37,39,42,43,44,47,59,60,62],automaticli:9,automobil:54,avail:[18,22,31,47],availabel:31,averag:[8,9,12,21,44,55,57,59,60,61,62],average_test_period:[43,44,60],average_window:61,averagepool:9,avg:[41,57],avgcost:[57,59,61,62],avgpool:[9,57],avoid:[18,41],avx:[31,32,34],await:48,awar:[16,18,32,47],aws:24,aws_account_id:47,awsaccountid:47,awskeymanagementservicepowerus:47,b2t:52,b363:48,b8561f5c79193550d64fa47418a9e67ebdd71546186e840f88de5026b8097465:48,ba5f:47,back:[3,18,32],backward:[6,9,10,22,37,39,44,46],backward_first:37,backwardactiv:39,bag:[57,61],baidu:[0,9,30,34,38,48,52],baik:52,balanc:[44,47,53],balasubramanyan:61,bank:60,bardward:10,bare:48,barrier:44,barrierstatset:41,base:[9,10,12,13,16,21,30,34,37,38,39,41,42,44,47,52,53,57,59,61,62],baseactiv:9,baseev:15,basematrix:39,basenam:8,basepoolingtyp:[9,10],basestr:[7,8,9,10,12,15,59],bash:[32,47,48],bashrc:31,basic:[3,9,15,32,38,39,57,58,61],batch:[3,9,10,11,13,15,16,18,20,39,42,44,47,48,53,54,55,57,59,60,61,62],batch_0:55,batch_id:15,batch_norm:10,batch_norm_typ:9,batch_read:[20,28],batch_siz:[3,13,30,42,52,53,54,57,59,61,62],batchsiz:[9,39],bazel:17,bbbbb:20,bcd:9,bcebo:13,beam:[9,37,44,60,62],beam_gen:[9,37],beam_search:[15,37],beam_siz:[9,37,43,44,46],beamsiz:62,becaus:[5,9,13,16,17,18,22,28,37,38,39,46,47,54,57,60],becom:[38,41],been:[3,21,31,38,54,57,60,61,62],befor:[5,9,10,18,25,28,32,38,42,47,54,59,61,62],begin:[5,8,9,22,25,39],beginiter:[15,16],beginn:37,beginpass:[15,16],begintrain:16,behavior:41,being:[18,28,53],belong:[9,62],below:[3,9,13,18,22,28,37,39,41,42,47,53,54,57,59],benefit:[10,23],bengio:9,bertolami:61,besid:[2,9,13,62],best:[9,31,32,44,57,59,61,62],best_model_path:60,besteffort:48,beta1:11,beta2:11,beta:55,better:[9,10,17,30,42,47,53,59],between:[9,11,17,18,22,27,30,38,47,53,57,58,61,62],bgr:55,bi_gru:10,bi_lstm:10,bia:[9,10,11,37,39,55],bias:[9,39],bias_attr:[9,10,30,37],bias_param_attr:10,biases_:39,biasparameter_:39,biassiz:39,bidi:48,bidirect:[10,37,60,62],bidirectional_lstm_net:61,big:41,bigger:18,biggest:61,bilinear:9,bilinear_interpol:9,bilinearfwdbwd:41,bin:[31,32,42,47,48,59],binari:[3,8,9,13,17,23,41,47,52,57,61],bird:54,bison:31,bit:57,bitext:62,bla:31,blank:[9,47],block:[9,19,21,22,30,39,41,44,55,61],block_i:9,block_x:9,blog:61,bn_attr:10,bn_bias_attr:10,bn_param_attr:10,bollen:61,book:13,bool:[3,7,8,9,10,12,13,39,44,46,57,59,61],boot:[9,37],boot_bia:9,boot_bias_active_typ:9,boot_lay:[9,37],boot_with_const_id:9,bootstrap:31,bos_id:[9,37],both:[0,6,7,9,10,16,17,18,32,37,39,41,47,53,55,57],bottleneck:[41,55],bottom:61,bow:[57,61],box:41,branch:[9,16,29,38],breadth:[44,62],brendan:61,brew:31,brief:22,briefli:41,broadcast:18,brows:32,browser:[32,47],bryan:61,bucket_nam:47,buf_siz:13,buffer:[3,13,22,28,44],buffered_read:28,bug:47,bui:61,build:[0,13,17,23,30,32,35,44,47,49,50,52,54,55,57,59,61,62],build_dict:13,built:[0,17,31,32,53,60],bunch:[41,57],bunk:61,button:[38,47],c11:26,c99:27,c99e:47,cach:[57,59,60],cache_pass_in_mem:[3,57,59,60],cachetyp:[3,57,59,60],calc_batch_s:[3,60],calcul:[3,8,9,10,11,18,22,37,39,41,44,46,53,59],call:[3,9,10,16,21,22,23,30,32,37,39,41,44,47,54,55,57,61,62],callabl:[3,9,13],callback:39,caller:47,caltech:54,can:[2,3,5,7,8,9,10,13,16,18,21,23,28,30,31,32,34,37,38,39,41,42,43,44,46,47,48,52,53,54,55,57,59,60,61,62],can_over_batch_s:[3,60],candid:9,cannot:39,caoi:62,capabl:[31,61],capac:47,capi:26,capi_prvi:27,caption:[30,62],captur:[30,42],card:42,care:[10,23,28,43,44,58],carefulli:[42,44,55],cat:[32,54,55,61],categor:60,categori:[9,13,18,57,61],categorig:13,categoryfil:48,caus:[18,25],caution:[47,48],cc_:17,cc_binari:17,cc_test:17,ccb2_pc30:62,cde:9,cdn:13,ceil:9,ceil_mod:9,cell:[9,10,61],center:3,ceph:[20,48],cephf:[20,23,24],certain:[2,43,60],certif:[16,24,47],cffi:26,cfg:48,cgo:26,chain:[13,39],challeng:18,chanc:[16,39,57],chang:[9,13,17,21,23,28,30,32,37,38,39,41,44,47,57,61],channel:[9,41,42,55],channl:[42,55],char_bas:59,charact:[57,59],character:30,characterist:[46,54],check:[3,13,30,31,32,38,44,46,47,58],check_align:13,check_eq:39,check_fail_continu:3,check_l:39,check_sparse_distribution_batch:[43,44],check_sparse_distribution_in_pserv:[43,44],check_sparse_distribution_ratio:[43,44],check_sparse_distribution_unbalance_degre:[43,44],checkgrad:44,checkgrad_ep:44,checkout:38,checksum:24,children:58,china:32,chines:56,chmod:[31,47],choic:[32,58],choos:[44,57,59],chosen:[2,58,62],chunk:[21,24,53,60],chunk_evalu:8,chunk_schem:8,chunktyp:8,cifar:[53,54],cifar_vgg_model:54,claim:47,claimnam:47,clang:[26,31,32,38],class1:61,class2:61,class_dim:61,classfic:[55,61],classfiic:54,classic:[9,30],classif:[3,5,9,46,55,56,57,61,62],classifc:61,classifi:[53,54,55,57,61],classification_cost:[54,57],classification_error_evalu:[8,53,57,61,62],classification_error_printer_evalu:8,claster:47,clean:[5,17,59],clear:17,cleric:58,cli:47,click:[38,41,47],client:38,clip:[7,44,57,61],clock:9,clone:[31,32],close:[3,28],closer:30,cloud:[18,23,24,25],cls:57,cludform:47,cluster:[16,18,22,43,44,48,57,62],cluster_train:42,cm469:47,cmake3:31,cmake:[27,31,39,41],cmakelist:[17,39],cmatrix:[26,27],cmd:48,cna:9,cname:47,cnn:[9,48,55,57],code:[0,3,5,13,16,17,28,30,31,32,33,37,39,40,41,42,47,48,53,57,58],coeff:9,coeffici:9,collabor:18,collect:[9,13,15,30,58],collectbia:39,colleg:58,color:[54,55],colour:13,column:[8,9,28,39,52,62],column_sum_evalu:8,colunm:62,com:[9,10,13,31,32,34,38,47,48,55],combin:[9,10,13,15,53,59,61],come:61,comedi:58,comma:[44,52],command:[2,5,17,23,25,30,31,32,34,38,39,40,41,42,47,48,49,50,52,53,54,55,59,60,61],commandlin:[41,61],commenc:57,comment:[10,17,38,57,61],commnun:42,common:[20,37,39,43],common_util:[42,59],commonli:[25,37,41,46],commun:[0,18,22,39,42,47],compani:61,compar:[39,53,57],compat:3,compet:61,competit:53,compil:[17,31,32,38,39],complet:[0,5,9,10,13,15,18,21,22,24,39,47,48,57],complex:[2,3,10,28,37,41,57],complic:9,compon:39,compos:[13,16,53,60],composenotalign:13,compress:21,comput:[9,10,16,18,30,31,32,37,39,41,46,47,57,59,60,61],computation:37,conat:9,concat:62,concat_lay:37,concaten:10,concept:[3,16,32,37],concern:16,concurr:18,concurrentremoteparameterupdat:44,condit:[9,21,37,42,48,62],conduct:41,conf:[5,9,42,52,53,55,62],conf_paddle_gradient_num:47,conf_paddle_n:47,conf_paddle_port:47,conf_paddle_ports_num:47,conf_paddle_ports_num_spars:47,confid:61,config:[3,7,9,10,20,25,30,39,42,43,44,47,48,52,53,54,55,57,61,62],config_:44,config_arg:[43,44,46,55,57,60,61],config_bas:[8,9,10,15],config_fil:60,config_gener:[42,59],config_lay:39,config_pars:[5,39],config_proto:22,configur:[1,2,3,5,9,22,23,30,38,39,41,44,52,54,55,61,62],confirm:25,conflict:38,confront:62,congest:44,conll05st:60,conll:[13,60],connect:[2,10,23,30,39,47,48,53,54,55,57,59,61],connectionist:[9,61],connor:61,consequ:[9,10],consid:[8,9,11,31,32,41,46,54],consider:[3,10],consist:[9,13,21,28,54,55,57,60,62],consol:[41,47],constant:39,construct:[3,5,16,37,59],construct_featur:59,constructor:39,consum:[18,61],contact:18,contain:[3,8,9,10,12,13,15,16,21,33,34,37,38,42,47,54,55,57,58,61,62],containerport:47,contemporan:61,content:[22,25,48,60,61],content_len:22,context:[9,10,13,37,52,57,59,60,61,62],context_attr:10,context_len:[9,10,57,59],context_proj_nam:10,context_proj_param_attr:10,context_project:[10,59],context_start:[9,10,57],contibut:38,contin:47,continu:[3,18,34,44],contrast:[9,62],contribut:[0,33,40,61],contributor:0,control:[7,32,44,47,48,62],conv:10,conv_act:10,conv_attr:10,conv_batchnorm_drop_r:10,conv_bias_attr:10,conv_filter_s:10,conv_num_filt:10,conv_op:9,conv_pad:10,conv_param_attr:10,conv_strid:10,conv_with_batchnorm:10,conveni:[16,42],convent:22,converg:[42,53,61],convert:[3,5,13,20,28,37,52,54,55,57,59],convlay:9,convolut:[9,10,53,55,59],convoper:9,convtran:9,convtranslay:9,cool:[3,38],coordin:18,copi:[15,16,21,25,32,47,53,59],copy_shared_paramet:53,copytonumpymat:53,core:[3,7,27,44,62],coreo:47,corespond:60,corpora:62,corpu:[13,60],correct:[3,8,9,39,47],correctli:[8,13,39,53],correl:[30,54,61],correspoind:16,correspond:[3,5,16,30,37,39,54,58,60,61,62],corss_entropi:16,cos:9,cos_sim:59,cosin:[9,59],cost:[5,11,15,16,30,44,53,57,59,61,62],cost_id:9,could:[3,5,9,13,15,16,21,28,32,41,42,47,57,59],count:[18,23,28,41,44,46,48,52,59,60,61,62],counter:[18,21],coupl:30,cours:23,coverag:31,coveral:31,coveralls_uploadpackag:31,cpickl:[55,59],cpp:[26,27,38,39,41,57,59,62],cpu:[2,3,7,9,23,29,31,34,41,44,48,53,60,61,62],cpuinfo:32,cpusparsematrix:27,craftsman:58,crash:[18,41,42,44],crazi:42,creat:[5,7,9,13,15,16,18,24,25,30,31,32,39,42,44,52,53,54,62],create_bias_paramet:39,create_input_paramet:39,createargu:53,createfromconfigproto:[5,53],createstack:47,creation:47,creationd:47,creator:[13,20],credenti:25,credit:53,crf:60,crime:58,critic:61,crop:55,crop_siz:55,cross:[9,57,60],cross_entropi:[9,16,53],cross_entropy_with_selfnorm:9,crt:24,csc:39,cslm:62,csr:39,csv:58,ctc:8,ctc_error_evalu:8,ctest:32,ctrl:[42,59],ctx:60,ctx_0:60,ctx_0_slot:60,ctx_n1:60,ctx_n1_slot:60,ctx_n2:60,ctx_n2_slot:60,ctx_p1:60,ctx_p1_slot:60,ctx_p2:60,ctx_p2_slot:60,cub:54,cuda:[17,31,32,34,41,42,44],cuda_dir:[43,44],cudaconfigurecal:41,cudadevicegetattribut:41,cudaeventcr:41,cudaeventcreatewithflag:41,cudafre:41,cudagetdevic:41,cudagetdevicecount:41,cudagetdeviceproperti:41,cudagetlasterror:41,cudahostalloc:41,cudalaunch:41,cudamalloc:41,cudamemcpi:41,cudaprofilerstart:41,cudaprofilerstop:41,cudaruntimegetvers:41,cudasetdevic:41,cudasetupargu:41,cudastreamcr:41,cudastreamcreatewithflag:41,cudastreamsynchron:41,cudeviceget:41,cudevicegetattribut:41,cudevicegetcount:41,cudevicegetnam:41,cudevicetotalmem:41,cudnn:[9,12,17,31,34,44],cudnn_batch_norm:9,cudnn_conv:9,cudnn_conv_workspace_limit_in_mb:[43,44],cudnn_convt:9,cudnn_dir:[43,44],cudrivergetvers:41,cuinit:41,cumul:9,curl:[31,47],current:[3,9,18,30,32,37,38,39,42,44,47,57,61,62],current_word:37,currentcost:[57,59,61,62],currentev:[57,59,61,62],curv:[16,54,60],custom:[2,3,16,23,39,47,58,61],custom_batch_read:28,cutoff:13,cycl:18,cyclic:9,cython:26,d3e0:47,dai:62,daili:61,dalla:3,dan:60,danger:3,darwin:47,dat:[13,20,42,59],data:[1,2,3,5,8,10,11,15,16,20,21,24,31,32,39,41,42,43,44,46,49,55,58],data_batch_gen:53,data_dir:[52,54,61,62],data_feed:13,data_fil:30,data_initialz:57,data_lay:[3,30,37,53,54,57,59,60],data_nam:13,data_read:[13,28],data_reader_creator_random_imag:28,data_sourc:53,data_typ:[9,13],databas:[13,61],datacent:[20,25],datacenter1:20,datacenter2:20,datacenter_1:20,datacenter_2:20,datacenter_nam:20,datadim:9,datalay:9,dataprovid:[2,30,37,42,59,60],dataprovider_bow:57,dataprovider_emb:57,dataproviderconvert:5,datasci:9,dataset:[1,3,20,23,28,30,44,52,54,55,57,60,61],date:60,db_lstm:60,dcgan:53,dcmake_install_prefix:31,dead:18,deal:[38,53],deb:[33,34],debian:[32,33],debug:[3,25],decai:[11,54],decent:21,decid:[16,28],declar:[9,59],decod:[9,10,37,60,62],decoder_boot:37,decoder_group_nam:37,decoder_input:37,decoder_mem:37,decoder_prev:10,decoder_s:37,decoder_st:[10,37],deconv:9,deconvolut:9,decor:[3,13,39],decreas:30,decrypt:47,deep:[0,9,30,32,35,41,53,54,55,57,60],deeper:[30,32,55],deer:54,def:[3,9,13,16,20,23,28,30,37,39,53,55,57,59,60],defalut:[9,44,46],default_devic:46,default_valu:46,defferenct:3,defin:[2,3,9,10,13,16,17,18,28,30,37,39,42,44,52,53,54,59,60],define_py_data_sources2:[3,30,54,55,57,59],defini:62,definit:[3,13,18,21,30,32,52,57,61],degre:9,del:59,delai:44,delar:57,delet:[18,23,24],deletestack:47,delimit:[8,58,59],demand:18,demo:[9,13,37,42,48,49,52,53,54,55,56,57,58,59,60,61,62],demograph:58,demolish:48,demonstr:[30,37,53,59],denot:[46,57,58,60],dens:[3,9,13,22,39,47,57,59],dense_vector:[3,5,9,13,30,59],dense_vector_sequ:13,dep:17,depend:[17,18,23,30,32,34,42,46,54,58],deploi:[42,46],deploy:[42,47],deriv:[6,16],descent:[9,18],describ:[16,17,21,30,39,47,48,53,57,60],describestack:47,describestackev:47,describestackresourc:47,descript:[5,17,31,37,45,47,54,59],deseri:15,design:[3,9,13,26,61],desir:[18,47,48,52],destin:[22,25],destructor:39,detail:[3,5,7,9,10,11,21,23,25,37,38,39,41,42,45,46,47,48,52,53,55,57,59,61,62],detect:8,determin:[3,9,13,39,53],dev:[31,32,54,59,62],devel:31,develop:[0,17,29,31,38,43,44,62],deverlop:44,deviat:7,devic:[7,44,62],deviceid:46,devid:[9,44],dez:61,diagnos:42,diagram:55,dict:[3,13,15,57,59,61,62],dict_dim:61,dict_fil:[8,37,57,60],dict_siz:13,dictionai:57,dictionari:[3,8,9,13,15,16,37,46,55,57,59,60,61,62],dictsiz:62,did:3,differ:[3,8,9,17,18,22,30,32,37,38,39,42,44,47,48,52,54,55,57,61,62],difficult:30,dig:[32,41,47],digit:[3,9],dim:[13,39,52,55,57,61],dimens:[6,9,12,13,39,46,52,57,59,61],dimension:[3,30,37,39,53,57],dimenst:52,dimes:9,din:59,dir:[42,55,57,59,60,61,62],dirctori:32,direct:[9,10,32,55,60],directli:[2,3,10,23,30,32,42,48,61],directori:[2,17,20,24,25,31,32,38,41,42,44,48,54,55,57,59,60,61,62],diretcoti:55,dis_conf:53,dis_train:53,dis_training_machin:53,disabl:3,discard:[13,18,21,44],discount:9,discov:[18,60],discoveri:47,discrep:41,discrimin:53,discriminator_train:53,discuss:[16,21,22],disk:48,dispatch:[42,44],displai:[23,25],disput:62,dist_train:[16,23],distanc:8,distibut:52,distinguish:[17,42,53,62],distribut:[9,21,22,31,40,48,49,50,53,57,60],distribute_test:[43,44],distributedli:39,disucss:16,divid:[11,43,54,62],diy_beam_search_prob_so:[43,44],dmkl_root:31,dns:47,do_forward_backward:28,doc:[5,10,13,31,32,42],docker:[29,33,47,49,50],docker_build:16,docker_push:16,dockerfil:32,dockerhub:32,doctor:58,document:[3,5,10,24,31,38,46,54,57,59,60,61],documentari:[3,58],doe:[3,5,10,18,21,22,23,28,30,34,37,39,41,57,59,60],doesn:[7,9,13,16,28,38,41,48,62],dog:[54,55],doing:41,domain:47,don:[10,16,17,28,30,47,61],done:[9,10,17,18,21,22,37,41,47,53,61],dopenblas_root:31,dot:[44,55,62],dot_period:[44,46,53,54,59,61,62],dotmuloper:9,dotmulproject:9,doubl:[3,31,44],down:[41,57],download:[13,18,24,32,34,53,54,57,60,61],download_cifar:54,downsampl:54,doxygen:[31,38],dpkg:34,drama:58,drop:3,drop_rat:7,dropout:[7,9,39,57],dropout_r:10,drwxr:48,dst:22,dtoh:41,dtype:[5,30,55],dubai:62,due:[21,58,59],dummi:21,duplic:58,durat:[21,41],dure:[2,3,9,18,21,23,30,38,39,43,44,47,57,59,60,62],durn:3,dwith_c_api:27,dwith_doc:31,dwith_profil:41,dwith_python:27,dwith_swig_pi:27,dwith_tim:41,dynam:[2,3,22,27,28,31,41,44],dynamic_cast:39,each:[2,3,5,8,9,12,13,15,17,18,21,22,23,28,30,32,37,38,39,42,44,46,47,52,54,55,57,58,59,60,61,62],each_feature_vector:6,each_meta:59,each_pixel_str:3,each_sequ:9,each_time_step_output:6,each_timestep:9,each_word:3,eaqual:9,eas:[13,28,55],easi:[0,28,32,39,42,57],easier:[16,28,39],easili:[16,28,30],echo:[32,59,61],edit:[8,32,47],editor:[32,38],edu:[13,47,48,54],educ:58,eeoi3ezpr86c:47,effect:[3,44,47],effici:[0,2,3,37,39],efg:9,efs:47,efs_dns_nam:47,efsvol:47,eight:60,either:[9,13,15,16,41,57,59],elb:47,elbapis:47,elec:57,electron:[48,57],elem_dim:9,element:[3,5,8,9,10,13,15,21,28,57,61,62],element_typ:22,elif:[16,59],elimin:60,els:[9,16,23,32,39,55,57,59],emac:[32,38],emailweixu:17,emb:[48,57],embed:[16,37,56,59,61],embedd:60,embedding_lay:[37,57,59],embedding_nam:[9,37],embedding_s:[9,37],emphas:41,empir:9,emplace_back:39,emploi:[37,58],empti:[8,13,18,30],emul:62,enabl:[3,7,17,21,41,42,44,47],enable_grad_shar:[43,44],enable_parallel_vector:44,enc_proj:[10,37],enc_seq:10,enc_vec:37,encapsul:22,encod:[10,21,37,62],encoded_proj:[10,37],encoded_sequ:[10,37],encoded_vector:37,encoder_last:9,encoder_proj:37,encoder_s:37,encrypt:47,encrypt_decrypt:47,end:[3,8,9,28,30,37,44,52,60,61,62],end_pass:16,enditer:[15,16],endpass:[15,16],endpoint:[20,47],endtrain:16,engin:[0,23,41,58],english:[3,9,62],enjoi:32,enough:30,ensembl:10,ensur:[3,18,39],enter:[32,58],entir:[9,10,22,61],entri:[13,21,23,39,47,58],entropi:[9,57,60],entry_point:23,enumer:[6,57,59],enumerate_data_types_of_data_lay:13,env:[38,47],environ:[16,31,32,34,41,42,43,44,47,48,53,54,59],eol:38,eos_id:[9,37],epel:31,epoch:58,epsilon:11,equal:[9,10,18,44],equat:[9,10,11,32],equilibrium:53,equip:[31,37],equival:[9,16],error:[7,8,9,11,16,21,25,30,34,39,42,44,47,54,55,57,58,59,61,62],error_clipping_threshold:7,especi:[3,10,60],essenc:16,essenti:[9,16,31,60,62],estat:30,estim:[9,16],eta:48,etc:[13,28,32,42,43,46,47,61,62],etcd:[18,21],eth0:[42,47],ethternet:42,eval:[8,57,59,61,62],eval_bleu:62,evalu:[2,9,14,15,41,42,57,61,62],evaluate_pass:61,evalut:[30,62],even:[16,28,41,44,61],evenli:[22,47],event:48,event_handl:[15,16],everi:[2,3,8,9,10,13,16,18,21,22,37,38,39,44,57,60,61,62],everyth:[30,38],exactli:[3,8,9,10,32,47,60],exampl:[2,3,9,10,11,13,15,23,25,28,30,31,32,37,39,41,42,43,44,46,47,48,54,55,56,57,61,62],exceed:9,except:[3,13,46,52,59,61],excluded_chunk_typ:8,exconv:9,exconvt:9,exdb:13,exec:[32,44],execut:[17,18,21,23,39,41,47,58,60,61],exist:[16,18,25,28,39,44,47,58,61],exit:[22,25,44,48],expand:[39,60,61,62],expand_a:9,expand_level:9,expandconvlay:9,expect:[9,41,61],expens:62,experi:46,expert:17,expir:18,explain:[3,8,18,42,53,61],explan:[9,23,57,62],explanatori:[30,32],explicit:39,explicitli:[3,16],exploit:54,explor:9,exponenti:6,expos:[32,47],express:[16,47,61],extend:[0,59],extens:[58,59,62],extent:27,extern:[3,17,26,27],external_librari:17,extra:[7,9,10,30],extra_input:9,extra_lay:15,extraattr:[7,46],extraattribut:[9,10],extraattributenon:9,extract:[9,47,54,60,61],extract_fea_c:55,extract_fea_pi:55,extract_para:52,extralayerattribut:[7,9],extrapaddl:10,extrem:[9,41],extremli:2,f120da72:48,f7e3:47,fa0wx:48,fabric:42,face:17,facotr:9,fact:55,factor:[7,9,11],factori:26,fail:[3,18,21,44,46,48,54],failur:[18,22],fake:53,fake_imag:28,falloc:24,fals:[3,7,8,9,10,11,13,28,30,37,39,44,46,48,52,57,59,60,61,62],false_label:28,false_read:28,famili:62,familiar:[3,30],fanscin:3,fantasi:58,fantast:57,far:0,farmer:58,fascinatingli:2,fast:[9,21,38,41],faster:[9,10,18,37,41,61],fault:21,favorit:38,favourit:32,fbd1f2bb71f4:48,fc1:[39,46],fc2:46,fc3:46,fc4:46,fc8a365:47,fc8a:47,fc_act:10,fc_attr:10,fc_bias_attr:10,fc_layer:[9,30,39,46,57,59],fc_mat:15,fc_name:10,fc_param_attr:10,fclayer:39,fdata:60,fea:55,fea_output:55,feat:61,featur:[3,6,9,13,38,44,54,57,61,62],feature_map:59,feed:[10,13,15,16,30,61],feedback:0,feeder:13,feedforward:54,femal:58,fernan:61,festiv:3,fetch:[13,18,37,39],few:[3,17,18,28,32],fewer:9,fg0:9,field1:15,field2:15,field:[9,15,41,47],figur:[16,17,37,39,41,52,53,54,55,60,61,62],file1:62,file2:62,file:[2,3,5,8,9,13,15,16,17,18,20,21,22,23,24,25,27,28,30,31,32,37,38,39,42,44,52,54,55,60,61,62],file_list:3,file_nam:[3,30,55,57,60],filenam:[3,20,59],fileoffset:24,filer:9,filesystem:[23,24,32,47],fill:[9,18,21,47,57],film:58,filter:[9,55],filter_s:[9,10],filter_size_i:9,finali:42,find:[9,11,18,32,41,54,61,62],fine:[7,21,59],fingerprint:47,finish:[3,18,21,23,32,42,47,48,54],finit:39,first:[3,9,13,16,18,21,23,25,30,32,34,37,38,39,41,44,46,47,52,53,54,55,57,59,60,61,62],first_seq:37,firstn:13,firstseen:48,fit:[2,13,38],five:[41,57],fix:[3,7,26,62],flag:[13,44,53,54,60],flexiabl:28,flexibl:[0,2,9,10,16,22,37],flight:62,float32:[5,13,28,30,53,55],floor:9,flow:[29,38],fly:[30,57],fnt03:47,focu:[3,41],folder:[20,23,25,31,47,54,61,62],follow:[2,3,8,9,10,11,13,16,17,18,21,23,28,31,32,34,37,38,39,41,42,46,47,48,49,50,52,53,54,55,57,58,59,60,61,62],fool:53,forbid:16,force_load:26,forecast:61,forget:[11,16,61],form:[2,3,10,11,41,60],format:[2,3,8,21,22,30,38,39,44,47,52,54,58,59,61],former:[16,17,62],formula:[9,10],formular:9,forward:[6,10,22,37,38,39,46,53,60,61],forwardactiv:39,forwardtest:5,found:[3,5,9,31,37,53,54,57,61],four:[3,34,52,55,57,59,60,61],frame:8,framework:[16,39,55,57,61],free:[13,62],french:62,frequenc:[13,41,52,57,61],frequent:[21,28,42,62],frog:54,from:[0,3,5,9,10,13,15,17,18,20,21,22,25,28,30,32,37,38,39,41,42,44,46,47,48,52,53,54,55,57,58,59,60,61,62],from_sequ:9,from_timestep:9,fromfil:[28,30,55],fulfil:41,full:[9,18,32,37,39],full_matrix_project:[10,37],fulli:[30,38,39,41,53,54,55,57,59,61],fullmatrixproject:9,fully_matrix_project:10,fullyconnect:52,fullyconnectedlay:39,func:[13,21],fundament:30,further:9,fusion:59,gain:9,game:53,gamma:55,gan:16,gan_train:53,gangliao:17,gap:44,gate:[9,10,61],gate_act:[9,10],gate_recurr:9,gather:[9,39,59],gauss:7,gaussian:53,gcc:[26,31,32],gdebi:34,gen:[9,62],gen_conf:[53,62],gen_data:62,gen_result:62,gen_train:53,gen_training_machin:53,gen_trans_fil:37,gender:[13,58,59],gener:[2,3,5,8,9,10,13,15,16,17,18,20,21,22,28,30,31,32,41,42,44,46,47,52,55,56,57,59,61],generated_word_embed:9,generatedinput:[9,37],generator_conf:53,generator_machin:53,generator_train:53,genert:3,genr:[58,59],gereat:8,get:[3,9,10,13,15,17,21,22,23,24,30,31,34,37,39,41,42,47,51,54,55,57,59,60,61],get_batch_s:60,get_best_pass:61,get_config_arg:[46,57,59,61],get_data:[48,57,60],get_dict:13,get_embed:13,get_imdb:61,get_input_lay:39,get_mnist_data:53,get_model:55,get_movie_title_dict:13,get_nois:53,get_output_attr:10,get_shap:15,get_training_loss:53,get_word_dict:13,getbatchs:39,getenv:[16,23],getinput:39,getinputgrad:39,getinputvalu:39,getoutputgrad:39,getoutputvalu:39,getparameterptr:39,getsiz:39,getslotvalu:53,gettask:21,gettempl:47,gettranspos:39,getw:39,getweight:39,getwgrad:39,gfortran:31,gildea:60,gist:10,git:[29,31,32,38],github:[9,10,31,32,34,55],give:[3,18,30,32,39,41,47,57],given:[3,13,15,22,28,39,44,53,57,60,61,62],global:[3,7,16,17,18,41,44,47,59,61],global_learning_r:7,globalstat:41,globalstatinfo:41,globe:3,go_librari:17,go_test:17,goal:[41,60],gob:21,godoc:26,goe:[9,10,18,30],going:[57,61],good:[9,28,41,61,62],goodfellow13:9,googl:16,googleapi:47,gpg2:47,gpg:47,gpu:[2,3,7,9,12,23,29,31,34,40,42,53,54,55,59,60,61,62],gpu_id:[44,46,53],gpugpu_id:43,grab:[18,61],grad:[22,44,58],grad_share_block_num:[43,44],gradient:[7,8,9,11,15,18,21,44,57,61],gradient_clipping_threshold:[7,57,61],gradient_machin:[15,27],gradient_printer_evalu:8,gradientmachin:[5,15,27,53,59,62],gradual:[30,41],grai:54,gram:[52,61],grant:47,graph:[9,15,17,18,52],graphviz:55,grave:61,grayscal:3,greater:9,grep:[32,61],groudtruth:37,ground:[8,9,57,62],group:[10,21,61],group_id:59,group_input:37,grouplen:[13,58],gru:[9,37,57,62],gru_attr:10,gru_bias_attr:10,gru_decod:37,gru_decoder_with_attent:37,gru_encoder_decod:[52,62],gru_memori:10,gru_siz:57,gru_step:[10,37],gru_step_lay:37,grumemori:[10,37],gserver:[9,39],gsizex:41,guarante:39,guess:[30,61],gui:41,guid:[24,33,37,38,39,41,47,48,52,54,61,62],guidenc:30,gur_group:10,gzip:[21,48],hack:[33,42],hadoop:16,half:47,hand:[58,59,61],handi:17,handl:[16,23,28,42,59,61],handler:15,handwrit:[3,61],happen:21,hard:[47,57],hardwar:[32,41],has:[3,5,9,10,11,13,16,18,21,22,32,37,39,41,47,48,52,54,57,58,59,60,61,62],has_kei:15,have:[2,3,5,8,9,10,13,16,17,18,21,22,23,28,30,31,32,37,38,39,41,42,44,46,47,52,54,57,58,59,61,62],hdf:[2,20],head:[38,52,61],header:[22,27,30,39,52,55,59],health:58,heavi:42,height:[9,13,26,28,39,54],held:18,hello:16,help:[3,5,25,38,42],helper:[9,10,39],here:[3,5,7,9,10,13,16,17,18,25,28,30,31,37,42,43,46,47,48,52,54,55,56,57,58,59,60,61,62],heurist:[9,44,62],hidden:[9,10,37,47,57,59,61],hidden_s:[10,59],hierarch:[9,37],high:[7,39,53],higher:2,highest:[13,62],highli:[2,3,13,37,46,59,61],him:16,hint:30,histor:61,hl_get_sync_flag:39,hold:[16,18,21,47],home:[20,25,42,47,48],homemak:58,honor:21,hook:[3,59,60],hope:0,horizont:[9,55],horror:58,hors:54,horst:61,host:[23,31,32,42,47,48],hostnam:[42,47],hostpath:48,hostport:47,hot:59,hour:62,hous:[3,13,30,52],how:[2,3,7,9,16,18,21,25,30,37,42,44,47,48,51,54,55,57,59],howev:[3,10,28,30,37,38,43,44,47,61,62],hpp:26,html:[13,32,54],htod:41,http:[9,10,13,23,31,32,34,38,47,48,53,54,55,62],huber:9,huge:[9,38],huina:61,human:62,hyper:[9,39],hyperplan:13,i0601:59,i0706:62,i0719:62,i1117:41,iamfullaccess:47,iamusersshkei:47,ib0:42,ics:13,icwsm:61,id_input:[8,37],idea:[9,17,28],ident:[30,32,47,58],identifi:[37,39],identityoffsetproject:9,identityproject:9,ids:[8,9,39,57,59],idx:[21,39],ieee:61,ies:25,ignor:[3,8,9,44,52],ijcnlp:61,illustr:[3,18,22,37,39,41,57],ilsvrc:55,imag:[3,12,13,16,28,30,33,46,47,49,50,53,55,56,62],image_a:28,image_b:28,image_classif:54,image_fil:28,image_lay:28,image_list_provid:55,image_nam:16,image_path:28,image_provid:54,image_reader_cr:28,image_s:55,imagenet:[20,56],imagepullpolici:47,imageri:9,images_reader_cr:28,imdb:58,imdber:61,img:[3,9,54],img_conv:10,img_featur:3,img_pool:10,img_siz:54,imgsiz:41,imgsizei:41,imgsizex:41,immedi:47,immutable_paramet:16,implement:[3,9,10,11,13,21,22,23,26,27,37,57,60],impli:17,importerror:59,improv:[0,41,47,61,62],inbound:47,includ:[2,3,9,10,13,16,17,22,23,26,27,31,32,37,39,41,44,47,48,52,57,58,60,62],inconsist:58,incorrect:9,increas:[18,21,44,62],increment:44,incupd:39,inde:[13,28,32],independ:[9,22,57],index:[3,8,9,12,13,15,18,21,37,42,47,59],indexslot:[9,60],indic:[3,8,9,22,30,42,47,60],individu:[18,30,47],industri:18,infer:[1,16,18,26,31],infiniband:42,info:[8,9,13,39,42],infom:38,inform:[5,13,23,25,39,41,44,47,58,59,60,61,62],infrastructur:[47,53],ingor:44,ininst:16,init:[7,39,46,47,53,57,59,60],init_hook:[57,59,60],init_model_path:[43,44,46,52,57,60],initi:[3,5,7,9,13,17,21,37,39,44,52,53,57,60],initial_max:7,initial_mean:[7,9],initial_min:7,initial_std:[7,9],initpaddl:[5,53],inlcud:10,inlin:47,inner:39,inner_param_attr:10,input1:[9,10],input2:9,input:[3,5,6,8,9,10,12,13,15,28,30,37,39,46,52,53,54,55,57,59,60,61,62],input_data:39,input_data_target:39,input_featur:6,input_fil:[30,60],input_hassub_sequence_data:39,input_id:9,input_imag:[10,54],input_index:39,input_label:39,input_lay:39,input_nam:16,input_sequence_data:39,input_sequence_label:39,input_sparse_float_value_data:39,input_sparse_non_value_data:39,input_t:39,input_typ:[30,37,57,59],inputdef:39,inputlayers_:39,inputtyp:[3,13],insid:[8,9,18,28,32,47],inspir:52,instal:[23,32,35,38,42,48,54,55,59,60,61],instanc:[9,18,20,37,39,41,44,60],instance_ip:47,instanti:18,instead:[9,10,12,17,23,28,32,38,42,57,62],instruct:[32,34,41,57],int32:44,int64:24,integ:[3,8,9,13,21,23,26,37,39,57,61],integer_valu:[3,13,57],integer_value_sequ:[3,13,37,57,60],integr:[31,60],intend:0,inter:[9,42],interact:[32,47],intercept:9,interest:[41,61],interfac:[1,5,7,9,10,21,23,25,42,47,54,59,61],interg:57,intergr:9,intermedi:[25,60],intern:[9,10,13,15,47],internet:[18,61],interpret:[3,8,31,41],interv:61,intrins:31,introduc:[3,18,48,59,61],introduct:[4,53],invalid:28,invari:54,invoc:17,invok:[3,9,15,41,47,59],involv:53,iob:8,ioe:8,ips:47,ipt:[9,37],ipython:16,is_discriminator_train:53,is_gener:[9,52,53,62],is_generator_train:53,is_kei:59,is_loc:15,is_predict:[57,59,61],is_seq:[9,37,59],is_sequ:59,is_stat:7,is_test:[55,60,61],is_train:3,isn:41,isol:32,isspars:39,issu:[31,32,41],item:[9,13,15,28],iter:[9,10,11,13,15,16,18,28,54,60,61],its:[3,8,9,10,16,18,21,39,41,44,47,52,53,54,57,61,62],itself:[10,18],java:26,jeremi:41,jie:[60,61],jmlr:9,job:[5,13,43,44,46,55,57,59,60,61,62],job_dispatch_packag:42,job_id:13,job_mod:52,job_nam:[23,47],job_namespac:47,job_path:47,job_workspac:42,jobpath:47,jobport0:47,jobport1:47,jobport2:47,jobport3:47,jobserv:23,johan:61,join:18,joint:[52,62],jointli:[10,62],journal:[60,61],journei:32,jpeg:54,jpg:55,json:[42,47,48,59],jth:10,judg:62,jupyt:[23,32],just:[3,6,8,9,10,13,21,22,30,38,42,46,47,52,54,59,60,61],jx4xr:47,jypyt:16,k8s_data:47,k8s_job:16,k8s_token:16,k8s_train:47,k8s_user:16,kafka:20,kaim:9,kaimingh:55,kebilinearinterpbw:41,kebilinearinterpfw:41,keep:[3,9,18],kei:[3,13,15,18,20,21,24,41,42,59,61],kernel:[9,41,57],key1:44,key2:44,key_pair_nam:47,keyid:47,keymetadata:47,keypair:47,keyserv:47,keystat:47,keyusag:47,keyword:3,kill:[18,47],kind:[2,3,16,18,30,47,48,53,57,59],kingsburi:60,kms:47,know:[3,10,16,21,30,39,41,47,59],knowledg:61,known:[53,61,62],kriz:[13,54],ksimonyan:10,kube_cluster_tl:16,kube_ctrl_start_job:16,kube_list_containers_in_job_and_return_current_containers_rank:16,kubeconfig:47,kubectl:48,kuberent:[18,47],kubernet:[16,18,40,42,49,50],kubernetes_service_host:16,kwarg:[3,8,9,10,11,13,57,59,60],l1_rate:7,l2_rate:7,l2regular:[54,57,61],label:[3,5,8,9,11,13,15,28,30,37,48,53,54,55,56,57,59,61],label_dict:60,label_dim:[9,57],label_fil:[28,60],label_lay:28,label_list:60,label_path:28,label_slot:60,labeledbow:61,labl:61,lag:44,lake:3,lambdacost:9,lambdarank:9,languag:[9,13,46,52,60,61,62],larg:[12,13,60,61,62],larger:[3,7,8,9,42],last:[8,9,10,30,37,42,44,57,61,62],last_time_step_output:9,lastseen:48,late:61,latenc:[42,47],later:[17,31,38,47,57],latest:[9,18,32,38,48,61],latter:62,launch:[44,47,61],launcher:16,lawyer:58,layer1:[9,10],layer2:9,layer3:9,layer:[5,7,8,10,12,13,14,15,28,30,37,40,43,44,52,53,54,55,57,59,60,61],layer_0:39,layer_attr:[9,37,46],layer_num:[46,55],layer_s:9,layer_typ:9,layerbas:39,layerconfig:39,layergradutil:39,layermap:39,layeroutout:9,layeroutput:[9,59],lbl:[8,54],ld_library_path:[31,34,42],lead:41,learn:[0,7,8,9,10,11,13,16,22,28,30,32,35,37,39,41,54,55,57,60,61,62],learnabl:9,learning_method:[30,52,54,57,59,61,62],learning_r:[7,22,30,52,54,57,59,61,62],leas:18,least:[8,9,18,31,58],leav:[3,47],lecun:13,left:[9,30,55],leman:62,len:[3,9,22,24,37,39,57,59,60],length:[9,10,13,22,37,44,48,61,62],less:[9,16,42,62],less_than:16,let02:48,let:[5,9,16,30,47,59],level:[7,9,42,44,53,59,61,62],lgtest:17,lgtest_main:17,lib64:[31,42,44],lib:27,libapi:17,libari:27,libcudnn:31,libjpeg:54,libpaddl:[26,27],libpaddle_capi:27,libpaddle_gserv:27,libpaddle_math:27,libpython:31,librari:[9,17,27,31,32,42,44,59],licens:60,life:18,like:[3,8,9,13,17,18,23,28,30,31,37,41,42,43,46,47,52,55,57,59,61,62],limit:[9,13,41,44],line:[2,3,5,8,13,17,23,25,30,38,40,41,42,46,47,52,54,55,59,60,61,62],linear:9,linearactiv:[9,30],linguist:60,link:[9,10,17,24,25,31,47,57,61],linux:[24,31,32,34,47,62],lipeng:52,lipton:61,list:[2,3,8,9,13,15,16,21,23,25,30,32,37,39,42,44,46,47,54,55,57,59,60,61,62],listen:44,literatur:61,littl:[2,3,22,44,57,61],lium:62,live:[18,32],liwicki:61,load:[2,3,5,9,16,18,30,44,47,55,59,60,61,62],load_featur:55,load_feature_c:55,load_feature_pi:55,load_missing_parameter_strategi:[43,44,46,52,60],load_uniform_data:53,loadparamet:5,loadsave_parameters_in_pserv:[43,44],local:[7,18,31,32,38,42,43,44,48,54,61],localhost:32,localpath:25,locat:[37,39,57,60],lock:[18,21,22],log:[3,21,25,38,39,42,44,47,48,54,59,60,61,62],log_barrier_abstract:44,log_barrier_lowest_nod:[43,44],log_barrier_show_log:[43,44],log_clip:[43,44],log_error_clip:[43,44],log_period:[44,46,48,53,54,57,59,60,61,62],log_period_serv:[43,44],logarithm:6,logger:3,logic:[3,42],login:32,longer:62,look:[3,23,30,42,43,47,48,53,57],lookup:57,loop:28,loss:[9,39,53,57,61,62],lot:43,low:9,lower:42,lowest:44,lpaddle_capi_shar:27,lpaddle_capi_whol:27,lst:59,lstm:[9,37,48,57],lstm_attr:10,lstm_bias_attr:10,lstm_cell_attr:10,lstm_group:10,lstm_size:57,lstm_step:10,lstmemori:[10,37],lstmemory_group:9,ltr:9,lucki:30,mac:[27,31,32],machan:10,machin:[9,10,13,15,30,38,39,43,44,46,47,48,57,59,61,62],made:[3,18,22,30,37,58],mai:[3,9,28,32,38,41,47,58],main:[3,5,38,47,54,60,61],mainli:44,maintain:[9,21,47],majel:17,major:[32,38,53,55,61,62],make:[3,16,17,18,21,22,28,31,32,38,39,41,42,47,54,57,59,61],male:58,malloc:39,man:24,manag:[18,22,25,38,42],manageri:58,mandarin:9,mani:[0,9,10,21,30,32,44,57,58,59,61],mannal:42,manual:38,manufactur:62,mao:61,map:[3,9,13,15,16,21,44,54,55,59],map_read:13,mapreduc:16,marcu:61,mark:[3,37,60],mark_slot:60,market:[30,58,61],martha:60,mask:[7,9],master:[16,29,38,44,61],mat:[26,27],mat_param_attr:10,match:41,math:[10,26,39,41],matirx:9,matplotlib:54,matric:[5,37,39],matrix:[8,9,10,13,15,26,27,37,39,43,46,55,60],matrixptr:39,matrixtyp:27,matter:3,max:[3,7,9,13,41,44,46,54,57,59],max_id:[9,15,57],max_job_id:13,max_length:[9,37],max_movie_id:13,max_sort_s:9,max_user_id:13,maxframe_printer_evalu:8,maxid:[8,57],maxid_lay:57,maxid_printer_evalu:8,maxim:[9,62],maximum:[8,13,22,37,41,44,57,60,61],maxinum:12,maxpool:9,mayb:[9,10,54],md5:[13,19],mean:[3,7,8,9,10,11,12,13,15,17,28,30,37,41,42,44,46,47,52,53,54,55,57,59,60,61,62],mean_img_s:54,mean_meta:55,mean_meta_224:55,mean_valu:55,measur:[30,41],mechan:[9,10,37,47,61],media:61,meet:60,mem:[9,23],member:[13,16],memcpi:41,memor:61,memori:[2,3,10,21,23,37,39,41,44,46,48,57,60,61,62],memory_nam:9,memory_threshold_on_load_data:44,mention:21,mere:10,merg:[22,38,44,52,62],mergedict:[52,62],messag:[30,44,48,59,61,62],meta:[42,54,55,57],meta_config:[42,59],meta_fil:59,meta_gener:[42,59],meta_path:54,meta_to_head:59,metadata:[24,47,48],metaplotlib:16,method:[3,9,11,15,32,39,41,44,46,57,59,61,62],might:[9,17,32,39,47],mileag:41,million:[13,46,58],min:[7,41,46,47,59],min_pool_s:3,min_word_freq:13,mind:42,mini:[3,9,13,15,18],mini_batch:28,minibatch:9,minibatch_data:13,minim:[3,11,30,44],minimum:9,minimun:44,minst:3,minut:[18,47,62],mirror:32,mislead:22,miss:[44,52,60],mit:47,mix:[10,37,60],mixed_attr:10,mixed_bias_attr:10,mixed_lay:[9,37,60],mixedlayertyp:9,mkdir:[25,31,32,47],mkl:31,mkl_path:31,mkl_root:31,ml_data:[42,59],mnist:[3,5,20,28],mnist_provid:3,mnist_random_image_batch_read:28,mnist_train:[3,28],mnist_train_batch_read:28,mod:60,modal:60,mode:[9,44,53,54,55,59,61,62],model:[1,2,5,9,10,13,18,19,38,39,40,44,47,59,60,61],model_config:[5,53],model_list:[44,46,60,61],model_output:61,model_path:46,model_zoo:[52,55],modifi:[5,37,38,39,42,47],modul:[2,3,5,10,13,15,30,31,54,55,57,59,60],modulo:9,momentum:[7,30,57],momentumoptim:[30,54],mon:48,monitor:[57,61],mono:9,month:[17,57,62],mood:61,more:[2,3,5,8,9,10,13,16,18,21,23,25,28,30,32,37,39,41,42,46,48,54,57,60,61,62],morin:9,mose:[61,62],moses_bleu:62,mosesdecod:61,most:[3,5,9,13,16,28,30,37,39,41,43,59,60,61,62],mostli:[54,58],mount:[23,32,47,48],mountpath:[47,48],move:[9,18,21,25,41,47,59,61],movement:[41,61],movi:[3,13,61],movie_categori:13,movie_featur:59,movie_head:59,movie_id:59,movie_info:13,movie_meta:59,movie_nam:59,movie_review:13,movieid:58,movieinfo:13,movielen:56,moving_average_fract:9,mpi:42,mse_cost:[30,59],much:[9,18,28,41],mul:39,mulit:42,multi:[9,39,43,44,55,62],multi_binary_label_cross_entropi:9,multi_crop:55,multinomi:9,multipl:[8,9,10,13,15,16,21,22,32,37,39,44,46,47,53,57,59,61],multipli:[8,9,39,54],multithread:3,music:58,must:[3,6,8,9,10,28,31,32,37,38,39,42,44,46,47,62],my_cluster_nam:47,my_cool_stuff_branch:38,my_external_dns_nam:47,mypaddl:48,mysteri:58,name:[3,7,8,9,10,12,13,15,16,18,20,22,23,27,30,32,37,39,41,42,44,46,48,49,50,52,53,54,55,57,59,61,62],name_prefix:20,namespac:[26,32,39,48],nano:38,nativ:9,natur:[21,46,60,61],nchw:9,ndarrai:[15,20],ndarri:15,ndcg:9,ndcg_num:9,nearest:57,necessari:[3,9,22,31,39,42,57,61],necessarili:39,need:[3,9,10,13,16,17,21,22,23,25,30,31,32,34,37,38,39,42,43,44,46,47,48,53,54,55,57,59,60,61,62],neg:[3,8,9,57,60,61],neg_distribut:9,negat:60,neighbor:57,nest:[3,9,13],net:[9,10],net_conf:61,net_diagram:55,network:[2,3,5,7,8,9,11,13,14,15,16,18,28,30,32,39,41,42,44,52,61,62],network_config:46,networkadministr:47,neural:[3,5,9,10,11,13,15,16,18,30,41,44,52,53,55,61,62],neuralnetwork:9,neuron:[5,39,57,61],never:[13,28,47,48],newest:[22,38],newtork:61,next:[9,13,18,37,39,41,44,47,48,60,61,62],nfs4:47,nfs:47,nfsver:47,nginx:32,ngram:13,nic:[42,43,44],nil:21,nine:[13,60],nlp:[3,9],nltk:13,nmt:62,nnz:39,no_cach:3,no_sequ:[3,9,59],noah:61,noavx:[32,34],node:[9,17,39,42,44,47,48,61,62],node_0:47,node_1:47,node_2:47,nodefil:42,noir:58,nois:[9,18,53],noise_dim:53,non:[9,18,39,44,47],none:[2,3,5,7,8,9,10,11,12,13,15,16,30,37,55,57],nonlinear:39,norm:53,norm_by_tim:9,normal:[3,5,9,10,13,34,37,39,42,44,48,52,53,55],normzal:55,north:54,notat:9,note:[3,5,7,9,10,12,15,16,21,23,28,31,41,44,46,47,52,54,59,61],notebook:[23,32],noth:[6,15,44],notic:[37,39],novel:61,now:[0,3,9,17,18,30,32,38,44,47,53,59,60],np_arrai:13,nproc:31,ntst1213:62,ntst14:62,nullptr:39,num:[9,42,44,57,60,61,62],num_channel:[9,10,54],num_chunk_typ:8,num_class:[9,10,54],num_filt:[9,10],num_gradient_serv:[43,44],num_group:9,num_neg_sampl:9,num_parameter_serv:16,num_pass:[15,30,43,44,46,48,57,59,60,61,62],num_repeat:9,num_result:8,num_results_per_sampl:9,num_shard:20,number:[3,8,9,13,18,20,28,30,39,42,44,47,52,54,55,57,60,61,62],numchunktyp:8,numdevices_:46,numlogicaldevices_:46,numofallsampl:8,numofwrongpredict:8,numpi:[13,15,20,28,30,31,53,55],numsampl:41,numtagtyp:8,numtimeout:21,nv_:17,nv_librari:17,nv_test:17,nvcc:[17,32],nvidia:[31,32,41,44],obj:[3,30,54,55,57,59],object:[3,5,7,9,10,11,13,15,16,26,41,53,54,55,57,60],observ:[11,30,39,41,62],obtain:[57,60,61],occup:[58,59],occur:[13,15,38],oct:48,odd:9,off:[27,32],offer:[5,60],offici:[32,47,54],offlin:[18,20],offset:[9,59],often:[42,57,62],ograd:39,old:[22,32,38,44],omit:57,on_init:3,on_travisexclud:31,onc:[3,9,18,21,32,38,39,47,57],one:[3,6,8,9,10,11,12,13,16,18,21,22,23,28,30,32,38,39,42,44,46,47,48,52,53,54,55,57,59,60,61,62],one_host_dens:59,one_hot_dens:59,onli:[2,3,5,8,9,10,12,13,15,16,17,21,22,23,25,30,31,37,38,39,41,43,44,46,47,48,52,55,57,58,61,62],onlin:[11,18,20,28],onto:47,open:[0,3,9,16,20,28,30,32,47,55,57,59,60],openbla:31,openblas_path:31,openblas_root:31,oper:[9,10,11,32,37,39,41,44,47,52,54,59],opinion:61,ops:17,ops_test:17,opt:[16,31],optim:[3,7,14,15,30,39,41,61],option:[3,8,9,16,17,30,38,39,42,46],order:[3,9,10,13,15,28,39,44,47,48,53,55,57,61,62],ordinari:61,oregon:47,org:[9,10,13,20,24,31,32,53],organ:[9,54,61,62],origin:[0,2,3,9,13,38,53,60,62],other:[3,8,9,10,13,18,22,25,31,32,34,37,38,46,47,48,52,53,54,55,57,58,59,60,61,62],otherchunktyp:8,otherwis:[2,9,13,16,18,22,28,37,42,46,59,62],our:[16,17,32,37,39,47,48,52,54,57,60,61,62],out:[9,15,16,17,21,30,37,41,44,47,48,54,61],out_dir:47,out_left:9,out_mem:37,out_right:9,out_size_i:9,out_size_x:9,outlin:45,outout_lay:15,outout_layer1:15,outout_layer2:15,outperform:60,output:[5,6,7,8,9,10,12,13,15,16,20,25,28,30,37,39,41,44,46,48,52,53,54,55,57,59,60,61,62],output_:[9,39],output_dir:55,output_fil:60,output_id:9,output_lay:[15,55],output_max_index:12,output_mem:[9,37],output_path:20,outputh:9,outputw:9,outsid:[3,9,10],outter_kwarg:3,outv:39,over:[2,9,10,16,38,39,41,57,60,61],overcom:61,overhead:41,overlap:39,overrid:[18,25,39],overview:[21,22],overwrit:25,owe:0,own:[22,32,38,42,47],pacakg:34,pack:32,packag:[3,13,21,23,32,33,47],pad:[37,57],pad_c:9,pad_h:9,pad_w:9,paddepaddl:2,padding_attr:9,padding_i:9,padding_x:9,paddl:[3,5,6,7,8,9,10,11,12,13,15,16,18,20,23,25,26,27,30,31,32,33,34,38,39,40,41,42,44,46,47,53,54,57,59,60,61,62],paddle_begin_init_param:22,paddle_element_typ:22,paddle_element_type_float32:22,paddle_element_type_float64:22,paddle_element_type_int32:22,paddle_element_type_int64:22,paddle_element_type_uint32:22,paddle_element_type_uint64:22,paddle_error:[26,27],paddle_exampl:23,paddle_finish_init_param:22,paddle_get_param:22,paddle_gradi:22,paddle_init_param:22,paddle_job:23,paddle_matrix:[26,27],paddle_matrix_cr:27,paddle_matrix_get_shap:26,paddle_matrix_shap:26,paddle_n:42,paddle_new_pserver_cli:22,paddle_on_cloud:23,paddle_output:48,paddle_paramet:22,paddle_port:42,paddle_ports_num:42,paddle_ports_num_for_spars:42,paddle_pserver2:42,paddle_pserver_cli:22,paddle_pserver_client_releas:22,paddle_root:52,paddle_save_model:22,paddle_send_grad:22,paddle_source_root:52,paddle_train:[27,29,42],paddledev:[47,48],paddlepaddl:[0,2,3,5,9,10,13,18,20,22,23,24,25,28,30,31,34,37,38,39,40,41,42,49,50,55,57,59,60,61],paddlepadl:3,paddlpaddl:0,paddpepaddl:3,page:[38,47,59],pai:32,pair:[8,60],palceholder_just_ignore_the_embed:52,palmer:60,paper:[9,52,53,55,60,61,62],paraconvert:52,paragraph:61,parallel:[41,44,46,47,48,62],parallel_nn:[7,43,44],param:[7,9,22,59],param_attr:[9,10,30,37],param_config_proto:22,paramattr:[7,9,30,37],paramet:[2,3,5,8,9,10,11,12,13,14,17,19,25,28,30,39,40,46,53,54,57,59,60,61,62],parameter_attribut:9,parameter_block_s:[43,44],parameter_block_size_for_spars:[43,44],parameter_learning_r:7,parameter_nam:[15,16],parameter_serv:16,parameterattribut:[7,9,10],parametermap:39,parameters_:39,parameterset:16,parametris:11,paramt:[47,52],paramutil:59,paraphras:62,paraphrase_data:52,paraphrase_model:52,paraspars:39,parent:39,pars:[5,13,17,46,47,53,59,60],parse_config:[5,53],parser:59,part:[3,30,37,38,39,41,53,57,59,60,61,62],parti:[41,59],partial:[9,53],participl:52,particular:41,partit:[18,20,47],pass:[3,9,13,15,18,28,30,38,39,41,42,44,47,48,53,54,57,59,60,61,62],pass_id:15,pass_idx:28,pass_test:53,passtyp:39,password:42,past:[16,32,47],patch:24,path:[2,3,8,13,15,18,21,22,23,28,30,31,37,42,44,46,47,48,52,54,55,57,60,61,62],pattern:[13,18,26,30,47,59,61],paul:60,paus:18,pave:62,pdf:[9,10],pem:[16,20,47],pend:[18,21],penn:60,per:[9,13,18,22,28,44,54,57],perfom:[44,46],perform:[2,9,10,22,30,37,38,39,40,42,43,53,54,57,61,62],period:[2,18,44,57,59,60,61,62],perl:[61,62],permiss:47,peroid:9,persist:47,persistentvolum:47,persistentvolumeclaim:47,person:16,perspect:41,perturb:39,pfs:[20,25],pfsclient:20,pfspath:25,pgp:47,phase:30,photo:54,pick:[3,47],pickl:59,pictur:57,piec:[9,10,30],pillow:[23,54],pip:[31,38,42,54,59],pipe:58,pipelin:60,pixel:[3,9,13],pixels_float:3,pixels_str:3,place:[2,3,18,39,41,42,55,62],placehold:[30,52],plai:[60,61],plain:[2,8,9,15,23,27],plan:[18,39],platform:[0,30,47],pleas:[3,5,7,9,10,11,16,18,21,22,28,31,32,33,37,38,39,47,52,54,57,59,60],plot:[16,54],plotcurv:54,png:[54,55],pnpair_evalu:8,pnpairvalidationlay:44,pnpairvalidationpredict_fil:43,pod:[20,23,47,48],pod_nam:47,point:[18,23,30,41],polar:[13,61],polici:47,polit:61,poll:61,poo:54,pool3:39,pool:[3,10,14,54,57,59],pool_attr:10,pool_bias_attr:10,pool_pad:10,pool_siz:[3,9,10],pool_size_i:9,pool_strid:10,pool_typ:[9,10],pooling_lay:57,pooling_typ:[9,57],poolingtyp:12,popular:[30,55],port:[17,32,42,43,44,47,48],port_num:43,ports_num:44,ports_num_for_spars:[43,44,46],pos:[59,61],pose:18,posit:[3,8,9,13,57,60,61,62],positive_label:8,possibl:[16,21,38,41,53],post1:31,post:[23,24],potenti:41,power:[57,62],practic:[9,30,37,39],pre:[3,9,10,13,16,32,47,48,52,54,60,61,62],pre_dictandmodel:52,precis:[8,31],precision_recall_evalu:8,pred:[57,60],predefin:61,predetermin:[9,44,62],predic:[13,60],predicate_dict:60,predicate_dict_fil:60,predicate_slot:60,predict:[3,4,8,9,11,15,30,37,42,44,52,57,62],predict_fil:44,predict_output_dir:[43,44,57],predict_sampl:5,predicted_label_id:57,prediction1:15,prediction2:15,predictor:59,predin:54,prefer:61,prefetch:39,prefix:[18,20,47],pregrad:39,preinstal:31,premodel:52,prepar:[5,23,49,57],preprcess:61,preprocess:[13,37,42,48,61],prerequisit:31,present:[16,55,60,62],preserv:25,pretti:30,prev_batch_st:[43,44],prevent:[2,11,16,18,21],previou:[9,10,18,25,39,44,47,60,62],previous:[9,48,55],price:[13,30],primarili:61,principl:16,print:[7,15,16,30,37,44,52,57,59,60,61,62],printallstatu:41,printer:8,printstatu:41,prite:8,privat:27,privileg:47,prob:[8,15,53],probabilist:[9,52],probability_of_label_0:57,probability_of_label_1:57,probabl:[8,9,15,37,38,55,57,60],problem:[5,9,11,16,17,57,60,61],proc:32,proc_from_raw_data:57,proce:[13,18,28,47],procedur:[52,60,62],proceed:[9,60],process:[2,3,5,7,9,10,16,20,21,30,32,37,42,44,46,47,48,52,54,55,57,59,60,61,62],process_pr:57,processdata:[54,55],processor:41,prod:32,produc:[10,13,18,28,32,55,57],product:[0,23,32,39,47,57,61],productgraph:48,profil:[25,31],proflier:41,program:[2,13,16,20,22,28,32,41,42,44],programm:58,progress:[18,21,44],proivid:3,proj:9,project:[9,10,23,27,31,37,39,59],promis:[9,10],prompt:[25,38],prone:16,prop:60,propag:[11,44,46],properli:57,properti:[3,44],propos:62,proposit:60,protect:39,proto:12,protobuf:[23,31],protocol:[22,44],prove:57,proven:62,provid:[0,9,13,16,22,23,30,32,37,41,42,47,52,53,54,55,58,61],providermemory_threshold_on_load_data:43,provis:47,provod:3,prune:9,ps_desir:18,pserver:[23,42,43,44,47],pserver_config_proto:22,pserver_cpu:23,pserver_id:19,pserver_mem:23,pserver_num_thread:[43,44],pserverstart_pserv:43,pseudo:[16,23],psize:39,ptr:27,pull:[29,32,52,62],punctuat:61,purchas:57,purpos:[0,18,41],push_back:39,put:[18,32,39,42,48,57],pvc:47,pwd:32,py_paddl:[5,13,53],pydataprovid:[2,3,57],pydataprovider2:[4,5,30,37,57,59,61],pyramid:9,pyramid_height:9,python:[2,3,4,15,16,26,29,30,31,38,42,52,53,54,60,61,62],pythonpath:54,pzo:61,qualifi:31,qualiti:57,queri:[9,47,62],question:[9,16,47,60],quick:[44,48,56,62],quick_start:[23,47,48,49,57],quick_start_data:48,quickli:30,quickstart:48,quit:41,quot:58,rac:9,rais:13,ramnath:61,ran:41,rand:[41,44,46,53,60],random:[3,7,9,13,20,28,30,44,53,54,60],random_imag:20,randomli:61,randomnumberse:43,rang:[3,9,13,20,28,44,46,54,58,60],rank:[9,16,47,55,57],rare:3,rate:[7,8,11,13,22,39,42,54,57,59,61,62],rather:[5,23,47,61],ratio:44,raw:[9,30,57,61],raw_meta:59,rdma:[31,44],rdma_tcp:[43,44],reach:[18,41,60],read:[2,3,13,15,16,18,20,28,30,37,42,47,55,57,59],read_from_realistic_imag:16,read_from_rng:16,read_lock:19,read_mnist_imag:16,read_ranking_model_data:16,reader:[1,15,20,62],reader_cr:20,reader_creator_bool:28,reader_creator_random_imag:[13,28],reader_creator_random_image_and_label:[13,28],readi:[18,30,47,48,54],readm:[27,58,59,61],readonesamplefromfil:3,readwritemani:47,real:[3,28,30,53],realist:16,reason:[10,16,18,32,48],rebas:38,recal:8,receiv:[18,23],recent:62,reciev:44,recogn:54,recognit:[3,9,55,61],recommand:3,recommend:[2,10,16,32,37,39,42,44,59],recommonmark:31,recompil:41,record:[21,47,59,60],recordio:[16,20,21],recov:[18,30,53],recoveri:21,rectangular:9,recurr:[60,61],recurrent_group:[10,37],recurrentgradientmachin:27,recurrentgroup:8,recurrentlay:44,recurs:25,recv:47,reduc:[11,42,44,46],refer:[2,5,7,9,10,11,18,21,22,37,39,42,48,52,54,57,59,62],referenc:[9,21],reflect:21,regard:60,regardless:62,regex:59,region:[41,60],regist:[39,41],register_gpu_profil:41,register_lay:39,register_timer_info:41,registri:[23,32,48],regress:56,regular:[7,39,47,54,57,61],rel:[2,10,42],relat:[3,18,23,32,34,48,59,61],relationship:[13,30,53],releas:[29,31,32,34,47,58,60],relev:[60,62],reli:31,reliabl:18,relu:[9,39],remain:57,rememb:9,remot:[7,38,39,42,44,46,47],remoteparameterupdat:44,remov:[13,25,42,44,61],renam:[25,62],reorgan:9,replac:[17,21,32,61],replicaset:23,repo:38,report:[21,41,42],reportdataset:21,repositori:38,repres:[3,5,9,13,21,37,39,47,54,57,58],represent:[22,57,61],reproduc:62,request:[18,29,47,48,52,62],requir:[2,8,9,16,18,22,23,25,39,42,47,48,53,54,57,59],requrest:38,res5_3_branch2c_bn:55,res5_3_branch2c_conv:55,res:60,research:[9,13,54,58,61],resembl:61,reserv:[3,25],reserveoutput:39,reset:[9,18],reshap:28,reshape_s:9,residu:55,resnet:56,resnet_101:55,resnet_152:55,resnet_50:55,resolv:[38,48],resourc:[32,47],respect:[3,30,37,39,44,54,55,60,62],respons:[9,47,48],rest:[3,9,23,24,30],restart:[18,22,47,48],restartpolici:[47,48],restrict:44,resu:28,result:[5,6,8,9,15,21,37,41,44,47,54,55,57,59,60,61],result_fil:[8,37],ret_val:59,retir:58,retran:47,retriev:[39,48],return_seq:10,reuqest:29,reus:[28,39],reveal:16,revers:[9,10,37,60,61],review:[13,38,48,57,61],reviews_electronics_5:48,revis:57,rewrit:[17,62],rgb:9,rgen:61,rho:11,rich:30,right:[3,9,17,23,55],rkt:23,rmsprop:57,rmspropoptim:59,rnn:[9,10,40,43,57,61],rnn_bias_attr:37,rnn_layer_attr:37,rnn_out:37,rnn_step:9,rnn_use_batch:[43,44],rnnlm:13,robot:54,role:[13,16,21,22,37,47,56,61],roman:61,romanc:58,root:[11,12,42,47,48],root_dir:42,rot:9,roughli:[3,53],routin:59,routledg:61,row:[5,8,9,13,39,55],row_id:9,rpc:21,rpcserver:21,rsize:47,rtype:[9,59],rule:[39,47],run:[16,18,23,32,38,39,40,41,44,47,49,50,52,54,55,57,59,61,62],runinitfunct:41,running_on_cloud:23,runtim:[2,3,31,32,42],s_fusion:59,s_id:59,s_param:53,s_recurrent_group:37,sacrif:2,safe:23,sai:[30,44,46],sake:39,sale:58,same:[3,5,8,9,10,16,21,22,37,42,46,47,52,57,59,60,61,62],samping_id:9,sampl:[3,5,8,13,42,44,46,52,53,55,57,59,60,61,62],sample_dim:53,sample_id:8,sample_num:8,santiago:61,satisfi:[42,47,57],save:[3,9,13,18,20,21,22,23,30,44,46,47,48,54,55,57,59,60,61,62],save_dir:[30,44,46,48,53,54,57,59,60,61,62],save_only_on:[43,44],saving_period:[43,44],saving_period_by_batch:[43,44,46,57],saw:3,scalabl:0,scalar:[3,9],scale:[0,6,55,58,59],scalingproject:9,scan:21,scatter:9,scenario:[30,43],scene:43,schdule:47,schedul:[21,23,47,53],scheduler_factor:7,schema:52,scheme:[8,60],schmidhub:61,schwenk:62,sci:58,scienc:61,scientist:[0,58],score:[8,9,59,61,62],screen:59,scrip:57,script:[5,13,42,47,54,55,57,60,61,62],seaplane_s_000978:54,search:[9,18,31,37,44,60,62],seat:62,second:[3,9,13,16,25,28,30,32,38,42,52,55,57,58,59,61],secret:47,section:[3,37,39,42,47,57],sed:61,see:[3,5,9,10,16,18,30,32,38,41,47,52,53,55,57,59,61,62],seed:[41,44],seem:17,segment:8,segmentor:52,sel_fc:9,select:[9,38,47,58,62],selectiv:9,selector:48,self:[30,39,58,61],selfnorm:9,semant:[13,16,29,37,56,61],semat:16,sen_len:60,send:[18,22,44,47],sent:[16,22,48],sent_id:37,sentenc:[3,9,13,37,57,60,61,62],sentiment:[3,30,56,57,60],sentiment_data:61,sentiment_net:61,sentimental_provid:3,separ:[3,8,44,52,57,58,59,60,62],seq:[9,13,59],seq_pool:9,seq_text_print:8,seq_to_seq_data:[52,62],seq_typ:[3,13,59],seqtext_printer_evalu:[8,37],seqtoseq:[9,37,52,62],seqtoseq_net:[9,37,52,62],sequel:3,sequenc:[3,6,8,9,10,12,13,39,52,57,59,60,61,62],sequence_conv_pool:57,sequence_layer_group:9,sequence_nest_layer_group:9,sequencestartposit:9,sequencetextprint:8,sequencetyp:[3,9],sequenti:[9,37,57,60],seri:[10,61],serial:[3,15,21,22],serv:[32,41,47,53],server:[16,17,32,39,42,43],serverless:18,servic:[32,58],session:[32,41],set:[2,3,5,7,8,9,10,13,15,16,18,23,30,31,32,34,37,39,40,41,42,43,44,46,47,48,52,54,55,57,58,59,60,61,62],set_active_typ:39,set_default_parameter_nam:7,set_drop_r:39,set_input:9,set_siz:39,set_typ:39,setp:47,settup:39,setup:[3,32,39,57],sever:[3,9,42,46,47,56,57,59,60,61,62],sgd:[11,15,16,18,23,42,53,61,62],sgdasync_count:43,shallow:60,shape:[9,15,55],shard:[18,19,20,21,22,47],share:[9,17,27,31,32,41,44,48,60],shared_bia:10,shared_bias:9,shared_librari:17,shared_ptr:[26,27],shell:[47,55],shift:55,ship:54,shold:61,shop:61,shorten:9,shorter:55,should:[3,5,8,9,13,15,16,22,23,28,30,34,37,38,42,47,54,57,59,60,61,62],should_be_fals:16,should_be_tru:16,should_shuffl:[3,60],shouldn:38,show:[5,11,13,18,25,30,38,44,47,48,52,55,57,59,60,61,62],show_check_sparse_distribution_log:[43,44],show_layer_stat:[43,44],show_parameter_stats_period:[43,44,46,48,57,60,61,62],shown:[3,8,9,16,37,39,41,47,53,54,55,57,59,61,62],shrink:39,shuf:59,shuffl:[3,13,59,61],sid:47,side:[9,15,55],sig:47,sigint:42,sigmoid:[9,10,39],sign:[24,47],signal:42,signatur:47,signific:41,similar:[9,28,47,57,59],similarli:[9,60],simpl:[2,3,6,8,9,10,13,15,31,32,38,41,44,57,59,60,61],simple_attent:37,simple_gru:57,simple_lstm:[9,57],simple_rnn:[9,37],simplest:47,simpli:[2,9,16,22,31,32,37,38,41,52,55,59,61,62],simplifi:[16,39,48],simultan:47,sinc:[9,18,21,28,30,32,41,47,53,57,58,62],sincer:[38,61],singl:[3,8,10,13,18,32,39,42,48,55,57,60,62],sinlg:15,site:47,six:[52,60,62],size:[3,8,9,10,11,13,18,20,28,30,37,39,42,44,53,54,55,57,58,59,60,61,62],size_a:9,size_b:9,size_t:39,sizeof:52,skill:62,skip:[28,30,42,47,55],slide:[9,11,13,18],slightli:54,slope:9,slot:[59,60],slot_dim:59,slot_nam:59,slottyp:59,slow:[3,41],small:[3,13,39,42,44,54,62],small_messag:[43,44],small_vgg:54,smaller:[9,18],smith:61,smooth:9,snap:48,snapshot:[19,47],snippet:[37,39,41,47,57],social:61,sock:23,sock_recv_buf_s:[43,44],sock_send_buf_s:[43,44],socket:44,softmax:[9,10,16,37,39,52,57,60,61],softmax_param_attr:10,softmax_selfnorm_alpha:9,softmaxactiv:[37,57],softwar:[32,41],solv:[16,60],solver:62,some:[3,7,9,13,15,16,17,21,22,23,30,31,38,39,41,43,44,46,47,53,57,58,59,60,61,62],some_c_api_funct:27,some_inst:27,some_python_class:26,somecppclass:26,somedata:15,somegotyp:26,someth:[3,9],sometim:[11,28,41,61],somewhat:22,soon:18,sophist:[30,39,42],sort:[9,13,44,47,59,61,62],sourc:[0,9,13,17,25,27,28,30,32,37,38,47,48,52,57,59,62],source_dict_dim:37,source_language_word:37,space:[8,32,37,41],space_seperated_tokens_from_dictionary_according_to_seq:8,space_seperated_tokens_from_dictionary_according_to_sub_seq:8,spars:[3,7,9,11,13,39,42,44,47,57],sparse_binary_vector:[3,13,57],sparse_binary_vector_sequ:13,sparse_float_vector:3,sparse_non_value_slot:13,sparse_upd:7,sparse_value_slot:13,sparse_vector:13,sparse_vector_sequ:13,sparseparam:39,sparseprefetchrowcpumatrix:39,spatial:[9,54],speak:[37,62],spec:[47,48],specfii:44,speci:54,special:[9,22,31,52,57,62],specif:[2,25,46,54,57,59],specifi:[2,3,8,9,13,16,21,22,23,25,30,31,37,39,44,47,53,54,55,57,58,59,61,62],speech:9,speed:[10,32],spefici:55,sphinx:[26,31,32],sphinx_rtd_them:31,split:[3,9,42,46,47,52,55,57,60],split_count:47,sql:2,squar:[9,11,12,30],squarerootnpool:9,squash:62,srand:44,src:[17,62],src_backward:37,src_dict:37,src_embed:37,src_forward:37,src_id:37,src_root:5,src_word_id:37,srl:[13,60],ssd:9,ssh:[32,42,47,48],sshd:32,ssl:31,sstabl:16,stabl:[29,47],stack:[30,47,57,60],stacked_lstm_net:61,stacked_num:61,stackexchang:9,stage:42,stake:62,stale:[18,38],stamp:41,standard:[7,52,54,60,61,62],stanford:[13,48],star:58,start:[9,15,18,21,22,23,30,32,37,38,41,42,44,51,52,56,59,62],start_pass:[43,44],start_pserv:44,startup:[18,23,47],stat:[31,41,44,60,61,62],state:[9,10,18,30,37,44,48,53,60,62],state_act:[9,10],statement:[39,47],staticinput:[9,37],statist:[9,44,57,60,61,62],statset:41,statu:[23,38,41,47,48],status:48,std:[17,26,27,39,44],stderr:42,stdout:42,step:[5,9,10,12,18,22,32,37,39,41,42,47,48,57,59,60,61,62],still:[21,55],stmt1482205552000:47,stmt1482205746000:47,stochast:[11,18,21],stock:61,stop:[9,32,42,44,48,59],storag:[24,47,48,54],store:[8,9,13,39,42,44,47,48,52,54,55,57,59,60,61,62],str:[15,23,46],straight:38,strategi:[3,12,18,44,60],street:[9,60],strength:53,strict:28,stride:9,stride_i:9,stride_x:9,string:[2,3,8,9,21,25,39,44,47,61],strip:[57,59,60],struct:[21,22,24,27],structur:[13,21,42,47,52,54,57,59,60,61,62],sts:47,stub:9,student:58,stuff:38,stun:3,style:[3,9,31,38],sub:[8,9,13,16,37,39,54,57,62],sub_sequ:[3,9],subcommand:25,subgradi:11,submit:[38,43,44,47],subnet0:47,subnet:[16,47],subobjectpath:48,subsequenceinput:9,subset:[39,62],substanti:55,substitut:62,succe:61,succeed:[21,48],success:[22,47,48,55,60],successfulcr:48,successfuli:61,successfulli:[55,59,61],successor:[44,62],sucessfulli:62,sudo:[31,34,47,54],suffic:[28,30],suffici:44,suffix:[23,62],suggest:[9,17,41],suitabl:[38,44,54],sum:[9,11,19,37,39],sum_:9,sum_evalu:8,summar:[57,61],sumpool:9,support:[7,8,9,12,13,18,23,28,31,32,34,37,39,41,44,47,60],suppos:[17,30,39,57],suppress:25,sure:[38,39,47,54,61],survei:61,swagger:24,swap_channel:55,swig:[5,26,27,31],swig_paddl:[5,13,53],symbol:[9,27],sync:[18,38,44,53],syncflag:39,synchron:[11,18,21,42,44,47],syntact:60,syntax:[28,59],synthect:30,synthes:53,synthet:30,sys:55,system:[17,18,22,24,31,32,42,48,57,60,61,62],t2b:52,t_i:9,tab:[32,57],tabl:[3,9,55,57,62],tableproject:9,tag:[8,13,32,37],tagtyp:8,take:[3,5,8,9,10,16,17,37,39,41,47,48,53,60,62],taken:[3,60],tanh:[9,10,39],tanhactiv:37,taobao:61,tar:[31,47],tarbal:47,target:[9,13,15,17,37,52,57,62],target_dict_dim:37,target_dictionary_dim:9,target_language_embed:9,target_language_word:37,target_link_librari:17,targetinlink:9,task:[3,8,9,30,37,46,52,55,60,61,62],task_queu:21,taskentri:21,taskqueu:21,tconf:61,tcp:[44,47],teach:57,tear:41,technic:18,technician:58,techniqu:[37,39],tee:[48,54,59,60,61,62],tell:[18,21,22,32,41,59],tellig:61,templat:[48,60],tempor:[9,57,60],temporari:23,tensor:17,tensor_test:17,term:[9,10,18,60,61],termin:48,terminolog:30,tese:2,tesh:60,test100:13,test10:13,test1:20,test:[2,3,9,13,15,16,17,27,28,29,31,32,34,38,41,42,43,52,54,55,57,58,62],test_all_data_in_one_period:[48,54,59,60,61],test_data:62,test_fcgrad:39,test_gpuprofil:41,test_layergrad:39,test_list:[3,30,54,57],test_part_000:61,test_pass:[43,44,46,62],test_period:[43,44,46],test_ratio:59,test_wait:[43,44],testa:16,testb:16,testbilinearfwdbwd:41,testconfig:39,tester:[59,62],testfcgrad:39,testfclay:39,testlayergrad:39,testmodel_list:43,testq:16,testresult:15,testsave_dir:43,testutil:39,text1:25,text:[2,3,8,10,13,16,32,37,47,52,56,57,59,61],text_conv:57,text_conv_pool:59,text_fil:[13,61],tflop:41,tgz:[13,31],than:[3,5,7,8,9,10,18,23,31,32,37,39,42,47,55,60,61,62],thank:[0,52,62],thei:[3,16,17,18,22,25,30,32,37,39,41,42,43,47,55,61],them:[2,3,10,16,18,23,28,30,32,37,41,43,44,47,54,55,57,59,61,62],theori:41,therefor:31,therein:9,therun:55,thi:[2,3,7,8,9,10,11,13,15,16,17,18,21,22,23,28,30,31,32,34,37,38,39,41,42,44,46,47,48,52,53,54,55,57,58,59,60,61,62],thing:[3,30,37,38,41,59,60],think:[16,17],third:[9,18,41,55,61],those:[17,18,55,60],thought:[17,41],thread:[39,41,44,46,59,60,61,62],thread_local_rand_use_global_se:[43,44],threadid:46,threadloc:41,three:[3,8,9,18,28,30,37,44,53,55,61,62],threshold:[7,8,18,21,44,61],thriller:58,through:[5,9,18,21,37,39,41,42,52,53,54,61,62],throughout:57,throughput:41,thu:[3,9,30,39,47,62],tier:48,tight:31,time:[3,9,10,12,13,16,17,18,21,28,30,37,41,44,46,48,57,58,60,61,62],timelin:[9,41],timeo:47,timeout:[18,21],timer:31,timestamp:[9,19,58],timestep:[3,9],titil:59,titl:[13,38,58,59],tls:24,tmall:61,todo:[8,10,13,18,21],toend:9,togeth:[3,9,10,13,15,37],token:[8,9,16,37,52,61,62],too:[13,32,34],tool:[32,37,38,47,61],toolchain:31,toolkit:[31,34],top:[8,55,60],top_k:8,topolog:[13,16,18],topolopi:15,toronto:[13,54],total:[8,15,18,28,41,42,48,52,62],total_pass:28,touch:61,tourism:61,tourist:62,toward:30,tra:62,track:[18,21],tractabl:9,tradesman:58,tradit:9,trail:13,train100:13,train10:13,train:[1,2,3,5,7,9,11,13,20,21,22,37,39,40,41,43,49,50,55],train_conf:[52,62],train_config_dir:47,train_data:62,train_id:47,train_list:[3,30,54,55,57],train_part_000:61,trainabl:9,traindot_period:43,trainer:[3,5,16,19,20,21,30,39,42,44,46,53,57,60,61,62],trainer_config:[2,3,30,42,47,48,57,59,61],trainer_config_help:[3,30,39,54,57,59],trainer_count:[43,44,46,47,48,59,60,61,62],trainer_cpu:23,trainer_cr:23,trainer_gpu:23,trainer_id:[44,47],trainer_mem:23,trainer_packag:23,trainerintern:[57,59,62],training_machin:53,trainingtest_period:43,trainonedatabatch:53,tran:[39,44],trane:3,transact:[18,21,61],transfer:[2,3],transform:[9,37,39,53,54,57,60],transform_param_attr:10,translat:[9,10,30,52,59,61,62],transpar:42,transport:44,transpos:[9,39,53],transposedfullmatrixproject:9,travel:3,travi:[31,38],treat:[9,22,37],treatment:22,tree:[9,38,44,62],trg:62,trg_dict:37,trg_dict_path:37,trg_embed:37,trg_id:37,trg_ids_next:37,triain:2,tricki:26,trivial:3,trn:57,truck:54,true_imag:28,true_label:28,true_read:28,truth:[8,9,57,62],tst:57,tune:[7,40,57,59,62],tuninglog_barrier_abstract:43,tupl:[3,9,13,15,28],ture:9,turn:[9,28,53],tutori:[32,37,38,39,41,42,47,48,49,50,55,57],tweet:61,twelv:62,twitter:61,two:[2,3,9,10,16,22,23,25,28,30,32,37,41,42,46,47,52,53,54,55,57,59,60,61,62],txt:[3,17,23,25,39,42,47,57,59,61],type:[3,8,9,10,12,13,15,16,18,21,23,24,25,26,27,28,30,32,37,39,44,46,47,48,54,55,57,59,60],type_nam:59,typedef:[22,26,27],typic:[5,8,32,41,61],ubuntu:[29,34],ubyt:28,uci:13,ufldl:9,uid:48,uint32:24,uint64:26,uint64_t:26,unawar:22,unbalanc:44,unbound:37,unconstrain:61,under:[21,30,31,32,47,58,61],underli:30,understand:[32,41,52,54,61],understudi:62,undeterminist:41,unemploi:58,unexist:60,uniform:[7,9,13,20,28,44,53],uniqu:[16,18,23,38,44,47],unique_ptr:39,unit:[9,10,17,30,31,32,37,38,60],unittest:27,unittestcheckgrad_ep:43,univ:62,unix:42,unk:[52,62],unk_idx:[57,60],unknown:9,unlabel:61,unlik:[60,61,62],unseg:9,unsup:61,unsupbow:61,until:[18,22,42,47,60],unus:59,unzip:59,updat:[7,9,18,21,22,24,31,39,42,44,46,61],update_equ:15,updatecallback:39,updatestack:47,upload:[18,23,24],upon:[0,60],upstream:38,uri:47,url:[13,34,61],urls_neg:61,urls_po:61,urls_unsup:61,usag:[2,3,8,9,10,13,15,30,41,52,53,59],use:[0,2,3,5,7,8,9,10,12,13,15,16,17,18,30,31,32,33,34,37,38,39,41,42,44,46,47,48,52,53,54,55,57,58,59,60,61,62],use_global_stat:9,use_gpu:[43,44,46,48,53,54,55,57,59,60,61,62],use_jpeg:54,use_old_updat:[43,44],use_seq:[30,59],use_seq_or_not:59,used:[2,3,5,8,9,10,11,12,13,15,16,18,28,30,33,34,37,39,41,42,43,44,46,47,52,54,55,57,59,60,61,62],useful:[2,3,9,10,37,39,46,57,60,61],usegpu:[39,53],useless:42,user:[2,3,7,9,10,13,15,16,20,21,23,25,28,30,32,38,42,43,44,47,55,57,60],user_featur:59,user_head:59,user_id:59,user_info:13,user_meta:59,user_nam:[20,59],usercert:20,userid:58,userinfo:13,userkei:20,usernam:[20,38],uses:[3,18,37,38,39,44,47,54,55,57,59,62],using:[2,3,5,7,9,10,13,16,17,18,21,22,23,25,28,30,32,37,38,39,41,44,46,47,48,52,53,54,55,57,60,61],usr:[31,42,44,47],usrdict:52,usrmodel:52,usual:[9,13,15,23,30,31,41,44,46,47,61],utf:52,util:[5,31,37,39,41,54,59,61],uuid:19,v28:9,valid:[28,47,55,61],valu:[3,5,7,8,9,11,12,13,15,18,30,37,39,44,46,47,53,54,55,60,61],value1:44,value2:44,value_printer_evalu:8,value_rang:13,vanilla:37,vanish:61,vari:[41,47],variabl:[3,9,13,16,30,31,34,39,42,47,48,61],varianc:[9,55],variant:32,vast:38,vector:[3,9,10,13,16,22,37,39,52,57,59,61,62],vectorenable_parallel_vector:43,verb:[13,60],verbos:25,veri:[3,9,12,21,37,41,54,57,61],verifi:[38,39],versa:31,version:[9,10,23,25,29,31,32,34,39,41,42,43,44,47,48,52,54,58,60,61,62],versu:16,vertic:[9,55],vgg:[10,54],vgg_16_cifar:54,via:[18,28,31,41,42,47,57],vice:31,view:9,vim:38,virtual:32,virtualenv:59,visibl:32,vision:54,visipedia:54,visual:[9,32,41],viterbi:60,voc_dim:57,vocab:61,volum:[32,48],volumemount:[47,48],volumn:47,voluntarili:58,vutbr:13,wai:[3,9,10,16,22,30,32,37,39,42,46,59,60,62],wait:[11,18,22,44],walk:[5,53],wall:60,want:[3,9,10,16,23,28,30,31,32,39,44,46,52,55,57,59,60,61],war:58,warn:[9,25],warp:[9,41],watch:18,wbia:[47,55],web:32,websit:[54,57,60,61],wei:[60,61],weight:[8,9,10,11,37,39,44,46,54,55],weight_act:10,weightlist:39,weights_:39,weights_t:39,welcom:[17,59,61],well:[23,32,39,44,47,54,57],were:17,west:47,western:58,wether:9,what:[7,9,10,11,17,30,42,57,59],wheel:31,when:[2,3,7,8,9,13,15,17,18,21,22,23,25,34,37,38,39,41,44,46,47,48,52,53,54,60,61,62],whenev:59,where:[3,9,10,11,16,18,30,37,39,41,42,44,46,52,55,60,62],wherea:21,whether:[8,9,10,28,39,44,53,54,59,61,62],which:[0,2,3,5,8,9,10,11,13,16,18,20,21,22,23,28,30,34,37,39,41,42,44,46,47,53,54,55,57,58,59,60,61,62],whichev:53,whl:31,who:[52,55,58],whoever:22,whole:[3,8,13,26,27,47,48,57,58,59,62],whole_cont:59,whose:[3,9,13,18,37,59,60],why:[10,27],wide:60,width:[8,9,13,26,28,39,54,62],wiki:9,wikipedia:[9,13],wilder:3,window:[9,12,13,32,61],wise:9,with_avx:32,with_avxcompil:31,with_coveragecompil:31,with_doccompil:31,with_doubl:39,with_doublecompil:31,with_dsocompil:31,with_gpu:32,with_gpucompil:31,with_profil:41,with_profilercompil:31,with_pythoncompil:31,with_rdmacompil:31,with_style_checkcompil:31,with_swig_pycompil:31,with_test:32,with_testingcompil:31,with_tim:41,with_timercompil:31,within:[9,21,30],without:[8,9,18,22,28,42,61],wmt14:62,wmt14_data:62,wmt14_model:62,wmt:62,wmt_shrinked_data:13,woboq:32,won:[41,55],wonder:3,word2vec:23,word:[3,8,9,13,37,46,56,59,60,61,62],word_dict:[57,60],word_dim:57,word_id:3,word_idx:13,word_slot:60,word_vector:57,word_vector_dim:[9,37,52],words_freq_sort:13,work:[3,5,13,16,17,18,28,31,37,38,39,41,42,44,47,48,57,59],worker:47,workercount:47,workflow:[32,38,47],workspac:[32,44,59],worri:30,wors:53,would:[15,17,18,28,32,42,47,53,57,60],wrap:60,wrapper:[10,17,41],writ:59,write:[3,13,16,18,28,32,37,38,40,42,47,54,59,60,62],write_lock:19,writelin:30,writer:[16,58],written:[59,61],wrong:[3,28],wsize:47,wsj:60,www:[9,13,54,62],x64:31,xarg:39,xgbe0:44,xgbe1:44,xiaojun:61,xrang:[28,30,39],xxbow:61,xxx:[16,55,62],xxxx:19,xxxxxxxxx:47,xxxxxxxxxx:47,xxxxxxxxxxxxx:47,xxxxxxxxxxxxxxxxxxx:47,xzf:31,y_i:9,y_predict:30,yaml:[47,59],yancey1989:23,yann:13,year:58,yeild:[15,54],yield:[3,13,16,20,28,30,37,57,59,60,61],you:[2,3,5,7,9,10,23,30,31,32,34,37,38,39,41,42,44,46,47,52,53,54,55,57,59,60,61,62],your:[3,9,16,17,23,25,31,32,39,41,42,46,47,57,61],your_access_key_id:47,your_secrete_access_kei:47,your_source_root:27,yum:31,yuyang18:[10,13],zachari:61,zeng:61,zero:[3,7,9,11,13,18,39,44,47,57],zhidao:52,zhou:[60,61],zip:[13,58],zone:47,zxvf:47},titles:["ABOUT","API","Introduction","PyDataProvider2","API","Python Prediction","Activation","Parameter Attribute","Evaluators","Layers","Networks","Optimizer","Pooling","Data Reader Interface and DataSets","Model Configuration","Training and Inference","PaddlePaddle Design Doc","Required CMake Function","Design Doc: Distributed Training","\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9\uff08Checkpointing\uff09","\u8bad\u7ec3\u6570\u636e\u7684\u5b58\u50a8\u548c\u5206\u53d1","Design Doc: Master Server","Design Doc: The Client Library of Parameter Server","Submit a Distributed Training Job","FileManager\u8bbe\u8ba1\u6587\u6863","PFSClient","Paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0","C-API \u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863","Python Data Reader Design Doc","Paddle\u53d1\u884c\u89c4\u8303","Simple Linear Regression","Installing from Sources","PaddlePaddle in Docker Containers","Install and Build","Debian Package installation guide","GET STARTED","RNN Models","RNN Configuration","Contribute Code","Write New Layers","HOW TO","Tune GPU Performance","Run Distributed Training","Argument Outline","Detail Description","Set Command-line Parameters","Use Case","Distributed PaddlePaddle Training on AWS with Kubernetes","Paddle On Kubernetes","<no title>","<no title>","PaddlePaddle Documentation","Chinese Word Embedding Model Tutorial","Generative Adversarial Networks (GAN)","Image Classification Tutorial","Model Zoo - ImageNet","TUTORIALS","Quick Start","MovieLens Dataset","Regression MovieLens Ratting","Semantic Role labeling Tutorial","Sentiment Analysis Tutorial","Text generation Tutorial"],titleterms:{"\u4e0a\u4f20\u8bad\u7ec3\u6587\u4ef6":20,"\u4e0d\u4f7f\u7528":26,"\u4e0d\u4f7f\u7528swig\u8fd9\u79cd\u4ee3\u7801\u751f\u6210\u5668":26,"\u4e0d\u5bfc\u51fapaddle\u5185\u90e8\u7684\u7ed3\u6784\u4f53":26,"\u4e0d\u5f15\u7528\u5176\u4ed6\u52a8\u6001\u5e93":26,"\u4ec5\u4ec5\u4f7f\u7528void":26,"\u4ece\u5feb\u7167\u6062\u590d":19,"\u4f7f\u7528\u52a8\u6001\u5e93\u6765\u5206\u53d1paddl":26,"\u4f7f\u7528\u8f6c\u6362\u5e93":20,"\u5177\u4f53\u67d0\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5177\u4f53\u67d0\u79cd\u7c7b\u578b\u7684\u5b9e\u73b0\u6587\u4ef6":27,"\u5206\u5757\u6587\u4ef6\u4f20\u8f93":24,"\u5206\u652f\u89c4\u8303":29,"\u52a0\u901f\u6267\u884c":19,"\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165\u4efb\u4f55\u5176\u4ed6\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u52a8\u6001\u6269\u5bb9":19,"\u539f\u56e0":26,"\u539f\u56e0\u5217\u8868":26,"\u53c2\u8003\u6587\u6863":24,"\u540d\u8bcd\u89e3\u91ca":24,"\u57fa\u672c\u8981\u6c42":26,"\u5b9e\u73b0":26,"\u5b9e\u73b0\u65b9\u5f0f":27,"\u5bfc\u51fac":26,"\u5feb\u7167\u4fdd\u5b58\u7684\u8bbe\u8ba1\u5982\u4e0b":19,"\u6307\u9488\u4f5c\u4e3a\u7c7b\u578b\u7684\u53e5\u67c4":26,"\u63a8\u6d4b\u6267\u884c":19,"\u652f\u6301\u7528\u6237\u81ea\u5b9a\u4e49\u7684\u6570\u636e\u9884\u5904\u7406job":20,"\u6587\u4ef6\u4f20\u8f93\u4f18\u5316":24,"\u6587\u4ef6\u8bbf\u95ee\u65b9\u5f0f":20,"\u6587\u4ef6\u8bbf\u95ee\u7684\u6743\u9650":20,"\u6587\u4ef6\u9884\u5904\u7406":20,"\u66b4\u9732\u63a5\u53e3\u539f\u5219":27,"\u672f\u8bed":19,"\u67b6\u6784\u56fe":24,"\u6846\u67b6\u751f\u6210":24,"\u6982\u5ff5\u89e3\u91ca":20,"\u6a21\u5757":24,"\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9":19,"\u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863":27,"\u6d41\u7a0b\u4ecb\u7ecd":20,"\u751f\u6210sparse\u6587\u4ef6":24,"\u7528\u6237\u4f7f\u7528\u6d41\u7a0b":24,"\u76ee\u5f55\u7ed3\u6784":27,"\u76ee\u6807":24,"\u793a\u4f8b\u7a0b\u5e8f":20,"\u7b26\u53f7":26,"\u7c7b":26,"\u7f16\u8bd1\u9009\u9879":27,"\u7f29\u5bb9":19,"\u800c\u662f\u624b\u5199\u591a\u8bed\u8a00\u7ed1\u5b9a":26,"\u80cc\u666f":26,"\u8986\u76d6\u4e0d\u4e00\u81f4\u7684\u90e8\u5206":24,"\u8bad\u7ec3\u6570\u636e\u5b58\u50a8":20,"\u8bad\u7ec3\u6570\u636e\u7684\u5b58\u50a8\u548c\u5206\u53d1":20,"\u8f6c\u6362\u5e93":20,"\u8fd9\u4e2a\u52a8\u6001\u5e93\u4f7f\u7528c99\u6807\u51c6\u7684\u5934\u6587\u4ef6\u5bfc\u51fa\u4e00\u4e9b\u51fd\u6570":26,"\u8fdb\u884c\u8bad\u7ec3":20,"book\u4e2d\u6240\u6709\u7ae0\u8282":29,"case":46,"class":39,"filemanager\u8bbe\u8ba1\u6587\u6863":24,"function":[17,52],"new":39,"paddle\u52a8\u6001\u5e93\u4e2d":26,"paddle\u53d1\u884c\u89c4\u8303":29,"paddle\u56de\u5f52\u6d4b\u8bd5\u5217\u8868":29,"paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0":26,"return":28,AWS:47,Abs:6,DNS:47,EFS:47,For:48,KMS:47,The:22,Use:[46,48],Using:[22,32,38],With:[23,32],about:0,access:47,account:47,activ:6,adadelta:11,adagrad:11,adam:11,adamax:11,add:47,address:47,addto:9,adversari:53,aggreg:9,aggregatelevel:9,algorithm:[18,57],analysi:61,api:[1,4,27,32],appendix:57,applic:4,approach:41,architectur:[37,57],argument:[25,28,43,46,57],asset:47,associ:47,async:44,attent:37,attribut:7,auc:8,avg:12,aws:47,background:30,base:23,basepool:12,batch:28,batch_norm:9,batch_siz:28,beam_search:9,between:16,bidirect:61,bidirectional_gru:10,bidirectional_lstm:10,bilinear_interp:9,bleu:62,block_expand:9,book:32,brelu:6,bucket:47,build:[31,33,48],built:41,cach:3,capi:27,capi_priv:27,cento:31,check:[9,39,42],checkpoint:[18,19],chines:52,choos:47,chunk:8,cifar:13,classif:[8,54],classification_error:8,classification_error_print:8,client:22,clone:38,cloudform:47,cluster:[42,46,47],cmake:17,code:[23,38],column_sum:8,command:[45,46,57,62],commit:[38,48],common:44,commun:44,compos:28,concat:9,concept:47,config:[4,46,59,60],configur:[14,37,40,42,47,57,59],conll05:13,connect:9,contain:[32,48],content:[27,41,47],context_project:9,contribut:38,conv:9,conv_oper:9,conv_project:9,conv_shift:9,convolut:[54,57],core:47,cos_sim:9,cost:9,cpu:[32,46],creat:[28,38,47,48],creation:21,creator:28,credenti:47,credit:0,crf:9,crf_decod:9,cross_channel_norm:9,cross_entropy_cost:9,cross_entropy_with_selfnorm_cost:9,ctc:9,ctc_error:8,cudnnavg:12,cudnnmax:12,custom:28,dat:58,data:[9,13,18,28,30,37,47,48,52,53,54,57,59,60,61,62],datafeed:13,dataprovid:[3,4,44],dataset:[13,18,21,58,59,62],datatyp:13,date:38,debian:34,decayedadagrad:11,decor:28,defin:[47,57,61,62],delet:47,delv:54,demo:47,depend:31,deploi:23,deriv:39,descript:[25,44,53,58,60],design:[16,18,21,22,28],destroi:47,detail:[44,54],develop:[32,40],devic:46,dictionari:[28,52],differ:46,directori:47,dispatch:[18,21],distribut:[16,18,23,42,44,47],doc:[16,18,21,22,28],docker:[23,32,48],document:[32,51],dotmul_oper:9,dotmul_project:9,down:47,download:[31,47,48,52,55,59,62],dropout_lay:10,dylib:27,dynam:18,ec2:47,elast:47,embed:[9,52,57],entri:28,environ:23,eos:9,equat:39,evalu:[8,30,59],evalutaion:62,event:[15,16],exampl:[16,17,27,52,53],exercis:54,exp:6,expand:9,expandlevel:9,extern:47,extract:[52,55,59,62],fault:18,featur:[55,58,59,60],field:59,file:[47,48,57,58,59],find:47,first_seq:9,fork:38,format:[18,57],from:[16,31,33],full_matrix_project:9,fulli:9,gan:53,gate:37,gener:[37,53,62],get:[35,48],get_output:9,github:38,gpu:[32,41,44,46],gradient:[22,39],gradient_print:8,group:[9,47],gru:[10,44],gru_group:10,gru_step:9,gru_unit:10,grumemori:9,guid:34,hand:41,handler:[16,26],hook:38,how:[28,40,41],hsigmoid:9,huber_cost:9,iam:47,ident:6,identity_project:9,imag:[9,10,23,32,48,54],imagenet:55,imdb:[13,61],img_cmrnorm:9,img_conv:9,img_conv_bn_pool:10,img_conv_group:10,img_pool:9,imikolov:13,implement:[17,28,39,53],infer:[15,57],info:55,ingredi:16,ingress:24,init_hook:3,initi:[22,46,47],input_typ:3,inspect:47,instal:[31,33,34,47,57],instanc:47,integr:47,interfac:[13,18,22,28,55],interpol:9,introduct:[2,52,55,61,62],isn:28,job:[18,23,42,47,48],join:9,keep:38,kei:47,kill:42,kube:47,kubectl:47,kubernet:[23,47,48],label:60,lambda_cost:9,last_seq:9,lastest:38,launch:42,layer:[9,16,39,46],libpaddle_capi_shar:27,libpaddle_capi_whol:27,librari:22,line:[45,57],linear:[6,30],linear_comb:9,list:[19,28],local:[46,47],log:[6,57],logic:21,logist:57,lstm:[10,44,60,61],lstm_step:9,lstmemori:9,lstmemory_group:10,lstmemory_unit:10,map:28,master:[18,21,23],math:9,matrix:44,max:12,maxframe_print:8,maxid:9,maxid_print:8,maxout:9,memori:9,meta:59,mini:28,minibatch:13,misc:10,mix:[9,46],mnist:[13,53],model:[3,4,14,16,22,30,32,36,37,42,46,52,53,54,55,56,57,62],modifi:48,momentum:11,movi:[58,59],movielen:[13,58,59],mse_cost:9,multi_binary_label_cross_entropy_cost:9,multipl:28,name:47,nce:9,need:[28,41],network:[10,37,46,53,54,55,57,59,60],neural:[37,54,57,59,60],neuralnetwork:30,nlp:[10,44],non:3,norm:9,nvprof:41,nvvp:41,object:[18,59],observ:[52,55],onli:[28,32],optim:[11,18,22,40,57],option:[25,31,52],order:25,outlin:43,output:[42,47],overview:57,packag:34,pad:9,paddl:[28,29,48],paddlejob:23,paddlepaddl:[16,32,33,47,51,52,62],pair:47,parallel_nn:46,paramet:[7,15,16,18,22,23,44,45,47,52,55],paraphras:52,partit:22,pass:46,path:25,perform:[41,44],persist:21,pfsclient:[24,25],pfsserver:24,pnpair:8,point:47,pool:[9,12],power:9,pre:38,precision_recal:8,predict:[5,54,55,59,60,61],prefetch:28,prepar:[30,37,42,47,52,53,54,59,61,62],preprocess:[52,54,57,59,62],prerequisit:42,pretrain:[52,62],print:8,privat:47,problem:30,process:[18,22,23],profil:41,project:17,provid:[3,28,57,59,60],pull:38,push:38,pydataprovider2:3,python:[5,23,28,32,39,55,57,59],queue:[18,21],quick:57,randomnumb:44,rank:8,rank_cost:9,rat:59,rate:58,reader:[13,16,28],recoveri:18,recurr:[9,10,37,57],recurrent_group:9,refer:[3,41,60,61],region:47,regress:[30,57,59],regular:22,relu:6,render:47,repeat:9,request:38,requir:[17,31,38],reshap:9,resnet:55,result:[42,48,62],retri:21,revis:[38,52],rmsprop:11,rnn:[36,37,44],role:60,rotat:9,route53:47,run:[42,48,60],runtim:23,sampl:9,sampling_id:9,scale:[9,18],scaling_project:9,script:48,secur:47,select:22,selective_fc:9,semant:60,sentiment:[13,61],seq_concat:9,seq_reshap:9,seqtext_print:8,sequenc:37,sequence_conv_pool:10,sequencesoftmax:6,sequenti:3,server:[18,21,22,23,44,47],servic:47,set:45,setup:[31,47],sgd:44,share:16,shuffl:28,sigmoid:6,simpl:[30,37],simple_attent:10,simple_gru2:10,simple_gru:10,simple_img_conv_pool:10,simple_lstm:10,singl:28,slice:9,slope_intercept:9,small_vgg:10,smooth_l1_cost:9,softmax:6,softrelu:6,sourc:[31,33],span:31,spars:[22,46],specifi:[46,52],split:59,spp:9,squar:6,squarerootn:12,stack:61,standard:57,stanh:6,start:[16,35,47,48,57],startup:48,store:18,structur:53,subcommond:25,submit:23,suffici:28,sum:[8,12],sum_cost:9,sum_to_one_norm:9,summar:16,summari:57,synopsi:25,system:47,tabl:27,table_project:9,take:28,tanh:6,task:[18,21],tear:47,templat:47,tensor:9,test:[39,44,46,59,60,61],text:62,text_conv_pool:10,timer:41,tip:41,todo:[19,20],toi:53,toler:18,tool:41,train:[15,16,18,23,28,30,32,42,44,46,47,48,52,53,54,57,59,60,61,62],trainer:[15,18,22,23,47,59],tran:9,trans_full_matrix_project:9,transfer:57,tune:[41,44],tutori:[52,54,56,60,61,62],ubuntu:31,uci_h:13,unit:[39,44],updat:[16,38,47],usag:[28,32,40],use:28,user:[18,52,58,59,61,62],util:8,value_print:8,vector:44,verifi:47,version:38,vgg_16_network:10,visual:55,volum:47,vpc:47,warp_ctc:9,what:41,why:[28,41],wmt14:13,word:[52,57],work:32,workflow:62,workspac:42,wrapper:39,write:[39,57],yaml:48,your:38,zoo:[55,56]}})
\ No newline at end of file
+Search.setIndex({docnames:["about/index_en","api/index_en","api/v1/data_provider/dataprovider_en","api/v1/data_provider/pydataprovider2_en","api/v1/index_en","api/v1/predict/swig_py_paddle_en","api/v2/config/activation","api/v2/config/attr","api/v2/config/evaluators","api/v2/config/layer","api/v2/config/networks","api/v2/config/optimizer","api/v2/config/pooling","api/v2/data","api/v2/model_configs","api/v2/run_logic","design/api","design/build_system/README","design/cluster_train/README","design/cluster_train/checkpointing","design/cluster_train/data_dispatch","design/cluster_train/master_server","design/cluster_train/pserver_client","design/cluster_train/submit-job","design/file_manager/README","design/file_manager/pfs/pfsclient","design/multi_language_interface/00.why_plain_c","design/multi_language_interface/01.inference_implementation","design/parameters_in_cpp","design/reader/README","design/releasing_process","getstarted/basic_usage/index_en","getstarted/build_and_install/build_from_source_en","getstarted/build_and_install/docker_install_en","getstarted/build_and_install/index_en","getstarted/build_and_install/ubuntu_install_en","getstarted/index_en","howto/deep_model/rnn/index_en","howto/deep_model/rnn/rnn_config_en","howto/dev/contribute_to_paddle_en","howto/dev/new_layer_en","howto/index_en","howto/optimization/gpu_profiling_en","howto/usage/cluster/cluster_train_en","howto/usage/cmd_parameter/arguments_en","howto/usage/cmd_parameter/detail_introduction_en","howto/usage/cmd_parameter/index_en","howto/usage/cmd_parameter/use_case_en","howto/usage/k8s/k8s_aws_en","howto/usage/k8s/k8s_en","howto/usage/k8s/src/k8s_data/README","howto/usage/k8s/src/k8s_train/README","index_en","tutorials/embedding_model/index_en","tutorials/gan/index_en","tutorials/image_classification/index_en","tutorials/imagenet_model/resnet_model_en","tutorials/index_en","tutorials/quick_start/index_en","tutorials/rec/ml_dataset_en","tutorials/rec/ml_regression_en","tutorials/semantic_role_labeling/index_en","tutorials/sentiment_analysis/index_en","tutorials/text_generation/index_en"],envversion:50,filenames:["about/index_en.rst","api/index_en.rst","api/v1/data_provider/dataprovider_en.rst","api/v1/data_provider/pydataprovider2_en.rst","api/v1/index_en.rst","api/v1/predict/swig_py_paddle_en.rst","api/v2/config/activation.rst","api/v2/config/attr.rst","api/v2/config/evaluators.rst","api/v2/config/layer.rst","api/v2/config/networks.rst","api/v2/config/optimizer.rst","api/v2/config/pooling.rst","api/v2/data.rst","api/v2/model_configs.rst","api/v2/run_logic.rst","design/api.md","design/build_system/README.md","design/cluster_train/README.md","design/cluster_train/checkpointing.md","design/cluster_train/data_dispatch.md","design/cluster_train/master_server.md","design/cluster_train/pserver_client.md","design/cluster_train/submit-job.md","design/file_manager/README.md","design/file_manager/pfs/pfsclient.md","design/multi_language_interface/00.why_plain_c.md","design/multi_language_interface/01.inference_implementation.md","design/parameters_in_cpp.md","design/reader/README.md","design/releasing_process.md","getstarted/basic_usage/index_en.rst","getstarted/build_and_install/build_from_source_en.md","getstarted/build_and_install/docker_install_en.rst","getstarted/build_and_install/index_en.rst","getstarted/build_and_install/ubuntu_install_en.rst","getstarted/index_en.rst","howto/deep_model/rnn/index_en.rst","howto/deep_model/rnn/rnn_config_en.rst","howto/dev/contribute_to_paddle_en.md","howto/dev/new_layer_en.rst","howto/index_en.rst","howto/optimization/gpu_profiling_en.rst","howto/usage/cluster/cluster_train_en.md","howto/usage/cmd_parameter/arguments_en.md","howto/usage/cmd_parameter/detail_introduction_en.md","howto/usage/cmd_parameter/index_en.rst","howto/usage/cmd_parameter/use_case_en.md","howto/usage/k8s/k8s_aws_en.md","howto/usage/k8s/k8s_en.md","howto/usage/k8s/src/k8s_data/README.md","howto/usage/k8s/src/k8s_train/README.md","index_en.rst","tutorials/embedding_model/index_en.md","tutorials/gan/index_en.md","tutorials/image_classification/index_en.md","tutorials/imagenet_model/resnet_model_en.md","tutorials/index_en.md","tutorials/quick_start/index_en.md","tutorials/rec/ml_dataset_en.md","tutorials/rec/ml_regression_en.rst","tutorials/semantic_role_labeling/index_en.md","tutorials/sentiment_analysis/index_en.md","tutorials/text_generation/index_en.md"],objects:{"paddle.trainer.PyDataProvider2":{provider:[3,0,1,""]}},objnames:{"0":["py","function","Python function"]},objtypes:{"0":"py:function"},terms:{"0000x":58,"00186201e":5,"00m":42,"02595v1":9,"03m":42,"0424m":42,"0473v3":10,"055ee37d":48,"05d":55,"0630u":42,"06u":42,"0810u":42,"08823112e":5,"0957m":42,"0ab":9,"0rc1":30,"0rc2":[30,33],"0th":63,"10007_10":62,"10014_7":62,"100gb":42,"100gi":48,"10g":23,"10m":42,"1150u":42,"11\u5b9e\u73b0\u4e86c":27,"11e6":49,"12194102e":5,"124n":42,"13m":49,"1490u":42,"15501715e":5,"1550u":42,"15mb":58,"1636k":63,"16mb":58,"16u":42,"173m":56,"173n":42,"1770u":42,"18ad":48,"18e457ce3d362ff5f3febf8e7f85ffec852f70f3b629add10aed84f930a68750":49,"197u":42,"1gb":42,"1st":[53,56,62,63],"202mb":63,"210u":42,"211839e770f7b538e2d8":10,"215n":42,"228u":42,"234m":56,"2520u":42,"252kb":58,"25639710e":5,"25k":58,"2680u":42,"27787406e":5,"279n":42,"27m":42,"285m":42,"2863m":42,"28m":42,"28x28":3,"2977m":42,"2cbf7385":48,"2nd":[9,62,63],"302n":42,"30u":42,"32777140e":5,"328n":42,"32u":42,"32x32":[13,55],"331n":42,"3320u":42,"36540484e":5,"365e":48,"36u":42,"3710m":42,"3768m":42,"387u":42,"38u":42,"3920u":42,"39u":42,"3rd":[60,62,63],"4035m":42,"4090u":42,"4096mb":45,"4279m":42,"43630644e":5,"43u":42,"448a5b355b84":49,"4560u":42,"4563m":42,"45u":42,"4650u":42,"4726m":42,"473m":49,"48565123e":5,"48684503e":5,"49316648e":5,"4gb":45,"50bd":48,"50gi":48,"51111044e":5,"514u":42,"525n":42,"526u":42,"53018653e":5,"536u":42,"5460u":42,"5470u":42,"54u":42,"55g":63,"5690m":42,"573u":42,"578n":42,"5798m":42,"586u":42,"58s":49,"5969m":42,"6080u":42,"6082v4":9,"6140u":42,"6305m":42,"639u":42,"655u":42,"6780u":42,"6810u":42,"682u":42,"6970u":42,"6ce9":48,"6node":43,"6th":63,"704u":42,"70634608e":5,"7090u":42,"72296313e":5,"72u":42,"73u":42,"75u":42,"760u":42,"767u":42,"783n":42,"784u":42,"78m":42,"7eamaa":13,"7kb":49,"8250u":42,"8300u":42,"830n":42,"849m":42,"85625684e":5,"861u":42,"864k":63,"8661m":42,"892m":42,"901n":42,"90u":42,"918u":42,"9247m":42,"924n":42,"9261m":42,"93137714e":5,"9330m":42,"94u":42,"9530m":42,"96644767e":5,"983m":42,"988u":42,"997u":42,"99982715e":5,"99m":56,"99u":42,"9f18":49,"\u4e00\u4e2a\u5178\u578b\u7684chunk\u5982\u4e0b\u6240\u793a":24,"\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc\u7684\u6a21\u578b\u7531\u5927\u91cf\u7684\u53c2\u6570\u7ec4\u6210":19,"\u4e00\u4e2achunk\u7531\u6240\u5728\u7684\u6587\u4ef6\u504f\u79fb":24,"\u4e00\u4e2aposix\u517c\u5bb9\u7684\u6587\u4ef6\u7cfb\u7edf":24,"\u4e00\u822c\u4e0d\u5141\u8bb8\u518d\u4ece":30,"\u4e0a\u4f20\u5230cloud\u6216\u8005\u4e0b\u8f7d\u5230\u672c\u5730\u7684\u65f6\u95f4\u53ef\u80fd\u6bd4\u8f83\u957f":24,"\u4e0a\u6ce8\u518c\u4e00\u4e0b":24,"\u4e0b\u5b58\u653e\u516c\u5171\u6570\u636e\u96c6\u5408":20,"\u4e0b\u8f7d":24,"\u4e0b\u8f7d\u5230\u672c\u5730":24,"\u4e0b\u9762\u5206\u522b\u4ecb\u7ecd\u67d0\u4e00\u7c7b\u6587\u4ef6\u7684\u5b9e\u73b0\u65b9\u5f0f":27,"\u4e0d\u4e00\u81f4\u7684\u7531pfsclient\u4e0b\u8f7d\u6216\u8005\u4f20\u8f93chunk\u5b8c\u6210":24,"\u4e0d\u4f7f\u7528\u9759\u6001\u5e93":26,"\u4e0d\u4f7f\u7528c":26,"\u4e0d\u4f7f\u7528swig":26,"\u4e0d\u540c\u7248\u672c\u7684\u7f16\u8bd1\u5668\u4e4b\u95f4":26,"\u4e0d\u540c\u8bed\u8a00\u7684\u63a5\u53e3\u9002\u5e94\u4e0d\u540c\u8bed\u8a00\u7684\u7279\u6027":26,"\u4e0d\u5728":27,"\u4e0d\u5bb9\u6613\u51fa\u9519":24,"\u4e0d\u5d4c\u5165\u5176\u4ed6\u8bed\u8a00\u89e3\u91ca\u5668":26,"\u4e0d\u5d4c\u5165python\u89e3\u91ca\u5668":26,"\u4e0d\u663e\u793a\u7684\u5199\u6bcf\u4e2a\u7c7b\u5177\u4f53\u5305\u542b\u4ec0\u4e48":26,"\u4e0d\u7528mount\u7684\u65b9\u5f0f\u6765\u8bbf\u95ee\u6570\u636e":20,"\u4e0e\u4e4b\u76f8\u5bf9\u7684\u662flocal":24,"\u4e0e\u529f\u80fd\u5206\u652f\u4e0d\u540c\u7684\u662f":30,"\u4e0e\u53ef\u80fd\u6709\u7684":30,"\u4e14\u589e\u52a0\u4e00\u4e2a\u7b2c\u4e09\u65b9\u8bed\u8a00":26,"\u4e14\u8c03\u7528\u65f6\u4e0d\u80fd\u629b\u51fa\u5f02\u5e38\u6216\u51fa\u73b0\u8fd0\u884c\u65f6\u9519\u8bef":27,"\u4e14c99\u652f\u6301bool\u7c7b\u578b\u548c\u5b9a\u957f\u6574\u6570":26,"\u4e14c99\u76f8\u5bf9\u4e8ec11\u4f7f\u7528\u66f4\u52a0\u5e7f\u6cdb":26,"\u4e2a\u6027\u5316\u63a8\u8350":30,"\u4e2d":[26,27],"\u4e2d\u5199\u5165json\u5185\u5bb9":19,"\u4e2d\u5b8c\u5168\u4e00\u81f4":26,"\u4e2d\u5b9e\u73b0\u7684\u7ed3\u6784\u4f53":27,"\u4e2d\u8fd0\u884c\u4efb\u52a1\u7684\u89d2\u5ea6":20,"\u4e3a\u4e86\u5e94\u5bf9\u4ee5\u4e0a\u7684\u95ee\u9898":24,"\u4e3a\u4e86\u66b4\u9732\u7684\u63a5\u53e3\u5c3d\u91cf\u7b80\u5355":27,"\u4e3b\u8981\u529f\u80fd\u5305\u62ec":24,"\u4e4b\u5916\u7684\u6240\u6709\u5934\u6587\u4ef6":27,"\u4e5f\u4e0d\u4f7f\u7528\u5176\u4ed6\u52a8\u6001\u5e93":26,"\u4e5f\u4e0d\u5e94\u8be5\u62a5\u9519":27,"\u4e5f\u4e0d\u751f\u6210":27,"\u4e66\u5199":26,"\u4eba\u8138\u8bc6\u522b":20,"\u4ec5\u4ec5\u4f7f\u7528":26,"\u4ece":30,"\u4ece\u78c1\u76d8\u6587\u4ef6\u4e2d\u52a0\u8f7duuid\u6587\u4ef6\u540d\u7684\u68c0\u67e5\u70b9\u5feb\u7167\u6587\u4ef6":19,"\u4eceetcd\u4e2d\u8bfb\u53d6\u8282\u70b9":19,"\u4ed6\u4e3b\u8981\u5305\u542b\u4e86\u5b9e\u9645\u66b4\u9732\u7684\u7c7b\u578b\u7ed3\u6784":27,"\u4ed6\u662f\u5c06":27,"\u4ed6\u7684\u76ee\u6807\u662f\u4f7f\u7528c":26,"\u4ee3\u7801\u751f\u6210\u7684\u7b26\u53f7\u53ef\u80fd\u4e0d\u4e00\u81f4":26,"\u4ee3\u8868\u8fd9\u4e2ashard\u7684\u6700\u5927index":20,"\u4ee3\u8868shard\u7684index":20,"\u4ee5\u4e0a\u4ee3\u7801\u7684reader\u8f93\u51fa\u7684data":20,"\u4ee5\u4e0a\u547d\u4ee4\u4f1a\u5728\u5f53\u524d\u76ee\u5f55\u4e0b\u751f\u6210100\u4e2a\u6587\u4ef6":20,"\u4ee5\u4e0b":20,"\u4ee5\u4fbf\u6211\u4eec\u53ef\u4ee5\u628a\u66f4\u591a\u7684\u7cbe\u529b\u653e\u5230\u903b\u8f91\u672c\u8eab\u4e0a":24,"\u4ee5\u53canumpi":20,"\u4efb\u610f\u65f6\u523b\u53ea\u53ef\u80fd\u540c\u65f6\u6709\u4e00\u53f0\u670d\u52a1\u5668\u6545\u969c":19,"\u4f1a\u5bfc\u81f4\u4e0d\u540c\u7248\u672cpython\u5728\u4e00\u4e2a\u8fdb\u7a0b\u91cc\u7684bug":26,"\u4f1a\u76f4\u63a5\u62a5\u9519\u9000\u51fa":26,"\u4f1a\u88abpickle\u5e8f\u5217\u5316\u6210\u5b57\u7b26\u4e32":20,"\u4f20\u5165":20,"\u4f46":27,"\u4f46\u4e0d\u66b4\u9732":27,"\u4f46\u5e76\u6ca1\u6709\u7ecf\u8fc7\u56de\u5f52\u6d4b\u8bd5":30,"\u4f46\u6240\u6709fork\u7684\u7248\u672c\u5e93\u7684\u6240\u6709\u5206\u652f\u90fd\u76f8\u5f53\u4e8e\u7279\u6027\u5206\u652f":30,"\u4f46\u662f\u53c8\u8fc7\u4e8e\u7410\u788e":27,"\u4f46\u662f\u89e3\u91ca\u6027\u8bed\u8a00":26,"\u4f5c\u4e3a\u5b58\u50a8\u7cfb\u7edf":20,"\u4f5c\u4e3a\u7c7b\u53e5\u67c4":26,"\u4f7f\u7528":[27,30],"\u4f7f\u7528\u4e0b\u9762\u547d\u4ee4":20,"\u4f7f\u7528\u52a8\u6001\u5e93":26,"\u4f7f\u7528\u540c\u6837\u7684\u8bad\u7ec3\u6570\u636eblock":19,"\u4f7f\u7528\u667a\u80fd\u6307\u9488\u7684\u539f\u56e0\u662f":27,"\u4f7f\u7528\u76f8\u5bf9\u8def\u5f84\u7684\u5f15\u7528\u65b9\u5f0f":27,"\u4f7f\u7528\u8fd9\u4e2a\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u5b8c\u6210\u5bf9\u65b0\u6570\u636e\u7684\u9884\u6d4b":19,"\u4f7f\u7528\u9759\u6001\u5e93\u548c\u52a8\u6001\u5e93\u96be\u5ea6\u5dee\u4e0d\u591a":26,"\u4f7f\u7528c":27,"\u4f7f\u7528c99\u505a\u63a5\u53e3":26,"\u4f7f\u7528c99\u800c\u4e0d\u4f7f\u7528c11\u7684\u539f\u56e0\u662f":26,"\u4f7f\u7528c99\u800c\u4e0d\u4f7f\u7528c89":26,"\u4f7f\u7528regress":30,"\u4f7f\u7528swig\u53ea\u652f\u6301cpython\u89e3\u91ca\u5668":26,"\u4f7f\u7528swig\u9700\u8981\u591a\u8bed\u8a00\u7ed1\u5b9a\u7684\u5f00\u53d1\u4eba\u5458\u719f\u7ec3\u638c\u63e1swig\u914d\u7f6e":26,"\u4f7f\u7528void":26,"\u4f8b\u5982":[20,26,27,30],"\u4f8b\u5982\u5bf9\u4e8ejava\u6216\u8005python":26,"\u4f8b\u5982\u5bf9\u4e8ejava\u6765\u8bf4":26,"\u4f8b\u5982\u5bf9\u4e8epython":26,"\u4f8b\u5982c":26,"\u4f8b\u5982java\u4e0epython\u7684\u9519\u8bef\u5904\u7406\u662f\u76f4\u63a5\u6254\u51fa\u6765except":26,"\u4f8b\u5982python\u53ef\u4ee5\u4f7f\u7528":26,"\u4f8b\u5982python\u7684":26,"\u4f9d\u6b21\u7c7b\u63a8":30,"\u4fbf\u662f\u5c06\u9759\u6001\u5e93\u52a0\u5165jvm\u4e2d":26,"\u4fee\u590d\u6240\u6709bug\u540e":30,"\u4fee\u590ddocker\u7f16\u8bd1\u955c\u50cf\u95ee\u9898":30,"\u4fee\u590dubuntu":30,"\u505a\u53ea\u8bfb\u6302\u8f7d":20,"\u505a\u5982\u4e0b\u51e0\u4e2a\u64cd\u4f5c":30,"\u505a\u63a5\u53e3":26,"\u505c\u6b62\u4fdd\u5b58\u68c0\u67e5\u70b9\u7684\u7ebf\u7a0b":19,"\u5148\u5b9e\u73b0\u6a21\u578b\u63a8\u65ad\u7684api":27,"\u5176\u4e2d":[26,30],"\u5176\u4ed6\u51fd\u6570\u5747\u8fd4\u56de":27,"\u5176\u4ed6\u7528\u6237\u7684fork\u7248\u672c\u5e93\u5e76\u4e0d\u9700\u8981\u4e25\u683c\u9075\u5b88":30,"\u5177\u4f53\u4f7f\u7528\u65b9\u6cd5\u4e3a":27,"\u5177\u4f53\u539f\u56e0\u53c2\u8003":27,"\u5177\u4f53\u8bf7\u53c2\u8003":27,"\u5185\u90e8\u9a71\u52a8python\u89e3\u91ca\u5668\u8fdb\u884c\u6a21\u578b\u914d\u7f6e\u89e3\u6790\u548c\u6570\u636e\u8bfb\u53d6":26,"\u518d\u5728\u6bcf\u4e00\u4e2aapi\u4e2d\u81ea\u5df1\u68c0\u67e5\u7c7b\u578b":26,"\u518d\u57fa\u4e8e":30,"\u5199\u4ee3\u7801":26,"\u5199\u5165\u5feb\u7167\u6570\u636e":19,"\u51fd\u6570\u5373\u53ef\u5b8c\u6210\u8f6c\u6362":20,"\u51fd\u6570\u540d\u4e3a":27,"\u51fd\u6570\u547d\u540d":26,"\u5206\u5e03\u5f0f\u5b58\u50a8\u670d\u52a1":19,"\u5206\u652f":30,"\u5206\u652f\u4e00\u65e6\u5efa\u7acb":30,"\u5206\u652f\u4e2d":30,"\u5206\u652f\u4e3a\u5f00\u53d1":30,"\u5206\u652f\u4e3a\u6bcf\u4e00\u6b21release\u65f6\u5efa\u7acb\u7684\u4e34\u65f6\u5206\u652f":30,"\u5206\u652f\u4e3a\u7a33\u5b9a":30,"\u5206\u652f\u529f\u80fd\u7684\u5c01\u95ed":30,"\u5206\u652f\u5408\u5165":30,"\u5206\u652f\u5408\u5165master\u5206\u652f":30,"\u5206\u652f\u540c\u6b65\u4e3b\u7248\u672c\u5e93\u7684":30,"\u5206\u652f\u540d\u4e3a":30,"\u5206\u652f\u5b58\u5728\u7684\u65f6\u5019":30,"\u5206\u652f\u6d3e\u751f\u51fa\u65b0\u7684\u5206\u652f":30,"\u5206\u652f\u7684\u7248\u672c\u90fd\u662f\u7ecf\u8fc7\u5355\u5143\u6d4b\u8bd5\u548c\u56de\u5f52\u6d4b\u8bd5\u7684\u7248\u672c":30,"\u5206\u652f\u7684\u7248\u672c\u90fd\u7ecf\u8fc7\u5355\u5143\u6d4b\u8bd5":30,"\u5206\u7247":19,"\u5219\u4f7f\u7528\u542f\u52a8\u53c2\u6570\u5b9a\u4e49\u7684\u521d\u59cb\u5316\u65b9\u6cd5\u521d\u59cb\u5316\u53c2\u6570":19,"\u5219\u5ffd\u7565":19,"\u5219\u628a\u53e6\u4e00\u4e2a\u6162\u901f\u7684kill\u6389":19,"\u5219\u76f4\u63a5\u5f15\u5165\u53e6\u4e00\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5219\u9700\u8981\u56de\u6eda\u5230\u4e0a\u4e00\u4e2a\u68c0\u67e5\u70b9":19,"\u5220\u9664\u78c1\u76d8\u76ee\u5f55\u4e2d\u4e0d\u662f\u5f53\u524duuid\u7684\u5feb\u7167\u6587\u4ef6":19,"\u5230":19,"\u529f\u80fd":24,"\u529f\u80fd\u7684\u6b63\u786e\u6027\u5305\u62ec\u9a8c\u8bc1paddle\u76ee\u524d\u7684":30,"\u52a8\u6001\u5e93":26,"\u5305\u542b\u4e86\u67d0\u79cd\u7c7b\u578b\u7684\u7c7b\u578b\u5b9a\u4e49\u548c\u66b4\u9732\u7684\u5168\u90e8\u51fd\u6570":27,"\u5305\u62ec":20,"\u5305\u62ec\u6743\u91cdw\u548c\u504f\u7f6eb":19,"\u534f\u540c\u5b8c\u6210releas":30,"\u5355\u4e2a\u503c":20,"\u5355\u70b9\u6545\u969c":19,"\u5373":27,"\u5373\u4f7f\u7528":27,"\u5373\u4f7f\u7528\u6237\u76f4\u63a5\u5f15\u7528\u67d0\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5373\u4f7fc":27,"\u5373\u4f8b\u5982":27,"\u5373\u4fbfpaddl":27,"\u5373\u5b8c\u6210\u67d0\u4e00\u4e2a\u4efb\u52a1\u7684\u6700\u5c11\u51fd\u6570":27,"\u5373\u66b4\u9732":27,"\u5373\u8fd9\u4e2a\u52a8\u6001\u5e93\u662f\u4e0d\u4f9d\u8d56\u4e8e\u5176\u4ed6\u4efb\u4f55\u6587\u4ef6\u7684":26,"\u53c2\u6570":26,"\u53c2\u8003":[24,26],"\u53cc\u5411\u9a8c\u8bc1":24,"\u53d1\u5e03\u5230dockerhub":30,"\u53d1\u5e03\u5230github":30,"\u53ea\u5bf9\u7279\u6b8a\u5728\u7ebf\u7cfb\u7edf\u8003\u8651\u4e24\u53f0\u4ee5\u4e0a\u540c\u65f6\u6545\u969c\u7684\u5bb9\u707e":19,"\u53ea\u66b4\u9732\u6982\u5ff5\u7684\u63a5\u53e3":27,"\u53ea\u80fd\u8c03\u7528paddle\u7684\u52a8\u6001\u5e93":26,"\u53ea\u9700\u8981\u6062\u590d\u8fd9\u53f0\u8282\u70b9":19,"\u53ef\u4ee5\u51cf\u5c0f\u7cfb\u7edf\u590d\u6742\u6027":19,"\u53ef\u4ee5\u5728\u4efb\u4f55\u673a\u5668\u4e0a\u6267\u884c\u7684":26,"\u53ef\u4ee5\u628a\u672c\u5730\u7684\u6570\u636e\u4e0a\u4f20\u5230\u5b58\u50a8\u96c6\u7fa4\u4e2d":20,"\u53ef\u4ee5\u6709\u6548\u7684\u907f\u514dparamet":19,"\u53ef\u4ee5\u7528":24,"\u53ef\u4ee5\u7528\u4ee5\u4e0b\u6307\u4ee4":20,"\u53ef\u4ee5\u7ee7\u7eed\u5728\u81ea\u5df1\u7684\u529f\u80fd\u5206\u652f\u63d0\u4ea4\u4ee3\u7801":30,"\u53ef\u4ee5\u901a\u8fc7\u9636\u6bb5\u6027\u7684\u4fdd\u5b58\u6bcf\u4e2aparamet":19,"\u53ef\u80fd\u4f1a\u9020\u6210\u7f51\u7edc\u62e5\u585e":19,"\u540c\u65f6\u518d\u5c06":30,"\u540c\u65f6\u63d0\u8d77":30,"\u540d\u5b57\u4fee\u9970":26,"\u5411\u6307\u5b9a\u7684\u76ee\u5f55\u4e2d\u4e00\u4e2a\u65b0\u7684\u6587\u4ef6":19,"\u5411paddle\u7684\u4e3b\u7248\u672c\u5e93\u63d0\u4ea4":30,"\u5426\u5219\u5f97\u628apaddle\u9759\u6001\u5e93\u94fe\u63a5\u5230\u89e3\u91ca\u5668\u91cc":26,"\u542f\u52a8\u4e00\u4e2a\u65b0\u7684\u7ebf\u7a0b\u5f00\u59cb\u4fdd\u5b58\u68c0\u67e5\u70b9":19,"\u548c":[20,26,27,30],"\u548c\u79bb\u7ebf\u6570\u636e\u7684\u65b9\u5f0f":20,"\u54ea\u4e2atrainer\u5148\u5b8c\u6210block\u7684\u8bad\u7ec3":19,"\u56e0\u4e3a\u8fd9\u6837\u505a\u4e5f\u6ca1\u6cd5\u4fdd\u8bc1\u6d88\u9664\u968f\u673a\u6027":19,"\u56e0\u4e3aswig\u5728\u7b2c\u4e09\u65b9\u8bed\u8a00\u4e2d\u66b4\u9732\u7684\u51fd\u6570\u540d":26,"\u56fe\u50cf\u5206\u7c7b":30,"\u5728":[27,30],"\u5728\u4e00\u4e2a\u4e0d\u53ef\u4e2d\u65ad\u5e76\u7f3a\u5c11\u5907\u4efd\u7684\u8bad\u7ec3\u4efb\u52a1\u4e2d":19,"\u5728\u4e0a\u56fe\u4e2d\u663e\u793a\u4e86\u5728\u4e00\u4e2a\u5b9e\u9645\u751f\u4ea7\u73af\u5883\u4e2d\u7684\u5e94\u7528":20,"\u5728\u51fa\u73b0\u5355\u70b9\u6545\u969c\u65f6":19,"\u5728\u5b9e\u73b0\u8fc7\u7a0b\u4e2d":27,"\u5728\u5f00\u59cb\u8bad\u7ec3\u4e4b\u524d":20,"\u5728\u5f02\u6784\u96c6\u7fa4\u4e2d":19,"\u5728\u5f15\u5165\u5176\u4ed6\u7c7b\u578b\u7684\u5934\u6587\u4ef6\u65f6":27,"\u5728\u5feb\u7167\u5199\u5165\u5b8c\u6210\u540e":19,"\u5728\u60a8\u7684\u5b9e\u9645\u73af\u5883\u4e2d":19,"\u5728\u672c\u6587\u6863\u4e2d":24,"\u5728\u673a\u7fa4\u4e0a\u8fd0\u884c\u8f6c\u6362\u7a0b\u5e8f":20,"\u5728\u6837\u4f8b\u4e2d":27,"\u5728\u7528\u6237\u4f7f\u7528c":27,"\u5728\u7ebf\u6a21\u578b\u9884\u6d4b\u670d\u52a1":20,"\u5728\u8bc4\u5ba1\u8fc7\u7a0b\u4e2d":30,"\u5728\u8fd9\u4e2a":30,"\u5728\u8fd9\u4e2a\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165\u4efb\u4f55\u5176\u4ed6\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u5728\u8fd9\u4e2a\u9636\u6bb5\u7684\u4ee3\u7801\u6b63\u5728\u7ecf\u5386\u56de\u5f52\u6d4b\u8bd5":30,"\u5728\u8fd9\u4e9b\u5934\u6587\u4ef6\u4e2d":27,"\u5728\u8fd9\u4e9b\u6587\u4ef6\u4e2d":27,"\u5728c":26,"\u5728c\u7684\u5934\u6587\u4ef6":26,"\u5728paddle\u4e4b\u4e0a\u8fd0\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u8f93\u51fa\u7684\u6a21\u578b\u4f1a\u63d0\u4f9b\u7ed9\u5728\u7ebf\u4eba\u8138\u8bc6\u522b\u7684\u5e94\u7528\u4f7f\u7528":20,"\u5728paramet":19,"\u5747\u4f1a\u88ab\u5b89\u88c5\u5230includ":27,"\u5747\u662f\u5728":27,"\u57fa\u4e8e\u7c98\u6027\u4f1a\u8bdd\u7684\u8d1f\u8f7d\u5747\u8861\u529f\u80fd":24,"\u591a\u4e2a\u503c":20,"\u591a\u4e2aparamet":19,"\u5927\u591a\u6570\u8bed\u8a00\u90fd\u652f\u6301\u4f7f\u7528c\u8bed\u8a00api":26,"\u5982\u56fe\u4e2dtrainer":19,"\u5982\u679c\u4e0a\u9762\u4e24\u6b65\u51fa\u73b0\u9519\u8bef":19,"\u5982\u679c\u4f7f\u7528swig\u6211\u4eec\u9700\u8981\u5c06\u5728interface\u6587\u4ef6\u91cc":26,"\u5982\u679c\u5931\u8d25":30,"\u5982\u679c\u5b58\u5728\u67d0\u4e9btrainer\u6267\u884c\u901f\u5ea6\u8fc7\u6162\u4f1a\u5f71\u54cd\u6574\u4f53\u96c6\u7fa4\u7684\u901f\u5ea6":19,"\u5982\u679c\u5df2\u7ecf\u6b63\u5728\u6267\u884c\u4fdd\u5b58\u68c0\u67e5\u70b9\u7684\u7ebf\u7a0b":19,"\u5982\u679c\u662f\u5176\u5b83\u7c7b\u578b":20,"\u5982\u679c\u6709bugfix\u7684\u884c\u4e3a":30,"\u5982\u679c\u67d0\u4e00\u4e2a\u7c7b\u578b\u9700\u8981\u5f15\u7528\u53e6\u4e00\u4e2a\u7c7b\u578b":27,"\u5982\u679c\u67d0\u4e00\u4e2apaddl":27,"\u5982\u679c\u67d0\u4e00\u4e2apaddle\u6982\u5ff5\u5fc5\u987b\u8981\u66b4\u9732":27,"\u5982\u679c\u6ee1\u8db3\u6761\u4ef6":19,"\u5982\u679c\u7528\u6237\u8981\u628apaddle\u7684\u9759\u6001\u5e93":26,"\u5982\u679c\u8c03\u7528\u9759\u6001\u5e93\u53ea\u80fd\u5c06\u9759\u6001\u5e93\u4e0e\u89e3\u91ca\u5668\u94fe\u63a5":26,"\u5982\u679cparamet":19,"\u5b57\u7b26\u4e32":20,"\u5b58\u50a8":20,"\u5b66\u4e60\u6210\u672c\u9ad8":26,"\u5b83\u4eec\u7684\u6587\u4ef6\u540d\u662f":20,"\u5b89\u88c5\u540e\u7684\u76ee\u5f55\u7ed3\u6784\u4e3a":27,"\u5b8c\u6210\u4e00\u4e2a\u4f20\u8f93\u52a8\u4f5c\u5b8c\u6210\u7684\u65f6\u95f4\u4e5f\u6bd4\u8f83\u77ed":24,"\u5b8c\u6210\u6570\u636e\u7684\u9884\u5904\u7406":20,"\u5b9e\u73b0\u7b80\u5355":26,"\u5bf9\u4e8e\u4e0d\u540c\u8bed\u8a00":26,"\u5bf9\u4e8e\u540c\u4e00\u6bb5c":26,"\u5bf9\u4e8e\u591a\u8bed\u8a00\u63a5\u53e3":26,"\u5bf9\u4e8e\u5927\u591a\u6570\u8bed\u8a00":26,"\u5bf9\u4e8e\u6bcf\u79cd\u7c7b\u578b":27,"\u5bf9\u4e8e\u6bcf\u79cdc":27,"\u5bf9\u6bd4":26,"\u5bf9\u8f93\u5165\u53c2\u6570\u7684\u5b89\u5168\u6027\u8fdb\u884c\u4e86\u5fc5\u8981\u7684\u5224\u65ad":27,"\u5bf9\u8fd9\u4e2a\u7248\u672c\u7684\u63d0\u4ea4":30,"\u5bfc\u51fa\u8fd9\u4e9b\u63a5\u53e3":27,"\u5c06":30,"\u5c06\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc\u53c2\u6570\u62c6\u5206\u6210\u591a\u4efd":19,"\u5c06\u5927\u91cf\u7684":26,"\u5c06\u65b0\u5206\u652f\u7684\u7248\u672c\u6253\u4e0atag":30,"\u5c06master\u5206\u652f\u7684\u5408\u5165commit\u6253\u4e0atag":30,"\u5c31\u9700\u8981\u5bf9\u8fd9\u4e2a\u7b2c\u4e09\u65b9\u8bed\u8a00\u589e\u52a0\u4e00\u4e9b\u5b9a\u4e49":26,"\u5e73\u5747\u6545\u969c\u4fee\u590d\u65f6\u95f4":19,"\u5e73\u5747\u6545\u969c\u7387":19,"\u5e76\u4e14\u4f7f\u7528":27,"\u5e76\u4e14\u5728\u5e38\u89c1\u7684\u5e73\u53f0\u4e0a":26,"\u5e76\u4e14\u628a\u7cfb\u7edf\u751f\u6210\u7684ca":24,"\u5e76\u4e14\u628a\u7ed3\u679c\u8fd4\u56depfsclient\u7aef":24,"\u5e76\u4e14\u8ba9\u63a5\u53e3\u8131\u79bb\u5b9e\u73b0\u7ec6\u8282":26,"\u5e76\u5220\u9664":30,"\u5e76\u5220\u9664\u66f4\u65e9\u7684\u5feb\u7167":19,"\u5e76\u52a0\u8f7d\u5176\u4e2d\u7684\u53c2\u6570":19,"\u5e76\u5728\u96c6\u7fa4\u4e2d\u8fd0\u884c\u591a\u4e2a\u5206\u5e03\u5f0f\u6570\u636e\u5904\u7406\u4efb\u52a1":20,"\u5e76\u5c06c":27,"\u5e76\u628a\u5feb\u7167\u4fdd\u5b58\u5230\u8fd9\u4e2a\u76ee\u5f55\u4e0b":19,"\u5e76\u6ca1\u6709paddle\u7279\u522b\u9700\u8981\u7684\u7279\u6027":26,"\u5e76\u88ab\u5b58\u50a8\u5728\u8bf8\u5982hadoop":20,"\u5e76\u9002\u5e94github\u7684\u7279\u6027\u505a\u4e86\u4e00\u4e9b\u533a\u522b":30,"\u5efa\u8bae":30,"\u5f00\u53d1\u4e86\u6a21\u578b\u9884\u6d4b\u7684\u6837\u4f8b\u4ee3\u7801":27,"\u5f00\u53d1\u8005\u4fee\u6539\u81ea\u5df1\u7684\u4ee3\u7801":30,"\u5f00\u53d1\u8005fork\u7684\u7248\u672c\u5e93\u4e2d":30,"\u5f00\u53d1\u8005fork\u7684\u7248\u672c\u5e93\u4f7f\u7528":30,"\u5f00\u59cb\u63d0\u4f9b\u670d\u52a1":19,"\u5f15\u5165\u4e86\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5f53\u529f\u80fd\u5206\u652f\u5f00\u53d1\u5b8c\u6bd5\u540e":30,"\u5f53\u7528\u6237\u4f7f\u7528\u5b8c\u8fd9\u4e2a\u53c2\u6570\u540e":27,"\u5f53destination\u6587\u4ef6\u4e0d\u5b58\u5728\u6216\u8005\u5927\u5c0f\u548csource\u6587\u4ef6\u4e0d\u4e00\u81f4\u65f6":24,"\u5f88\u96be\u4fdd\u8bc1\u591a\u8bed\u8a00\u4ee3\u7801\u98ce\u683c\u7684\u4e00\u81f4\u6027":26,"\u5f97\u4f7f\u7528":26,"\u5fc5\u8981":27,"\u60c5\u611f\u5206\u6790":30,"\u6211\u4eec\u4e5f\u53ef\u4ee5\u786e\u5b9a\u6bcf\u4e00\u4e2a\u53c2\u6570\u7684\u7c7b\u578b":27,"\u6211\u4eec\u4f7f\u7528\u52a8\u6001\u5e93\u6765\u5206\u53d1paddl":26,"\u6211\u4eec\u63d0\u4f9b\u4e24\u4e2a\u8f6c\u6362\u65b9\u5f0f":20,"\u6211\u4eec\u63d0\u51fa\u4e86chunk\u7684\u6982\u5ff5":24,"\u6211\u4eec\u6700\u7ec8\u7684\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165python\u6216\u8005\u5176\u4ed6\u4efb\u4f55\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u6211\u4eec\u8bbe\u8ba1\u8bf4\u660e\u4e86\u540d\u4e3afilemanager\u7cfb\u7edf":24,"\u6211\u4eec\u9009\u62e9":20,"\u6211\u4eec\u90fd\u63d0\u4f9bpython\u7684\u8f6c\u6362\u5e93":20,"\u6216\u8005":[26,27],"\u6216\u8005\u5c06\u8fd9\u53f0\u8282\u70b9\u8fc1\u79fb\u5230\u53e6\u4e00\u4e2a\u8282\u70b9\u5e76\u542f\u52a8\u5373\u53ef\u6062\u590d\u8bad\u7ec3\u4efb\u52a1":19,"\u6216\u8005\u7528tuple\u8868\u793a\u7684\u591a\u4e2a\u503c":20,"\u6216\u8005\u7531\u5b83\u4eec\u7ec4\u6210\u7684list":20,"\u6240\u4ee5\u5728\u5199\u5165\u5feb\u7167\u7684\u8fc7\u7a0b\u4e2d":19,"\u6240\u4ee5\u7528\u6237\u9700\u8981\u9996\u5148\u5728":24,"\u6240\u6709\u4e0e\u7c7b\u578b\u76f8\u5173\u7684\u51fd\u6570":27,"\u6240\u6709\u7684\u63a5\u53e3\u5747\u4e3ac\u63a5\u53e3":27,"\u6240\u6709\u7c7b\u578b\u540d\u4e3a":27,"\u624b\u5199\u591a\u8bed\u8a00\u7ed1\u5b9a":26,"\u6253\u5f00\u8fd9\u4e2a\u7f16\u8bd1\u9009\u9879":27,"\u628a":20,"\u628a\u4e4b\u524d\u793a\u4f8b\u4e2d\u8f6c\u6362\u5b8c\u6bd5\u7684random":20,"\u6307\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u4e4b\u540e\u5f97\u5230\u7684\u6240\u6709\u53c2\u6570":19,"\u63a5\u53e3":[26,27],"\u63a5\u53e3\u5c42\u505a\u8fc7\u591a\u5c01\u88c5":27,"\u63a5\u53e3\u662f":20,"\u63a5\u6536\u5904\u7406pfsclient\u7aef\u7684\u6587\u4ef6\u7ba1\u7406\u8bf7\u6c42":24,"\u63a7\u5236\u7528\u6237\u6743\u9650":20,"\u63d0\u4f9b\u4e03\u5c42\u534f\u8bae\u7684\u53cd\u5411\u4ee3\u7406":24,"\u63d0\u4f9b\u5e38\u7528\u7684\u547d\u4ee4\u884c\u7ba1\u7406\u547d\u4ee4\u7ba1\u7406\u6587\u4ef6\u548c\u76ee\u5f55":24,"\u63d0\u4f9b\u7528\u6237\u7ba1\u7406\u6587\u4ef6\u7684\u547d\u4ee4":24,"\u63d0\u4f9b\u7ed9paddle\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e":20,"\u652f\u6301\u5927\u6587\u4ef6\u7684\u65ad\u70b9\u4e0a\u4f20":24,"\u6570\u636e":24,"\u6570\u636e\u8bfb\u53d6\u5747\u4ea4\u7531\u5176\u4ed6\u8bed\u8a00\u5b8c\u6210":26,"\u6570\u636e\u957f\u5ea6\u53ca\u6821\u9a8c\u503c\u7ec4\u6210":24,"\u6570\u636e\u96c6\u9700\u8981\u9884\u5148\u88ab\u8f6c\u6362\u6210paddlepaddle\u5206\u5e03\u5f0f\u8bad\u7ec3\u4f7f\u7528\u7684\u5b58\u50a8\u683c":20,"\u6570\u636e\u9884\u5904\u7406\u4efb\u52a1":20,"\u6587\u4ef6":26,"\u6587\u4ef6\u4f20\u8f93\u7684\u7684\u5173\u952e\u5728\u4e8e\u9700\u8981pfsclient\u7aef\u5bf9\u6bd4source\u548cdestination\u7684\u6587\u4ef6chunks\u7684checksum\u662f\u5426\u4fdd\u6301\u4e00\u81f4":24,"\u6587\u4ef6\u5185\u5bb9\u4e3a":26,"\u6587\u4ef6\u540d\u4e3a\u6b64uuid":19,"\u6587\u4ef6\u5bf9\u5e94\u7684data":20,"\u6587\u4ef6\u7684\u4e0a\u4f20\u548c\u4e0b\u8f7d\u90fd\u662f\u901a\u8fc7\u5bf9chunk\u7684\u64cd\u4f5c\u6765\u5b9e\u73b0\u7684":24,"\u65b0\u624b\u5165\u95e8\u7ae0\u8282":30,"\u65b9\u4fbf\u6d4b\u8bd5\u4eba\u5458\u6d4b\u8bd5paddle\u7684\u884c\u4e3a":30,"\u65b9\u4fbf\u7528\u6237\u4e0a\u4f20\u81ea\u5df1\u7684\u8bad\u7ec3\u6570\u636e\u4ee5\u8fdb\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3":24,"\u65e0\u6cd5\u505a\u5230\u5bf9\u4e8e\u5404\u79cd\u8bed\u8a00\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u7684\u9002\u914d":26,"\u65e0\u8bba\u5728\u672c\u5730\u8fd8\u662f\u5728\u4e91\u7aef":20,"\u65e0\u8bba\u662f\u4ece":20,"\u65e0\u8bba\u662f\u5728\u672c\u5730\u6216\u662f\u4e91\u7aef\u8f6c\u6362":20,"\u65f6":19,"\u662f":24,"\u662f\u4e00\u4e2a\u591a\u8bed\u8a00\u63a5\u53e3\u7684\u4ee3\u7801\u751f\u6210\u5668":26,"\u662f\u4e00\u4e2a\u7c7b\u578b\u7684\u6807\u5fd7":27,"\u662f\u4e0d\u5e38\u89c1\u7684\u505a\u6cd5":26,"\u662f\u5404\u4e2a\u5b9e\u73b0\u4e2d\u5171\u4eab\u7684\u5934\u6587\u4ef6":27,"\u662f\u56e0\u4e3ac99\u652f\u6301":26,"\u662f\u5bf9\u7528\u6237\u6587\u4ef6\u5b58\u50a8\u7a7a\u95f4\u7684\u62bd\u8c61":24,"\u662f\u6307":27,"\u662f\u7528\u6237\u4f7f\u7528c":27,"\u662fc":27,"\u6682\u65f6\u4e0d\u8003\u8651\u591a\u4e2aparamet":19,"\u66b4\u9732\u8fd9\u4e2a\u6982\u5ff5\u5fc5\u8981\u51fd\u6570":27,"\u6700\u540e\u5220\u9664":30,"\u6700\u5e38\u89c1\u7684\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u662fexcept":26,"\u6709\u6807\u51c6\u7684":26,"\u6709\u7684\u65f6\u5019":26,"\u672c\u5217\u8868\u8bf4\u660epaddle\u53d1\u7248\u4e4b\u524d\u9700\u8981\u6d4b\u8bd5\u7684\u529f\u80fd\u70b9":30,"\u672c\u6587\u6863\u63cf\u8ff0paddl":27,"\u673a\u5668\u7ffb\u8bd1":30,"\u6765\u4fdd\u8bc1\u8bad\u7ec3\u8fc7\u7a0b\u53ef\u4ee5\u4ece\u4e2d\u95f4\u72b6\u6001\u91cd\u65b0\u542f\u52a8":19,"\u6765\u786e\u4fdd\u628a":26,"\u6765\u8868\u793apaddle\u5185\u90e8\u7c7b":26,"\u6765\u8bbf\u95ee\u7528\u6237\u81ea\u5df1\u7684\u6570\u636e":20,"\u6765\u8fdb\u884c\u8ba8\u8bba":27,"\u6807\u51c6\u8868\u793apaddle\u7248\u672c\u53f7":30,"\u68c0\u67e5\u70b9\u4fdd\u5b58\u7a0b\u5e8f\u6d41\u7a0b":19,"\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9\u901a\u8fc7\u5b9a\u671f\u5411\u78c1\u76d8\u4e0a\u4fdd\u5b58\u4e00\u4efd\u5b58\u50a8\u5728paramet":19,"\u6a21\u578b\u6570\u636e\u68c0\u67e5\u70b9\u7684\u5b9e\u73b0":19,"\u6a21\u578b\u914d\u7f6e\u89e3\u6790":26,"\u6b64\u65f6master\u5c06\u8d1f\u8d23\u542f\u52a8\u4e00\u4e2a\u65b0\u7684train":19,"\u6bcf\u4e00\u4e2a":30,"\u6bcf\u4e00\u4e2a\u6587\u4ef6\u662f\u6570\u636e\u96c6\u7684\u4e00\u4e2ashard":20,"\u6bcf\u4e2a\u503c\u7684\u7c7b\u578b\u53ef\u4ee5\u662f\u6574\u5f62":20,"\u6bcf\u4e2adata":20,"\u6bcf\u4e2aparamet":19,"\u6bcf\u4e2ashard\u5206\u522b\u5b58\u50a8\u5728\u5176\u4e2d\u4e00\u53f0paramet":19,"\u6bcf\u6b21\u8f93\u51fa\u4e00\u4e2adata":20,"\u6bcf\u969410\u5206\u949f":19,"\u6bd4\u5982":20,"\u6bd4\u5982\u6bcf\u969410\u5206\u949f\u6700\u65b0\u7684\u5feb\u7167":19,"\u6bd4\u5982\u6d41\u5f0f\u6570\u636e\u5904\u7406":20,"\u6bd4\u5982imagenet\u8fd9\u4e2a\u6570\u636e\u96c6\u53ef\u80fd\u88ab\u5206\u62101000\u4e2ashard":20,"\u6ce8":19,"\u6d4b\u8bd5docker\u955c\u50cf":30,"\u6d6e\u70b9\u578b\u6570\u636e":20,"\u7136\u540e\u5728etcd\u7684":19,"\u7136\u540e\u5c31\u53ef\u4ee5\u5e76\u53d1\u5199\u5165\u591a\u4e2achunk":24,"\u7136\u540e\u624d\u80fd\u4f7f\u7528pfsclient":24,"\u7248\u672c\u5206\u652f":30,"\u7248\u672c\u53f7":30,"\u7248\u672c\u53f7rc":30,"\u7248\u672cfork\u51fa\u81ea\u5df1\u7684\u529f\u80fd\u5206\u652f":30,"\u73b0\u9636\u6bb5paddle\u6709\u4e00\u4e2a\u95ee\u9898\u662f":26,"\u751f\u4ea7\u73af\u5883\u4e2d\u7684\u8bad\u7ec3\u6570\u636e\u96c6\u901a\u5e38\u4f53\u79ef\u5f88\u5927":20,"\u751f\u4ea7\u73af\u5883\u7684\u65e5\u5fd7\u6570\u636e\u4f1a\u901a\u8fc7\u5b9e\u65f6\u6d41\u7684\u65b9\u5f0f":20,"\u751f\u6210\u5404\u79cd\u8bed\u8a00\u7684\u7ed1\u5b9a\u4ee3\u7801":26,"\u751f\u6210\u6587\u6863":26,"\u751f\u6210\u7684":20,"\u751f\u6210\u7ed9\u5b9a":20,"\u751f\u6210api\u6587\u6863":26,"\u751f\u6210pfsclient\u548cpfsserver\u7684\u6846\u67b6\u90e8\u5206":24,"\u7528":24,"\u7528\u6237\u4e0a\u4f20\u6570\u636e\u540e":20,"\u7528\u6237\u4e5f\u53ef\u4ee5\u4e0a\u4f20label":20,"\u7528\u6237\u53ef\u4ee5\u5b89\u5168\u7684\u91ca\u653e\u67d0\u4e2ac":27,"\u7528\u6237\u53ef\u4ee5\u628a\u81ea\u5df1\u7684\u6570\u636e\u5206\u4eab\u7ed9\u522b\u4eba":20,"\u7528\u6237\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u8fd9\u4e2a\u52a8\u6001\u5e93\u6765\u5f15\u5165paddl":27,"\u7528\u6237\u5728\u672c\u5730\u8f6c\u6362\u597d\u518d\u4e0a\u4f20":20,"\u7528\u6237\u6587\u4ef6\u53ef\u80fd\u662f\u6bd4\u8f83\u5927\u7684":24,"\u7528\u6237\u901a\u8fc7c":27,"\u7531\u4e8e\u5bf9parameters\u7684\u66f4\u65b0\u9700\u8981\u83b7\u53d6parameters\u5185\u5b58\u7684":19,"\u7531\u4e8e\u96c6\u7fa4\u4e2d\u540c\u65f6\u5b58\u5728\u4e24\u53f0\u673a\u5668\u6545\u969c\u7684\u6982\u7387\u6781\u4f4e":19,"\u7531\u4e8ec":26,"\u7531\u4e8echunk\u6bd4\u8f83\u5c0f":24,"\u7533\u8bf7\u7528\u6237\u7a7a\u95f4":24,"\u7684\u547d\u540d\u98ce\u683c\u5e76\u4e0d\u80fd\u9002\u5e94\u5176\u4ed6\u7b2c\u4e09\u65b9\u8bed\u8a00":26,"\u7684\u5934\u6587\u4ef6":26,"\u7684\u63a5\u53e3\u6837\u5f0f":26,"\u7684\u6570\u636e\u6d41\u56fe":20,"\u7684\u6e90\u7801\u91cc\u4f7f\u7528\u4e86":26,"\u7684\u7f29\u5199":24,"\u7684\u89c4\u8303":26,"\u7684\u89d2\u5ea6":20,"\u7684\u914d\u7f6e\u5199\u5230\u914d\u7f6e\u6587\u4ef6\u4e2d":20,"\u76ee\u524d\u53ea\u8003\u8651\u52a8\u6001\u6269\u5bb9trainer\u6570\u91cf":19,"\u76ee\u524d\u5d4c\u5165python\u89e3\u91ca\u5668":26,"\u76ee\u524d\u6211\u4eec\u7528cephfs\u6765\u642d\u5efa":24,"\u76ee\u524dpaddle\u7684\u8fdb\u7a0b\u6a21\u578b\u662fc":26,"\u76ee\u5f55\u4e0b":27,"\u76f4\u63a5\u4f7f\u7528c\u8bed\u8a00\u7684":26,"\u76f4\u63a5\u5220\u9664\u8fd9\u4e2a\u53c2\u6570\u5373\u53ef":27,"\u76f4\u63a5\u5bfc\u51fa\u5230c\u7684\u63a5\u53e3\u6bd4\u8f83\u56f0\u96be":26,"\u793e\u533a\u53c2\u4e0e\u56f0\u96be":26,"\u793e\u533a\u8d21\u732e\u4ee3\u7801\u5b66\u4e60\u6210\u672c\u9ad8":26,"\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u53c2\u6570":19,"\u79bb\u7ebf\u6279\u5904\u7406":20,"\u7b2c\u4e00\u4e2atag\u4e3a":30,"\u7b2c\u4e09\u6b65\u5b8c\u6210\u540e":30,"\u7b2c\u4e8c\u4e2a\u4e3a":30,"\u7b49":27,"\u7b49\u5168\u90e8\u9759\u6001\u5e93\u4e2d\u7684\u76ee\u6807\u6587\u4ef6\u5168\u90e8\u6253\u5305\u540e\u4ea7\u751f\u7684\u6587\u4ef6":27,"\u7b49\u6587\u4ef6":27,"\u7c7b\u4f3c":27,"\u7c7b\u540d\u548cc":26,"\u7c7b\u578b":26,"\u7ea2\u697c\u68a6":53,"\u7ed3\u8bba":26,"\u7edf\u4e00\u7528":20,"\u7f16\u8bd1\u5668\u6ca1\u6709":26,"\u7f16\u8bd1\u578b\u8bed\u8a00":26,"\u7f16\u8bd1\u8fd9\u4e2a\u7248\u672c\u7684docker\u53d1\u884c\u955c\u50cf":30,"\u7f16\u8bd1\u8fd9\u4e2a\u7248\u672c\u7684ubuntu":30,"\u7f16\u8bd1c":27,"\u7f16\u8bd1master\u5206\u652f\u7684docker\u53d1\u884c\u955c\u50cf":30,"\u7f16\u8bd1ubuntu\u7684deb\u5305":30,"\u800c\u4e0d\u5fc5\u5728\u610fpaddl":27,"\u800c\u4e0d\u652f\u6301pypy\u89e3\u91ca\u5668":26,"\u800c\u4e0d\u66b4\u9732\u6982\u5ff5\u7684\u5b9e\u73b0":27,"\u800c\u4e14\u5728\u4f20\u8f93\u7684\u8fc7\u7a0b\u4e2d\u4e5f\u53ef\u80fd\u51fa\u73b0\u7f51\u7edc\u4e0d\u7a33\u5b9a\u7684\u60c5\u51b5":24,"\u800c\u51fa\u73b0\u9636\u6bb5\u6027\u7684\u8fd0\u884c\u505c\u6ede":19,"\u800c\u5728cpp\u91cc\u9762\u5b9e\u73b0\u8fd9\u4e2ac\u7684\u63a5\u53e3":26,"\u800c\u591a\u8bed\u8a00\u63a5\u53e3\u9700\u8981\u76f4\u63a5\u8bfb\u53d6\u751f\u6210\u7684\u4e8c\u8fdb\u5236":26,"\u800c\u5bf9\u4e8egolang":26,"\u800c\u5bf9\u4e8egolang\u9519\u8bef\u5904\u7406\u5e94\u8be5\u4f7f\u7528\u8fd4\u56de\u503c":26,"\u800c\u662f\u76f4\u63a5\u4fee\u6539paddl":27,"\u800c\u662f\u76f4\u63a5\u7528api\u7684\u63a5\u53e3\u8fdc\u7a0b\u8bbf\u95ee":20,"\u800cswig\u53ea\u80fd\u7b80\u5355\u7684\u66b4\u9732c":26,"\u81ea\u52a8\u6302\u8f7d\u5206\u5e03\u5f0f\u5b58\u50a8\u76ee\u5f55":19,"\u81f3\u4e8e\u4e3a\u4ec0\u4e48\u9700\u8981c":27,"\u826f\u597d\u7684\u6587\u6863":26,"\u83b7\u53d6\u6700\u65b0\u7684\u68c0\u67e5\u70b9\u7684\u6587\u4ef6uuid":19,"\u867d\u7136\u4e0d\u9f13\u52b1\u8fd9\u6837":27,"\u89e3\u91ca\u578b\u8bed\u8a00\u53ea\u80fd\u8c03\u7528\u52a8\u6001\u5e93":26,"\u89e3\u91ca\u6027\u8bed\u8a00\u5b9e\u9645\u8fd0\u884c\u7684\u4e8c\u8fdb\u5236\u662f\u89e3\u91ca\u5668\u672c\u8eab":26,"\u8ba1\u7b97\u8fd9\u4e2a\u6587\u4ef6\u7684md5":19,"\u8ba9paddle\u6838\u5fc3\u4e2d":27,"\u8bad\u7ec3\u4efb\u52a1\u7684\u8fd0\u884c\u53ef\u80fd\u4f1a\u5360\u6ee1trainer\u548cparamet":19,"\u8bad\u7ec3\u548c\u7eaf\u4f7f\u7528":30,"\u8bad\u7ec3\u6a21\u578b\u6b63\u786e\u6027":30,"\u8bb0\u5f55\u4e0b\u6240\u6709\u5931\u8d25\u7684\u4f8b\u5b50":30,"\u8bbe\u7f6e":27,"\u8bc6\u522b\u6570\u5b57":30,"\u8bcd\u5411\u91cf":30,"\u8be6\u7ec6\u8bbe\u8ba1":24,"\u8bed\u610f\u89d2\u8272\u6807\u6ce8":30,"\u8bf4\u660e":19,"\u8bf7\u53c2\u8003":27,"\u8f6c\u6362\u751f\u6210\u7684\u6587\u4ef6\u540d\u4f1a\u662f\u4ee5\u4e0b\u683c\u5f0f":20,"\u8fbe\u5230\u5bb9\u707e\u7684\u76ee\u7684":19,"\u8fd4\u56de\u7b2c\u4e8c\u6b65":30,"\u8fd8\u662f\u4ece":20,"\u8fd9\u4e00\u5c42\u8fdb\u884c\u5c01\u88c5":27,"\u8fd9\u4e00\u6982\u5ff5\u4e0d\u518d\u7410\u788e":27,"\u8fd9\u4e09\u4e2a\u5206\u652f":30,"\u8fd9\u4e2a\u52a8\u6001\u5e93\u7684\u8fde\u63a5\u53c2\u6570\u4e0epaddle\u7684\u5176\u4ed6\u4e8c\u8fdb\u5236":27,"\u8fd9\u4e2a\u53c2\u6570\u4e5f\u4e0d\u4f1a\u4e00\u5e76\u5220\u9664":27,"\u8fd9\u4e2a\u5934\u6587\u4ef6\u4e0d\u5047\u8bbe\u5176\u4ed6\u6587\u4ef6\u7684\u5f15\u7528\u987a\u5e8f":27,"\u8fd9\u4e2a\u63a5\u53e3\u9700\u8981\u505a\u5230":26,"\u8fd9\u4e2a\u6587\u4ef6\u5177\u6709\u72ec\u7279\u7684\u8bed\u6cd5":26,"\u8fd9\u4e2a\u76ee\u5f55\u4e2d\u9664\u4e86":27,"\u8fd9\u4e2a\u7ed3\u6784\u4f53\u4e2d\u7684\u53e6\u4e00\u4e2a\u9879\u76ee\u662f":27,"\u8fd9\u4e2a\u7ed3\u6784\u4f53\u5305\u542b\u4e24\u4e2a\u9879\u76ee":27,"\u8fd9\u4e2a\u9759\u6001\u5e93\u5305\u542b\u4e86paddle\u7684\u5168\u90e8\u7b26\u53f7":27,"\u8fd9\u4e2ainstance\u53ef\u4ee5\u662f\u5355\u4e2a\u503c":20,"\u8fd9\u4e9b\u5206\u5e03\u5f0f\u5b58\u50a8\u670d\u52a1\u901a\u5e38\u4f1a\u628a\u6570\u636e\u5207\u5272\u6210\u591a\u4e2a\u5206\u7247\u5206\u5e03\u5f0f\u7684\u5b58\u50a8\u5728\u591a\u4e2a\u8282\u70b9\u4e4b\u4e0a":20,"\u8fd9\u5bf9\u4e8e\u901a\u5e38\u7684java\u7684\u5f00\u53d1\u8005\u6765\u8bf4":26,"\u8fd9\u662f\u56e0\u4e3a":26,"\u8fd9\u6837":27,"\u8fd9\u6837\u4fdd\u8bc1":30,"\u8fd9\u6837\u5c31\u53ef\u4ee5\u5728\u4e91\u7aef\u6267\u884c\u591a\u79cd\u6570\u636e\u7c7b\u8ba1\u7b97\u4efb\u52a1":20,"\u8fd9\u6837\u5df2\u7ecf\u4f20\u8f93\u6210\u529f\u7684\u90e8\u5206\u5c31\u4e0d\u7528\u91cd\u65b0\u4f20\u8f93\u4e86":24,"\u8fd9\u90fd\u9700\u8981\u8fd9\u4e2a\u63a5\u53e3\u6309\u7167\u7ea6\u5b9a\u4fd7\u6210\u7684\u89c4\u5219\u6765\u6ce8\u91ca\u5b8c\u5907":26,"\u8fd9\u91cc\u9700\u8981\u7528\u6237\u989d\u5916\u6ce8\u610f":19,"\u8fdb\u800c\u8fdb\u884c\u4ee3\u7801\u8bc4\u5ba1":30,"\u900f\u4f20\u7528\u6237\u8eab\u4efd\u7684\u529e\u6cd5":24,"\u901a\u5e38":27,"\u901a\u5e38\u6307\u5c06\u4e00\u4e2a\u6574\u4f53\u62c6\u5206\u6210\u591a\u4efd\u7684\u5176\u4e2d\u7684\u4e00\u4efd":19,"\u901a\u8fc7\u6a21\u578b\u63a8\u65adapi\u7684\u5b9e\u73b0\u4f5c\u4e3a\u4e00\u4e2a\u6837\u4f8b":27,"\u903b\u8f91\u5212\u4e0a\u6587\u4ef6\u5206\u5757\u7684\u5355\u4f4d":24,"\u9075\u5faa\u4ee5\u4e0b\u6d41\u7a0b":30,"\u90a3\u4e48":27,"\u90fd\u662f\u4e94\u4f4d\u7684\u6570\u5b57":20,"\u90fd\u662fabi\u8c03\u7528\u6807\u51c6\u7684":26,"\u914d\u7f6e\u7684\u65b9\u6cd5\u53c2\u8003":24,"\u91ca\u653e\u5bf9paramters\u5185\u5b58\u7684\u9501\u5b9a":19,"\u91cc\u6240\u6709\u7684\u7b26\u53f7\u90fd\u5199\u5165\u81ea\u5df1\u7684\u7a0b\u5e8f\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u91cc":26,"\u91cd\u547d\u540d\u6210":26,"\u94fe\u63a5\u5230\u81ea\u5df1\u7684\u7a0b\u5e8f\u91cc":26,"\u9519\u8bef\u5904\u7406":26,"\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u662f\u8fd4\u56de\u503c":26,"\u9519\u8bef\u5904\u7406\u7684\u65b9\u5f0f\u4e5f\u4e0d\u5c3d\u76f8\u540c":26,"\u9664\u6784\u9020\u67d0\u79cd\u7c7b\u578b\u7684\u51fd\u6570":27,"\u9700\u8981":20,"\u9700\u8981\u53ef\u4ee5\u8de8\u5e73\u53f0\u6267\u884c":24,"\u9700\u8981\u5728cmake\u7684\u65f6\u5019":27,"\u9700\u8981\u5c06bugfix\u7684\u5206\u652f\u540c\u65f6merge\u5230":30,"\u9700\u8981\u5f15\u7528":27,"\u9700\u8981\u6709\u7a33\u5b9a\u7684\u5bfc\u51fa\u7b26\u53f7":26,"\u9700\u8981\u6ce8\u610f\u7684\u662f":30,"\u9700\u8981\u88ab\u66b4\u9732\u5230\u5176\u4ed6\u8bed\u8a00":27,"\u9ed8\u8ba4256k":24,"\ufb01xed":63,"abstract":[40,45],"api\u4e2d\u4f7f\u7528":26,"api\u5bfc\u51fa\u7684\u52a8\u6001\u5e93":27,"api\u5bfc\u51fa\u7684\u9759\u6001\u5e93":27,"api\u63a5\u53d7\u7684\u7c7b\u578b\u5168\u662f":27,"api\u63a5\u53e3":24,"api\u63a5\u53e3\u7684\u53c2\u6570\u8f6c\u53d1\u7ed9":27,"api\u65f6":27,"api\u65f6\u6240\u552f\u4e00\u9700\u8981\u5f15\u5165\u7684\u5934\u6587\u4ef6":27,"api\u662f\u591a\u8bed\u8a00api\u7684\u57fa\u7840\u90e8\u5206":27,"api\u66b4\u9732\u7684\u7c7b\u578b":27,"api\u751f\u6210\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u4f1a\u88ab\u5b89\u88c5\u5230":27,"api\u7684\u5b9e\u4f8b":27,"api\u7684\u5b9e\u73b0\u7ec6\u8282":27,"api\u7684\u63a5\u53e3":27,"api\u7684\u65f6\u5019\u63a8\u8350paddle\u4e0d\u5d4c\u5165python\u89e3\u91ca\u5668":27,"api\u7684\u7f16\u8bd1\u9009\u9879\u9ed8\u8ba4\u5173\u95ed":27,"api\u76ee\u5f55\u7ed3\u6784\u5982\u4e0a\u56fe\u8868\u6240\u793a":27,"api\u83b7\u5f97\u4e86\u795e\u7ecf\u7f51\u7edc\u7684\u53c2\u6570\u5b9e\u4f8b":27,"block\u6784\u6210\u4e00\u4e2amodel":19,"book\u4e2d\u6240\u6709\u7ae0\u8282\u529f\u80fd\u7684\u6b63\u786e\u6027":30,"boolean":[9,25,26],"break":58,"bugfix\u5206\u652f\u4e5f\u662f\u5728\u5f00\u53d1\u8005\u81ea\u5df1\u7684fork\u7248\u672c\u5e93\u7ef4\u62a4":30,"bugfix\u5206\u652f\u9700\u8981\u5206\u522b\u7ed9\u4e3b\u7248\u672c\u5e93\u7684":30,"byte":24,"c99\u662f\u76ee\u524dc\u6700\u5e7f\u6cdb\u7684\u4f7f\u7528\u6807\u51c6":26,"c\u6709\u6807\u51c6\u7684abi":26,"c\u8bed\u8a00\u662f\u6709\u5bfc\u51fa\u7b26\u53f7\u7684\u6807\u51c6\u7684":26,"case":[9,27,29,31,38,39,40,42,46,48,54,58],"char":[22,60],"class":[5,6,7,9,11,12,13,15,16,26,44,55,62],"const":[22,28,40],"core\u4e2d\u7684\u6a21\u578b\u8fd8\u5728\u4f7f\u7528\u8fd9\u4e2a\u53c2\u6570":27,"core\u4e2d\u8fd9\u4e00\u7c7b\u578b\u63a5\u53e3\u7684\u667a\u80fd\u6307\u9488":27,"core\u662f\u5426\u8fd8\u5728\u4f7f\u7528\u8fd9\u4e2a\u5b9e\u4f8b":27,"core\u6982\u5ff5":27,"data\u5230\u5206\u5e03\u5f0f\u5b58\u50a8\u8865\u5145\u8bad\u7ec3\u6570\u636e":20,"deb\u5305":30,"deb\u5305\u7f16\u8bd1\u95ee\u9898":30,"default":[3,7,8,9,10,12,13,15,16,17,33,43,45,47,48,49,58,60,62,63],"enum":22,"export":[32,55],"final":[10,31,32,40,60,62],"float":[3,7,8,9,11,13,31,40,42,47,53,56,60],"function":[3,5,9,10,11,13,16,18,21,22,23,28,29,31,38,40,42,43,45,54,55,58,61,62,63],"golang\u53ef\u4ee5\u4f7f\u7528":26,"golang\u7684":26,"h\u5e76\u4e0d\u56f0\u96be":26,"images\u6570\u636e\u96c6\u4e0a\u4f20\u5230\u4e91\u7aef\u7684":20,"import":[3,5,8,9,16,31,38,42,48,53,54,55,56,58,60,62,63],"ingress\u9700\u8981\u628apfsclient\u7684\u8eab\u4efd\u4fe1\u606f\u4f20\u7ed9pfsserv":24,"instance\u4e0e\u751f\u6210\u6570\u636e\u96c6\u65f6":20,"instance\u5305\u6db5\u4e24\u4e2a\u503c":20,"instance\u662f\u4e00\u6a21\u4e00\u6837\u7684":20,"int":[3,7,8,9,10,13,21,22,23,26,27,29,40,47,58,60,61],"interface\u6587\u4ef6\u7684\u5199\u6cd5\u975e\u5e38":26,"list\u4f5c\u4e3a\u68c0\u67e5\u5217\u8868":30,"long":[2,9,10,13,33,42,61,62],"model\u505a\u5206\u652f\u7ba1\u7406":30,"ndarray\u7c7b\u578b\u7684\u503c\u548c\u6574\u578b\u7684\u503c":20,"new":[3,9,13,17,18,21,22,29,39,41,48,49,54,58,61,62],"note\u7684\u4e66\u5199":30,"null":[9,40,45,60],"paddle\u4e00\u4e2a\u52a8\u6001\u5e93\u53ef\u4ee5\u5728\u4efb\u4f55linux\u7cfb\u7edf\u4e0a\u8fd0\u884c":26,"paddle\u4f7f\u7528git":30,"paddle\u5185\u5d4c\u7684python\u89e3\u91ca\u5668\u548c\u5916\u90e8\u4f7f\u7528\u7684python\u5982\u679c\u7248\u672c\u4e0d\u540c":26,"paddle\u5185\u90e8\u7684\u7c7b\u4e3ac":26,"paddle\u5f00\u53d1\u8fc7\u7a0b\u4f7f\u7528":30,"paddle\u6bcf\u6b21\u53d1\u65b0\u7684\u7248\u672c":30,"paddle\u6bcf\u6b21\u53d1\u7248\u672c\u9996\u5148\u8981\u4fdd\u8bc1paddl":30,"paddle\u7684\u4e3b\u7248\u672c\u5e93\u9075\u5faa":30,"paddle\u7684\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0\u5305\u62ec\u4e00\u4e0b\u51e0\u4e2a\u65b9\u9762":26,"paddle\u7684\u7c7b\u578b\u5168\u90e8\u9000\u5316\u6210":27,"paddle\u7684\u94fe\u63a5\u65b9\u5f0f\u6bd4\u8f83\u590d\u6742":26,"paddle\u7684c":27,"paddle\u8bad\u7ec3\u4efb\u52a1":20,"paddle\u8def\u5f84\u4e0b":27,"paddle\u9700\u8981\u4e00\u4e2a\u591a\u8bed\u8a00\u63a5\u53e3":26,"paddle\u9700\u8981\u66b4\u9732\u7684api\u5f88\u591a":27,"paddle\u9759\u6001\u5e93\u94fe\u63a5\u590d\u6742":26,"paddle_\u7c7b\u578b\u540d":27,"paddle_\u7c7b\u578b\u540d_\u51fd\u6570\u540d":27,"paddlepaddle\u63d0\u4f9b\u4e13\u7528\u7684":20,"patch\u53f7":30,"patch\u53f7\u52a0\u4e00":30,"pfsclient\u9700\u8981\u548cingress\u4e4b\u95f4\u505a\u53cc\u5411\u9a8c\u8bc1":24,"pfsclient\u9700\u8981\u5728\u4f20\u8f93\u5b8c\u6bd5\u6700\u540e\u4e00\u4e2achunk\u7684\u65f6\u5019\u68c0\u67e5destination\u6587\u4ef6\u7684md5\u503c\u662f\u5426\u548csource\u6587\u4ef6\u4e00\u81f4":24,"pfsserver\u63d0\u4f9brest":24,"public":[13,40,43,48,49,62],"reader\u7684\u4f7f\u7528\u65b9\u5f0f\u90fd\u662f\u4e00\u81f4\u7684":20,"reader\u8f93\u51fa\u7684data":20,"release\u9875\u9762":30,"return":[3,8,9,10,12,13,15,16,20,22,23,31,38,40,48,54,56,58,59,60,63],"s3\u4e4b\u7c7b\u7684\u5206\u5e03\u5f0f\u5b58\u50a8\u4e4b\u4e0a":20,"server\u4e4b\u4e0a":19,"server\u4e4b\u95f4\u7684\u7f51\u7edc\u5e26\u5bbd":19,"server\u4f1a\u6682\u505c\u53c2\u6570\u66f4\u65b0\u5e76\u7b49\u5f85":19,"server\u4f1a\u83b7\u53d6parameters\u5185\u5b58\u7684":19,"server\u5185\u5b58\u4e2d\u7684\u6a21\u578b\u6570\u636e\u7684\u5b8c\u6574\u955c\u50cf":19,"server\u540c\u6b65\u7684\u4fdd\u5b58\u4e00\u4e2a\u7279\u5b9a\u65f6\u95f4\u70b9\u7684\u5168\u5c40\u68c0\u67e5\u70b9":19,"server\u5728\u96c6\u7fa4\u4e2d\u542f\u52a8\u540e":19,"server\u6545\u969c\u540e\u88abkubernetes\u91cd\u65b0\u542f\u52a8":19,"server\u6b64\u65f6\u8fd8\u9700\u8981\u901a\u8fc7\u7f51\u7edc\u8bbf\u95ee\u5206\u5e03\u5f0f\u5b58\u50a8\u4ee5\u4fdd\u5b58\u5feb\u7167":19,"server\u751f\u6210\u4e00\u4e2auuid":19,"server\u7684\u5355\u70b9\u6216\u591a\u70b9\u540c\u65f6\u6545\u969c":19,"server\u7684\u6570\u636e\u5feb\u7167":19,"server\u7684\u68c0\u67e5\u70b9\u5404\u81ea\u72ec\u7acb\u4fdd\u5b58":19,"server\u7b2c\u4e00\u6b21\u542f\u52a8\u6216\u4efb\u610f\u65f6\u95f4paramet":19,"short":[9,10,31,60,61,62],"static":[22,27,48],"super":40,"swig\u652f\u6301\u7684\u8bed\u8a00\u6216\u8005\u89e3\u91ca\u5668\u6709\u5c40\u9650":26,"swig\u66b4\u9732\u7684\u63a5\u53e3\u4fdd\u7559\u4e86c":26,"swig\u751f\u6210\u7684\u4ee3\u7801\u4e0d\u80fd\u4fdd\u8bc1\u591a\u8bed\u8a00\u4ee3\u7801\u98ce\u683c\u7684\u4e00\u81f4\u6027":26,"swig\u76f4\u63a5\u8bfb\u53d6c":26,"swig\u9700\u8981\u5199\u4e00\u4e2ainterface\u6587\u4ef6":26,"switch":[27,48,62],"tag\u4e3a":30,"throw":48,"true":[3,7,8,9,10,12,13,15,16,29,31,38,40,45,47,48,56,60,61,62,63],"try":[11,17,18,21,22,29,42,54,60],"type\u5b57\u6bb5\u5747\u4e0d\u5c3d\u76f8\u540c":27,"ubuntu\u5b89\u88c5\u5305\u7684\u529f\u80fd\u6b63\u786e\u6027":30,"void":[22,26,27,40],"while":[2,3,7,13,28,29,33,38,45,54,58,62,63],AGE:[48,49],AND:60,ARE:60,AWS:[20,41,50,51],Age:59,And:[3,8,9,11,13,23,29,35,39,47,48,49,53,56,60,62,63],But:[3,9,10,13],EOS:9,For:[2,3,8,9,11,13,16,21,22,23,28,29,31,32,33,38,40,42,43,44,45,47,53,55,56,58,62,63],Going:62,Has:3,IDs:[13,58],Ids:58,Into:48,Its:[3,38,48,60],Not:[16,18,43],ONE:3,One:[8,10,15,38,40,45,54,58,62,63],PFS:24,QoS:49,THE:3,TLS:[16,24,48],That:[9,13,29,33,45,47],The:[2,3,5,6,7,8,9,10,11,13,15,16,17,18,21,23,25,27,29,31,32,33,34,35,38,39,40,42,43,45,47,48,49,53,54,55,56,58,59,60,61,62,63],Their:[3,9,18],Then:[5,9,32,33,38,39,40,42,48,49,53,55,60,61,62],There:[8,9,13,15,16,18,22,23,25,28,31,33,35,42,48,54,55,56,57,58,60,63],These:[8,43,47,55,61],USE:60,USING:60,Use:[3,8,16,25,29,33,40,42,45,46,48,60],Used:10,Useful:3,Using:[18,49,62],VPS:48,WITH:39,Will:[13,15],With:[3,9,10,31,54,61],YES:23,Yes:33,___fc_layer_0__:48,__file__:23,__init__:40,__list_to_map__:60,__main__:56,__meta__:60,__name__:56,__rnn_step__:38,_binari:17,_error:54,_librari:17,_link:10,_proj:9,_res2_1_branch1_bn:56,_source_language_embed:[38,53],_target_language_embed:[38,53],_test:17,aaaaa:20,aaaaaaaaaaaaa:48,abc:9,abl:[9,16,54,62],about:[5,9,10,23,25,31,42,44,45,48,52,61,62,63],abov:[3,5,8,9,16,17,18,21,31,33,42,48,49,54,56,58,61],abs:[10,54],absolut:[2,43],academ:59,acceler:[19,47],accept:[3,5,13,16,29,58,61],acceptor:61,access:[2,9,10,16,21,23,38,63],accessmod:48,accident:59,accomplish:33,accord:[2,3,8,9,22,38,39,43,44,45,47],accordingli:[5,8,9,40],accordingto:61,accrod:10,accumul:[18,22],accuraci:[8,40,58,59,62],achiev:[42,55],ack:45,acl:62,aclimdb:62,across:[9,13],act:[9,10,31,38,58],act_typ:58,action:[48,59],activ:[0,5,9,10,14,31,32,40,45,58,62],activi:10,actual:[3,9,31],adadelta:58,adagrad:58,adam:[16,22,58,62,63],adamax:58,adamoptim:[53,58,62,63],adapt:[8,11,31,62,63],add:[3,9,10,13,17,31,32,39,40,42,47,58,60],add_depend:17,add_execut:17,add_input:40,add_test:[17,40],add_to:9,add_unittest_without_exec:40,addbia:40,added:[3,8,40],adding:56,addit:[9,10,33,58],address:[18,33,42,45],addrow:40,addtion:43,addtolay:9,adject:62,adjust:31,admin:59,adopt:61,advanc:[38,42,45],advantag:62,adventur:59,adverb:62,adversari:29,advic:42,affect:9,afford:21,afi:3,aforement:[17,43],after:[9,13,21,22,25,28,32,35,38,40,43,45,47,48,49,54,55,56,58,60,61,62,63],again:[16,18,42],against:48,age:[13,60],agg_level:9,aggreg:48,ago:17,aid:42,aim:[62,63],aircraft:63,airplan:55,aistat:9,alex:[9,62],alexnet_pass1:47,alexnet_pass2:47,algorithm:[9,11,21,31,38,53,55,62,63],alia:[6,7,9],align:[9,10,13,63],all:[0,3,7,8,9,11,16,18,22,23,25,27,31,33,38,39,40,42,43,44,45,47,48,49,53,54,56,58,59,60,61,62,63],alloc:[7,23,40,47],allow:[16,22,33,39,40,42,45,48,58],allow_only_one_model_on_one_gpu:[44,45,47],almost:[10,31,43,53],along:62,alreadi:[18,33,42,43,45,48,49,62],alreali:[44,63],also:[2,3,9,10,13,16,17,28,29,32,33,38,40,42,43,49,54,55,56,58,61,62],although:31,alwai:[5,9,10,15,29,31,45,48,63],amaz:55,amazon:[48,49,58,62],amazonaw:48,amazonec2fullaccess:48,amazonelasticfilesystemfullaccess:48,amazonroute53domainsfullaccess:48,amazonroute53fullaccess:48,amazons3fullaccess:48,amazonvpcfullaccess:48,ambigu:[29,61],amd64:48,amend:39,american:55,among:[48,62],amount:[42,62],analysi:[31,42,57,61],analyz:[58,62],andd:48,ani:[2,3,9,10,13,16,17,18,22,23,29,38,39,42,48,58,60,63],anim:59,annot:61,annual:61,anoth:[3,9,16,23,33,45,48,61,62],ans:48,answer:[31,48,61],anyth:[13,29,39,48,61],api:[13,15,16,17,22,23,24,28,30,32,40,42,48,52,54,58,60,62],api_shar:17,api_test:17,api_trainer_config_helpers_data_sourc:60,api_trainer_config_helpers_lay:38,api_trainer_config_helpers_layers_context_project:60,api_trainer_config_helpers_layers_cos_sim:60,api_trainer_config_helpers_layers_data_lay:60,api_trainer_config_helpers_layers_embedding_lay:60,api_trainer_config_helpers_layers_fc_lay:60,api_trainer_config_helpers_layers_pooling_lay:60,apiserv:48,apivers:[48,49],apo:63,appar:63,appear:61,append:[3,15,29,38,40,43,60],append_gradient_machin:15,appleclang:32,appleyard:42,appli:[0,9,10,38,40,55,58],applic:[33,42,48,49,62],appreci:[39,62],approach:[9,28],apt:[32,35,55],arbitrari:9,architectur:[53,61,62,63],architecur:62,archiv:[13,26,27],arg:[3,8,10,13,31,44,54,55,56,58,60,61,62],arg_nam:9,argu:61,argument:[3,5,9,13,21,28,38,40,45,46,53,54,55,56,60,61,62,63],argv:56,arn:48,around:[3,9,48],arrai:[5,9,13,15,22,29,31,56],art:[31,61],articl:[43,49],artifact:48,artifici:54,artist:59,arxiv:[9,10,54,62],ask:18,aspect:62,assign:[8,9,21,45,48],associ:[28,61,62,63],assum:[8,9,38,47,53],assur:2,astyp:[29,54],asyc:18,async:[18,44],async_count:45,async_lagged_grad_discard_ratio:45,async_lagged_ratio_default:[44,45],async_lagged_ratio_min:[44,45],asynchron:[18,45],atla:32,atlas_root:32,attenion:10,attent:[9,10,33,63],attitud:62,attr1:9,attr2:9,attr:[7,9,10],attribut:[3,9,10,14,40,53,61],auc:44,aucvalidationlay:45,authent:48,author:[24,48,56],authorized_kei:43,autmot:39,auto:[26,40,42,57,60],autom:[48,63],automak:32,automat:[9,16,22,32,38,40,43,44,45,48,60,61,63],automaticli:9,automobil:55,avail:[18,22,32,48],availabel:32,averag:[8,9,12,21,45,56,58,60,61,62,63],average_test_period:[44,45,61],average_window:62,averagepool:9,avg:[42,58],avgcost:[58,60,62,63],avgpool:[9,58],avoid:[18,28,42],avx:[32,33,35],await:49,awar:[16,18,33,48],aws:24,aws_account_id:48,awsaccountid:48,awskeymanagementservicepowerus:48,b2t:53,b363:49,b8561f5c79193550d64fa47418a9e67ebdd71546186e840f88de5026b8097465:49,ba5f:48,back:[3,18,33],backward:[6,9,10,22,28,38,40,45,47],backward_first:38,backwardactiv:40,bag:[58,62],baidu:[0,9,31,35,39,49,53],baik:53,balanc:[45,48,54],balasubramanyan:62,bank:61,bardward:10,bare:49,barrier:45,barrierstatset:42,base:[9,12,13,16,21,31,35,38,39,40,42,43,45,48,53,54,58,60,62,63],baseactiv:10,baseev:15,basematrix:40,basenam:8,basepoolingtyp:[9,10],basestr:[7,8,9,10,12,15,60],bash:[33,48,49],bashrc:32,basic:[3,9,15,33,39,40,58,59,62],batch:[3,9,10,11,13,15,16,18,20,40,43,45,48,49,54,55,56,58,60,61,62,63],batch_0:56,batch_id:15,batch_norm_lay:10,batch_norm_typ:9,batch_read:[20,29],batch_siz:[3,13,31,43,53,54,55,58,60,62,63],batchsiz:[9,40],bazel:17,bbbbb:20,bcd:9,bcebo:13,beam:[9,38,45,61,63],beam_gen:[9,38],beam_search:[15,38],beam_siz:[9,38,44,45,47],beamsiz:63,becaus:[5,8,9,13,16,17,18,22,28,29,38,39,40,47,48,55,58,61],becom:[39,42],been:[3,21,32,39,55,58,61,62,63],befor:[5,9,10,18,25,28,29,33,39,43,48,55,60,62,63],begin:[5,8,9,22,25,40],beginiter:[15,16],beginn:38,beginpass:[15,16],begintrain:16,behavior:42,being:[18,29,54],belong:[9,63],below:[3,9,13,18,22,28,29,38,40,42,43,48,54,55,58,60],benefit:[10,23],bengio:9,bertolami:62,besid:[2,9,13,63],best:[9,32,33,45,58,60,62,63],best_model_path:61,besteffort:49,beta1:11,beta2:11,beta:56,better:[9,10,17,31,43,48,54,60],between:[8,9,11,17,18,22,27,28,31,39,48,54,58,59,62,63],bgr:56,bi_gru:10,bi_lstm:10,bia:[9,10,11,38,40,56],bias:[9,40],bias_attr:[9,10,31,38],bias_param_attr:10,biases_:40,biasparameter_:40,biassiz:40,bidi:49,bidirect:[10,38,61,63],bidirectional_lstm_net:62,big:42,bigger:18,biggest:62,bilinear:9,bilinear_interpol:9,bilinearfwdbwd:42,bin:[32,33,43,48,49,60],binari:[3,8,9,13,17,23,42,48,53,58,62],bird:55,bison:32,bit:58,bitext:63,bla:32,blank:[9,48],block:[9,19,22,31,40,42,45,56,62],block_i:9,block_x:9,blog:62,bn_bias_attr:10,bn_layer_attr:10,bn_param_attr:10,bollen:62,book:13,bool:[3,7,8,9,10,12,13,40,45,47,58,60,62],boot:[9,38],boot_bia:9,boot_bias_active_typ:9,boot_lay:38,boot_with_const_id:9,bootstrap:32,bos_id:[9,38],both:[0,6,7,9,10,16,17,18,33,38,40,42,48,54,56,58],bottleneck:[42,56],bottom:62,boundri:8,bow:[58,62],box:42,branch:[9,16,30,39],breadth:[45,63],brendan:62,brew:32,brief:22,briefli:42,broadcast:18,brows:33,browser:[33,48],bryan:62,bucket_nam:48,buf_siz:13,buffer:[3,13,29,45],buffered_read:29,bug:48,bui:62,build:[0,13,17,23,31,33,36,45,48,50,51,53,55,56,58,60,62,63],build_dict:13,built:[0,17,32,33,54,61],bunch:[42,58],bunk:62,button:[39,48],c11:26,c99:27,c99e:48,cach:[58,60,61],cache_pass_in_mem:[3,58,60,61],cachetyp:[3,58,60,61],calc_batch_s:[3,61],calcul:[3,8,9,10,11,18,22,38,40,42,45,47,54,60],call:[3,8,9,10,16,21,22,23,31,33,38,40,42,45,48,55,56,58,62,63],callabl:[3,9,13],callback:40,caller:48,caltech:55,can:[2,3,5,7,8,9,10,13,16,18,21,23,28,29,31,32,33,35,38,39,40,42,43,44,45,47,48,49,53,54,55,56,58,60,61,62,63],can_over_batch_s:[3,61],candid:9,cannot:40,caoi:63,capabl:[32,62],capac:48,capi:26,capi_prvi:27,caption:[31,63],captur:[31,43],card:43,care:[10,23,29,44,45,59],carefulli:[43,45,56],cat:[33,55,56,62],categor:61,categori:[9,13,18,58,62],categorig:13,categoryfil:49,caus:[18,25],caution:[48,49],cc_:17,cc_binari:17,cc_test:17,ccb2_pc30:63,cde:9,cdn:13,ceil:9,ceil_mod:9,cell:[9,10,62],center:3,ceph:[20,49],cephf:[20,23,24],certain:[2,44,61],certif:[16,24,48],cffi:26,cfg:49,cgo:26,chain:[13,40],challeng:18,chanc:[16,40,58],chang:[9,13,17,21,23,28,29,31,33,38,39,40,42,45,48,58,62],channel:[9,42,43,56],channl:[43,56],char_bas:60,charact:[58,60],character:31,characterist:[47,55],check:[3,13,31,32,33,39,45,47,48,59],check_align:13,check_eq:40,check_fail_continu:3,check_l:40,check_sparse_distribution_batch:[44,45],check_sparse_distribution_in_pserv:[44,45],check_sparse_distribution_ratio:[44,45],check_sparse_distribution_unbalance_degre:[44,45],checkgrad:45,checkgrad_ep:45,checkout:39,checksum:24,children:59,china:33,chines:57,chmod:[32,48],choic:[33,59],choos:[45,58,60],chosen:[2,59,63],chunk:[21,24,54,61],chunk_schem:8,chunktyp:8,cifar:[54,55],cifar_vgg_model:55,claim:48,claimnam:48,clang:[26,32,33,39],clarifi:8,class1:62,class2:62,class_dim:62,classfic:[56,62],classfiic:55,classic:[9,31],classif:[3,5,9,47,56,57,58,62,63],classifc:62,classifi:[54,55,56,58,62],classification_cost:[55,58],classification_error_evalu:[8,54,58,62,63],classification_evalu:8,claster:48,clean:[5,17,28,60],clear:[8,17,28],clearer:28,cleric:59,cli:48,click:[39,42,48],client:39,clip:[7,45,58,62],clock:9,clone:[32,33],close:[3,29],closer:31,cloud:[18,23,24,25],cls:58,cludform:48,cluster:[16,18,22,44,45,49,58,63],cluster_train:43,cm469:48,cmake3:32,cmake:[27,32,40,42],cmakelist:[17,40],cmatrix:[26,27],cmd:49,cna:9,cname:48,cnn:[9,49,56,58],code:[0,3,5,13,16,17,28,29,31,32,33,34,38,40,41,42,43,48,49,54,58,59],coeff:9,coeffici:9,collabor:18,collect:[9,13,15,31,59],collectbia:40,colleg:59,color:[55,56],colour:13,column:[8,9,29,40,53,63],column_evalu:8,colunm:63,com:[9,10,13,32,33,35,39,48,49,56],combin:[8,9,10,13,15,54,60,62],come:62,comedi:59,comma:[45,53],command:[2,5,17,23,25,31,32,33,35,39,40,41,42,43,48,49,50,51,53,54,55,56,60,61,62],commandlin:[42,62],commenc:58,comment:[10,17,39,58,62],commnun:43,common:[20,38,40,44],common_util:[43,60],commonli:[25,38,42,47],commun:[0,18,22,40,43,48],compani:62,compar:[40,54,58],compat:3,compet:62,competit:54,compil:[17,32,33,39,40],complet:[0,5,9,10,13,15,18,21,22,24,40,48,49,58],complex:[2,3,10,29,38,42,58],complic:9,compon:40,compos:[13,16,54,61],composenotalign:13,compress:21,comput:[9,10,16,18,31,32,33,38,40,42,47,48,58,60,61,62],computation:38,conat:9,concat:63,concat_lay:38,concaten:10,concept:[3,8,16,28,33,38],concern:16,concurr:18,concurrentremoteparameterupdat:45,condit:[9,21,38,43,49,63],conduct:42,conf:[5,9,43,53,54,56,63],conf_paddle_gradient_num:48,conf_paddle_n:48,conf_paddle_port:48,conf_paddle_ports_num:48,conf_paddle_ports_num_spars:48,confid:62,config:[3,7,9,10,20,25,31,40,43,44,45,48,49,53,54,55,56,58,62,63],config_:45,config_arg:[44,45,47,56,58,61,62],config_bas:[8,9,15],config_fil:61,config_gener:[43,60],config_lay:40,config_len:22,config_pars:[5,40],config_proto:22,configur:[1,2,3,5,9,22,23,31,39,40,42,45,53,55,56,62,63],confirm:25,conflict:39,confront:63,congest:45,conll05st:61,conll:[13,61],connect:[2,10,23,31,40,48,49,54,55,56,58,60,62],connectionist:[9,62],connor:62,consequ:[9,10],consid:[8,9,11,32,33,42,47,55],consider:[3,10],consist:[8,9,13,21,29,55,56,58,61,63],consol:[42,48],constant:40,construct:[3,5,8,16,38,60],construct_featur:60,constructor:40,consum:[18,62],contact:18,contain:[3,8,9,10,12,13,15,16,21,28,34,35,38,39,43,48,55,56,58,59,62,63],containerport:48,contemporan:62,content:[22,25,49,61,62],content_len:22,context:[9,10,13,38,53,58,60,61,62,63],context_attr:10,context_len:[9,10,58,60],context_proj_layer_nam:10,context_proj_param_attr:10,context_project:[10,60],context_start:[9,10,58],contibut:39,contin:48,continu:[3,8,18,35,45],contrast:[9,63],contribut:[0,34,41,62],contributor:0,control:[7,33,45,48,49,63],conv:10,conv_act:10,conv_batchnorm_drop_r:10,conv_bias_attr:10,conv_filter_s:10,conv_layer_attr:10,conv_num_filt:10,conv_op:9,conv_pad:10,conv_param_attr:10,conv_strid:10,conv_with_batchnorm:10,conveni:[16,43],convent:22,converg:[43,54,62],convert:[3,5,13,20,29,38,53,55,56,58,60],convlay:9,convolut:[9,10,13,54,56,60],convoper:9,convtran:9,convtranslay:9,cool:[3,39],coordin:18,copi:[15,16,21,25,33,48,54,60],copy_shared_paramet:54,copytonumpymat:54,core:[3,7,27,28,45,63],coreo:48,corespond:61,corpora:63,corpu:[13,61],correct:[3,9,40,48],correctli:[8,13,40,54],correl:[31,55,62],correspoind:16,correspond:[3,5,16,31,38,40,55,59,61,62,63],corss_entropi:16,cos:9,cos_sim:60,cosin:[9,60],cost:[5,11,15,16,31,45,54,58,60,62,63],cost_id:9,could:[3,5,9,13,15,16,21,28,29,33,42,43,48,58,60],count:[18,23,29,42,45,47,49,53,60,61,62,63],counter:[18,21],coupl:31,cours:[8,23],coverag:32,coveral:32,coveralls_uploadpackag:32,cpickl:[56,60],cpp:[26,27,28,39,40,42,58,60,63],cpu:[2,3,7,9,23,28,30,32,35,42,45,49,54,61,62,63],cpuinfo:33,cpusparsematrix:27,craftsman:59,crash:[18,42,43,45],crazi:43,creat:[5,7,9,13,15,16,18,24,25,28,31,32,33,40,43,45,53,54,55,63],create_bias_paramet:40,create_input_paramet:40,createargu:54,createfromconfigproto:[5,54],createstack:48,creation:48,creationd:48,creator:[13,20],credenti:25,credit:54,crf:61,crime:59,critic:62,crop:56,crop_siz:56,cross:[9,58,61],cross_entropi:[9,16,54],cross_entropy_with_selfnorm:9,crt:24,csc:40,cslm:63,csr:40,csv:59,ctc:8,ctc_evalu:8,ctest:33,ctrl:[43,60],ctx:61,ctx_0:61,ctx_0_slot:61,ctx_n1:61,ctx_n1_slot:61,ctx_n2:61,ctx_n2_slot:61,ctx_p1:61,ctx_p1_slot:61,ctx_p2:61,ctx_p2_slot:61,cub:55,cuda:[17,32,33,35,42,43,45],cuda_dir:[44,45],cudaconfigurecal:42,cudadevicegetattribut:42,cudaeventcr:42,cudaeventcreatewithflag:42,cudafre:42,cudagetdevic:42,cudagetdevicecount:42,cudagetdeviceproperti:42,cudagetlasterror:42,cudahostalloc:42,cudalaunch:42,cudamalloc:42,cudamemcpi:42,cudaprofilerstart:42,cudaprofilerstop:42,cudaruntimegetvers:42,cudasetdevic:42,cudasetupargu:42,cudastreamcr:42,cudastreamcreatewithflag:42,cudastreamsynchron:42,cudeviceget:42,cudevicegetattribut:42,cudevicegetcount:42,cudevicegetnam:42,cudevicetotalmem:42,cudnn:[9,12,17,32,35,45],cudnn_batch_norm:9,cudnn_conv:9,cudnn_conv_workspace_limit_in_mb:[44,45],cudnn_convt:9,cudnn_dir:[44,45],cudrivergetvers:42,cuinit:42,cumul:9,curl:[32,48],current:[3,9,18,28,31,33,38,39,40,43,45,48,58,62,63],current_word:38,currentcost:[58,60,62,63],currentev:[58,60,62,63],curv:[16,55,61],custom:[2,3,16,23,40,48,59,62],custom_batch_read:29,cutoff:13,cycl:18,cyclic:9,cython:26,d3e0:48,dai:63,daili:62,dalla:3,dan:61,danger:3,darwin:48,dat:[13,20,43,60],data:[1,2,3,5,8,10,11,15,16,20,21,24,28,32,33,40,42,43,44,45,47,50,56,59],data_batch_gen:54,data_dir:[53,55,62,63],data_feed:13,data_fil:31,data_initialz:58,data_lay:[3,31,38,54,55,58,60,61],data_nam:13,data_read:[13,29],data_reader_creator_random_imag:29,data_sourc:54,data_typ:13,databas:[13,62],datacent:[20,25],datacenter1:20,datacenter2:20,datacenter_1:20,datacenter_2:20,datacenter_nam:20,datadim:9,dataprovid:[2,31,38,43,60,61],dataprovider_bow:58,dataprovider_emb:58,dataproviderconvert:5,datasci:9,dataset:[1,3,20,23,29,31,45,53,55,56,58,61,62],date:61,db_lstm:61,dcgan:54,dcmake_install_prefix:32,dead:18,deal:[39,54],deb:[34,35],debian:[33,34],debug:[3,25],decai:[11,55],decent:21,decid:[16,29],declar:[9,60],decod:[9,10,38,61,63],decoder_boot:38,decoder_group_nam:38,decoder_input:38,decoder_mem:38,decoder_prev:10,decoder_s:38,decoder_st:[10,38],deconv:9,deconvolut:9,decor:[3,13,40],decreas:31,decrypt:48,deep:[0,9,31,33,36,42,54,55,56,58,61],deeper:[31,33,56],deer:55,def:[3,9,13,16,20,23,29,31,38,40,54,56,58,60,61],defalut:[9,45,47],default_devic:47,default_valu:47,defect:28,defferenct:3,defin:[2,3,9,10,13,16,17,18,29,31,38,40,43,45,53,54,55,60,61],define_py_data_sources2:[3,31,55,56,58,60],defini:63,definit:[3,13,18,21,31,33,53,58,62],degre:9,del:60,delai:45,delar:58,delet:[18,23,24],deletestack:48,delimit:[8,59,60],demand:18,demo:[9,13,38,43,49,50,53,54,55,56,57,58,59,60,61,62,63],demograph:59,demolish:49,demonstr:[31,38,54,60],denot:[47,58,59,61],dens:[3,9,13,22,40,48,58,60],dense_arrai:13,dense_vector:[3,5,13,31,60],dense_vector_sequ:13,dep:17,depend:[17,18,23,31,33,35,43,47,55,59],deploi:[43,47],deploy:[43,48],deriv:[6,16],descent:[9,18],describ:[16,17,21,28,31,40,48,49,54,58,61],describestack:48,describestackev:48,describestackresourc:48,descript:[5,8,17,32,38,46,48,55,60],deseri:[15,28],design:[3,9,13,26,62],desir:[18,48,49,53],destin:[22,25],destructor:40,detail:[3,5,7,8,9,10,11,21,23,25,38,39,40,42,43,46,47,48,49,53,54,56,58,60,62,63],detect:8,determin:[3,9,13,40,54],dev:[32,33,55,60,63],devel:32,develop:[0,17,28,30,32,39,44,45,63],deverlop:45,deviat:7,devic:[7,28,45,63],deviceid:47,devid:[9,45],dez:62,diagnos:43,diagram:56,dict:[3,8,13,15,58,60,62,63],dict_dim:62,dict_fil:[8,38,58,61],dict_siz:13,dictionai:58,dictionari:[3,8,9,13,15,16,38,47,56,58,60,61,62,63],dictsiz:63,did:[3,28],differ:[3,8,9,17,18,22,31,33,38,39,40,43,45,48,49,53,55,56,58,62,63],difficult:31,dig:[33,42,48],digit:[3,9],dim:[13,40,53,56,58,62],dimens:[6,9,12,13,40,47,53,58,60,62],dimension:[3,31,38,40,54,58],dimenst:53,dimes:9,din:60,dir:[43,56,58,60,61,62,63],dirctori:33,direct:[9,10,33,56,61],directli:[2,3,10,23,28,31,33,43,49,62],directori:[2,17,20,24,25,32,33,39,42,43,45,49,55,56,58,60,61,62,63],diretcoti:56,dis_conf:54,dis_train:54,dis_training_machin:54,disabl:3,discard:[13,18,21,45],discount:9,discov:[18,61],discoveri:48,discrep:42,discrimin:54,discriminator_train:54,discuss:[16,21,22],disk:49,dispatch:[28,43,45],displai:[23,25],disput:63,dist_train:[16,23],distanc:8,distibut:53,distinguish:[17,43,54,63],distribut:[9,21,22,32,41,49,50,51,54,58,61],distribute_test:[44,45],distributedli:40,disucss:16,divid:[11,44,55,63],diy_beam_search_prob_so:[44,45],dmkl_root:32,dns:48,do_forward_backward:29,doc:[5,10,13,32,33,43],docker:[30,34,48,50,51],docker_build:16,docker_push:16,dockerfil:33,dockerhub:33,doctor:59,document:[3,5,10,24,32,39,47,55,58,60,61,62],documentari:[3,59],doe:[3,5,10,18,21,22,23,28,29,31,35,38,40,42,58,60,61],doesn:[7,9,13,16,29,39,42,49,63],dog:[55,56],doing:42,domain:48,don:[10,16,17,29,31,48,62],done:[8,9,10,17,18,21,22,38,42,48,54,62],dopenblas_root:32,dot:[45,56,63],dot_period:[45,47,54,55,60,62,63],dotmuloper:9,dotmulproject:9,doubl:[3,32,45],down:[42,58],download:[13,18,24,33,35,54,55,58,61,62],download_cifar:55,downsampl:55,doxygen:[32,39],dpkg:35,drama:59,drop:3,drop_rat:7,dropout:[7,9,40,58],dropout_r:10,drwxr:49,dst:22,dtoh:42,dtype:[5,31,56],dubai:63,due:[21,59,60],dummi:21,duplic:59,durat:[21,42],dure:[2,3,9,18,21,23,31,39,40,44,45,48,58,60,61,63],durn:3,dwith_c_api:27,dwith_doc:32,dwith_profil:42,dwith_python:27,dwith_swig_pi:27,dwith_tim:42,dynam:[2,3,22,27,29,32,42,45],dynamic_cast:40,each:[2,3,5,8,9,12,13,15,17,18,21,22,23,28,29,31,33,38,39,40,43,45,47,48,53,55,56,58,59,60,61,62,63],each_feature_vector:6,each_meta:60,each_pixel_str:3,each_sequ:9,each_time_step_output:6,each_timestep:9,each_word:3,eaqual:9,eas:[13,29,56],easi:[0,29,33,40,43,58],easier:[16,29,40],easili:[16,29,31],echo:[33,60,62],edit:[8,33,48],editor:[33,39],edu:[13,48,49,55],educ:59,eeoi3ezpr86c:48,effect:[3,45,48],effici:[0,2,3,38,40],efg:9,efs:48,efs_dns_nam:48,efsvol:48,eight:61,either:[9,13,15,16,42,58,60],elb:48,elbapis:48,elec:58,electron:[49,58],elem_dim:9,element:[3,5,8,9,10,13,15,21,29,58,62,63],element_typ:22,elif:[16,60],elimin:61,els:[9,16,23,33,40,56,58,60],emac:[33,39],emailweixu:17,emb:[49,58],embed:[16,38,57,60,62],embedd:61,embedding_lay:[38,58,60],embedding_nam:[9,38],embedding_s:[9,38],emphas:42,empir:9,emplace_back:40,emploi:[38,59],empti:[8,13,18,31],emul:63,enabl:[3,7,17,21,42,43,45,48],enable_grad_shar:[44,45],enable_parallel_vector:45,enc_proj:[10,38],enc_seq:10,enc_vec:38,encapsul:22,encod:[10,21,38,63],encoded_proj:[10,38],encoded_sequ:[10,38],encoded_vector:38,encoder_last:9,encoder_proj:38,encoder_s:38,encrypt:48,encrypt_decrypt:48,end:[3,8,9,28,29,31,38,45,53,61,62,63],end_pass:16,enditer:[15,16],endpass:[15,16],endpoint:[20,48],endtrain:16,engin:[0,23,42,59],english:[3,9,63],enjoi:33,enough:31,ensembl:10,ensur:[3,18,40],enter:[33,59],entir:[9,10,22,62],entiti:8,entri:[13,21,23,40,48,59],entropi:[9,58,61],entry_point:23,enumer:[6,58,60],enumerate_data_types_of_data_lay:13,env:[39,48],environ:[16,32,33,35,42,43,44,45,48,49,54,55,60],eol:39,eos_id:[9,38],epel:32,epoch:59,epsilon:11,equal:[9,10,18,45],equat:[8,9,10,11,33],equilibrium:54,equip:[32,38],equival:[9,16],error:[7,8,9,11,16,21,25,31,35,40,43,45,48,55,56,58,59,60,62,63],error_clipping_threshold:7,especi:[3,10,61],essenc:16,essenti:[9,16,32,61,63],estat:31,estim:[9,16],eta:49,etc:[8,13,29,33,43,44,47,48,62,63],etcd:[18,21],eth0:[43,48],ethternet:43,eval:[8,58,60,62,63],eval_bleu:63,evalu:[2,9,14,15,42,43,58,62,63],evaluate_pass:62,evalut:[31,63],even:[16,29,42,45,62],evenli:[22,48],event:49,event_handl:[15,16],everi:[2,3,8,9,10,13,16,18,21,22,38,39,40,45,58,61,62,63],everyth:[31,39],evid:28,exactli:[3,9,10,33,48,61],exampl:[2,3,8,9,10,11,13,15,23,25,28,29,31,32,33,38,40,42,43,44,45,47,48,49,55,56,57,58,62,63],exceed:9,except:[3,13,47,53,60,62],exchang:28,excluded_chunk_typ:8,exconv:9,exconvt:9,exdb:13,exec:[33,45],execut:[17,18,21,23,40,42,48,59,61,62],exist:[16,18,25,29,40,45,48,59,62],exit:[22,25,45,49],expand:[40,61,62,63],expand_a:9,expand_level:9,expandconvlay:9,expect:[9,42,62],expens:63,experi:47,expert:17,expir:18,explain:[3,8,18,43,54,62],explan:[9,23,58,63],explanatori:[31,33],explicit:40,explicitli:[3,16],exploit:55,explor:9,exponenti:6,expos:[33,48],express:[16,48,62],extend:[0,8,60],extens:[59,60,63],extent:27,extern:[3,17,26,27],external_librari:17,extra:[7,9,10,31],extra_lay:15,extraattr:[7,47],extraattribut:9,extraattributenon:9,extract:[8,9,28,48,55,61,62],extract_fea_c:56,extract_fea_pi:56,extract_para:53,extralayerattribut:[7,10],extralayeroutput:10,extrem:[9,42],extremli:2,f120da72:49,f7e3:48,fa0wx:49,fabric:43,face:17,facotr:9,fact:56,factor:[7,9,11],factori:26,fail:[3,18,21,45,47,49,55],failur:[18,22],fake:54,fake_imag:29,falloc:24,fals:[3,7,8,9,10,11,13,29,31,38,40,45,47,49,53,58,60,61,62,63],false_label:29,false_read:29,famili:63,familiar:[3,31],fanscin:3,fantasi:59,fantast:58,far:0,farmer:59,fascinatingli:2,fast:[9,21,39,42],faster:[9,10,18,38,42,62],fault:21,favorit:39,favourit:33,fbd1f2bb71f4:49,fc1:[40,47],fc2:47,fc3:47,fc4:47,fc8a365:48,fc8a:48,fc_act:10,fc_attr:10,fc_bias_attr:10,fc_layer:[31,40,47,58,60],fc_layer_nam:10,fc_mat:15,fc_param_attr:10,fclayer:40,fdata:61,fea:56,fea_output:56,feat:62,featur:[3,6,9,13,39,45,55,58,62,63],feature_map:60,feed:[10,13,15,16,31,62],feedback:0,feeder:13,feedforward:55,femal:59,fernan:62,festiv:3,fetch:[13,18,38,40],few:[3,17,18,29,33],fewer:9,fg0:9,field1:15,field2:15,field:[9,15,42,48],figur:[16,17,38,40,42,53,54,55,56,61,62,63],file1:63,file2:63,file:[2,3,5,8,9,13,15,16,17,18,20,21,22,23,24,25,27,29,31,32,33,38,39,40,43,45,53,55,56,61,62,63],file_list:3,file_nam:[3,31,56,58,61],filenam:[3,20,60],fileoffset:24,filer:9,filesystem:[23,24,33,48],fill:[9,18,21,48,58],film:59,filter:[9,56],filter_s:[9,10],filter_size_i:9,finali:43,find:[9,11,18,33,42,55,62,63],fine:[7,21,60],fingerprint:48,finish:[3,18,21,23,33,43,48,49,55],finit:40,first:[3,9,13,16,18,21,23,25,31,33,35,38,39,40,42,45,47,48,53,54,55,56,58,60,61,62,63],first_seq:38,firstli:8,firstn:13,firstseen:49,fit:[2,13,39],five:[42,58],fix:[3,7,26,63],flag:[13,45,54,55,61],flexiabl:29,flexibl:[0,2,9,10,16,22,38],flight:63,float32:[5,13,29,31,54,56],floor:9,flow:[30,39],fly:[31,58],fnt03:48,focu:[3,42],folder:[20,23,25,32,48,55,62,63],follow:[2,3,8,9,10,11,13,16,17,18,21,23,29,32,33,35,38,39,40,42,43,47,48,49,50,51,53,54,55,56,58,59,60,61,62,63],fool:54,forbid:16,force_load:26,forecast:62,forget:[11,16,62],form:[2,3,10,11,42,61],format:[2,3,8,21,31,39,40,45,48,53,55,59,60,62],former:[16,17,63],formula:[9,10],formular:9,forward:[6,10,22,28,38,39,40,47,54,61,62],forwardactiv:40,forwardtest:5,found:[3,5,9,32,38,54,55,58,62],four:[3,8,35,53,56,58,60,61,62],frame:8,framework:[16,40,56,58,62],free:[13,63],french:63,frequenc:[13,42,53,58,62],frequent:[21,29,43,63],frog:55,from:[0,3,5,8,9,10,13,15,17,18,20,21,22,25,28,29,31,33,38,39,40,42,43,45,47,48,49,53,54,55,56,58,59,60,61,62,63],from_sequ:9,from_timestep:9,fromfil:[29,31,56],fulfil:42,full:[9,18,33,38,40],full_matrix_project:[10,38],fulli:[31,39,40,42,54,55,56,58,60,62],fullmatrixproject:9,fully_matrix_project:10,fullyconnect:53,fullyconnectedlay:40,func:[13,21],fundament:31,further:9,fusion:60,gain:9,game:54,gamma:56,gan:16,gan_train:54,gangliao:17,gap:45,gate:[9,10,62],gate_act:[9,10],gate_recurr:9,gather:[9,28,40,60],gauss:7,gaussian:54,gcc:[26,32,33],gdebi:35,gen:[9,63],gen_conf:[54,63],gen_data:63,gen_result:63,gen_train:54,gen_training_machin:54,gen_trans_fil:38,gender:[13,59,60],gener:[2,3,5,8,9,10,13,15,16,17,18,20,21,22,29,31,32,33,42,43,45,47,48,53,56,57,58,60,62],generated_word_embed:9,generatedinput:[9,38],generator_conf:54,generator_machin:54,generator_train:54,genert:3,genr:[59,60],gereat:8,get:[3,8,9,10,13,15,17,21,22,23,24,31,32,35,38,40,42,43,48,52,55,56,58,60,61,62],get_batch_s:61,get_best_pass:62,get_config_arg:[47,58,60,62],get_data:[49,58,61],get_dict:13,get_embed:13,get_imdb:62,get_input_lay:40,get_mnist_data:54,get_model:56,get_movie_title_dict:13,get_nois:54,get_output_layer_attr:10,get_shap:15,get_training_loss:54,get_word_dict:13,getbatchs:40,getenv:[16,23],getinput:40,getinputgrad:40,getinputvalu:40,getoutputgrad:40,getoutputvalu:40,getparameterptr:40,getsiz:40,getslotvalu:54,gettask:21,gettempl:48,gettranspos:40,getw:40,getweight:40,getwgrad:40,gfortran:32,gildea:61,gist:10,git:[30,32,33,39],github:[9,10,32,33,35,56],give:[3,18,31,33,40,42,48,58],given:[3,13,15,22,29,40,45,54,58,61,62,63],global:[3,7,16,17,18,28,42,45,48,60,62],global_learning_r:7,globalstat:42,globalstatinfo:42,globe:3,go_librari:17,go_test:17,goal:[42,61],gob:21,godoc:26,goe:[9,10,18,31],going:[58,62],good:[9,29,42,62,63],goodfellow13:9,googl:16,googleapi:48,gpg2:48,gpg:48,gpu:[2,3,7,9,12,23,28,30,32,35,41,43,54,55,56,60,61,62,63],gpu_id:[45,47,54],gpugpu_id:44,grab:[18,62],grad:[22,45,59],grad_share_block_num:[44,45],gradient:[7,8,9,11,15,18,21,28,45,58,62],gradient_clipping_threshold:[7,58,62],gradient_evalu:8,gradient_machin:[15,27],gradientmachin:[5,15,27,28,54,60,63],gradual:[31,42],grai:55,gram:[53,62],grant:48,graph:[9,15,17,18,53],graphviz:56,grave:62,grayscal:3,greater:9,grep:[33,62],groudtruth:38,ground:[8,9,58,63],group:[10,21,62],group_id:60,group_input:38,grouplen:[13,59],gru:[9,38,58,63],gru_bias_attr:10,gru_decod:38,gru_decoder_with_attent:38,gru_encoder_decod:[53,63],gru_layer_attr:10,gru_memori:10,gru_siz:58,gru_step:38,gru_step_lay:[10,38],grumemori:[10,38],gserver:[9,40],gsizex:42,guarante:40,guess:[31,62],gui:42,guid:[24,34,38,39,40,42,48,49,53,55,62,63],guidenc:31,gur_group:10,gzip:[21,49],hack:[34,43],hadoop:16,half:48,hand:[59,60,62],handi:17,handl:[16,23,28,29,43,60,62],handler:15,handwrit:[3,62],happen:21,hard:[48,58],hardwar:[33,42],has:[3,5,8,9,10,11,13,16,18,21,22,33,38,40,42,48,49,53,55,58,59,60,61,62,63],has_kei:15,have:[2,3,5,9,10,13,16,17,18,21,22,23,28,29,31,32,33,38,39,40,42,43,45,47,48,53,55,58,59,60,62,63],hdf:[2,20],head:[39,53,62],header:[22,27,31,40,53,56,60],health:59,heavi:43,height:[9,13,26,29,40,55],held:18,hello:16,help:[3,5,25,39,43],helper:[9,10,40],here:[3,5,7,8,9,10,13,16,17,18,25,29,31,32,38,43,44,47,48,49,53,55,56,57,58,59,60,61,62,63],heurist:[9,45,63],hidden:[9,10,38,48,58,60,62],hidden_s:[10,60],hierarch:[9,38],high:[7,40,54],higher:2,highest:[13,63],highli:[2,3,13,38,47,60,62],him:16,hint:31,histor:62,hl_get_sync_flag:40,hold:[16,18,21,48],home:[20,25,43,48,49],homemak:59,honor:21,hook:[3,60,61],hope:0,horizont:[9,56],horror:59,hors:55,horst:62,host:[23,32,33,43,48,49],hostnam:[43,48],hostpath:49,hostport:48,hot:60,hour:63,hous:[3,13,31,53],how:[2,3,7,9,16,18,21,25,28,31,38,43,45,48,49,52,55,56,58,60],howev:[3,10,28,29,31,38,39,44,45,48,62,63],hpp:26,html:[13,33,55],htod:42,http:[9,10,13,23,32,33,35,39,48,49,54,55,56,63],huber:9,huge:[9,39],huina:62,human:63,hyper:[9,40],hyperplan:13,i0601:60,i0706:63,i0719:63,i1117:42,iamfullaccess:48,iamusersshkei:48,ib0:43,ics:13,icwsm:62,id_input:[8,38],idea:[9,17,29],ident:[31,33,48,59],identifi:[38,40],identityoffsetproject:9,identityproject:9,ids:[8,9,40,58,60],idx:[21,40],ieee:62,ies:25,ignor:[3,9,45,53],ijcnlp:62,illustr:[3,8,18,22,38,40,42,58],ilsvrc:56,imag:[3,12,13,16,29,31,34,47,48,50,51,54,56,57,63],image_a:29,image_b:29,image_classif:55,image_fil:29,image_lay:29,image_list_provid:56,image_nam:16,image_path:29,image_provid:55,image_reader_cr:29,image_s:56,imagenet:[20,57],imagepullpolici:48,imageri:9,images_reader_cr:29,imdb:59,imdber:62,img:[3,9,55],img_conv_lay:10,img_featur:3,img_pool_lay:10,img_siz:55,imgsiz:42,imgsizei:42,imgsizex:42,immedi:48,immutable_paramet:16,implement:[3,9,10,11,13,21,22,23,26,27,28,38,58,61],impli:17,importerror:60,improv:[0,42,48,62,63],inbound:48,includ:[2,3,8,9,10,13,16,17,22,23,26,27,32,33,38,40,42,45,48,49,53,58,59,61,63],inconsist:59,incorrect:9,increas:[18,21,45,63],increment:45,incupd:40,inde:[13,29,33],independ:[9,22,58],index:[3,8,9,12,13,15,18,21,38,43,48,60],indexslot:[9,61],indic:[3,8,9,22,31,43,48,61],individu:[18,31,48],industri:18,ineffici:28,infer:[1,16,18,26,32],infiniband:43,info:[8,9,13,40,43],infom:39,inform:[5,13,23,25,40,42,45,48,59,60,61,62,63],infrastructur:[48,54],ingor:45,ininst:16,init:[7,40,47,48,54,58,60,61],init_hook:[58,60,61],init_model_path:[44,45,47,53,58,61],initi:[3,5,7,9,13,17,21,38,40,45,53,54,58,61],initial_max:7,initial_mean:[7,9],initial_min:7,initial_std:[7,9],initpaddl:[5,54],inlcud:10,inlin:48,inner:40,inner_param_attr:10,input1:[9,10],input2:9,input:[3,5,6,8,9,10,12,13,15,28,29,31,38,40,47,53,54,55,56,58,60,61,62,63],input_data:40,input_data_target:40,input_featur:6,input_fil:[31,61],input_hassub_sequence_data:40,input_id:9,input_imag:[10,55],input_index:40,input_label:40,input_lay:40,input_nam:16,input_sequence_data:40,input_sequence_label:40,input_sparse_float_value_data:40,input_sparse_non_value_data:40,input_t:40,input_typ:[31,38,58,60],inputdef:40,inputlayers_:40,inputtyp:[3,13],insid:[8,9,18,28,29,33,48],inspir:53,instal:[23,33,36,39,43,49,55,56,60,61,62],instanc:[9,18,20,38,40,42,45,61],instance_ip:48,instanti:18,instead:[9,10,12,17,23,29,33,39,43,58,63],instruct:[33,35,42,58],int32:45,int64:24,integ:[3,8,9,13,21,23,26,38,40,58,62],integer_valu:[3,13,58],integer_value_sequ:[3,13,38,58,61],integr:[32,61],intend:0,inter:[9,43],interact:[33,48],intercept:9,interest:[42,62],interfac:[1,5,7,9,10,21,23,25,28,43,48,55,60,62],interg:58,intergr:9,intermedi:[25,61],intern:[9,10,13,15,48],internet:[18,62],interpret:[3,8,32,42],interv:62,intrins:32,introduc:[3,18,49,60,62],introduct:[4,54],invalid:29,invari:55,invoc:17,invok:[3,9,15,28,42,48,60],involv:54,iob:8,ioe:8,ips:48,ipt:[9,38],ipython:16,is_discriminator_train:54,is_gener:[9,53,54,63],is_generator_train:54,is_kei:60,is_loc:15,is_predict:[58,60,62],is_seq:[9,38,60],is_sequ:60,is_stat:7,is_test:[56,61,62],is_train:3,isn:42,isol:33,isspars:40,issu:[32,33,42],item:[9,13,15,29],iter:[9,10,11,13,15,16,18,29,55,61,62],its:[3,9,10,16,18,21,28,40,42,45,48,53,54,55,58,62,63],itself:[10,18],java:26,jeremi:42,jie:[61,62],jmlr:9,job:[5,13,44,45,47,56,58,60,61,62,63],job_dispatch_packag:43,job_id:13,job_mod:53,job_nam:[23,48],job_namespac:48,job_path:48,job_workspac:43,jobpath:48,jobport0:48,jobport1:48,jobport2:48,jobport3:48,jobserv:23,johan:62,join:18,joint:[53,63],jointli:[10,63],journal:[61,62],journei:33,jpeg:55,jpg:56,json:[43,48,49,60],jth:10,judg:63,jupyt:[23,33],just:[3,6,8,9,10,13,21,22,28,31,39,43,47,48,53,55,60,61,62],jx4xr:48,jypyt:16,k8s_data:48,k8s_job:16,k8s_token:16,k8s_train:48,k8s_user:16,kafka:20,kaim:9,kaimingh:56,kebilinearinterpbw:42,kebilinearinterpfw:42,keep:[3,9,18],kei:[3,13,15,18,20,21,24,42,43,60,62],kernel:[9,42,58],key1:45,key2:45,key_pair_nam:48,keyid:48,keymetadata:48,keypair:48,keyserv:48,keystat:48,keyusag:48,keyword:3,kill:[18,48],kind:[2,3,16,18,31,48,49,54,58,60],kingsburi:61,kms:48,know:[3,10,16,21,31,40,42,48,60],knowledg:62,known:[54,62,63],kriz:[13,55],ksimonyan:10,kube_cluster_tl:16,kube_ctrl_start_job:16,kube_list_containers_in_job_and_return_current_containers_rank:16,kubeconfig:48,kubectl:49,kuberent:[18,48],kubernet:[16,18,41,43,50,51],kubernetes_service_host:16,kwarg:[3,10,11,13,58,60,61],l1_rate:7,l2_rate:7,l2regular:[55,58,62],label:[3,5,8,9,11,13,15,29,31,38,49,54,55,56,57,58,60,62],label_dict:61,label_dim:[9,58],label_fil:[29,61],label_lay:29,label_list:61,label_path:29,label_slot:61,labeledbow:62,labl:62,lag:45,lake:3,lambdacost:9,lambdarank:9,languag:[9,13,47,53,61,62,63],larg:[12,13,61,62,63],larger:[3,7,8,9,43],last:[8,9,10,31,38,43,45,58,62,63],last_time_step_output:9,lastseen:49,late:62,latenc:[43,48],later:[17,32,39,48,58],latest:[9,18,33,39,49,62],latter:63,launch:[45,48,62],launcher:16,lawyer:59,layer1:[9,10],layer2:9,layer3:9,layer:[5,7,8,10,12,13,14,15,29,31,38,41,44,45,53,54,55,56,58,60,61,62],layer_0:40,layer_attr:[9,38,47],layer_num:[47,56],layer_s:9,layer_typ:9,layerbas:40,layerconfig:40,layergradutil:40,layermap:40,layeroutout:9,layeroutput:[10,60],lbl:[8,55],ld_library_path:[32,35,43],lead:42,learn:[0,7,8,9,10,11,13,16,22,29,31,33,36,38,40,42,55,56,58,61,62,63],learnabl:9,learning_method:[31,53,55,58,60,62,63],learning_r:[7,22,31,53,55,58,60,62,63],leas:18,least:[8,9,18,32,59],leav:[3,48],lecun:13,left:[9,31,56],leman:63,len:[3,9,22,24,38,40,58,60,61],length:[9,10,13,22,38,45,49,62,63],less:[9,16,43,63],less_than:16,let02:49,let:[5,8,9,16,31,48,60],level:[7,9,43,45,54,60,62,63],lgtest:17,lgtest_main:17,lib64:[32,43,45],lib:27,libapi:17,libari:27,libcudnn:32,libjpeg:55,libpaddl:[26,27],libpaddle_capi:27,libpaddle_gserv:27,libpaddle_math:27,libpython:32,librari:[9,17,27,32,33,43,45,60],licens:61,life:18,like:[3,8,9,13,17,18,23,29,31,32,38,42,43,44,47,48,53,56,58,60,62,63],limit:[9,13,42,45],line:[2,3,5,8,13,17,23,25,31,39,41,42,43,47,48,53,55,56,60,61,62,63],linear:9,linearactiv:31,linguist:61,link:[9,10,17,24,25,32,48,58,62],linux:[24,32,33,35,48,63],lipeng:53,lipton:62,list:[2,3,8,9,13,15,16,21,23,25,28,31,33,38,40,43,45,47,48,55,56,58,60,61,62,63],listen:45,literatur:62,littl:[2,3,22,45,58,62],lium:63,live:[18,33],liwicki:62,load:[2,3,5,9,16,18,31,45,48,56,60,61,62,63],load_featur:56,load_feature_c:56,load_feature_pi:56,load_missing_parameter_strategi:[44,45,47,53,61],load_uniform_data:54,loadparamet:5,loadsave_parameters_in_pserv:[44,45],loc:8,local:[7,18,32,33,39,43,44,45,49,55,62],localhost:33,localpath:25,locat:[38,40,58,61],lock:[18,21,22],log:[3,21,25,39,40,43,45,48,49,55,60,61,62,63],log_barrier_abstract:45,log_barrier_lowest_nod:[44,45],log_barrier_show_log:[44,45],log_clip:[44,45],log_error_clip:[44,45],log_period:[45,47,49,54,55,58,60,61,62,63],log_period_serv:[44,45],logarithm:6,logger:3,logic:[3,28,43],login:33,longer:63,look:[3,8,23,31,43,44,48,49,54,58],lookup:58,loop:29,loss:[9,40,54,58,62,63],lot:44,low:9,lower:43,lowest:45,lpaddle_capi_shar:27,lpaddle_capi_whol:27,lst:60,lstm:[9,38,49,58],lstm_bias_attr:10,lstm_cell_attr:10,lstm_group:10,lstm_layer_attr:10,lstm_size:58,lstm_step:10,lstmemori:[10,38],lstmemory_group:9,ltr:9,lucki:31,mac:[27,32,33],machan:10,machin:[9,10,13,15,31,39,40,44,45,47,48,49,58,60,62,63],made:[3,18,22,31,38,59],mai:[3,9,29,33,39,42,48,59],main:[3,5,39,48,55,61,62],mainli:45,maintain:[9,21,48],majel:17,major:[33,39,54,56,62,63],make:[3,8,16,17,18,21,22,28,29,32,33,39,40,42,43,48,55,58,60,62],male:59,malloc:40,man:24,manag:[18,22,25,39,43],manageri:59,mandarin:9,mani:[0,9,10,21,28,31,33,45,58,59,60,62],mannal:43,manual:39,manufactur:63,mao:62,map:[3,8,9,13,15,16,21,45,55,56,60],map_read:13,mapreduc:16,marcu:62,mark:[3,38,61],mark_slot:61,market:[31,59,62],martha:61,mask:[7,9],master:[16,30,39,45,62],mat:[26,27],mat_param_attr:10,match:42,math:[10,26,40,42],matirx:9,matplotlib:55,matric:[5,38,40],matrix:[8,9,10,13,15,26,27,38,40,44,47,56,61],matrixptr:40,matrixtyp:27,matter:3,max:[3,7,9,13,42,45,47,55,58,60],max_id:[9,15,58],max_job_id:13,max_length:[9,38],max_movie_id:13,max_sort_s:9,max_user_id:13,maxframe_evalu:8,maxid:[8,58],maxid_evalu:8,maxid_lay:58,maxim:[9,63],maximum:[8,13,22,38,42,45,58,61,62],maxinum:12,maxpool:9,mayb:[9,10,55],md5:[13,19],mean:[3,7,8,9,10,11,12,13,15,17,29,31,38,42,43,45,47,48,53,54,55,56,58,60,61,62,63],mean_img_s:55,mean_meta:56,mean_meta_224:56,mean_valu:56,measur:[31,42],mechan:[9,10,38,48,62],media:62,meet:61,mem:[9,23],member:[13,16,28],memcpi:[28,42],memor:62,memori:[2,3,10,21,23,38,40,42,45,47,49,58,61,62,63],memory_nam:9,memory_threshold_on_load_data:45,mention:21,mere:10,merg:[22,28,39,45,53,63],mergedict:[53,63],messag:[31,45,49,60,62,63],meta:[43,55,56,58],meta_config:[43,60],meta_fil:60,meta_gener:[43,60],meta_path:55,meta_to_head:60,metadata:[24,48,49],metaplotlib:16,method:[3,9,11,15,33,40,42,45,47,58,60,62,63],might:[9,17,33,40,48],mileag:42,million:[13,47,59],min:[7,42,47,48,60],min_pool_s:3,min_word_freq:13,mind:43,mini:[3,9,13,15,18],mini_batch:29,minibatch:9,minibatch_data:13,minim:[3,11,31,45],minimum:9,minimun:45,minst:3,minut:[18,48,63],mirror:33,mislead:22,miss:[45,53,61],mit:48,mix:[10,38,61],mixed_bias_attr:10,mixed_lay:[10,38,61],mixed_layer_attr:10,mixedlayertyp:9,mkdir:[25,32,33,48],mkl:32,mkl_path:32,mkl_root:32,ml_data:[43,60],mnist:[3,5,20,29],mnist_provid:3,mnist_random_image_batch_read:29,mnist_train:[3,29],mnist_train_batch_read:29,mod:61,modal:61,mode:[9,45,54,55,56,60,62,63],model:[1,2,5,9,10,13,18,19,39,40,41,45,48,60,61,62],model_config:[5,54],model_list:[45,47,61,62],model_output:62,model_path:47,model_zoo:[53,56],modifi:[5,38,39,40,43,48],modul:[2,3,5,10,13,15,31,32,55,56,58,60,61],modulo:9,momentum:[7,31,58],momentumoptim:[31,55],mon:49,monitor:[58,62],mono:9,month:[17,58,63],mood:62,more:[2,3,5,8,9,10,13,16,18,21,23,25,29,31,33,38,40,42,43,47,49,55,58,61,62,63],morin:9,mose:[62,63],moses_bleu:63,mosesdecod:62,most:[3,5,9,13,16,29,31,38,40,42,44,60,61,62,63],mostli:[55,59],mount:[23,33,48,49],mountpath:[48,49],move:[9,18,21,25,42,48,60,62],movement:[42,62],movi:[3,13,62],movie_categori:13,movie_featur:60,movie_head:60,movie_id:60,movie_info:13,movie_meta:60,movie_nam:60,movie_review:13,movieid:59,movieinfo:13,movielen:57,moving_average_fract:9,mpi:43,mse_cost:[31,60],much:[9,18,29,42],mul:40,mulit:43,multi:[9,28,40,44,45,56,63],multi_binary_label_cross_entropi:9,multi_crop:56,multigradientmachin:28,multinomi:9,multipl:[8,9,10,13,15,16,21,22,33,38,40,45,47,48,54,58,60,62],multipli:[8,9,40,55],multithread:3,music:59,must:[3,6,8,9,10,13,29,32,33,38,39,40,43,45,47,48,63],my_cluster_nam:48,my_cool_stuff_branch:39,my_external_dns_nam:48,mypaddl:49,mysteri:59,name:[3,7,8,9,10,12,13,15,16,18,20,22,23,27,31,33,38,40,42,43,45,47,49,50,51,53,54,55,56,58,60,62,63],name_prefix:20,namespac:[26,33,40,49],nano:39,nativ:9,natur:[21,47,61,62],nchw:9,ndarrai:[15,20],ndarri:15,ndcg:9,ndcg_num:9,nearest:58,necessari:[3,9,22,28,32,40,43,58,62],necessarili:40,need:[3,8,9,10,13,16,17,21,22,23,25,28,31,32,33,35,38,39,40,43,44,45,47,48,49,54,55,56,58,60,61,62,63],neg:[3,8,9,58,61,62],neg_distribut:9,negat:61,neighbor:58,ner:8,nest:[3,9,13],net:[9,10],net_conf:62,net_diagram:56,network:[2,3,5,7,8,9,11,13,14,15,16,18,29,31,33,40,42,43,45,53,62,63],network_config:47,networkadministr:48,neural:[3,5,9,10,11,13,15,16,18,31,42,45,53,54,56,62,63],neuralnetwork:28,neuron:[5,40,58,62],never:[13,29,48,49],newest:[22,39],newtork:62,next:[9,13,18,38,40,42,45,48,49,61,62,63],nfs4:48,nfs:48,nfsver:48,nginx:33,ngram:13,nic:[43,44,45],nil:21,nine:[13,61],nlp:[3,9],nltk:13,nmt:63,nnz:40,no_cach:3,no_sequ:[3,9,60],noah:62,noavx:[33,35],node:[9,17,40,43,45,48,49,62,63],node_0:48,node_1:48,node_2:48,nodefil:43,noir:59,nois:[9,18,54],noise_dim:54,non:[9,18,40,45,48],none:[2,3,5,7,8,9,10,11,12,13,15,16,31,38,56,58],nonlinear:40,norm:54,norm_by_tim:9,normal:[3,5,9,10,13,35,38,40,43,45,49,53,54,56],normzal:56,north:55,notat:9,note:[3,5,7,9,10,12,15,16,21,23,29,32,42,45,47,48,53,55,60,62],notebook:[23,33],noth:[6,15,45],notic:[38,40],novel:62,now:[0,3,9,13,17,18,31,33,39,45,48,54,60,61],np_arrai:13,nproc:32,ntst1213:63,ntst14:63,nullptr:40,num:[9,43,45,58,61,62,63],num_channel:[9,10,55],num_chunk_typ:8,num_class:[9,10,55],num_filt:[9,10],num_gradient_serv:[44,45],num_group:9,num_neg_sampl:9,num_parameter_serv:16,num_pass:[15,31,44,45,47,49,58,60,61,62,63],num_repeat:9,num_result:8,num_results_per_sampl:9,num_shard:20,number:[3,8,9,13,18,20,29,31,40,43,45,48,53,55,56,58,61,62,63],numchunktyp:8,numdevices_:47,numlogicaldevices_:47,numofallsampl:8,numofwrongpredict:8,numpi:[13,15,20,29,31,32,54,56],numsampl:42,numtagtyp:8,numtimeout:21,nv_:17,nv_librari:17,nv_test:17,nvcc:[17,33],nvidia:[32,33,42,45],obei:8,obj:[3,31,55,56,58,60],object:[3,5,7,9,10,11,13,15,16,26,42,54,55,56,58,61],observ:[11,31,40,42,63],obtain:[58,61,62],occup:[59,60],occur:[13,15,39],oct:49,odd:9,off:[27,33],offer:[5,61],offici:[33,48,55],offlin:[18,20],offset:[9,60],often:[43,58,63],ograd:40,old:[22,33,39,45],omit:58,on_init:3,on_travisexclud:32,onc:[3,9,18,21,33,39,40,48,58],one:[3,6,8,9,10,11,12,13,16,18,21,22,23,28,29,31,33,39,40,43,45,47,48,49,53,54,55,56,58,60,61,62,63],one_host_dens:60,one_hot_dens:60,onli:[2,3,5,8,9,10,12,13,15,16,17,21,22,23,25,28,31,32,38,39,40,42,44,45,47,48,49,53,56,58,59,62,63],onlin:[11,18,20,29],onto:48,open:[0,3,9,16,20,29,31,33,48,56,58,60,61],openbla:32,openblas_path:32,openblas_root:32,oper:[9,10,11,13,33,38,40,42,45,48,53,55,60],opinion:62,ops:17,ops_test:17,opt:[16,32],optim:[3,7,14,15,28,31,40,42,62],option:[3,8,9,16,17,31,39,40,43,47],order:[3,9,10,13,15,29,40,45,48,49,54,56,58,62,63],ordinari:62,oregon:48,org:[8,9,10,13,20,24,32,33,54],organ:[8,9,55,62,63],origin:[0,2,3,9,13,39,54,61,63],other:[3,8,9,10,13,18,22,25,32,33,35,38,39,47,48,49,53,54,55,56,58,59,60,61,62,63],otherchunktyp:8,otherwis:[2,9,13,16,18,22,29,38,43,47,60,63],our:[16,17,33,38,40,48,49,53,55,58,61,62,63],out:[9,15,16,17,21,31,38,42,45,48,49,55,62],out_dir:48,out_left:9,out_mem:38,out_right:9,out_size_i:9,out_size_x:9,outlin:46,outout_lay:15,outout_layer1:15,outout_layer2:15,outperform:61,output:[5,6,7,8,9,10,12,13,15,16,20,25,29,31,38,40,42,45,47,49,53,54,55,56,58,60,61,62,63],output_:[9,40],output_dir:56,output_fil:61,output_id:9,output_lay:[15,56],output_max_index:12,output_mem:[9,38],output_path:20,outputh:9,outputw:9,outsid:[3,9,10],outter_kwarg:3,outv:40,over:[2,9,10,16,39,40,42,58,61,62],overcom:62,overhead:42,overlap:40,overrid:[18,25,40],overview:[21,22],overwrit:25,owe:0,own:[22,33,39,43,48],pacakg:35,pack:33,packag:[3,13,21,23,33,34,48],pad:[38,58],pad_c:9,pad_h:9,pad_w:9,paddepaddl:2,padding_attr:9,padding_i:9,padding_x:9,paddl:[3,5,6,7,8,9,10,11,12,13,15,16,18,20,23,25,26,27,28,31,32,33,34,35,39,40,41,42,43,45,47,48,54,55,58,60,61,62,63],paddle_begin_init_param:22,paddle_element_typ:22,paddle_element_type_float32:22,paddle_element_type_float64:22,paddle_element_type_int32:22,paddle_element_type_int64:22,paddle_element_type_uint32:22,paddle_element_type_uint64:22,paddle_error:[26,27],paddle_exampl:23,paddle_finish_init_param:22,paddle_get_param:22,paddle_gradi:22,paddle_init_param:22,paddle_job:23,paddle_matrix:[26,27],paddle_matrix_cr:27,paddle_matrix_get_shap:26,paddle_matrix_shap:26,paddle_n:43,paddle_new_pserver_cli:22,paddle_on_cloud:23,paddle_output:49,paddle_paramet:22,paddle_port:43,paddle_ports_num:43,paddle_ports_num_for_spars:43,paddle_pserver2:43,paddle_pserver_cli:22,paddle_pserver_client_releas:22,paddle_root:53,paddle_save_model:22,paddle_send_grad:22,paddle_source_root:53,paddle_train:[27,30,43],paddledev:[48,49],paddlepaddl:[0,2,3,5,9,10,13,18,20,22,23,24,25,29,31,32,35,38,39,40,41,42,43,50,51,56,58,60,61,62],paddlepadl:3,paddlpaddl:0,paddpepaddl:3,page:[39,48,60],pai:33,pair:[8,61],palceholder_just_ignore_the_embed:53,palmer:61,paper:[9,53,54,56,61,62,63],paraconvert:53,paragraph:62,parallel:[42,45,47,48,49,63],parallel_nn:[7,44,45],param:[7,9,13,22,28,60],param_attr:[9,10,31,38],param_config_proto:22,paramattr:[7,9,31,38],paramet:[2,3,5,8,9,10,11,12,13,14,17,19,25,29,31,40,41,47,54,55,58,60,61,62,63],parameter_attribut:9,parameter_block_s:[44,45],parameter_block_size_for_spars:[44,45],parameter_learning_r:7,parameter_nam:[15,16],parameter_serv:16,parameterattribut:[7,9,10],parametermap:40,parameters_:40,parameterset:16,parameterupdat:28,parametris:11,paramt:[48,53],paramutil:60,paraphras:63,paraphrase_data:53,paraphrase_model:53,paraspars:40,parent:40,pars:[5,13,17,47,48,54,60,61],parse_config:[5,54],parser:60,part:[3,8,31,38,39,40,42,54,58,60,61,62,63],parti:[42,60],partial:[9,54],participl:53,particular:42,partit:[18,20,48],pass:[3,9,13,15,18,28,29,31,39,40,42,43,45,48,49,54,55,58,60,61,62,63],pass_id:15,pass_idx:29,pass_test:54,passtyp:40,password:43,past:[16,33,48],patch:24,path:[2,3,8,13,15,18,21,22,23,29,31,32,38,43,45,47,48,49,53,55,56,58,61,62,63],pattern:[13,18,26,31,48,60,62],paul:61,paus:18,pave:63,pdf:[9,10],pem:[16,20,48],pend:[18,21],penn:61,per:[8,9,13,18,22,29,45,55,58],perfom:[45,47],perform:[2,9,10,22,28,31,38,39,40,41,43,44,54,55,58,62,63],period:[2,18,45,58,60,61,62,63],perl:[62,63],permiss:48,peroid:9,persist:48,persistentvolum:48,persistentvolumeclaim:48,person:[8,16],perspect:42,perturb:40,pfs:[20,25],pfsclient:20,pfspath:25,pgp:48,phase:31,photo:55,pick:[3,48],pickl:60,pictur:58,piec:[9,10,31],pillow:[23,55],pip:[32,39,43,55,60],pipe:59,pipelin:61,pixel:[3,9,13],pixels_float:3,pixels_str:3,place:[2,3,18,40,42,43,56,63],placehold:[31,53],plai:[61,62],plain:[2,8,9,15,23,27],plan:[18,40],platform:[0,31,48],pleas:[3,5,7,9,10,11,16,18,21,22,29,32,33,34,38,39,40,48,53,55,58,60,61],plot:[16,55],plotcurv:55,png:[55,56],pnpairvalidationlay:45,pnpairvalidationpredict_fil:44,pod:[20,23,48,49],pod_nam:48,point:[18,23,31,42],polar:[13,62],polici:48,polit:62,poll:62,poo:55,pool3:40,pool:[3,10,14,55,58,60],pool_attr:10,pool_bias_attr:10,pool_layer_attr:10,pool_pad:10,pool_siz:[3,9,10],pool_size_i:9,pool_strid:10,pool_typ:[9,10],pooling_lay:[10,58],pooling_typ:[9,58],poolingtyp:12,popular:[31,56],port:[17,33,43,44,45,48,49],port_num:44,ports_num:45,ports_num_for_spars:[44,45,47],pos:[60,62],pose:18,posit:[3,8,9,13,58,61,62,63],positive_label:8,possibl:[16,21,39,42,54],post1:32,post:[23,24],potenti:42,power:[58,63],practic:[9,31,38,40],pre:[3,9,10,13,16,33,48,49,53,55,61,62,63],pre_dictandmodel:53,precis:[8,32],precision_evalu:8,pred:[58,61],predefin:62,predetermin:[9,45,63],predic:[13,61],predicate_dict:61,predicate_dict_fil:61,predicate_slot:61,predict:[3,4,8,9,11,15,31,38,43,45,53,58,63],predict_fil:45,predict_output_dir:[44,45,58],predict_sampl:5,predicted_label_id:58,prediction1:15,prediction2:15,predictor:60,predin:55,prefer:62,prefetch:40,prefix:[8,18,20,48],pregrad:40,preinstal:32,premodel:53,prepar:[5,23,28,50,58],preprcess:62,preprocess:[13,38,43,49,62],prerequisit:32,present:[16,56,61,63],preserv:25,pretti:31,prev_batch_st:[44,45],prevent:[2,11,16,18,21],previou:[9,10,18,25,40,45,48,61,63],previous:[9,49,56],price:[13,31],primarili:62,principl:16,print:[7,15,16,31,38,45,53,58,60,61,62,63],printallstatu:42,printer:8,printstatu:42,prite:8,privat:27,privileg:48,prob:[8,15,54],probabilist:[9,53],probability_of_label_0:58,probability_of_label_1:58,probabl:[8,9,15,38,39,56,58,61],problem:[5,9,11,16,17,58,61,62],proc:33,proc_from_raw_data:58,proce:[13,18,29,48],procedur:[53,61,63],proceed:[9,61],process:[2,3,5,7,9,10,16,20,21,31,33,38,43,45,47,48,49,53,55,56,58,60,61,62,63],process_pr:58,processdata:[55,56],processor:42,prod:33,produc:[10,13,18,29,33,56,58],product:[0,23,33,40,48,58,62],productgraph:49,profil:[25,32],proflier:42,program:[2,13,16,20,22,29,33,42,43,45],programm:59,progress:[18,21,45],proivid:3,proj:9,project:[9,10,23,27,32,38,40,60],promis:[9,10],prompt:[25,39],prone:16,prop:61,propag:[11,45,47],properli:58,properti:[3,45],propos:63,proposit:61,protect:40,proto:12,protobuf:[23,32],protocol:[8,45],prove:58,proven:63,provid:[0,9,13,16,22,23,31,33,38,42,43,48,53,54,55,56,59,62],providermemory_threshold_on_load_data:44,provis:48,provod:3,prune:9,ps_desir:18,pserver:[23,43,44,45,48],pserver_cpu:23,pserver_id:19,pserver_mem:23,pserver_num_thread:[44,45],pserverstart_pserv:44,pseudo:[16,23],psize:40,ptr:27,pull:[30,33,53,63],punctuat:62,purchas:58,purpos:[0,18,42],push_back:40,put:[18,33,40,43,49,58],pvc:48,pwd:33,py_paddl:[5,13,54],pydataprovid:[2,3,58],pydataprovider2:[4,5,31,38,58,60,62],pyramid:9,pyramid_height:9,python:[2,3,4,15,16,26,28,30,31,32,39,43,53,54,55,61,62,63],pythonpath:55,pzo:62,qualifi:32,qualiti:58,queri:[9,48,63],question:[9,16,48,61],quick:[45,49,57,63],quick_start:[23,48,49,50,58],quick_start_data:49,quickli:31,quickstart:49,quit:42,quot:59,rac:9,rais:13,ramnath:62,ran:42,rand:[42,45,47,54,61],random:[3,7,9,13,20,28,29,31,45,54,55,61],random_imag:20,randomli:62,randomnumberse:44,rang:[3,9,13,20,29,45,47,55,59,61],rank:[9,16,48,56,58],rare:3,rate:[7,8,11,13,22,40,43,55,58,60,62,63],rather:[5,23,48,62],ratio:45,raw:[9,31,58,62],raw_meta:60,rdma:[32,45],rdma_tcp:[44,45],reach:[18,42,61],read:[2,3,13,15,16,18,20,29,31,38,43,48,56,58,60],read_from_realistic_imag:16,read_from_rng:16,read_lock:19,read_mnist_imag:16,read_ranking_model_data:16,reader:[1,15,20,63],reader_cr:20,reader_creator_bool:29,reader_creator_random_imag:[13,29],reader_creator_random_image_and_label:[13,29],readi:[18,31,48,49,55],readm:[27,59,60,62],readonesamplefromfil:3,readwritemani:48,real:[3,29,31,54],realist:16,reason:[10,16,18,33,49],rebas:39,recal:8,receiv:[18,23],recent:63,reciev:45,recogn:55,recognit:[3,9,56,62],recommand:3,recommend:[2,10,16,33,38,40,43,45,60],recommonmark:32,recompil:42,record:[21,48,60,61],recordio:[16,20,21],recov:[18,31,54],recoveri:21,rectangular:9,recurr:[61,62],recurrent_group:[10,38],recurrent_lay:10,recurrentgradientmachin:27,recurrentgroup:8,recurrentlay:45,recurs:25,recv:48,reduc:[11,43,45,47],refactor:28,refer:[2,5,7,9,10,11,18,21,22,38,40,43,49,53,55,58,60,63],referenc:[9,21],reflect:21,regard:61,regardless:63,regex:60,region:[42,61],regist:[40,42],register_gpu_profil:42,register_lay:40,register_timer_info:42,registri:[23,33,49],regress:57,regular:[7,40,48,55,58,62],rel:[2,10,43],relat:[3,18,23,33,35,49,60,62],relationship:[13,31,54],releas:[30,32,33,35,48,59,61],relev:[61,63],reli:32,reliabl:18,relu:[9,40],remain:58,rememb:9,remot:[7,39,40,43,45,47,48],remoteparameterupdat:45,remov:[13,25,43,45,62],renam:[25,63],reorgan:9,replac:[17,21,33,62],replicaset:23,repo:39,report:[21,42,43],reportdataset:21,repositori:39,repres:[3,5,9,13,21,38,40,48,55,58,59],represent:[22,58,62],reproduc:63,request:[18,30,48,49,53,63],requir:[2,8,9,16,18,22,23,25,40,43,48,49,54,55,58,60],requrest:39,res5_3_branch2c_bn:56,res5_3_branch2c_conv:56,res:61,research:[9,13,55,59,62],resembl:62,reserv:[3,25],reserveoutput:40,reset:[9,18],reshap:29,reshape_s:9,residu:56,resnet:57,resnet_101:56,resnet_152:56,resnet_50:56,resolv:[39,49],resourc:[33,48],respect:[3,31,38,40,45,55,56,61,63],respons:[9,28,48,49],rest:[3,9,23,24,31],restart:[18,22,48,49],restartpolici:[48,49],restrict:45,resu:29,result:[5,6,8,9,15,21,38,42,45,48,55,56,58,60,61,62],result_fil:[8,38],ret_val:60,retir:59,retran:48,retriev:[40,49],return_seq:10,reuqest:30,reus:[29,40],reveal:16,revers:[9,10,38,61,62],review:[13,39,49,58,62],reviews_electronics_5:49,revis:58,rewrit:[17,63],rgb:9,rgen:62,rho:11,rich:31,right:[3,9,17,23,56],rkt:23,rmsprop:58,rmspropoptim:60,rnn:[9,10,41,44,58,62],rnn_bias_attr:38,rnn_layer_attr:38,rnn_out:38,rnn_step:9,rnn_use_batch:[44,45],rnnlm:13,robot:55,role:[13,16,21,22,38,48,57,62],roman:62,romanc:59,root:[11,12,43,48,49],root_dir:43,rot:9,roughli:[3,54],routin:60,routledg:62,row:[5,8,9,13,40,56],row_id:9,rpc:21,rpcserver:21,rsize:48,rtype:[9,60],rule:[8,40,48],run:[16,18,23,33,39,40,41,42,45,48,50,51,53,55,56,58,60,62,63],runinitfunct:42,running_on_cloud:23,runtim:[2,3,32,33,43],s_fusion:60,s_id:60,s_param:54,s_recurrent_group:38,sacrif:2,safe:23,sai:[31,45,47],sake:40,sale:59,same:[3,5,8,9,10,16,21,22,38,43,47,48,53,58,60,61,62,63],samping_id:9,sampl:[3,5,8,13,43,45,47,53,54,56,58,60,61,62,63],sample_dim:54,sample_id:8,sample_num:8,santiago:62,satifi:8,satisfi:[43,48,58],save:[3,9,13,18,20,21,22,23,31,45,47,48,49,55,56,58,60,61,62,63],save_dir:[31,45,47,49,54,55,58,60,61,62,63],save_only_on:[44,45],saving_period:[44,45],saving_period_by_batch:[44,45,47,58],saw:3,scalabl:0,scalar:[3,9],scale:[0,6,56,59,60],scalingproject:9,scan:21,scatter:9,scenario:[31,44],scene:44,schdule:48,schedul:[21,23,48,54],scheduler_factor:7,schema:53,scheme:[8,61],schmidhub:62,schwenk:63,sci:59,scienc:62,scientist:[0,59],score:[8,9,60,62,63],screen:60,scrip:58,script:[5,13,43,48,55,56,58,61,62,63],seaplane_s_000978:55,search:[9,18,32,38,45,61,63],seat:63,second:[3,9,13,16,25,29,31,33,39,43,53,56,58,59,60,62],secret:48,section:[3,38,40,43,48,58],sed:62,see:[3,5,9,10,16,18,31,33,39,42,48,53,54,56,58,60,62,63],seed:[42,45],seem:17,segment:8,segmentor:53,sel_fc:9,select:[9,39,48,59,63],selectiv:9,selector:49,self:[31,40,59,62],selfnorm:9,semant:[13,16,30,38,57,62],semat:16,sen_len:61,send:[18,22,45,48],sent:[16,22,49],sent_id:38,sentenc:[3,9,13,38,58,61,62,63],sentiment:[3,31,57,58,61],sentiment_data:62,sentiment_net:62,sentimental_provid:3,separ:[3,8,45,53,58,59,60,61,63],seq:[9,13,60],seq_pool:9,seq_text_print:8,seq_to_seq_data:[53,63],seq_typ:[3,13,60],seqtext_evalu:8,seqtext_printer_evalu:38,seqtoseq:[9,38,53,63],seqtoseq_net:[9,38,53,63],sequel:3,sequenc:[3,6,8,9,10,12,13,40,53,58,60,61,62,63],sequence_conv_pool:58,sequence_group:9,sequence_nest_group:9,sequencestartposit:9,sequencetextprint:8,sequencetyp:[3,9],sequenti:[9,38,58,61],seri:[10,62],serial:[3,15,21,28],serv:[33,42,48,54],server:[16,17,33,40,43,44],serverless:18,servic:[33,59],session:[33,42],set:[2,3,5,7,8,9,10,13,15,16,18,23,31,32,33,35,38,40,41,42,43,44,45,47,48,49,53,55,56,58,59,60,61,62,63],set_active_typ:40,set_default_parameter_nam:7,set_drop_r:40,set_input:9,set_siz:40,set_typ:40,setp:48,settup:40,setup:[3,33,40,58],sever:[3,8,9,28,43,47,48,57,58,60,61,62,63],sgd:[11,15,16,18,23,28,43,54,62,63],sgdasync_count:44,shallow:61,shape:[9,13,15,56],shard:[18,19,20,21,22,48],share:[9,17,27,28,32,33,42,45,49,61],shared_bia:10,shared_bias:9,shared_librari:17,shared_ptr:[26,27],shell:[48,56],shift:56,ship:55,shold:62,shop:62,shorten:9,shorter:56,should:[3,5,8,9,13,15,16,22,23,28,29,31,35,38,39,43,48,55,58,60,61,62,63],should_be_fals:16,should_be_tru:16,should_shuffl:[3,61],shouldn:39,show:[5,8,11,13,18,25,31,39,45,48,49,53,56,58,60,61,62,63],show_check_sparse_distribution_log:[44,45],show_layer_stat:[44,45],show_parameter_stats_period:[44,45,47,49,58,61,62,63],shown:[3,9,16,38,40,42,48,54,55,56,58,60,62,63],shrink:40,shuf:60,shuffl:[3,13,60,62],sid:48,side:[9,15,28,56],sig:48,sigint:43,sigmoid:[9,40],sigmoidactiv:10,sign:[24,48],signal:43,signatur:48,signific:42,similar:[9,29,48,58,60],similarli:[9,13,61],simpl:[2,3,6,8,9,10,13,15,32,33,39,42,45,58,60,61,62],simple_attent:38,simple_gru:58,simple_lstm:[9,58],simple_rnn:[9,38],simpler:28,simplest:48,simpli:[2,9,16,22,32,33,38,39,42,53,56,60,62,63],simplifi:[16,40,49],simultan:48,sinc:[9,18,21,29,31,33,42,48,54,58,59,63],sincer:[39,62],singl:[3,8,10,13,18,33,40,43,49,56,58,61,63],sinlg:15,site:48,six:[53,61,63],size:[3,8,9,10,11,13,18,20,29,31,38,40,43,45,54,55,56,58,59,60,61,62,63],size_a:9,size_b:9,size_t:40,sizeof:53,skill:63,skip:[29,31,43,48,56],slide:[9,11,13,18],slightli:55,slope:9,slot:[60,61],slot_dim:60,slot_nam:60,slottyp:60,slow:[3,42],small:[3,13,40,43,45,55,63],small_messag:[44,45],small_vgg:55,smaller:[9,18],smith:62,smooth:9,snap:49,snapshot:[19,48],snippet:[38,40,42,48,58],social:62,sock:23,sock_recv_buf_s:[44,45],sock_send_buf_s:[44,45],socket:45,softmax:[9,10,16,38,40,53,58,61,62],softmax_param_attr:10,softmax_selfnorm_alpha:9,softmaxactiv:[38,58],softwar:[33,42],solv:[16,61],solver:63,some:[3,7,9,13,15,16,17,21,22,23,31,32,39,40,42,44,45,47,48,54,58,59,60,61,62,63],some_c_api_funct:27,some_inst:27,some_python_class:26,somecppclass:26,somedata:15,somegotyp:26,someth:[3,9],sometim:[11,29,42,62],somewhat:22,soon:18,sophist:[31,40,43],sort:[9,13,45,48,60,62,63],sourc:[0,9,13,17,25,27,29,31,33,38,39,48,49,53,58,60,63],source_dict_dim:38,source_language_word:38,space:[8,33,38,42],space_seperated_tokens_from_dictionary_according_to_seq:8,space_seperated_tokens_from_dictionary_according_to_sub_seq:8,spars:[3,7,9,11,13,40,43,45,48,58],sparse_binary_vector:[3,13,58],sparse_binary_vector_sequ:13,sparse_float_vector:3,sparse_non_value_slot:13,sparse_upd:7,sparse_value_slot:13,sparse_vector:13,sparse_vector_sequ:13,sparseparam:40,sparseprefetchrowcpumatrix:40,spatial:[9,55],speak:[38,63],spec:[48,49],specfii:45,speci:55,special:[9,22,32,53,58,63],specif:[2,25,47,55,58,60],specifi:[2,3,8,9,13,16,21,22,23,25,31,32,38,40,45,48,54,55,56,58,59,60,62,63],speech:9,speed:[10,33],spefici:56,sphinx:[26,32,33],sphinx_rtd_them:32,split:[3,9,43,47,48,53,56,58,61],split_count:48,sql:2,squar:[9,11,12,31],squarerootnpool:9,squash:63,srand:45,src:[17,63],src_backward:38,src_dict:38,src_embed:38,src_forward:38,src_id:38,src_root:5,src_word_id:38,srl:[13,61],ssd:9,ssh:[33,43,48,49],sshd:33,ssl:32,sstabl:16,stabl:[30,48],stack:[31,48,58,61],stacked_lstm_net:62,stacked_num:62,stackexchang:9,stage:43,stake:63,stale:[18,39],stamp:42,standard:[7,53,55,61,62,63],stanford:[13,49],star:59,start:[9,15,18,21,22,23,28,31,33,38,39,42,43,45,52,53,57,60,63],start_pass:[44,45],start_pserv:45,startup:[18,23,48],stat:[32,42,45,61,62,63],state:[9,10,18,31,38,45,49,54,61,63],state_act:[9,10],statement:[40,48],staticinput:[9,38],statist:[9,45,58,61,62,63],statset:42,statu:[23,39,42,48,49],status:49,std:[17,26,27,40,45],stderr:43,stdout:43,step:[5,9,10,12,18,22,28,33,38,40,42,43,48,49,58,60,61,62,63],still:[21,56],stmt1482205552000:48,stmt1482205746000:48,stochast:[11,18,21],stock:62,stop:[9,33,43,45,49,60],storag:[24,48,49,55],store:[8,9,13,28,40,43,45,48,49,53,55,56,58,60,61,62,63],str:[15,23,47],straight:39,strategi:[3,12,18,45,61],street:[9,61],strength:54,strict:29,stride:9,stride_i:9,stride_x:9,string:[2,3,8,9,21,25,40,45,48,62],strip:[58,60,61],struct:[21,22,24,27],structur:[13,21,43,48,53,55,58,60,61,62,63],sts:48,stub:9,student:59,stuff:39,stun:3,style:[3,9,32,39],sub:[8,9,13,16,28,38,40,55,58,63],sub_sequ:[3,9],subcommand:25,subgradi:11,submit:[39,44,45,48],subnet0:48,subnet:[16,48],subobjectpath:49,subsequenceinput:9,subset:[40,63],substanti:56,substitut:63,succe:62,succeed:[21,49],success:[22,48,49,56,61],successfulcr:49,successfuli:62,successfulli:[56,60,62],successor:[45,63],sucessfulli:63,sudo:[32,35,48,55],suffic:[29,31],suffici:45,suffix:[23,63],suggest:[9,17,42],suitabl:[39,45,55],sum:[9,11,19,38,40],sum_:9,summar:[58,62],sumpool:9,support:[7,8,9,12,13,18,23,28,29,32,33,35,38,40,42,45,48,61],suppos:[17,31,40,58],suppress:25,sure:[39,40,48,55,62],survei:62,swagger:24,swap_channel:56,swig:[5,26,27,32],swig_paddl:[5,13,54],symbol:[9,27],sync:[18,39,45,54],syncflag:40,synchron:[11,18,21,43,45,48],syntact:61,syntax:[29,60],synthect:31,synthes:54,synthet:31,sys:56,system:[17,18,22,24,32,33,43,49,58,61,62,63],t2b:53,t_i:9,tab:[33,58],tabl:[3,8,9,56,58,63],tableproject:9,tag:[8,13,33,38],tagtyp:8,take:[3,5,8,9,10,16,17,38,40,42,48,49,54,61,63],taken:[3,61],tanh:[9,10,40],tanhactiv:[10,38],taobao:62,tar:[32,48],tarbal:48,target:[9,13,15,17,38,53,58,63],target_dict_dim:38,target_dictionary_dim:9,target_language_embed:9,target_language_word:38,target_link_librari:17,targetinlink:9,task:[3,8,9,31,38,47,53,56,61,62,63],task_queu:21,taskentri:21,taskqueu:21,tconf:62,tcp:[45,48],teach:58,tear:42,technic:18,technician:59,techniqu:[38,40],tee:[49,55,60,61,62,63],tell:[18,21,22,33,42,60],tellig:62,templat:[49,61],tempor:[9,58,61],temporari:23,tensor:17,tensor_test:17,term:[9,10,18,61,62],termin:49,terminolog:31,tese:2,tesh:61,test100:13,test10:13,test1:20,test:[2,3,9,13,15,16,17,27,29,30,32,33,35,39,42,43,44,53,55,56,58,59,63],test_all_data_in_one_period:[49,55,60,61,62],test_data:63,test_fcgrad:40,test_gpuprofil:42,test_layergrad:40,test_list:[3,31,55,58],test_part_000:62,test_pass:[44,45,47,63],test_period:[44,45,47],test_ratio:60,test_wait:[44,45],testa:16,testb:16,testbilinearfwdbwd:42,testconfig:40,tester:[60,63],testfcgrad:40,testfclay:40,testlayergrad:40,testmodel_list:44,testq:16,testresult:15,testsave_dir:44,testutil:40,text1:25,text:[2,3,8,10,13,16,33,38,48,53,57,58,60,62],text_conv:58,text_conv_pool:60,text_fil:[13,62],tflop:42,tgz:[13,32],than:[3,5,7,8,9,10,18,23,32,33,38,40,43,48,56,61,62,63],thank:[0,53,63],thei:[3,16,17,18,22,25,31,33,38,40,42,43,44,48,56,62],them:[2,3,10,16,18,23,29,31,33,38,42,44,45,48,55,56,58,60,62,63],theori:42,therefor:32,therein:9,therun:56,thi:[2,3,7,8,9,10,11,13,15,16,17,18,21,22,23,28,29,31,32,33,35,38,39,40,42,43,45,47,48,49,53,54,55,56,58,59,60,61,62,63],thing:[3,31,38,39,42,60,61],think:[16,17],third:[9,18,42,56,62],those:[17,18,56,61],thought:[17,42],thread:[40,42,45,47,60,61,62,63],thread_local_rand_use_global_se:[44,45],threadid:47,threadloc:42,three:[3,8,9,18,28,29,31,38,45,54,56,62,63],threshold:[7,8,18,21,45,62],thriller:59,through:[5,9,18,21,38,40,42,43,53,54,55,62,63],throughout:58,throughput:42,thu:[3,9,31,40,48,63],tier:49,tight:32,time:[3,9,10,12,13,16,17,18,21,29,31,38,42,45,47,49,58,59,61,62,63],timelin:[9,42],timeo:48,timeout:[18,21],timer:32,timestamp:[9,19,59],timestep:[3,9],titil:60,titl:[13,39,59,60],tls:24,tmall:62,todo:[8,10,13,18,21],toend:9,togeth:[3,9,10,13,15,38],token:[8,9,16,38,53,62,63],too:[13,33,35],tool:[33,38,39,48,62],toolchain:32,toolkit:[32,35],top:[8,56,61],top_k:8,topolog:[13,16,18,28],topolopi:15,toronto:[13,55],total:[15,18,29,42,43,49,53,63],total_pass:29,touch:62,tourism:62,tourist:63,toward:31,tra:63,track:[18,21],tractabl:9,tradesman:59,tradit:9,trail:13,train100:13,train10:13,train:[1,2,3,5,7,8,9,11,13,20,21,22,28,38,40,41,42,44,50,51,56],train_conf:[53,63],train_config_dir:48,train_data:63,train_id:48,train_list:[3,31,55,56,58],train_part_000:62,trainabl:9,traindot_period:44,trainer:[3,5,16,19,20,21,28,31,40,43,45,47,54,58,61,62,63],trainer_config:[2,3,31,43,48,49,58,60,62],trainer_config_help:[3,31,40,55,58,60],trainer_count:[44,45,47,48,49,60,61,62,63],trainer_cpu:23,trainer_cr:23,trainer_gpu:23,trainer_id:[45,48],trainer_mem:23,trainer_packag:23,trainerintern:[58,60,63],training_machin:54,trainingtest_period:44,trainonedatabatch:54,tran:[40,45],trane:3,transact:[18,21,62],transfer:[2,3],transform:[9,38,40,54,55,58,61],transform_param_attr:10,translat:[9,10,31,53,60,62,63],transpar:43,transport:45,transpos:[9,40,54],transposedfullmatrixproject:9,travel:3,travi:[32,39],treat:[9,22,38],treatment:22,tree:[9,39,45,63],trg:63,trg_dict:38,trg_dict_path:38,trg_embed:38,trg_id:38,trg_ids_next:38,triain:2,tricki:26,trigger:28,trivial:3,trn:58,truck:55,true_imag:29,true_label:29,true_read:29,truth:[8,9,58,63],tst:58,tune:[7,41,58,60,63],tuninglog_barrier_abstract:44,tupl:[3,9,13,15,29],ture:9,turn:[9,29,54],tutori:[33,38,39,40,42,43,48,49,50,51,56,58],tweet:62,twelv:63,twitter:62,two:[2,3,8,9,10,16,22,23,25,29,31,33,38,42,43,47,48,53,54,55,56,58,60,61,62,63],txt:[3,17,23,25,40,43,48,58,60,62],type:[3,8,9,10,12,13,15,16,18,21,23,24,25,26,27,29,31,33,38,40,45,47,48,49,55,56,58,60,61],type_nam:60,typedef:[22,26,27],typic:[5,8,33,42,62],ubuntu:[30,35],ubyt:29,uci:13,ufldl:9,uid:49,uint32:24,uint64:26,uint64_t:26,unawar:22,unbalanc:45,unbound:38,unconstrain:62,under:[21,31,32,33,48,59,62],underli:31,understand:[33,42,53,55,62],understudi:63,undeterminist:42,unemploi:59,unexist:61,uniform:[7,9,13,20,29,45,54],uniqu:[16,18,23,39,45,48],unique_ptr:40,unit:[9,10,17,31,32,33,38,39,61],unittest:27,unittestcheckgrad_ep:44,univ:63,unix:43,unk:[53,63],unk_idx:[58,61],unknown:9,unlabel:62,unlik:[61,62,63],unseg:9,unsign:22,unsup:62,unsupbow:62,until:[18,22,43,48,61],unus:60,unzip:60,updat:[7,9,18,21,22,24,28,32,40,43,45,47,62],update_equ:15,updatecallback:40,updatestack:48,upload:[18,23,24],upon:[0,61],upstream:39,uri:48,url:[13,35,62],urls_neg:62,urls_po:62,urls_unsup:62,usag:[2,3,8,9,10,13,15,28,31,42,53,54,60],use:[0,2,3,5,7,8,9,10,12,13,15,16,17,18,28,31,32,33,34,35,38,39,40,42,43,45,47,48,49,53,54,55,56,58,59,60,61,62,63],use_global_stat:9,use_gpu:[44,45,47,49,54,55,56,58,60,61,62,63],use_jpeg:55,use_old_updat:[44,45],use_seq:[31,60],use_seq_or_not:60,used:[2,3,5,8,9,10,11,12,13,15,16,18,28,29,31,34,35,38,40,42,43,44,45,47,48,53,55,56,58,60,61,62,63],useful:[2,3,9,10,38,40,47,58,61,62],usegpu:[40,54],useless:43,user:[2,3,7,9,10,13,15,16,20,21,23,25,29,31,33,39,43,44,45,48,56,58,61],user_featur:60,user_head:60,user_id:60,user_info:13,user_meta:60,user_nam:[20,60],usercert:20,userid:59,userinfo:13,userkei:20,usernam:[20,39],uses:[3,18,28,38,39,40,45,48,55,56,58,60,63],using:[2,3,5,7,9,10,13,16,17,18,21,22,23,25,29,31,33,38,39,40,42,45,47,48,49,53,54,55,56,58,61,62],usr:[32,43,45,48],usrdict:53,usrmodel:53,usual:[9,13,15,23,31,32,42,45,47,48,62],utf:53,util:[5,32,38,40,42,55,60,62],uuid:19,v28:9,valid:[29,48,56,62],valu:[3,5,7,8,9,11,12,13,15,18,28,31,38,40,45,47,48,54,55,56,61,62],value1:45,value2:45,value_evalu:8,value_rang:13,vanilla:38,vanish:62,vari:[42,48],variabl:[3,9,13,16,31,32,35,40,43,48,49,62],varianc:[9,56],variant:33,vast:39,vector:[3,9,10,13,16,22,38,40,53,58,60,62,63],vectorenable_parallel_vector:44,verb:[13,61],verbos:25,veri:[3,9,12,21,28,38,42,55,58,62],verifi:[39,40],versa:32,version:[9,10,23,25,30,32,33,35,40,42,43,44,45,48,49,53,55,59,61,62,63],versu:16,vertic:[9,56],vgg:[10,55],vgg_16_cifar:55,via:[18,29,32,42,43,48,58],vice:32,view:9,vim:39,virtual:33,virtualenv:60,visibl:33,vision:55,visipedia:55,visual:[9,33,42],viterbi:61,voc_dim:58,vocab:62,volum:[33,49],volumemount:[48,49],volumn:48,voluntarili:59,vutbr:13,wai:[3,8,9,10,16,22,31,33,38,40,43,47,60,61,63],wait:[11,18,22,45],walk:[5,54],wall:61,want:[3,9,10,16,23,29,31,32,33,40,45,47,53,56,58,60,61,62],war:59,warn:[9,25],warp:[9,42],watch:18,wbia:[48,56],web:33,websit:[55,58,61,62],wei:[61,62],weight:[8,9,10,11,38,40,45,47,55,56],weight_act:10,weightlist:40,weights_:40,weights_t:40,welcom:[17,60,62],well:[23,33,40,45,48,55,58],were:[8,17],west:48,western:59,wether:9,what:[7,9,10,11,17,31,43,58,60],wheel:32,when:[2,3,7,8,9,13,15,17,18,21,22,23,25,28,35,38,39,40,42,45,47,48,49,53,54,55,61,62,63],whenev:60,where:[3,9,10,11,16,18,31,38,40,42,43,45,47,53,56,61,63],wherea:21,whether:[8,9,10,29,40,45,54,55,60,62,63],which:[0,2,3,5,8,9,10,11,13,16,18,20,21,22,23,28,29,31,35,38,40,42,43,45,47,48,54,55,56,58,59,60,61,62,63],whichev:54,whl:32,who:[53,56,59],whoever:22,whole:[3,8,13,26,27,48,49,58,59,60,63],whole_cont:60,whose:[3,9,13,18,38,60,61],why:[10,27],wide:61,width:[8,9,13,26,29,40,55,63],wiki:9,wikipedia:[9,13],wilder:3,window:[9,12,13,33,62],wise:9,with_avx:33,with_avxcompil:32,with_coveragecompil:32,with_doccompil:32,with_doubl:40,with_doublecompil:32,with_dsocompil:32,with_gpu:33,with_gpucompil:32,with_profil:42,with_profilercompil:32,with_pythoncompil:32,with_rdmacompil:32,with_style_checkcompil:32,with_swig_pycompil:32,with_test:33,with_testingcompil:32,with_tim:42,with_timercompil:32,within:[9,21,31],without:[8,9,18,22,29,43,62],wmt14:63,wmt14_data:63,wmt14_model:63,wmt:63,wmt_shrinked_data:13,woboq:33,won:[42,56],wonder:3,word2vec:23,word:[3,8,9,13,38,47,57,60,61,62,63],word_dict:[58,61],word_dim:58,word_id:3,word_idx:13,word_slot:61,word_vector:58,word_vector_dim:[9,38,53],words_freq_sort:13,work:[3,5,13,16,17,18,29,32,38,39,40,42,43,45,48,49,58,60],worker:48,workercount:48,workflow:[33,39,48],workspac:[33,45,60],worri:31,wors:54,would:[15,17,18,28,29,33,43,48,54,58,61],wrap:61,wrapper:[10,17,42],writ:60,write:[3,13,16,18,29,33,38,39,41,43,48,55,60,61,63],write_lock:19,writelin:31,writer:[16,59],written:[60,62],wrong:[3,29],wsize:48,wsj:61,www:[9,13,55,63],x64:32,xarg:[8,40],xgbe0:45,xgbe1:45,xiaojun:62,xrang:[29,31,40],xxbow:62,xxx:[16,56,63],xxxx:19,xxxxxxxxx:48,xxxxxxxxxx:48,xxxxxxxxxxxxx:48,xxxxxxxxxxxxxxxxxxx:48,xzf:32,y_i:9,y_predict:31,yaml:[48,60],yancey1989:23,yann:13,year:59,yeild:[15,55],yield:[3,13,16,20,29,31,38,58,60,61,62],you:[2,3,5,7,9,10,23,31,32,33,35,38,39,40,42,43,45,47,48,53,54,55,56,58,60,61,62,63],your:[3,9,16,17,23,25,32,33,40,42,43,47,48,58,62],your_access_key_id:48,your_secrete_access_kei:48,your_source_root:27,yum:32,yuyang18:[10,13],zachari:62,zeng:62,zero:[3,7,9,11,13,18,28,40,45,48,58],zhidao:53,zhou:[61,62],zip:[13,59],zone:48,zxvf:48},titles:["ABOUT","API","Introduction","PyDataProvider2","API","Python Prediction","Activation","Parameter Attribute","Evaluators","Layers","Networks","Optimizer","Pooling","Data Reader Interface and DataSets","Model Configuration","Training and Inference","PaddlePaddle Design Doc","Required CMake Function","Design Doc: Distributed Training","\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9\uff08Checkpointing\uff09","\u8bad\u7ec3\u6570\u636e\u7684\u5b58\u50a8\u548c\u5206\u53d1","Design Doc: Master Server","Design Doc: The Client Library of Parameter Server","Submit a Distributed Training Job","FileManager\u8bbe\u8ba1\u6587\u6863","PFSClient","Paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0","C-API \u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863","Design Doc: The C++ Class Parameters","Python Data Reader Design Doc","Paddle\u53d1\u884c\u89c4\u8303","Simple Linear Regression","Installing from Sources","PaddlePaddle in Docker Containers","Install and Build","Debian Package installation guide","GET STARTED","RNN Models","RNN Configuration","Contribute Code","Write New Layers","HOW TO","Tune GPU Performance","Run Distributed Training","Argument Outline","Detail Description","Set Command-line Parameters","Use Case","Distributed PaddlePaddle Training on AWS with Kubernetes","Paddle On Kubernetes","<no title>","<no title>","PaddlePaddle Documentation","Chinese Word Embedding Model Tutorial","Generative Adversarial Networks (GAN)","Image Classification Tutorial","Model Zoo - ImageNet","TUTORIALS","Quick Start","MovieLens Dataset","Regression MovieLens Ratting","Semantic Role labeling Tutorial","Sentiment Analysis Tutorial","Text generation Tutorial"],titleterms:{"\u4e0a\u4f20\u8bad\u7ec3\u6587\u4ef6":20,"\u4e0d\u4f7f\u7528":26,"\u4e0d\u4f7f\u7528swig\u8fd9\u79cd\u4ee3\u7801\u751f\u6210\u5668":26,"\u4e0d\u5bfc\u51fapaddle\u5185\u90e8\u7684\u7ed3\u6784\u4f53":26,"\u4e0d\u5f15\u7528\u5176\u4ed6\u52a8\u6001\u5e93":26,"\u4ec5\u4ec5\u4f7f\u7528void":26,"\u4ece\u5feb\u7167\u6062\u590d":19,"\u4f7f\u7528\u52a8\u6001\u5e93\u6765\u5206\u53d1paddl":26,"\u4f7f\u7528\u8f6c\u6362\u5e93":20,"\u5177\u4f53\u67d0\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5177\u4f53\u67d0\u79cd\u7c7b\u578b\u7684\u5b9e\u73b0\u6587\u4ef6":27,"\u5206\u5757\u6587\u4ef6\u4f20\u8f93":24,"\u5206\u652f\u89c4\u8303":30,"\u52a0\u901f\u6267\u884c":19,"\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165\u4efb\u4f55\u5176\u4ed6\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u52a8\u6001\u6269\u5bb9":19,"\u539f\u56e0":26,"\u539f\u56e0\u5217\u8868":26,"\u53c2\u8003\u6587\u6863":24,"\u540d\u8bcd\u89e3\u91ca":24,"\u57fa\u672c\u8981\u6c42":26,"\u5b9e\u73b0":26,"\u5b9e\u73b0\u65b9\u5f0f":27,"\u5bfc\u51fac":26,"\u5feb\u7167\u4fdd\u5b58\u7684\u8bbe\u8ba1\u5982\u4e0b":19,"\u6307\u9488\u4f5c\u4e3a\u7c7b\u578b\u7684\u53e5\u67c4":26,"\u63a8\u6d4b\u6267\u884c":19,"\u652f\u6301\u7528\u6237\u81ea\u5b9a\u4e49\u7684\u6570\u636e\u9884\u5904\u7406job":20,"\u6587\u4ef6\u4f20\u8f93\u4f18\u5316":24,"\u6587\u4ef6\u8bbf\u95ee\u65b9\u5f0f":20,"\u6587\u4ef6\u8bbf\u95ee\u7684\u6743\u9650":20,"\u6587\u4ef6\u9884\u5904\u7406":20,"\u66b4\u9732\u63a5\u53e3\u539f\u5219":27,"\u672f\u8bed":19,"\u67b6\u6784\u56fe":24,"\u6846\u67b6\u751f\u6210":24,"\u6982\u5ff5\u89e3\u91ca":20,"\u6a21\u5757":24,"\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9":19,"\u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863":27,"\u6d41\u7a0b\u4ecb\u7ecd":20,"\u751f\u6210sparse\u6587\u4ef6":24,"\u7528\u6237\u4f7f\u7528\u6d41\u7a0b":24,"\u76ee\u5f55\u7ed3\u6784":27,"\u76ee\u6807":24,"\u793a\u4f8b\u7a0b\u5e8f":20,"\u7b26\u53f7":26,"\u7c7b":26,"\u7f16\u8bd1\u9009\u9879":27,"\u7f29\u5bb9":19,"\u800c\u662f\u624b\u5199\u591a\u8bed\u8a00\u7ed1\u5b9a":26,"\u80cc\u666f":26,"\u8986\u76d6\u4e0d\u4e00\u81f4\u7684\u90e8\u5206":24,"\u8bad\u7ec3\u6570\u636e\u5b58\u50a8":20,"\u8bad\u7ec3\u6570\u636e\u7684\u5b58\u50a8\u548c\u5206\u53d1":20,"\u8f6c\u6362\u5e93":20,"\u8fd9\u4e2a\u52a8\u6001\u5e93\u4f7f\u7528c99\u6807\u51c6\u7684\u5934\u6587\u4ef6\u5bfc\u51fa\u4e00\u4e9b\u51fd\u6570":26,"\u8fdb\u884c\u8bad\u7ec3":20,"book\u4e2d\u6240\u6709\u7ae0\u8282":30,"case":47,"class":[28,40],"filemanager\u8bbe\u8ba1\u6587\u6863":24,"function":[17,53],"new":40,"paddle\u52a8\u6001\u5e93\u4e2d":26,"paddle\u53d1\u884c\u89c4\u8303":30,"paddle\u56de\u5f52\u6d4b\u8bd5\u5217\u8868":30,"paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0":26,"return":29,AWS:48,Abs:6,DNS:48,EFS:48,For:49,KMS:48,The:[22,28],Use:[47,49],Using:[22,33,39],With:[23,33],about:0,access:48,account:48,activ:6,adadelta:11,adagrad:11,adam:11,adamax:11,add:48,address:48,addto:9,adversari:54,aggreg:9,aggregatelevel:9,algorithm:[18,58],analysi:62,api:[1,4,27,33],appendix:58,applic:4,approach:42,architectur:[38,58],argument:[25,29,44,47,58],asset:48,associ:48,async:45,attent:38,attribut:7,auc:8,avg:12,aws:48,background:31,base:23,basepool:12,batch:29,batch_norm:9,batch_siz:29,beam_search:9,between:16,bidirect:62,bidirectional_gru:10,bidirectional_lstm:10,bilinear_interp:9,bleu:63,block_expand:9,book:33,brelu:6,bucket:48,build:[32,34,49],built:42,cach:3,capi:27,capi_priv:27,cento:32,check:[9,40,43],checkpoint:[18,19],chines:53,choos:48,chunk:8,cifar:13,classif:[8,55],classification_error:8,classification_error_print:8,client:22,clone:39,cloudform:48,cluster:[43,47,48],cmake:17,code:[23,39],column_sum:8,command:[46,47,58,63],commit:[39,49],common:45,commun:45,compos:29,concat:9,concept:48,config:[4,47,60,61],configur:[14,38,41,43,48,58,60],conll05:13,connect:9,contain:[33,49],content:[27,42,48],context_project:9,contribut:39,conv:9,conv_oper:9,conv_project:9,conv_shift:9,convolut:[55,58],core:48,cos_sim:9,cost:9,cpu:[33,47],creat:[29,39,48,49],creation:21,creator:29,credenti:48,credit:0,crf:9,crf_decod:9,cross_channel_norm:9,cross_entropy_cost:9,cross_entropy_with_selfnorm_cost:9,ctc:9,ctc_error:8,cudnnavg:12,cudnnmax:12,custom:29,dat:59,data:[9,13,18,29,31,38,48,49,53,54,55,58,60,61,62,63],datafeed:13,dataprovid:[3,4,45],dataset:[13,18,21,59,60,63],datatyp:13,date:39,debian:35,decayedadagrad:11,decor:29,defin:[48,58,62,63],delet:48,delv:55,demo:48,depend:32,deploi:23,deriv:40,descript:[25,45,54,59,61],design:[16,18,21,22,28,29],destroi:48,detail:[45,55],develop:[33,41],devic:47,dictionari:[29,53],differ:47,directori:48,dispatch:[18,21],distribut:[16,18,23,43,45,48],doc:[16,18,21,22,28,29],docker:[23,33,49],document:[33,52],dotmul_oper:9,dotmul_project:9,down:48,download:[32,48,49,53,56,60,63],dropout_lay:10,dylib:27,dynam:18,ec2:48,elast:48,embed:[9,53,58],entri:29,environ:23,eos:9,equat:40,evalu:[8,31,60],evalutaion:63,event:[15,16],exampl:[16,17,27,53,54],exercis:55,exp:6,expand:9,expandlevel:9,extern:48,extract:[53,56,60,63],fault:18,featur:[56,59,60,61],field:60,file:[48,49,58,59,60],find:48,first_seq:9,fork:39,format:[18,58],from:[16,32,34],full_matrix_project:9,fulli:9,gan:54,gate:38,gener:[38,54,63],get:[36,49],get_output:9,github:39,gpu:[33,42,45,47],gradient:[22,40],gradient_print:8,group:[9,48],gru:[10,45],gru_group:10,gru_step:9,gru_unit:10,grumemori:9,guid:35,hand:42,handler:[16,26],hook:39,how:[29,41,42],hsigmoid:9,huber_cost:9,iam:48,ident:6,identity_project:9,imag:[9,10,23,33,49,55],imagenet:56,imdb:[13,62],img_cmrnorm:9,img_conv:9,img_conv_bn_pool:10,img_conv_group:10,img_pool:9,imikolov:13,implement:[17,29,40,54],infer:[15,58],info:56,ingredi:16,ingress:24,init_hook:3,initi:[22,47,48],input_typ:3,inspect:48,instal:[32,34,35,48,58],instanc:48,integr:48,interfac:[13,18,22,29,56],interpol:9,introduct:[2,53,56,62,63],isn:29,job:[18,23,43,48,49],join:9,keep:39,kei:48,kill:43,kube:48,kubectl:48,kubernet:[23,48,49],label:61,lambda_cost:9,last_seq:9,lastest:39,launch:43,layer:[9,16,40,47],libpaddle_capi_shar:27,libpaddle_capi_whol:27,librari:22,line:[46,58],linear:[6,31],linear_comb:9,list:[19,29],local:[47,48],log:[6,58],logic:21,logist:58,lstm:[10,45,61,62],lstm_step:9,lstmemori:9,lstmemory_group:10,lstmemory_unit:10,map:29,master:[18,21,23],math:9,matrix:45,max:12,maxframe_print:8,maxid:9,maxid_print:8,maxout:9,memori:9,meta:60,mini:29,minibatch:13,misc:10,mix:[9,47],mnist:[13,54],model:[3,4,14,16,22,31,33,37,38,43,47,53,54,55,56,57,58,63],modifi:49,momentum:11,movi:[59,60],movielen:[13,59,60],mse_cost:9,multi_binary_label_cross_entropy_cost:9,multipl:29,name:48,nce:9,need:[29,42],network:[10,38,47,54,55,56,58,60,61],neural:[38,55,58,60,61],neuralnetwork:31,nlp:[10,45],non:3,norm:9,nvprof:42,nvvp:42,object:[18,60],observ:[53,56],onli:[29,33],optim:[11,18,22,41,58],option:[25,32,53],order:25,outlin:44,output:[43,48],overview:58,packag:35,pad:9,paddl:[29,30,49],paddlejob:23,paddlepaddl:[16,33,34,48,52,53,63],pair:48,parallel_nn:47,paramet:[7,15,16,18,22,23,28,45,46,48,53,56],paraphras:53,partit:22,pass:47,path:25,perform:[42,45],persist:21,pfsclient:[24,25],pfsserver:24,pnpair:8,point:48,pool:[9,12],power:9,pre:39,precision_recal:8,predict:[5,55,56,60,61,62],prefetch:29,prepar:[31,38,43,48,53,54,55,60,62,63],preprocess:[53,55,58,60,63],prerequisit:43,pretrain:[53,63],print:8,privat:48,problem:31,process:[18,22,23],profil:42,project:17,provid:[3,29,58,60,61],pull:39,push:39,pydataprovider2:3,python:[5,23,29,33,40,56,58,60],queue:[18,21],quick:58,randomnumb:45,rank:8,rank_cost:9,rat:60,rate:59,reader:[13,16,29],recoveri:18,recurr:[9,10,38,58],recurrent_group:9,refer:[3,42,61,62],region:48,regress:[31,58,60],regular:22,relu:6,render:48,repeat:9,request:39,requir:[17,32,39],reshap:9,resnet:56,result:[43,49,63],retri:21,revis:[39,53],rmsprop:11,rnn:[37,38,45],role:61,rotat:9,route53:48,run:[43,49,61],runtim:23,sampl:9,sampling_id:9,scale:[9,18],scaling_project:9,script:49,secur:48,select:22,selective_fc:9,semant:61,sentiment:[13,62],seq_concat:9,seq_reshap:9,seqtext_print:8,sequenc:38,sequence_conv_pool:10,sequencesoftmax:6,sequenti:3,server:[18,21,22,23,45,48],servic:48,set:46,setup:[32,48],sgd:45,share:16,shuffl:29,sigmoid:6,simpl:[31,38],simple_attent:10,simple_gru2:10,simple_gru:10,simple_img_conv_pool:10,simple_lstm:10,singl:29,slice:9,slope_intercept:9,small_vgg:10,smooth_l1_cost:9,softmax:6,softrelu:6,sourc:[32,34],span:32,spars:[22,47],specifi:[47,53],split:60,spp:9,squar:6,squarerootn:12,stack:62,standard:58,stanh:6,start:[16,36,48,49,58],startup:49,store:18,structur:54,subcommond:25,submit:23,suffici:29,sum:[8,12],sum_cost:9,sum_to_one_norm:9,summar:16,summari:58,synopsi:25,system:48,tabl:27,table_project:9,take:29,tanh:6,task:[18,21],tear:48,templat:48,tensor:9,test:[40,45,47,60,61,62],text:63,text_conv_pool:10,timer:42,tip:42,todo:[19,20],toi:54,toler:18,tool:42,train:[15,16,18,23,29,31,33,43,45,47,48,49,53,54,55,58,60,61,62,63],trainer:[15,18,22,23,48,60],tran:9,trans_full_matrix_project:9,transfer:58,tune:[42,45],tutori:[53,55,57,61,62,63],ubuntu:32,uci_h:13,unit:[40,45],updat:[16,39,48],usag:[29,33,41],use:29,user:[18,53,59,60,62,63],util:8,value_print:8,vector:45,verifi:48,version:39,vgg_16_network:10,visual:56,volum:48,vpc:48,warp_ctc:9,what:42,why:[29,42],wmt14:13,word:[53,58],work:33,workflow:63,workspac:43,wrapper:40,write:[40,58],yaml:49,your:39,zoo:[56,57]}})
\ No newline at end of file
diff --git a/develop/doc_cn/_sources/design/cluster_train/master_server.md.txt b/develop/doc_cn/_sources/design/cluster_train/master_server.md.txt
index bb8307652587b4dc56cd668a3a5e64722734d194..4bf3c506f101361875043f8bfd97972b8c981a22 100644
--- a/develop/doc_cn/_sources/design/cluster_train/master_server.md.txt
+++ b/develop/doc_cn/_sources/design/cluster_train/master_server.md.txt
@@ -10,7 +10,7 @@ A dataset is a list of files in *RecordIO* format. A RecordIO file consists of c
 
 ## Task Queue
 
-As mentioned in [distributed training design doc](./README.md), a *task* is a data shard that the master server assigns to the trainer process to train on. A task consists of one or multiple *blocks* from one or multiple files. The master server maintains *task queues* to track the training progress.
+As mentioned in [distributed training design doc](./README.md), a *task* is a data shard that the master server assigns to the trainer process to train on. A task consists of one or multiple *chunks* from one or multiple files. The master server maintains *task queues* to track the training progress.
 
 ### Task Queue Creation
 
@@ -21,23 +21,23 @@ As mentioned in [distributed training design doc](./README.md), a *task* is a da
 	func (m *RPCServer) ReportDataset(Paths []string, dummy *int) error {
 	}
 	```
-1. The master server will scan through each RecordIO file to generate the *block index* and know how many blocks does each file have. A block can be referenced by the file path and the index of the block within the file. The block index is in memory data structure that enables fast access to each block, and the index of the block with the file is an integer start from 0, representing the n-th block within the file.
+1. The master server will scan through each RecordIO file to generate the *chunk index* and know how many chunks does each file have. A chunk can be referenced by the file path and the index of the chunk within the file. The chunk index is in memory data structure that enables fast access to each chunk, and the index of the chunk with the file is an integer start from 0, representing the n-th chunk within the file.
 
-	The definition of the block is:
+	The definition of the chunk is:
 	```go
-	type Block struct {
-		Idx   int // index of the block within the file
+	type Chunk struct {
+		Idx   int // index of the chunk within the file
 		Path  string
-		Index recordio.Index // block index
+		Index recordio.Index // chunk index
 	}
 	```
-1. Blocks are grouped into tasks, and tasks are filled into the todo queue. The pending queue and the done queue are initialized with no element.
+1. Chunks are grouped into tasks, and tasks are filled into the todo queue. The pending queue and the done queue are initialized with no element.
 
 	The definition of the task is:
 	```go
 	type Task struct {
 		Index  int
-		Blocks []Block
+		Chunks []Chunk
 	}
 	```
 
diff --git a/develop/doc_cn/_sources/design/cluster_train/pserver_client.md.txt b/develop/doc_cn/_sources/design/cluster_train/pserver_client.md.txt
index 392bab25e9de6bf5aa7cc1b0ad345ef12f1d9e5d..b3e4079010490b69db1de28157f0cab80cad2381 100644
--- a/develop/doc_cn/_sources/design/cluster_train/pserver_client.md.txt
+++ b/develop/doc_cn/_sources/design/cluster_train/pserver_client.md.txt
@@ -55,7 +55,7 @@ The trainer select process is encapsulated in the C API function:
 ```c
 int paddle_begin_init_params(paddle_pserver_client* client, const char* config_proto);
 ```
-The selected trainer's call to `paddle_begin_init_params` will return with 1, and the other trainers' call to `paddle_begin_init_params` will block until initialization is done, and return 0. As illustrated below:
+The selected trainer's call to `paddle_begin_init_params` will return with 1, and the other trainers' call to `paddle_begin_init_params` will return 0. `paddle_get_params` will be blocked until initialization is completed. As illustrated below:
 
 
 
@@ -89,16 +89,13 @@ void paddle_pserver_client_release(paddle_pserver_client* client);
  *
  * paddle_begin_init_params will be called from multiple trainers,
  * only one trainer will be selected to initialize the parameters on
- * parameter servers. Other trainers will be blocked until the
- * initialization is done, and they need to get the initialized
+ * parameter servers. Other trainers need to get the initialized
  * parameters from parameter servers using @paddle_get_params.
  *
- * @param pserver_config_proto serialized parameter server configuration in
- * Protocol Buffers format.
  * @return 1 if the trainer is selected to initialize parameter
  * servers, otherwise 0.
  */
-int paddle_begin_init_params(paddle_pserver_client* client, const char* pserver_config_proto);
+int paddle_begin_init_params(paddle_pserver_client* client);
 
 /**
  * @brief paddle_init_param initializes the parameter on parameter
@@ -106,12 +103,13 @@ int paddle_begin_init_params(paddle_pserver_client* client, const char* pserver_
  *
  * @param param the parameter to initialize.
  * @param param_config_proto the configuration for the parameter.
+ * @param config_len the length of param_config_proto
  * @return 0 if successful, otherwise -1. On failure, the trainer
  * needs to restart the entire initialization process (starting from
  * @paddle_begin_init_param). Or simply exit the program and wait for
  * the cluster management system to restart the trainer.
  */
-int paddle_init_param(paddle_pserver_client* client, paddle_parameter params, const char* param_config_proto);
+int paddle_init_param(paddle_pserver_client* client, paddle_parameter param, const unsigned char* param_config_proto, int config_len);
 
 /**
  * @brief paddle_finish_init_params tells parameter servers client has
@@ -138,6 +136,9 @@ int paddle_send_grads(paddle_pserver_client* client, const paddle_gradient* grad
 /**
  * @brief paddle_get_params gets parameters from parameter servers.
  *
+ * paddle_get_params will block until parameters are initialized on
+ * the parameter servers.
+ *
  * @param names the array of names of the parameters to get.
  * @param dst the destination array of parameters to save to.
  * @param len the length of the names array and the paddle_parameter
diff --git a/develop/doc_cn/_sources/design/parameters_in_cpp.md.txt b/develop/doc_cn/_sources/design/parameters_in_cpp.md.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b6f99bc7d9d6fafacb0a4bcff806b65d9aef98cc
--- /dev/null
+++ b/develop/doc_cn/_sources/design/parameters_in_cpp.md.txt
@@ -0,0 +1,41 @@
+# Design Doc: The C++ Class `Parameters`
+
+`Parameters` is a concept we designed in Paddle V2 API. `Parameters` is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of `Parameter` in [api.md](./api.md).
+
+We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation:
+* We just use `memcpy` to share Parameters between topologies, but this is very inefficient. 
+* We did not implement share Parameters while training. We just trigger `memcpy` when start training.
+
+It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with `Parameters`:
+
+1. `paddle::Parameter`. A `Parameters` is a container for `paddle::Parameter`.
+It is evident that we should use `paddle::Parameter` when developing `Parameters`.
+However, the `Parameter` class contains many functions and does not have a clear interface.
+It contains `create/store Parameter`, `serialize/deserialize`, `optimize(i.e SGD)`, `randomize/zero`.
+When we developing `Parameters`, we only use `create/store Parameter` functionality.
+We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation.
+
+2. `paddle::GradientMachine` and its sub-classes, e.g., `paddle::MultiGradientMachine`, `paddle::NeuralNetwork`.
+We should pass `Parameters` to `paddle::GradientMachine` when `forward/backward` to avoid `memcpy` between topologies.
+Also, we should handle multi-GPU/CPU training, because `forward` and `backward` would perform on multi-GPUs and multi-CPUs.
+`Parameters` should dispatch the parameter value to each device, and gather the parameter gradient from each device.
+
+3. `paddle::ParameterUpdater`. The ParameterUpdater is used to update parameters in Paddle. 
+So `Parameters` should be used by `paddle::ParameterUpdater`, and `paddle::ParameterUpdater` should optimize `Parameters` (by SGD).
+
+
+The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one.
+
+1. Clean `paddle::Parameter` interface. Extract the functionalities of `paddle::Parameter` to prepare for the implementation of Parameters.
+
+2. Implementation a `Parameters` class. It just stores the `paddle::Parameter` inside. Make `GradientMachine` uses `Parameters` as a class member.
+
+3. Make `Parameters` support Multi-CPU and Multi-GPU training to prepare for sharing `Parameter` between topologies.
+Because we need share `Parameters` between topologies, it is `Parameters`'s response to exchange Parameters between GPUs.
+`GradientMachine` should not handle how to exchange Parameters because `GradientMachine` only used to train one topology and we need to support train many topologies in Paddle, i.e., there could be many GradientMachines use one `Parameters`.
+   * We should use a global function to exchange Parameters between GPUs, not a member function in `Parameters`. The `MultiGradientMachine` invoke this function, which uses `Parameters` as this function inputs.
+   * The MultiGradientMachine contains many functionalities. Extracting the Parameters exchanging logic could make MultiGradientMachine clearer and simpler.
+
+4. Make `Parameters` as an argument for `forward/backward` function, not a data member for `GradientMachine`. For example, `forward` could be `forward(const Parameters& params, ...)` and `backward` could be `backward(Parameters* params, ...)`. After this step, Paddle could share `Parameters` between topologies.
+
+5. `ParameterUpdater` is invoked by `GradientMachine` and `Trainer`, but it updates `Parameters`. In the end of this code refactoring, we could change `ParameterUpdater` directly uses `Parameters` to make `ParameterUpdater`'s implementation clear.
diff --git a/develop/doc_cn/api/v2/config/evaluators.html b/develop/doc_cn/api/v2/config/evaluators.html
index 658b136af3f58f75929ea925cfbe5bb3249e97b9..f05ee25e6bf10c068c1d0c4cedf87c366d2d1f00 100644
--- a/develop/doc_cn/api/v2/config/evaluators.html
+++ b/develop/doc_cn/api/v2/config/evaluators.html
@@ -196,15 +196,15 @@
 
    Classification
    
 
    
 
    classification_error
    
-
    
+
    
 
    
-class paddle.v2.evaluator.classification_error(*args, **kwargs)
    
+paddle.v2.evaluator.classification_error(*args, **xargs)
 
    Classification Error Evaluator. It will print error rate for classification.
    
 
    The classification error is:
    
 
    
 \[classification\_error = \frac{NumOfWrongPredicts}{NumOfAllSamples}\]
    
 
    The simple usage is:
    
-
    eval =  classification_error_evaluator(input=prob,label=lbl)
+
    eval =  classification_evaluator.error(input=prob,label=lbl)
 
    
 
    
 
    • 
 
 

 
 
    
@@ -235,12 +235,12 @@ important this sample is.
    
 
    auc
    
-
    
+
    
 
    
-class paddle.v2.evaluator.auc(*args, **kwargs)
    
+paddle.v2.evaluator.auc(*args, **xargs)
 
    Auc Evaluator which adapts to binary classification.
    
 
    The simple usage:
    
-
    eval = auc_evaluator(input, label)
+
    eval = evaluator.auc(input, label)
 
    
 
    
 
    
 
 
    
@@ -263,12 +263,12 @@ important this sample is.
    
 
    ctc_error
    
-
    
+
    
 
    
-class paddle.v2.evaluator.ctc_error(*args, **kwargs)
    
+paddle.v2.evaluator.ctc_error(*args, **xargs)
 
    This evaluator is to calculate sequence-to-sequence edit distance.
    
 
    The simple usage is :
    
-
    eval = ctc_error_evaluator(input=input, label=lbl)
+
    eval = ctc_evaluator.error(input=input, label=lbl)
 
    
 
    
 
    
 
 
    
@@ -290,32 +290,68 @@ label for ctc
    
 
    chunk
    
-
    
+
    
 
    
-class paddle.v2.evaluator.chunk(*args, **kwargs)
    
+paddle.v2.evaluator.chunk(*args, **xargs)
 
    Chunk evaluator is used to evaluate segment labelling accuracy for a
-sequence. It calculates the chunk detection F1 score.
    
-
    A chunk is correctly detected if its beginning, end and type are correct.
-Other chunk type is ignored.
    
-
    For each label in the label sequence, we have:
    
-
    tagType = label % numTagType
-chunkType = label / numTagType
-otherChunkType = numChunkTypes
+sequence. It calculates precision, recall and F1 scores for the chunk detection.
    
+
    To use chunk evaluator, several concepts need to be clarified firstly.
    
+
      
+
    • Chunk type is the type of the whole chunk and a chunk consists of one or several words.  (For example in NER, ORG for organization name, PER for person name etc.)
      • 
+
    • Tag type indicates the position of a word in a chunk. (B for begin, I for inside, E for end, S for single)
      • 
+
    
+
    We can name a label by combining tag type and chunk type. (ie. B-ORG for begining of an organization name)
    
+
    The construction of label dictionary should obey the following rules:
    
+
      
+
    • Use one of the listed labelling schemes. These schemes differ in ways indicating chunk boundry.
      • 
+
    
+
    Scheme    Description
+plain    Use the same label for the whole chunk.
+IOB      Two labels for chunk type X, B-X for chunk begining and I-X for chunk inside.
+IOE      Two labels for chunk type X, E-X for chunk ending and I-X for chunk inside.
+IOBES    Four labels for chunk type X, B-X for chunk begining, I-X for chunk inside, E-X for chunk end and S-X for single word chunk.
+
    
+
    
+
    To make it clear, let’s illustrate by an NER example.
+Assuming that there are three named entity types including ORG, PER and LOC which are called ‘chunk type’ here,
+if ‘IOB’ scheme were used, the label set will be extended to a set including B-ORG, I-ORG, B-PER, I-PER, B-LOC, I-LOC and O,
+in which B-ORG for begining of ORG and I-ORG for inside of ORG.
+Prefixes which are called ‘tag type’ here are added to chunk types and there are two tag types including B and I.
+Of course, the training data should be labeled accordingly.
    
+
      
+
    • Mapping is done correctly by the listed equations and assigning protocol.
      • 
+
    
+
    The following table are equations to extract tag type and chunk type from a label.
    
+
    tagType = label % numTagType
+chunkType = label / numTagType
+otherChunkType = numChunkTypes
+
    
+
    
+
    The following table shows the mapping rule between tagType and tag type in each scheme.
    
+
    Scheme Begin Inside End   Single
+plain  0     -      -     -
+IOB    0     1      -     -
+IOE    -     0      1     -
+IOBES  0     1      2     3
 
    
 
    
-
    The total number of different labels is numTagType*numChunkTypes+1.
-We support 4 labelling scheme.
-The tag type for each of the scheme is shown as follows:
    
-
    Scheme Begin Inside End   Single
-plain  0     -      -     -
-IOB    0     1      -     -
-IOE    -     0      1     -
-IOBES  0     1      2     3
+
    Continue the NER example, and the label dict should look like this to satify above equations:
    
+
    B-ORG  0
+I-ORG  1
+B-PER  2
+I-PER  3
+B-LOC  4
+I-LOC  5
+O      6
 
    
 
    
-
    ‘plain’ means the whole chunk must contain exactly the same chunk label.
    
+
    In this example, chunkType has three values: 0 for ORG, 1 for PER, 2 for LOC, because the scheme is
+“IOB” so tagType has two values: 0 for B and 1 for I.
+Here we will use I-LOC to explain the above mapping rules in detail.
+For I-LOC, the label id is 5, so we can get tagType=1 and chunkType=2, which means I-LOC is a part of NER chunk LOC
+and the tag is I.
    
 
    The simple usage is:
    
-
    eval = chunk_evaluator(input, label, chunk_scheme, num_chunk_types)
+
    eval = evaluator.chunk(input, label, chunk_scheme, num_chunk_types)
 
    
 
    
 
    
 
 
    
@@ -340,9 +376,9 @@ The tag type for each of the scheme is shown as follows:
    
 
    precision_recall
    
-
    
+
    
 
    
-class paddle.v2.evaluator.precision_recall(*args, **kwargs)
    
+paddle.v2.evaluator.precision_recall(*args, **xargs)
 
    An Evaluator to calculate precision and recall, F1-score.
 It is adapt to the task with multiple labels.
    
 
      
@@ -352,7 +388,7 @@ F1-score of all labels.
 F1-score of this label.
 
    
 
    The simple usage:
    
-
    eval = precision_recall_evaluator(input, label)
+
    eval = precision_evaluator.recall(input, label)
 
    
 
    
 
    
 
 
    
@@ -379,13 +415,13 @@ F1-score of this label.
    Rank
    
 
    pnpair
    
-
    
+
    
 
    
-class paddle.v2.evaluator.pnpair(*args, **kwargs)
    
+paddle.v2.evaluator.pnpair(*args, **xargs)
 
    Positive-negative pair rate Evaluator which adapts to rank task like
 learning to rank. This evaluator must contain at least three layers.
    
 
    The simple usage:
    
-
    eval = pnpair_evaluator(input, info, label)
+
    eval = evaluator.pnpair(input, info, label)
 
    
 
    
 
    
 
 
    
@@ -412,12 +448,12 @@ learning to rank. This evaluator must contain at least three layers.
    Utils
    
 
    sum
    
-
    
+
    
 
    
-class paddle.v2.evaluator.sum(*args, **kwargs)
    
+paddle.v2.evaluator.sum(*args, **xargs)
 
    An Evaluator to sum the result of input.
    
 
    The simple usage:
    
-
    eval = sum_evaluator(input)
+
    eval = evaluator.sum(input)
 
    
 
    
 
    
 
 
    
@@ -439,12 +475,12 @@ learning to rank. This evaluator must contain at least three layers.
    
 
    column_sum
    
-
    
+
    
 
    
-class paddle.v2.evaluator.column_sum(*args, **kwargs)
    
+paddle.v2.evaluator.column_sum(*args, **xargs)
 
    This Evaluator is used to sum the last column of input.
    
 
    The simple usage is:
    
-
    eval = column_sum_evaluator(input, label)
+
    eval = column_evaluator.sum(input, label)
 
    
 
    
 
    
 
 
    
@@ -467,12 +503,12 @@ learning to rank. This evaluator must contain at least three layers.
    Print
    
 
    classification_error_printer
    
-
    
+
    
 
    
-class paddle.v2.evaluator.classification_error_printer(*args, **kwargs)
    
+paddle.v2.evaluator.classification_error_printer(*args, **xargs)
 
    This Evaluator is used to print the classification error of each sample.
    
 
    The simple usage is:
    
-
    eval = classification_error_printer_evaluator(input)
+
    eval = classification_error_evaluator.printer(input)
 
    
 
    
 
    
 
 
    
@@ -493,13 +529,13 @@ learning to rank. This evaluator must contain at least three layers.
    
 
    gradient_printer
    
-
    
+
    
 
    
-class paddle.v2.evaluator.gradient_printer(*args, **kwargs)
    
+paddle.v2.evaluator.gradient_printer(*args, **xargs)
 
    This Evaluator is used to print the gradient of input layers. It contains
 one or more input layers.
    
 
    The simple usage is:
    
-
    eval = gradient_printer_evaluator(input)
+
    eval = gradient_evaluator.printer(input)
 
    
 
    
 
    
 
 
    
@@ -519,14 +555,14 @@ one or more input layers.
    
 
    maxid_printer
    
-
    
+
    
 
    
-class paddle.v2.evaluator.maxid_printer(*args, **kwargs)
    
+paddle.v2.evaluator.maxid_printer(*args, **xargs)
 
    This Evaluator is used to print maximum top k values and their indexes
 of each row of input layers. It contains one or more input layers.
 k is specified by num_results.
    
 
    The simple usage is:
    
-
    eval = maxid_printer_evaluator(input)
+
    eval = maxid_evaluator.printer(input)
 
    
 
    
 
    
 
 
    
@@ -548,9 +584,9 @@ It is 1 by default.
    
 
    maxframe_printer
    
-
    
+
    
 
    
-class paddle.v2.evaluator.maxframe_printer(*args, **kwargs)
    
+paddle.v2.evaluator.maxframe_printer(*args, **xargs)
 
    This Evaluator is used to print the top k frames of each input layers.
 The input layers should contain sequences info or sequences type.
 k is specified by num_results.
@@ -560,7 +596,7 @@ It contains one or more input layers.
    
 
    The width of each frame is 1.
    
 
    The simple usage is:
    
-
    eval = maxframe_printer_evaluator(input)
+
    eval = maxframe_evaluator.printer(input)
 
    
 
    
 
    
 
 
 
    
@@ -580,9 +616,9 @@ It contains one or more input layers.
    
 
    seqtext_printer
    
-
    
+
    
 
    
-class paddle.v2.evaluator.seqtext_printer(*args, **kwargs)
    
+paddle.v2.evaluator.seqtext_printer(*args, **xargs)
 
    Sequence text printer will print text according to index matrix and a
 dictionary. There can be multiple input to this layer:
    
 
    1. If there is no id_input, the input must be a matrix containing
@@ -614,7 +650,7 @@ the sequence of indices;
    
 
    Typically SequenceTextPrinter layer takes output of maxid or RecurrentGroup
 with maxid (when generating) as an input.
    
 
    The simple usage is:
    
-
    eval = seqtext_printer_evaluator(input=maxid,
+
    eval = seqtext_evaluator.printer(input=maxid,
                                  id_input=sample_id,
                                  dict_file=dict_file,
                                  result_file=result_file)
@@ -654,13 +690,13 @@ Default is True. No space is added if set to False.
 
    
 
    
 
    value_printer
    
-
    
+
    
 
    
-class paddle.v2.evaluator.value_printer(*args, **kwargs)
    
+paddle.v2.evaluator.value_printer(*args, **xargs)
 
    This Evaluator is used to print the values of input layers. It contains
 one or more input layers.
    
 
    The simple usage is:
    
-
    eval = value_printer_evaluator(input)
+
    eval = value_evaluator.printer(input)
 
    
 
    
 
    
 
 
    
diff --git a/develop/doc_cn/api/v2/config/layer.html b/develop/doc_cn/api/v2/config/layer.html
index 35ce6a4d5a28d56a37263e6d0808bd93b267a7d6..28f21a2ebfa21b60ab090ec43bf537daae62f494 100644
--- a/develop/doc_cn/api/v2/config/layer.html
+++ b/develop/doc_cn/api/v2/config/layer.html
@@ -196,35 +196,10 @@
 
    Data layer
    
 
    data
    
-
    
+
    
 
    
-class paddle.v2.layer.data(name, type, **kwargs)
    
-
    Define DataLayer For NeuralNetwork.
    
-
    The example usage is:
    
-
    data = paddle.layer.data(name="input", type=paddle.data_type.dense_vector(1000))
-
    
-
    
-
    
 
    
---
-
-
-
-
-
-
-
-
    


    参数:
      
-
    • name (basestring) – Name of this data layer.
      • 
-
    • type – Data type of this data layer
      • 
-
    • height (int|None) – Height of this data layer, used for image
      • 
-
    • width (int|None) – Width of this data layer, used for image
      • 
-
    • layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
      • 
-
    
-
    返回:
    paddle.v2.config_base.Layer object.
    
-
    返回类型:
    paddle.v2.config_base.Layer
    
-
    
+paddle.v2.layer.data
+
    name 的别名
    
 
    
 
 
    
@@ -235,12 +210,12 @@
 
    fc
    
 
    
 
    
-class paddle.v2.layer.fc(*args, **kwargs)
    
+class paddle.v2.layer.fc
 
    Helper for declare fully connected layer.
    
 
    The example usage is:
    
 
    fc = fc(input=layer,
               size=1024,
-              act=paddle.v2.Activation.Linear(),
+              act=paddle.v2.activation.Linear(),
               bias_attr=False)
 
    
 
    
@@ -257,7 +232,7 @@
 
  • name (basestring) – The Layer Name.
    • 
 
  • input (paddle.v2.config_base.Layer|list|tuple) – The input layer. Could be a list/tuple of input layer.
    • 
 
  • size (int) – The layer dimension.
    • 
-
  • act (paddle.v2.Activation.Base) – Activation Type. Default is tanh.
    • 
+
  • act (paddle.v2.activation.Base) – Activation Type. Default is tanh.
    • 
 
  • param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute|list.
    • 
 
  • bias_attr (paddle.v2.attr.ParameterAttribute|None|Any) – The Bias Attribute. If no bias, then pass False or
 something not type of paddle.v2.attr.ParameterAttribute. None will get a
@@ -281,13 +256,13 @@ default Bias.
    • 
 
    selective_fc
    
 
    
 
    
-class paddle.v2.layer.selective_fc(*args, **kwargs)
    
+class paddle.v2.layer.selective_fc
 
    Selectived fully connected layer. Different from fc, the output
 of this layer maybe sparse. It requires an additional input to indicate
 several selected columns for output. If the selected columns is not
 specified, selective_fc acts exactly like fc.
    
 
    The simple usage is:
    
-
    sel_fc = selective_fc(input=input, size=128, act=paddle.v2.Activation.Tanh())
+
    sel_fc = selective_fc(input=input, size=128, act=paddle.v2.activation.Tanh())
 
    
 
    
 
@@ -301,7 +276,7 @@ specified, selective_fc acts exactly like fc.
    
 sparse binary matrix, and treat as the mask of selective fc.
 If is None, acts exactly like fc.
  • size (int) – The layer dimension.
    • 
-
  • act (paddle.v2.Activation.Base) – Activation Type. Default is tanh.
    • 
+
  • act (paddle.v2.activation.Base) – Activation Type. Default is tanh.
  • param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute.
  • bias_attr (paddle.v2.attr.ParameterAttribute|None|Any) – The Bias Attribute. If no bias, then pass False or
 something not type of paddle.v2.attr.ParameterAttribute. None will get a
@@ -328,7 +303,7 @@ default Bias.
    • conv_operator
    
 
    
-class paddle.v2.layer.conv_operator(**kwargs)
    
+class paddle.v2.layer.conv_operator
 
    Different from img_conv, conv_op is an Operator, which can be used
 in mixed. And conv_op takes two inputs to perform convolution.
 The first input is the image and the second is filter kernel. It only
@@ -376,7 +351,7 @@ the filter’s shape can be (filter_size, filter_size_y).
 
    conv_projection
    
 
    
 
    
-class paddle.v2.layer.conv_projection(**kwargs)
    
+class paddle.v2.layer.conv_projection
 
    Different from img_conv and conv_op, conv_projection is an Projection,
 which can be used in mixed and conat. It use cudnn to implement
 conv and only support GPU mode.
    
@@ -424,7 +399,7 @@ the filter’s shape can be (filter_size, filter_size_y).
 
    conv_shift
    
 
    
 
    
-class paddle.v2.layer.conv_shift(*args, **kwargs)
    
+class paddle.v2.layer.conv_shift
 
    
 
    This layer performs cyclic convolution for two input. For example:
    
 
      
@@ -477,7 +452,7 @@ the right size (which is the end of array) to the left.
 
      img_conv
      
 
      
 
      
-class paddle.v2.layer.img_conv(*args, **kwargs)
      
+class paddle.v2.layer.img_conv
 
      Convolution layer for image. Paddle can support both square and non-square
 input currently.
      
 
      The details of convolution layer, please refer UFLDL’s convolution .
      
@@ -501,7 +476,7 @@ rest channels will be processed by rest group of filters.
      
                       num_channels=8,
                       num_filters=16, stride=1,
                       bias_attr=False,
-                      act=paddle.v2.Activation.Relu())
+                      act=paddle.v2.activation.Relu())
 
 
 
    
 
 
 
 
 
    
@@ -517,7 +492,7 @@ two image dimension.
 currently supports rectangular filters, the filter’s
 shape will be (filter_size, filter_size_y).
  • num_filters – Each filter group’s number of filter
    • 
-
  • act (paddle.v2.Activation.Base) – Activation type. Default is tanh
    • 
+
  • act (paddle.v2.activation.Base) – Activation type. Default is tanh
  • groups (int) – Group size of filters.
  • stride (int|tuple|list) – The x dimension of the stride. Or input a tuple for two image
 dimension.
    • 
@@ -555,7 +530,7 @@ otherwise layer_type has to be either “exconv” or
 
    context_projection
    
 
    
-class paddle.v2.layer.context_projection(**kwargs)
    
+class paddle.v2.layer.context_projection
 
    Context Projection.
    
 
    It just simply reorganizes input sequence, combines “context_len” sequence
 to one context from context_start. “context_start” will be set to
@@ -598,7 +573,7 @@ parameter attribute is set by this parameter.
 
    img_pool
    
 
    
 
    
-class paddle.v2.layer.img_pool(*args, **kwargs)
    
+class paddle.v2.layer.img_pool
 
    Image pooling Layer.
    
 
    The details of pooling layer, please refer ufldl’s pooling .
    
 
      
@@ -662,7 +637,7 @@ Defalut is True. If set false, Otherwise use floor.
 
      spp
      
 
      
 
      
-class paddle.v2.layer.spp(*args, **kwargs)
      
+class paddle.v2.layer.spp
 
      Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
 The details please refer to
 Kaiming He’s paper.
      
@@ -702,7 +677,7 @@ The details please refer to
 
      maxout
      
 
      
 
      
-class paddle.v2.layer.maxout(*args, **kwargs)
      
+class paddle.v2.layer.maxout
 
      
 
      A layer to do max out on conv layer output.
      
 
        
@@ -759,7 +734,7 @@ automatically from previous output.
 
        img_cmrnorm
        
 
        
 
        
-class paddle.v2.layer.img_cmrnorm(*args, **kwargs)
        
+class paddle.v2.layer.img_cmrnorm
 
        Response normalization across feature maps.
 The details please refer to
 Alex’s paper.
        
@@ -798,7 +773,7 @@ num_channels is None, it will be set automatically.
 
        batch_norm
        
 
        
 
        
-class paddle.v2.layer.batch_norm(*args, **kwargs)
        
+class paddle.v2.layer.batch_norm
 
        Batch Normalization Layer. The notation of this layer as follow.
        
 
        \(x\) is the input features over a mini-batch.
        
 
        
@@ -812,7 +787,7 @@ y_i &\gets \gamma \hat{x_i} + \beta \qquad &//\ scale\ and\ shift\end{sp
 
        The details of batch normalization please refer to this
 paper.
        
 
        The example usage is:
        
-
        norm = batch_norm(input=net, act=paddle.v2.Activation.Relu())
+
        norm = batch_norm(input=net, act=paddle.v2.activation.Relu())
 
        
 
        
 
    
 
 
 
 
    
@@ -832,7 +807,7 @@ automaticly select cudnn_batch_norm for GPU and
 batch_norm for CPU. Otherwise, select batch norm
 type based on the specified type. If you use cudnn_batch_norm,
 we suggested you use latest version, such as v5.1.
-
  • act (paddle.v2.Activation.Base) – Activation Type. Better be relu. Because batch
+
  • act (paddle.v2.activation.Base) – Activation Type. Better be relu. Because batch
 normalization will normalize input near zero.
  • num_channels (int) – num of image channels or previous layer’s number of
 filters. None will automatically get from layer’s
@@ -870,7 +845,7 @@ computation, referred to as facotr,
 
    sum_to_one_norm
    
 
    
 
    
-class paddle.v2.layer.sum_to_one_norm(*args, **kwargs)
    
+class paddle.v2.layer.sum_to_one_norm
 
    A layer for sum-to-one normalization,
 which is used in NEURAL TURING MACHINE.
    
 
    
@@ -907,7 +882,7 @@ and \(out\) is a (batchSize x dataDim) output vector.<
 
    cross_channel_norm
    
 
    
 
    
-class paddle.v2.layer.cross_channel_norm(*args, **kwargs)
    
+class paddle.v2.layer.cross_channel_norm
 
    Normalize a layer’s output. This layer is necessary for ssd.
 This layer applys normalize across the channels of each sample to
 a conv layer’s output and scale the output by a group of trainable
@@ -938,7 +913,7 @@ factors which dimensions equal to the channel’s number.
    
 
    recurrent
    
 
    
 
    
-class paddle.v2.layer.recurrent(*args, **kwargs)
    
+class paddle.v2.layer.recurrent
 
    Simple recurrent unit layer. It is just a fully connect layer through both
 time and neural network.
    
 
    For each sequence [start, end] it performs the following computation:
    
@@ -955,7 +930,7 @@ out_{i} = act(in_{i} + out_{i+1} * W) \ \ \text{for} \ start <= i < end\en
 
    • 
-
-
@@ -1160,9 +1135,9 @@ sequence input. This is extremely usefull for attention based model, or
 Neural Turning Machine like models.
    The basic usage (time steps) is:
    def step(input):
-    output = fc_layer(input=layer,
+    output = fc(input=layer,
                       size=1024,
-                      act=LinearActivation(),
+                      act=paddle.v2.activation.Linear(),
                       bias_attr=False)
     return output
 
@@ -1172,8 +1147,8 @@ Neural Turning Machine like models.
    
 
    
 
    You can see following configs for further usages:
    
 
      
-
    • time steps: lstmemory_group, paddle/gserver/tests/sequence_layer_group.conf,                   demo/seqToseq/seqToseq_net.py
      • 
-
    • sequence steps: paddle/gserver/tests/sequence_nest_layer_group.conf
      • 
+
    • time steps: lstmemory_group, paddle/gserver/tests/sequence_group.conf,                   demo/seqToseq/seqToseq_net.py
      • 
+
    • sequence steps: paddle/gserver/tests/sequence_nest_group.conf
      • 
 
    
 
    
 
 
    参数:
      
 
    • input (paddle.v2.config_base.Layer) – Input Layer
      • 
-
    • act (paddle.v2.Activation.Base) – activation.
      • 
+
    • act (paddle.v2.activation.Base) – activation.
      • 
 
    • bias_attr (paddle.v2.attr.ParameterAttribute) – bias attribute.
      • 
 
    • param_attr (paddle.v2.attr.ParameterAttribute) – parameter attribute.
      • 
 
    • name (basestring) – name of the layer
      • 
@@ -978,7 +953,7 @@ out_{i} = act(in_{i} + out_{i+1} * W) \ \ \text{for} \ start <= i < end\en
 
      lstmemory
      
 
      
 
      
-class paddle.v2.layer.lstmemory(*args, **kwargs)
      
+class paddle.v2.layer.lstmemory
 
      Long Short-term Memory Cell.
      
 
      The memory cell was implemented as follow equations.
      
 
      
@@ -1002,9 +977,9 @@ more details about LSTM.
      
 
    • name (basestring) – The lstmemory layer name.
      • 
 
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
 
    • reverse (bool) – is sequence process reversed or not.
      • 
-
    • act (paddle.v2.Activation.Base) – activation type, paddle.v2.Activation.Tanh by default. \(h_t\)
      • 
-
    • gate_act (paddle.v2.Activation.Base) – gate activation type, paddle.v2.Activation.Sigmoid by default.
      • 
-
    • state_act (paddle.v2.Activation.Base) – state activation type, paddle.v2.Activation.Tanh by default.
      • 
+
    • act (paddle.v2.activation.Base) – activation type, paddle.v2.activation.Tanh by default. \(h_t\)
      • 
+
    • gate_act (paddle.v2.activation.Base) – gate activation type, paddle.v2.activation.Sigmoid by default.
      • 
+
    • state_act (paddle.v2.activation.Base) – state activation type, paddle.v2.activation.Tanh by default.
      • 
 
    • bias_attr (paddle.v2.attr.ParameterAttribute|None|False) – Bias attribute. None means default bias. False means no
 bias.
      • 
 
    • param_attr (paddle.v2.attr.ParameterAttribute|None|False) – Parameter Attribute.
      • 
@@ -1027,7 +1002,7 @@ bias.
 
      grumemory
      
 
      
 
      
-class paddle.v2.layer.grumemory(*args, **kwargs)
      
+class paddle.v2.layer.grumemory
 
      Gate Recurrent Unit Layer.
      
 
      The memory cell was implemented as follow equations.
      
 
      1. update gate \(z\): defines how much of the previous memory to
@@ -1067,9 +1042,9 @@ Recurrent Neural Networks on Sequence Modeling.
      
 
    • name (None|basestring) – The gru layer name.
      • 
 
    • input (paddle.v2.config_base.Layer.) – input layer.
      • 
 
    • reverse (bool) – Whether sequence process is reversed or not.
      • 
-
    • act (paddle.v2.Activation.Base) – activation type, paddle.v2.Activation.Tanh by default. This activation
+
    • act (paddle.v2.activation.Base) – activation type, paddle.v2.activation.Tanh by default. This activation
 affects the \({\tilde{h_t}}\).
      • 
-
    • gate_act (paddle.v2.Activation.Base) – gate activation type, paddle.v2.Activation.Sigmoid by default.
+
    • gate_act (paddle.v2.activation.Base) – gate activation type, paddle.v2.activation.Sigmoid by default.
 This activation affects the \(z_t\) and \(r_t\). It is the
 \(\sigma\) in the above formula.
      • 
 
    • bias_attr (paddle.v2.attr.ParameterAttribute|None|False) – Bias attribute. None means default bias. False means no
@@ -1099,7 +1074,7 @@ will get a warning.
      • 
 
      memory
      
 
      
 
      
-class paddle.v2.layer.memory(name, extra_input=None, **kwargs)
      
+class paddle.v2.layer.memory
 
      The memory layers is a layer cross each time step. Reference this output
 as previous time step layer name ‘s output.
      
 
      The default memory is zero in first time step, previous time step’s
@@ -1108,12 +1083,12 @@ output in the rest time steps.
      
 with activation.
      
 
      If boot_with_const_id, then the first time stop is a IndexSlot, the
 Arguments.ids()[0] is this cost_id.
      
-
      If boot_layer is not null, the memory is just the boot_layer’s output.
+
      If boot is not null, the memory is just the boot’s output.
 Set is_seq is true boot layer is sequence.
      
 
      The same name layer in recurrent group will set memory on each time
 step.
      
 
      mem = memory(size=256, name='state')
-state = fc_layer(input=mem, size=256, name='state')
+state = fc(input=mem, size=256, name='state')
 
      
 
      
 
      If you do not want to specify the name, you can equivalently use set_input()
@@ -1129,18 +1104,18 @@ name of the layer which this memory remembers.
 
    • size (int) – size of memory.
      • 
 
    • memory_name (basestring) – the name of the memory.
 It is ignored when name is provided.
      • 
-
    • is_seq (bool) – is sequence for boot_layer
      • 
-
    • boot_layer (LayerOutput|None) – boot layer of memory.
      • 
-
    • boot_bias (ParameterAttribute|None) – boot layer’s bias
      • 
-
    • boot_bias_active_type (BaseActivation) – boot layer’s active type.
      • 
+
    • is_seq (bool) – is sequence for boot
      • 
+
    • boot (paddle.v2.config_base.Layer|None) – boot layer of memory.
      • 
+
    • boot_bias (paddle.v2.attr.ParameterAttribute|None) – boot layer’s bias
      • 
+
    • boot_bias_active_type (paddle.v2.activation.Base) – boot layer’s active type.
      • 
 
    • boot_with_const_id (int) – boot layer’s id.
      • 
 
    
     		
 
    返回:
    LayerOutput object which is a memory.
    
+
    返回:
    paddle.v2.config_base.Layer object which is a memory.
    
     		
 
    返回类型:
    LayerOutput
    
+
    返回类型:
    paddle.v2.config_base.Layer
    
     		
 
    
     
 
 
    @@ -1189,24 +1164,24 @@ a time step result. Then gather each time step of output into
 layer group’s output.
  • name (basestring) – recurrent_group’s name.
    • 
-
  • input (LayerOutput|StaticInput|SubsequenceInput|list|tuple) – 
    Input links array.
    
-
    LayerOutput will be scattered into time steps.
+
  • input (paddle.v2.config_base.Layer|StaticInput|SubsequenceInput|list|tuple) – 
    Input links array.
    
+
    paddle.v2.config_base.Layer will be scattered into time steps.
 SubsequenceInput will be scattered into sequence steps.
 StaticInput will be imported to each time step, and doesn’t change
 through time. It’s a mechanism to access layer outside step function.
    
 
  • reverse (bool) – If reverse is set true, the recurrent unit will process the
 input sequence in a reverse order.
    • 
-
  • targetInlink (LayerOutput|SubsequenceInput) – 
    the input layer which share info with layer group’s output
    
+
  • targetInlink (paddle.v2.config_base.Layer|SubsequenceInput) – 
    the input layer which share info with layer group’s output
    
 
    Param input specifies multiple input layers. For
 SubsequenceInput inputs, config should assign one input
 layer that share info(the number of sentences and the number
 of words in each sentence) with all layer group’s outputs.
 targetInlink should be one of the layer group’s input.
    
 
    • 
-
  • is_generating – If is generating, none of input type should be LayerOutput;
+
  • is_generating – If is generating, none of input type should be paddle.v2.config_base.Layer;
 else, for training or testing, one of the input type must
-be LayerOutput.
    • 
+be paddle.v2.config_base.Layer.
@@ -1217,9 +1192,9 @@ be LayerOutput.
-
+
-
+
    
 

 
 
 
 
 
 
 
 
 
    返回:LayerOutput object.
    返回:paddle.v2.config_base.Layer object.
 
    返回类型:LayerOutput
    返回类型:paddle.v2.config_base.Layer
 
    
     
 
    
@@ -1230,7 +1205,7 @@ be LayerOutput.
 
    lstm_step
    
 
    
 
    
-class paddle.v2.layer.lstm_step(*args, **kwargs)
    
+class paddle.v2.layer.lstm_step
 
    LSTM Step Layer. It used in recurrent_group. The lstm equations are shown
 as follow.
    
 
    
@@ -1255,10 +1230,10 @@ output is \(o_t\), which name is ‘state’ a
 state.size.
 
  • input (paddle.v2.config_base.Layer) – input layer. \(Wx_t + Wh_{t-1}\)
    • 
 
  • state (paddle.v2.config_base.Layer) – State Layer. \(c_{t-1}\)
    • 
-
  • act (paddle.v2.Activation.Base) – Activation type. Default is tanh
    • 
-
  • gate_act (paddle.v2.Activation.Base) – Gate Activation Type. Default is sigmoid, and should
+
  • act (paddle.v2.activation.Base) – Activation type. Default is tanh
    • 
+
  • gate_act (paddle.v2.activation.Base) – Gate Activation Type. Default is sigmoid, and should
 be sigmoid only.
    • 
-
  • state_act (paddle.v2.Activation.Base) – State Activation Type. Default is sigmoid, and should
+
  • state_act (paddle.v2.activation.Base) – State Activation Type. Default is sigmoid, and should
 be sigmoid only.
    • 
 
  • bias_attr (paddle.v2.attr.ParameterAttribute) – Bias Attribute.
    • 
 
  • layer_attr (paddle.v2.attr.ExtraAttribute) – layer’s extra attribute.
    • 
@@ -1280,7 +1255,7 @@ be sigmoid only.
 
    gru_step
    
 
    
 
    
-class paddle.v2.layer.gru_step(*args, **kwargs)
    
+class paddle.v2.layer.gru_step
 
    @@ -1321,7 +1296,7 @@ to maintain tractability.
    The example usage is:
    def rnn_step(input):
     last_time_step_output = memory(name='rnn', size=512)
-    with mixed_layer(size=512, name='rnn') as simple_rnn:
+    with mixed(size=512, name='rnn') as simple_rnn:
         simple_rnn += full_matrix_projection(input)
         simple_rnn += last_time_step_output
     return simple_rnn
@@ -1383,7 +1358,7 @@ beam size.
 
    
-
@@ -1395,7 +1370,7 @@ beam size.
    get_output
    
 
    
-class paddle.v2.layer.get_output(*args, **kwargs)
    
+class paddle.v2.layer.get_output
 
    Get layer’s output by name. In PaddlePaddle, a layer might return multiple
 values, but returns one layer’s output. If the user wants to use another
 output besides the default one, please use get_output first to get
@@ -1436,17 +1411,17 @@ multiple outputs.
 Each inputs is a projection or operator.
    
 
    There are two styles of usages.
    
 
      
-
    1. When not set inputs parameter, use mixed_layer like this:
      2. 
+
    2. When not set inputs parameter, use mixed like this:
      3. 
 
    
-
    with mixed_layer(size=256) as m:
+
    with mixed(size=256) as m:
     m += full_matrix_projection(input=layer1)
     m += identity_projection(input=layer2)
 
    
 
    
 
      
-
    1. You can also set all inputs when invoke mixed_layer as follows:
      2. 
+
    2. You can also set all inputs when invoke mixed as follows:
      3. 
 
    
-
    m = mixed_layer(size=256,
+
    m = mixed(size=256,
                 input=[full_matrix_projection(input=layer1),
                        full_matrix_projection(input=layer2)])
 
    
@@ -1460,11 +1435,11 @@ Each inputs is a projection or operator.
    
 
  • size (int) – layer size.
    • 
 
  • input – inputs layer. It is an optional parameter. If set,
 then this function will just return layer’s name.
    • 
-
  • act (BaseActivation) – Activation Type.
    • 
-
  • bias_attr (ParameterAttribute or None or bool) – The Bias Attribute. If no bias, then pass False or
-something not type of ParameterAttribute. None will get a
+
  • act (paddle.v2.activation.Base) – Activation Type.
    • 
+
  • bias_attr (paddle.v2.attr.ParameterAttribute or None or bool) – The Bias Attribute. If no bias, then pass False or
+something not type of paddle.v2.attr.ParameterAttribute. None will get a
 default Bias.
    • 
-
  • layer_attr (ExtraLayerAttribute) – The extra layer config. Default is None.
    • 
+
  • layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer config. Default is None.
    • 
 
 
 
@@ -1483,7 +1458,7 @@ default Bias.
 
    embedding
    
 
    
 
    
-class paddle.v2.layer.embedding(*args, **kwargs)
    
+class paddle.v2.layer.embedding
 
    Define a embedding Layer.
    
 
    
 
 

 
 
    返回:
    The generated word index.
    
     		
 
    返回类型:
    LayerOutput
    
+
    返回类型:
    paddle.v2.config_base.Layer
    
     		
 
    
     
 
 
    @@ -1514,7 +1489,7 @@ for details.
    scaling_projection
    
 
    
-class paddle.v2.layer.scaling_projection(**kwargs)
    
+class paddle.v2.layer.scaling_projection
 
    scaling_projection multiplies the input with a scalar parameter and add to
 the output.
    
 
    
@@ -1548,7 +1523,7 @@ the output.
    
 
    dotmul_projection
    
 
    
 
    
-class paddle.v2.layer.dotmul_projection(**kwargs)
    
+class paddle.v2.layer.dotmul_projection
 
    DotMulProjection with a layer as input.
 It performs element-wise multiplication with weight.
    
 
    
@@ -1583,7 +1558,7 @@ It performs element-wise multiplication with weight.
    
 
    dotmul_operator
    
 
    
 
    
-class paddle.v2.layer.dotmul_operator(**kwargs)
    
+class paddle.v2.layer.dotmul_operator
 
    DotMulOperator takes two inputs and performs element-wise multiplication:
    
 
    
 \[out.row[i] += scale * (a.row[i] .* b.row[i])\]
    
@@ -1619,7 +1594,7 @@ scale is a config scalar, its default value is one.
    
 
    full_matrix_projection
    
 
    
 
    
-class paddle.v2.layer.full_matrix_projection(**kwargs)
    
+class paddle.v2.layer.full_matrix_projection
 
    Full Matrix Projection. It performs full matrix multiplication.
    
 
    
 \[out.row[i] += in.row[i] * weight\]
    
@@ -1665,7 +1640,7 @@ scale is a config scalar, its default value is one.
    
 
    identity_projection
    
 
    
 
    
-class paddle.v2.layer.identity_projection(**kwargs)
    
+class paddle.v2.layer.identity_projection
 
      
 
    1. IdentityProjection if offset=None. It performs:
      2. 
 
    
@@ -1711,7 +1686,7 @@ It select dimesions [offset, offset+layer_size) from input:
    
 
    table_projection
    
 
    
 
    
-class paddle.v2.layer.table_projection(**kwargs)
    
+class paddle.v2.layer.table_projection
 
    Table Projection. It selects rows from parameter where row_id
 is in input_ids.
    
 
    
@@ -1760,7 +1735,7 @@ and \(i\) is row_id.
    
 
    trans_full_matrix_projection
    
 
    
 
    
-class paddle.v2.layer.trans_full_matrix_projection(**kwargs)
    
+class paddle.v2.layer.trans_full_matrix_projection
 
    Different from full_matrix_projection, this projection performs matrix
 multiplication, using transpose of weight.
    
 
    
@@ -1828,7 +1803,7 @@ sequence of a nested sequence, 
    pooling
    
 
    
 
    
-class paddle.v2.layer.pooling(*args, **kwargs)
    
+class paddle.v2.layer.pooling
 
    Pooling layer for sequence inputs, not used for Image.
    
 
    The example usage is:
    
 
    seq_pool = pooling(input=layer,
@@ -1867,7 +1842,7 @@ SumPooling, SquareRootNPooling.
 
    last_seq
    
 
    
 
    
-class paddle.v2.layer.last_seq(*args, **kwargs)
    
+class paddle.v2.layer.last_seq
 
    Get Last Timestamp Activation of a sequence.
    
 
    If stride > 0, this layer slides a window whose size is determined by stride,
 and return the last value of the window as the output. Thus, a long sequence
@@ -1905,7 +1880,7 @@ of stride is -1.
    
 
    first_seq
    
 
    
 
    
-class paddle.v2.layer.first_seq(*args, **kwargs)
    
+class paddle.v2.layer.first_seq
 
    Get First Timestamp Activation of a sequence.
    
 
    If stride > 0, this layer slides a window whose size is determined by stride,
 and return the first value of the window as the output. Thus, a long sequence
@@ -1943,7 +1918,7 @@ of stride is -1.
    
 
    concat
    
 
    
 
    
-class paddle.v2.layer.concat(*args, **kwargs)
    
+class paddle.v2.layer.concat
 
    Concat all input vector into one huge vector.
 Inputs can be list of paddle.v2.config_base.Layer or list of projection.
    
 
    The example usage is:
    
@@ -1957,7 +1932,7 @@ Inputs can be list of paddle.v2.config_base.Layer or list of projection.
    
 
    
@@ -1977,7 +1952,7 @@ Inputs can be list of paddle.v2.config_base.Layer or list of projection.
    
 
    seq_concat
    
 
    
 
    
-class paddle.v2.layer.seq_concat(*args, **kwargs)
    
+class paddle.v2.layer.seq_concat
 
    Concat sequence a with sequence b.
    
 
    
 
    Inputs:
    
@@ -2001,7 +1976,7 @@ Inputs can be list of paddle.v2.config_base.Layer or list of projection.
    
 
  • name (basestring) – Layer name.
    • 
 
  • a (paddle.v2.config_base.Layer) – input sequence layer
    • 
 
  • b (paddle.v2.config_base.Layer) – input sequence layer
    • 
-
  • act (paddle.v2.Activation.Base) – Activation type.
    • 
+
  • act (paddle.v2.activation.Base) – Activation type.
    • 
 
  • layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
    • 
 
  • bias_attr (paddle.v2.attr.ParameterAttribute or None or bool) – The Bias Attribute. If no bias, then pass False or
 something not type of paddle.v2.attr.ParameterAttribute. None will get a
@@ -2027,7 +2002,7 @@ default Bias.
    • 
 
    block_expand
    
 
    
 
    
-class paddle.v2.layer.block_expand(*args, **kwargs)
    
+class paddle.v2.layer.block_expand
 
    
 
    Expand feature map to minibatch matrix.
    
 
      
@@ -2103,7 +2078,7 @@ sequence of a nested sequence, 
      
 
      
-class paddle.v2.layer.expand(*args, **kwargs)
      
+class paddle.v2.layer.expand
 
      A layer for “Expand Dense data or (sequence data where the length of each
 sequence is one) to sequence data.”
      
 
      The example usage is:
      
@@ -2142,7 +2117,7 @@ bias.
 
      repeat
      
 
      
 
      
-class paddle.v2.layer.repeat(*args, **kwargs)
      
+class paddle.v2.layer.repeat
 
      A layer for repeating the input for num_repeats times. This is equivalent
 to apply concat() with num_repeats same input.
      
 
      
@@ -2178,7 +2153,7 @@ to apply concat() with num_repeats same input.
      
 
      rotate
      
 
      
 
      
-class paddle.v2.layer.rotate(*args, **kwargs)
      
+class paddle.v2.layer.rotate
 
      A layer for rotating 90 degrees (clock-wise) for each feature channel,
 usually used when the input sample is some image or feature map.
      
 
      
@@ -2217,7 +2192,7 @@ usually used when the input sample is some image or feature map.
      
 
      seq_reshape
      
 
      
 
      
-class paddle.v2.layer.seq_reshape(*args, **kwargs)
      
+class paddle.v2.layer.seq_reshape
 
      A layer for reshaping the sequence. Assume the input sequence has T instances,
 the dimension of each instance is M, and the input reshape_size is N, then the
 output sequence has T*M/N instances, the dimension of each instance is N.
      
@@ -2234,7 +2209,7 @@ output sequence has T*M/N instances, the dimension of each instance is N.
      
 
    • input (paddle.v2.config_base.Layer) – Input layer.
      • 
 
    • reshape_size (int) – the size of reshaped sequence.
      • 
 
    • name (basestring) – Layer name.
      • 
-
    • act (paddle.v2.Activation.Base) – Activation type.
      • 
+
    • act (paddle.v2.activation.Base) – Activation type.
      • 
 
    • layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
      • 
 
    • bias_attr (paddle.v2.attr.ParameterAttribute or None or bool) – The Bias Attribute. If no bias, then pass False or
 something not type of paddle.v2.attr.ParameterAttribute. None will get a
@@ -2260,7 +2235,7 @@ default Bias.
      • 
 
      addto
      
 
      
 
      
-class paddle.v2.layer.addto(*args, **kwargs)
      
+class paddle.v2.layer.addto
 
      AddtoLayer.
      
 
      
 \[y = f(\sum_{i} x_i + b)\]
      
@@ -2268,7 +2243,7 @@ default Bias.
 and \(f\) is activation function.
      
 
      The example usage is:
      
 
      addto = addto(input=[layer1, layer2],
-                    act=paddle.v2.Activation.Relu(),
+                    act=paddle.v2.activation.Relu(),
                     bias_attr=False)
 
      
 
      
@@ -2290,7 +2265,7 @@ Please refer to dropout for details.
      
 
    • name (basestring) – Layer name.
      • 
 
    • input (paddle.v2.config_base.Layer|list|tuple) – Input layers. It could be a paddle.v2.config_base.Layer or list/tuple of
 paddle.v2.config_base.Layer.
      • 
-
    • act (paddle.v2.Activation.Base) – Activation Type, default is tanh.
      • 
+
    • act (paddle.v2.activation.Base) – Activation Type, default is tanh.
      • 
 
    • bias_attr (paddle.v2.attr.ParameterAttribute|bool) – Bias attribute. If False, means no bias. None is default
 bias.
      • 
 
    • layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer attribute.
      • 
@@ -2312,7 +2287,7 @@ bias.
 
      linear_comb
      
 
      
 
      
-class paddle.v2.layer.linear_comb(*args, **kwargs)
      
+class paddle.v2.layer.linear_comb
 
      
 
      A layer for weighted sum of vectors takes two inputs.
      
 
        
@@ -2375,7 +2350,7 @@ processed in one batch.
        
 
        interpolation
        
 
        
 
        
-class paddle.v2.layer.interpolation(*args, **kwargs)
        
+class paddle.v2.layer.interpolation
 
        This layer is for linear interpolation with two inputs,
 which is used in NEURAL TURING MACHINE.
        
 
        
@@ -2414,7 +2389,7 @@ which is used in NEURAL TURING MACHINE.
        
 
        bilinear_interp
        
 
        
 
        
-class paddle.v2.layer.bilinear_interp(*args, **kwargs)
        
+class paddle.v2.layer.bilinear_interp
 
        This layer is to implement bilinear interpolation on conv layer output.
        
 
        Please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation
        
 
        The simple usage is:
        
@@ -2449,7 +2424,7 @@ which is used in NEURAL TURING MACHINE.
        
 
        power
        
 
        
 
        
-class paddle.v2.layer.power(*args, **kwargs)
        
+class paddle.v2.layer.power
 
        This layer applies a power function to a vector element-wise,
 which is used in NEURAL TURING MACHINE.
        
 
        
@@ -2487,7 +2462,7 @@ and \(y\) is a output vector.
        
 
        scaling
        
 
        
 
        
-class paddle.v2.layer.scaling(*args, **kwargs)
        
+class paddle.v2.layer.scaling
 
        A layer for multiplying input vector by weight scalar.
        
 
        
 \[y  = w x\]
        
@@ -2526,7 +2501,7 @@ processed in one batch.
        
 
        slope_intercept
        
 
        
 
        
-class paddle.v2.layer.slope_intercept(*args, **kwargs)
        
+class paddle.v2.layer.slope_intercept
 
        This layer for applying a slope and an intercept to the input
 element-wise. There is no activation and weight.
        
 
        
@@ -2563,7 +2538,7 @@ element-wise. There is no activation and weight.
        
 
        tensor
        
 
        
 
        
-class paddle.v2.layer.tensor(*args, **kwargs)
        
+class paddle.v2.layer.tensor
 
        This layer performs tensor operation for two input.
 For example, each sample:
        
 
        
@@ -2592,7 +2567,7 @@ For example, each sample:
        
 
      • a (paddle.v2.config_base.Layer) – Input layer a.
        • 
 
      • b (paddle.v2.config_base.Layer) – input layer b.
        • 
 
      • size (int.) – the layer dimension.
        • 
-
      • act (paddle.v2.Activation.Base) – Activation Type. Default is tanh.
        • 
+
      • act (paddle.v2.activation.Base) – Activation Type. Default is tanh.
        • 
 
      • param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute.
        • 
 
      • bias_attr (paddle.v2.attr.ParameterAttribute|None|Any) – The Bias Attribute. If no bias, then pass False or
 something not type of paddle.v2.attr.ParameterAttribute. None will get a
@@ -2616,7 +2591,7 @@ default Bias.
        • 
 
        cos_sim
        
 
        
 
        
-class paddle.v2.layer.cos_sim(*args, **kwargs)
        
+class paddle.v2.layer.cos_sim
 
        Cosine Similarity Layer. The cosine similarity equation is here.
        
 
        
 \[similarity = cos(\theta) = {\mathbf{a} \cdot \mathbf{b}
@@ -2659,7 +2634,7 @@ processed in one batch.
        
 
        trans
        
 
        
 
        
-class paddle.v2.layer.trans(*args, **kwargs)
        
+class paddle.v2.layer.trans
 
        A layer for transposing a minibatch matrix.
        
 
        
 \[y = x^\mathrm{T}\]
        
@@ -2697,7 +2672,7 @@ processed in one batch.
        
 
        maxid
        
 
        
 
        
-class paddle.v2.layer.max_id(*args, **kwargs)
        
+class paddle.v2.layer.max_id
 
        A layer for finding the id which has the maximal value for each sample.
 The result is stored in output.ids.
        
 
        The example usage is:
        
@@ -2730,7 +2705,7 @@ The result is stored in output.ids.
        
 
        sampling_id
        
 
        
 
        
-class paddle.v2.layer.sampling_id(*args, **kwargs)
        
+class paddle.v2.layer.sampling_id
 
        A layer for sampling id from multinomial distribution from the input layer.
 Sampling one id for one sample.
        
 
        The simple usage is:
        
@@ -2766,7 +2741,7 @@ Sampling one id for one sample.
        
 
        pad
        
 
        
 
        
-class paddle.v2.layer.pad(*args, **kwargs)
        
+class paddle.v2.layer.pad
 
        This operation pads zeros to the input data according to pad_c,pad_h
 and pad_w. pad_c, pad_h, pad_w specifies the which dimension and size
 of padding. And the input data shape is NCHW.
        
@@ -2835,7 +2810,7 @@ in width dimension.
        
 
        cross_entropy_cost
        
 
        
 
        
-class paddle.v2.layer.cross_entropy_cost(*args, **kwargs)
        
+class paddle.v2.layer.cross_entropy_cost
 
        A loss layer for multi class entropy.
        
 
        cost = cross_entropy(input=input,
                      label=label)
@@ -2873,7 +2848,7 @@ will not be calculated for weight.
 
        cross_entropy_with_selfnorm_cost
        
 
        
 
        
-class paddle.v2.layer.cross_entropy_with_selfnorm_cost(*args, **kwargs)
        
+class paddle.v2.layer.cross_entropy_with_selfnorm_cost
 
        A loss layer for multi class entropy with selfnorm.
 Input should be a vector of positive numbers, without normalization.
        
 
        cost = cross_entropy_with_selfnorm(input=input,
@@ -2909,7 +2884,7 @@ Input should be a vector of positive numbers, without normalization.
        
 
        multi_binary_label_cross_entropy_cost
        
 
        
 
        
-class paddle.v2.layer.multi_binary_label_cross_entropy_cost(*args, **kwargs)
        
+class paddle.v2.layer.multi_binary_label_cross_entropy_cost
 
        A loss layer for multi binary label cross entropy.
        
 
        cost = multi_binary_label_cross_entropy(input=input,
                                         label=label)
@@ -2943,7 +2918,7 @@ Input should be a vector of positive numbers, without normalization.
        
 
        huber_cost
        
 
        
 
        
-class paddle.v2.layer.huber_cost(*args, **kwargs)
        
+class paddle.v2.layer.huber_cost
 
        A loss layer for huber loss.
        
 
        cost = huber_cost(input=input,
                   label=label)
@@ -2977,7 +2952,7 @@ Input should be a vector of positive numbers, without normalization.
        
 
        lambda_cost
        
 
        
 
        
-class paddle.v2.layer.lambda_cost(*args, **kwargs)
        
+class paddle.v2.layer.lambda_cost
 
        lambdaCost for lambdaRank LTR approach.
        
 
        The simple usage:
        
 
        cost = lambda_cost(input=input,
@@ -3023,7 +2998,7 @@ entire list of get gradient.
 
        mse_cost
        
 
        
 
        
-class paddle.v2.layer.mse_cost(*args, **kwargs)
        
+class paddle.v2.layer.mse_cost
 
        
 
        mean squared error cost:
        
 
        
@@ -3076,7 +3051,7 @@ It is an optional argument.
 
        rank_cost
        
 
        
 
        
-class paddle.v2.layer.rank_cost(*args, **kwargs)
        
+class paddle.v2.layer.rank_cost
 
        A cost Layer for learning to rank using gradient descent. Details can refer
 to papers.
 This layer contains at least three inputs. The weight is an optional
@@ -3131,7 +3106,7 @@ It is an optional argument.
 
        sum_cost
        
 
        
 
        
-class paddle.v2.layer.sum_cost(*args, **kwargs)
        
+class paddle.v2.layer.sum_cost
 
        A loss layer which calculate the sum of the input as loss
        
 
        cost = sum_cost(input=input)
 
        
@@ -3162,7 +3137,7 @@ It is an optional argument.
 
        crf
        
 
        
 
        
-class paddle.v2.layer.crf(*args, **kwargs)
        
+class paddle.v2.layer.crf
 
        A layer for calculating the cost of sequential conditional random
 field model.
        
 
        The simple usage:
        
@@ -3203,7 +3178,7 @@ optional argument.
 
        crf_decoding
        
 
        
 
        
-class paddle.v2.layer.crf_decoding(*args, **kwargs)
        
+class paddle.v2.layer.crf_decoding
 
        A layer for calculating the decoding sequence of sequential conditional
 random field model. The decoding sequence is stored in output.ids.
 If a second input is provided, it is treated as the ground-truth label, and
@@ -3243,7 +3218,7 @@ decoding or 0 for correct decoding.
        
 
        ctc
        
 
        
 
        
-class paddle.v2.layer.ctc(*args, **kwargs)
        
+class paddle.v2.layer.ctc
 
        Connectionist Temporal Classification (CTC) is designed for temporal
 classication task. That is, for sequence labeling problems where the
 alignment between the inputs and the target labels is unknown.
        
@@ -3294,7 +3269,7 @@ should also be num_classes + 1.
        
 
        warp_ctc
        
 
        
 
        
-class paddle.v2.layer.warp_ctc(*args, **kwargs)
        
+class paddle.v2.layer.warp_ctc
 
        A layer intergrating the open-source warp-ctc
 <https://github.com/baidu-research/warp-ctc> library, which is used in
 Deep Speech 2: End-toEnd Speech Recognition in English and Mandarin
@@ -3354,7 +3329,7 @@ should be consistent as that used in your labels.
 
        nce
        
 
        
 
        
-class paddle.v2.layer.nce(*args, **kwargs)
        
+class paddle.v2.layer.nce
 
        Noise-contrastive estimation.
 Implements the method in the following paper:
 A fast and simple algorithm for training neural probabilistic language models.
        
@@ -3374,7 +3349,7 @@ A fast and simple algorithm for training neural probabilistic language models.label (paddle.v2.config_base.Layer) – label layer
 
      • weight (paddle.v2.config_base.Layer) – weight layer, can be None(default)
        • 
 
      • num_classes (int) – number of classes.
        • 
-
      • act (paddle.v2.Activation.Base) – Activation, default is Sigmoid.
        • 
+
      • act (paddle.v2.activation.Base) – Activation, default is Sigmoid.
        • 
 
      • param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute|list.
        • 
 
      • num_neg_samples (int) – number of negative samples. Default is 10.
        • 
 
      • neg_distribution (list|tuple|collections.Sequence|None) – The distribution for generating the random negative labels.
@@ -3400,7 +3375,7 @@ If not None, its length must be equal to num_classes.
        • 
 
        hsigmoid
        
 
        
 
        
-class paddle.v2.layer.hsigmoid(*args, **kwargs)
        
+class paddle.v2.layer.hsigmoid
 
        Organize the classes into a binary tree. At each node, a sigmoid function
 is used to calculate the probability of belonging to the right branch.
 This idea is from “F. Morin, Y. Bengio (AISTATS 05):
@@ -3442,7 +3417,7 @@ False means no bias.
 
        smooth_l1_cost
        
 
        
 
        
-class paddle.v2.layer.smooth_l1_cost(*args, **kwargs)
        
+class paddle.v2.layer.smooth_l1_cost
 
        This is a L1 loss but more smooth. It requires that the
 size of input and label are equal. The formula is as follows,
        
 
        
@@ -3486,7 +3461,7 @@ size of input and label are equal. The formula is as follows,
        
 
        eos
        
 
        
 
        
-class paddle.v2.layer.eos(*args, **kwargs)
        
+class paddle.v2.layer.eos
 
        A layer for checking EOS for each sample:
 - output_id = (input_id == conf.eos_id)
        
 
        The result is stored in output_.ids.
diff --git a/develop/doc_cn/api/v2/config/networks.html b/develop/doc_cn/api/v2/config/networks.html
index e0b442391abb841e1aa9ce0c948ec2abc3a32e23..484ae13f761f7ab62895022ad48125712faa3997 100644
--- a/develop/doc_cn/api/v2/config/networks.html
+++ b/develop/doc_cn/api/v2/config/networks.html
@@ -197,9 +197,9 @@
 
        NLP
        
 
        
 
        sequence_conv_pool
        
-
        
+
        
 
        
-class paddle.v2.networks.sequence_conv_pool(*args, **kwargs)
        
+paddle.v2.networks.sequence_conv_pool(*args, **kwargs)
 
        Text convolution pooling layers helper.
        
 
        Text input => Context Projection => FC Layer => Pooling => Output.
        
 
    
 

 
 
    参数:
      
 
    • name (basestring) – Layer name.
      • 
 
    • input (list|tuple|collections.Sequence) – input layers or projections
      • 
-
    • act (paddle.v2.Activation.Base) – Activation type.
      • 
+
    • act (paddle.v2.activation.Base) – Activation type.
      • 
 
    • layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
      • 
 
    
 
    
@@ -208,34 +208,34 @@
 
-
@@ -245,9 +245,9 @@ False if no bias.
 
 
    
 
    text_conv_pool
    
-
    
+
    
 
    
-class paddle.v2.networks.text_conv_pool(*args, **kwargs)
    
+paddle.v2.networks.text_conv_pool(*args, **kwargs)
 
    Text convolution pooling layers helper.
    
 
    Text input => Context Projection => FC Layer => Pooling => Output.
    
 
    
 
    参数:
      
 
    • name (basestring) – name of output layer(pooling layer name)
      • 
-
    • input (paddle.v2.config_base.Layer) – name of input layer
      • 
+
    • input (LayerOutput) – name of input layer
      • 
 
    • context_len (int) – context projection length. See
 context_projection’s document.
      • 
 
    • hidden_size (int) – FC Layer size.
      • 
 
    • context_start (int or None) – context projection length. See
 context_projection’s context_start.
      • 
-
    • pool_type (BasePoolingType.) – pooling layer type. See pooling’s document.
      • 
-
    • context_proj_name (basestring) – context projection layer name.
+
    • pool_type (BasePoolingType.) – pooling layer type. See pooling_layer’s document.
      • 
+
    • context_proj_layer_name (basestring) – context projection layer name.
 None if user don’t care.
      • 
-
    • context_proj_param_attr (paddle.v2.attr.ParameterAttribute or None.) – context projection parameter attribute.
+
    • context_proj_param_attr (ParameterAttribute or None.) – context projection parameter attribute.
 None if user don’t care.
      • 
-
    • fc_name (basestring) – fc layer name. None if user don’t care.
      • 
-
    • fc_param_attr (paddle.v2.attr.ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
      • 
-
    • fc_bias_attr (paddle.v2.attr.ParameterAttribute or None) – fc bias parameter attribute. False if no bias,
+
    • fc_layer_name (basestring) – fc layer name. None if user don’t care.
      • 
+
    • fc_param_attr (ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
      • 
+
    • fc_bias_attr (ParameterAttribute or None) – fc bias parameter attribute. False if no bias,
 None if user don’t care.
      • 
-
    • fc_act (paddle.v2.Activation.Base) – fc layer activation type. None means tanh
      • 
-
    • pool_bias_attr (paddle.v2.attr.ParameterAttribute or None.) – pooling layer bias attr. None if don’t care.
+
    • fc_act (BaseActivation) – fc layer activation type. None means tanh
      • 
+
    • pool_bias_attr (ParameterAttribute or None.) – pooling layer bias attr. None if don’t care.
 False if no bias.
      • 
-
    • fc_attr (paddle.v2.attr.ExtraAttribute) – fc layer extra attribute.
      • 
-
    • context_attr (paddle.v2.attr.ExtraAttribute) – context projection layer extra attribute.
      • 
-
    • pool_attr (paddle.v2.attr.ExtraAttribute) – pooling layer extra attribute.
      • 
+
    • fc_attr (ExtraLayerAttribute) – fc layer extra attribute.
      • 
+
    • context_attr (ExtraLayerAttribute) – context projection layer extra attribute.
      • 
+
    • pool_attr (ExtraLayerAttribute) – pooling layer extra attribute.
      • 
 
    
     		
 
    
 
    返回:
    output layer name.
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
 
    
@@ -256,34 +256,34 @@ False if no bias.
 
-
@@ -296,9 +296,9 @@ False if no bias.
 
    Images
    
 
    
 
    img_conv_bn_pool
    
-
    
+
    
 
    
-class paddle.v2.networks.img_conv_bn_pool(*args, **kwargs)
    
+paddle.v2.networks.img_conv_bn_pool(*args, **kwargs)
 
    Convolution, batch normalization, pooling group.
    
 
    
 
    参数:
      
 
    • name (basestring) – name of output layer(pooling layer name)
      • 
-
    • input (paddle.v2.config_base.Layer) – name of input layer
      • 
+
    • input (LayerOutput) – name of input layer
      • 
 
    • context_len (int) – context projection length. See
 context_projection’s document.
      • 
 
    • hidden_size (int) – FC Layer size.
      • 
 
    • context_start (int or None) – context projection length. See
 context_projection’s context_start.
      • 
-
    • pool_type (BasePoolingType.) – pooling layer type. See pooling’s document.
      • 
-
    • context_proj_name (basestring) – context projection layer name.
+
    • pool_type (BasePoolingType.) – pooling layer type. See pooling_layer’s document.
      • 
+
    • context_proj_layer_name (basestring) – context projection layer name.
 None if user don’t care.
      • 
-
    • context_proj_param_attr (paddle.v2.attr.ParameterAttribute or None.) – context projection parameter attribute.
+
    • context_proj_param_attr (ParameterAttribute or None.) – context projection parameter attribute.
 None if user don’t care.
      • 
-
    • fc_name (basestring) – fc layer name. None if user don’t care.
      • 
-
    • fc_param_attr (paddle.v2.attr.ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
      • 
-
    • fc_bias_attr (paddle.v2.attr.ParameterAttribute or None) – fc bias parameter attribute. False if no bias,
+
    • fc_layer_name (basestring) – fc layer name. None if user don’t care.
      • 
+
    • fc_param_attr (ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
      • 
+
    • fc_bias_attr (ParameterAttribute or None) – fc bias parameter attribute. False if no bias,
 None if user don’t care.
      • 
-
    • fc_act (paddle.v2.Activation.Base) – fc layer activation type. None means tanh
      • 
-
    • pool_bias_attr (paddle.v2.attr.ParameterAttribute or None.) – pooling layer bias attr. None if don’t care.
+
    • fc_act (BaseActivation) – fc layer activation type. None means tanh
      • 
+
    • pool_bias_attr (ParameterAttribute or None.) – pooling layer bias attr. None if don’t care.
 False if no bias.
      • 
-
    • fc_attr (paddle.v2.attr.ExtraAttribute) – fc layer extra attribute.
      • 
-
    • context_attr (paddle.v2.attr.ExtraAttribute) – context projection layer extra attribute.
      • 
-
    • pool_attr (paddle.v2.attr.ExtraAttribute) – pooling layer extra attribute.
      • 
+
    • fc_attr (ExtraLayerAttribute) – fc layer extra attribute.
      • 
+
    • context_attr (ExtraLayerAttribute) – context projection layer extra attribute.
      • 
+
    • pool_attr (ExtraLayerAttribute) – pooling layer extra attribute.
      • 
 
    
     		
 
    
 
    返回:
    output layer name.
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
 
    @@ -306,33 +306,33 @@ False if no bias.
 
-
@@ -342,9 +342,9 @@ False if no bias.
 
 
    
 
    img_conv_group
    
-
    
+
    
 
    
-class paddle.v2.networks.img_conv_group(**kwargs)
    
+paddle.v2.networks.img_conv_group(*args, **kwargs)
 
    Image Convolution Group, Used for vgg net.
    
 
    TODO(yuyang18): Complete docs
    
 
    
 

 
    参数:
      
 
    • name (basestring) – group name
      • 
-
    • input (paddle.v2.config_base.Layer) – layer’s input
      • 
-
    • filter_size (int) – see img_conv’s document
      • 
-
    • num_filters (int) – see img_conv’s document
      • 
-
    • pool_size (int) – see img_pool’s document.
      • 
-
    • pool_type (BasePoolingType) – see img_pool’s document.
      • 
-
    • act (paddle.v2.Activation.Base) – see batch_norm’s document.
      • 
-
    • groups (int) – see img_conv’s document
      • 
-
    • conv_stride (int) – see img_conv’s document.
      • 
-
    • conv_padding (int) – see img_conv’s document.
      • 
-
    • conv_bias_attr (paddle.v2.attr.ParameterAttribute) – see img_conv’s document.
      • 
-
    • num_channel (int) – see img_conv’s document.
      • 
-
    • conv_param_attr (paddle.v2.attr.ParameterAttribute) – see img_conv’s document.
      • 
-
    • shared_bias (bool) – see img_conv’s document.
      • 
-
    • conv_attr (Extrapaddle.v2.config_base.Layer) – see img_conv’s document.
      • 
-
    • bn_param_attr (paddle.v2.attr.ParameterAttribute.) – see batch_norm’s document.
      • 
-
    • bn_bias_attr – see batch_norm’s document.
      • 
-
    • bn_attr – paddle.v2.attr.ParameterAttribute.
      • 
-
    • pool_stride (int) – see img_pool’s document.
      • 
-
    • pool_padding (int) – see img_pool’s document.
      • 
-
    • pool_attr (paddle.v2.attr.ExtraAttribute) – see img_pool’s document.
      • 
+
    • input (LayerOutput) – layer’s input
      • 
+
    • filter_size (int) – see img_conv_layer’s document
      • 
+
    • num_filters (int) – see img_conv_layer’s document
      • 
+
    • pool_size (int) – see img_pool_layer’s document.
      • 
+
    • pool_type (BasePoolingType) – see img_pool_layer’s document.
      • 
+
    • act (BaseActivation) – see batch_norm_layer’s document.
      • 
+
    • groups (int) – see img_conv_layer’s document
      • 
+
    • conv_stride (int) – see img_conv_layer’s document.
      • 
+
    • conv_padding (int) – see img_conv_layer’s document.
      • 
+
    • conv_bias_attr (ParameterAttribute) – see img_conv_layer’s document.
      • 
+
    • num_channel (int) – see img_conv_layer’s document.
      • 
+
    • conv_param_attr (ParameterAttribute) – see img_conv_layer’s document.
      • 
+
    • shared_bias (bool) – see img_conv_layer’s document.
      • 
+
    • conv_layer_attr (ExtraLayerOutput) – see img_conv_layer’s document.
      • 
+
    • bn_param_attr (ParameterAttribute.) – see batch_norm_layer’s document.
      • 
+
    • bn_bias_attr – see batch_norm_layer’s document.
      • 
+
    • bn_layer_attr – ParameterAttribute.
      • 
+
    • pool_stride (int) – see img_pool_layer’s document.
      • 
+
    • pool_padding (int) – see img_pool_layer’s document.
      • 
+
    • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer’s document.
      • 
 
    
     		
 
    
 
    返回:
    Layer groups output
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
 
    
@@ -376,9 +376,9 @@ False if no bias.
 
 
    
 
    simple_img_conv_pool
    
-
    
+
    
 
    
-class paddle.v2.networks.simple_img_conv_pool(*args, **kwargs)
    
+paddle.v2.networks.simple_img_conv_pool(*args, **kwargs)
 
    Simple image convolution and pooling group.
    
 
    Input => conv => pooling
    
 
    
@@ -387,30 +387,30 @@ False if no bias.
 
-
@@ -423,9 +423,9 @@ False if no bias.
 
 
    
 
    vgg_16_network
    
-
    
+
    
 
    
-class paddle.v2.networks.vgg_16_network(**kwargs)
    
+paddle.v2.networks.vgg_16_network(input_image, num_channels, num_classes=1000)
 
    Same model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8
    
 
    
 
    参数:
      
 
    • name (basestring) – group name
      • 
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
-
    • filter_size (int) – see img_conv for details
      • 
-
    • num_filters (int) – see img_conv for details
      • 
-
    • pool_size (int) – see img_pool for details
      • 
-
    • pool_type (BasePoolingType) – see img_pool for details
      • 
-
    • act (paddle.v2.Activation.Base) – see img_conv for details
      • 
-
    • groups (int) – see img_conv for details
      • 
-
    • conv_stride (int) – see img_conv for details
      • 
-
    • conv_padding (int) – see img_conv for details
      • 
-
    • bias_attr (paddle.v2.attr.ParameterAttribute) – see img_conv for details
      • 
-
    • num_channel (int) – see img_conv for details
      • 
-
    • param_attr (paddle.v2.attr.ParameterAttribute) – see img_conv for details
      • 
-
    • shared_bias (bool) – see img_conv for details
      • 
-
    • conv_attr (paddle.v2.attr.ExtraAttribute) – see img_conv for details
      • 
-
    • pool_stride (int) – see img_pool for details
      • 
-
    • pool_padding (int) – see img_pool for details
      • 
-
    • pool_attr (paddle.v2.attr.ExtraAttribute) – see img_pool for details
      • 
+
    • input (LayerOutput) – input layer name.
      • 
+
    • filter_size (int) – see img_conv_layer for details
      • 
+
    • num_filters (int) – see img_conv_layer for details
      • 
+
    • pool_size (int) – see img_pool_layer for details
      • 
+
    • pool_type (BasePoolingType) – see img_pool_layer for details
      • 
+
    • act (BaseActivation) – see img_conv_layer for details
      • 
+
    • groups (int) – see img_conv_layer for details
      • 
+
    • conv_stride (int) – see img_conv_layer for details
      • 
+
    • conv_padding (int) – see img_conv_layer for details
      • 
+
    • bias_attr (ParameterAttribute) – see img_conv_layer for details
      • 
+
    • num_channel (int) – see img_conv_layer for details
      • 
+
    • param_attr (ParameterAttribute) – see img_conv_layer for details
      • 
+
    • shared_bias (bool) – see img_conv_layer for details
      • 
+
    • conv_layer_attr (ExtraLayerAttribute) – see img_conv_layer for details
      • 
+
    • pool_stride (int) – see img_pool_layer for details
      • 
+
    • pool_padding (int) – see img_pool_layer for details
      • 
+
    • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer for details
      • 
 
    
     		
 
    
 
    返回:
    Layer’s output
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
 
    @@ -433,7 +433,7 @@ False if no bias.
 
@@ -453,9 +453,9 @@ False if no bias.
 
    LSTM
    
 
    
 
    lstmemory_unit
    
-
    
+
    
 
    
-class paddle.v2.networks.lstmemory_unit(*args, **kwargs)
    
+paddle.v2.networks.lstmemory_unit(*args, **kwargs)
 
    Define calculations that a LSTM unit performs in a single time step.
 This function itself is not a recurrent layer, so that it can not be
 directly applied to sequence input. This function is always used in
@@ -469,9 +469,9 @@ for more details about LSTM. The link goes as follows:
 
    The example usage is:
    
 
    lstm_step = lstmemory_unit(input=[layer1],
                            size=256,
-                           act=paddle.v2.Activation.Tanh(),
-                           gate_act=paddle.v2.Activation.Sigmoid(),
-                           state_act=paddle.v2.Activation.Tanh())
+                           act=TanhActivation(),
+                           gate_act=SigmoidActivation(),
+                           state_act=TanhActivation())
 
    
 
    
 
    
 

 
    参数:
      
 
    • num_classes – 
      • 
-
    • input_image (paddle.v2.config_base.Layer) – 
      • 
+
    • input_image (LayerOutput) – 
      • 
 
    • num_channels (int) – 
      • 
 
    
 
    
@@ -479,27 +479,27 @@ for more details about LSTM. The link goes as follows:
 
-
@@ -509,9 +509,9 @@ False means no bias, None means default bias.
 
 
    
 
    lstmemory_group
    
-
    
+
    
 
    
-class paddle.v2.networks.lstmemory_group(*args, **kwargs)
    
+paddle.v2.networks.lstmemory_group(*args, **kwargs)
 
    lstm_group is a recurrent layer group version of Long Short Term Memory. It
 does exactly the same calculation as the lstmemory layer (see lstmemory in
 layers.py for the maths) does. A promising benefit is that LSTM memory
@@ -524,14 +524,14 @@ lstmemory_group.
    
 multiplications:
 \(W_{xi}x_{t}\) , \(W_{xf}x_{t}\),
 \(W_{xc}x_t\), \(W_{xo}x_{t}\) are not done in lstmemory_unit to
-speed up the calculations. Consequently, an additional mixed with
+speed up the calculations. Consequently, an additional mixed_layer with
 full_matrix_projection must be included before lstmemory_unit is called.
    
 
    The example usage is:
    
 
    lstm_step = lstmemory_group(input=[layer1],
                             size=256,
-                            act=paddle.v2.Activation.Tanh(),
-                            gate_act=paddle.v2.Activation.Sigmoid(),
-                            state_act=paddle.v2.Activation.Tanh())
+                            act=TanhActivation(),
+                            gate_act=SigmoidActivation(),
+                            state_act=TanhActivation())
 
    
 
    
 
    
 
 
    参数:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – lstmemory unit name.
      • 
 
    • size (int) – lstmemory unit size.
      • 
-
    • param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default.
      • 
-
    • act (paddle.v2.Activation.Base) – lstm final activiation type
      • 
-
    • gate_act (paddle.v2.Activation.Base) – lstm gate activiation type
      • 
-
    • state_act (paddle.v2.Activation.Base) – lstm state activiation type.
      • 
-
    • mixed_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias parameter attribute of mixed layer.
+
    • param_attr (ParameterAttribute) – Parameter config, None if use default.
      • 
+
    • act (BaseActivation) – lstm final activiation type
      • 
+
    • gate_act (BaseActivation) – lstm gate activiation type
      • 
+
    • state_act (BaseActivation) – lstm state activiation type.
      • 
+
    • mixed_bias_attr (ParameterAttribute|False) – bias parameter attribute of mixed layer.
 False means no bias, None means default bias.
      • 
-
    • lstm_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias parameter attribute of lstm layer.
+
    • lstm_bias_attr (ParameterAttribute|False) – bias parameter attribute of lstm layer.
 False means no bias, None means default bias.
      • 
-
    • mixed_attr (paddle.v2.attr.ExtraAttribute) – mixed layer’s extra attribute.
      • 
-
    • lstm_attr (paddle.v2.attr.ExtraAttribute) – lstm layer’s extra attribute.
      • 
-
    • get_output_attr (paddle.v2.attr.ExtraAttribute) – get output layer’s extra attribute.
      • 
+
    • mixed_layer_attr (ExtraLayerAttribute) – mixed layer’s extra attribute.
      • 
+
    • lstm_layer_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
      • 
+
    • get_output_layer_attr (ExtraLayerAttribute) – get output layer’s extra attribute.
      • 
 
    
     		
 
    
 
    返回:
    lstmemory unit name.
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
 
    
@@ -539,28 +539,28 @@ full_matrix_projection must be included before lstmemory_unit is called.
    
     
-
@@ -570,9 +570,9 @@ False means no bias, None means default bias.
 
 
    
 
    simple_lstm
    
-
    
+
    
 
    
-class paddle.v2.networks.simple_lstm(*args, **kwargs)
    
+paddle.v2.networks.simple_lstm(*args, **kwargs)
 
    Simple LSTM Cell.
    
 
    It just combine a mixed layer with fully_matrix_projection and a lstmemory
 layer. The simple lstm cell was implemented as follow equations.
    
@@ -586,25 +586,25 @@ want to know what lstm is. 
 
    
-
@@ -614,9 +614,9 @@ means default bias.
 
 
    
 
    bidirectional_lstm
    
-
    
+
    
 
    
-class paddle.v2.networks.bidirectional_lstm(*args, **kwargs)
    
+paddle.v2.networks.bidirectional_lstm(*args, **kwargs)
 
    A bidirectional_lstm is a recurrent unit that iterates over the input
 sequence both in forward and bardward orders, and then concatenate two
 outputs form a final output. However, concatenation of two outputs
@@ -636,7 +636,7 @@ The link goes as follows:
 
    
-
-
@@ -662,9 +662,9 @@ concatenated and returned.
 
    GRU
    
 
    
 
    gru_unit
    
-
    
+
    
 
    
-class paddle.v2.networks.gru_unit(*args, **kwargs)
    
+paddle.v2.networks.gru_unit(*args, **kwargs)
 
    Define calculations that a gated recurrent unit performs in a single time
 step. This function itself is not a recurrent layer, so that it can not be
 directly applied to sequence input. This function is almost always used in
@@ -676,19 +676,19 @@ mechanism.
    
 
    
-
@@ -698,9 +698,9 @@ mechanism.
    
 
 
    
 
    gru_group
    
-
    
+
    
 
    
-class paddle.v2.networks.gru_group(*args, **kwargs)
    
+paddle.v2.networks.gru_group(*args, **kwargs)
 
    gru_group is a recurrent layer group version of Gated Recurrent Unit. It
 does exactly the same calculation as the grumemory layer does. A promising
 benefit is that gru hidden states are accessible to the user. This is
@@ -711,8 +711,8 @@ to use the grumemory, which is relatively faster.
    
 
    The example usage is:
    
 
    gru = gur_group(input=[layer1],
                 size=256,
-                act=paddle.v2.Activation.Tanh(),
-                gate_act=paddle.v2.Activation.Sigmoid())
+                act=TanhActivation(),
+                gate_act=SigmoidActivation())
 
    
 
    
 
    
 
 
    参数:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – lstmemory group name.
      • 
 
    • size (int) – lstmemory group size.
      • 
 
    • reverse (bool) – is lstm reversed
      • 
-
    • param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default.
      • 
-
    • act (paddle.v2.Activation.Base) – lstm final activiation type
      • 
-
    • gate_act (paddle.v2.Activation.Base) – lstm gate activiation type
      • 
-
    • state_act (paddle.v2.Activation.Base) – lstm state activiation type.
      • 
-
    • mixed_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias parameter attribute of mixed layer.
+
    • param_attr (ParameterAttribute) – Parameter config, None if use default.
      • 
+
    • act (BaseActivation) – lstm final activiation type
      • 
+
    • gate_act (BaseActivation) – lstm gate activiation type
      • 
+
    • state_act (BaseActivation) – lstm state activiation type.
      • 
+
    • mixed_bias_attr (ParameterAttribute|False) – bias parameter attribute of mixed layer.
 False means no bias, None means default bias.
      • 
-
    • lstm_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias parameter attribute of lstm layer.
+
    • lstm_bias_attr (ParameterAttribute|False) – bias parameter attribute of lstm layer.
 False means no bias, None means default bias.
      • 
-
    • mixed_attr (paddle.v2.attr.ExtraAttribute) – mixed layer’s extra attribute.
      • 
-
    • lstm_attr (paddle.v2.attr.ExtraAttribute) – lstm layer’s extra attribute.
      • 
-
    • get_output_attr (paddle.v2.attr.ExtraAttribute) – get output layer’s extra attribute.
      • 
+
    • mixed_layer_attr (ExtraLayerAttribute) – mixed layer’s extra attribute.
      • 
+
    • lstm_layer_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
      • 
+
    • get_output_layer_attr (ExtraLayerAttribute) – get output layer’s extra attribute.
      • 
 
    
     		
 
    
 
    返回:
    the lstmemory group.
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
 
    参数:
      
 
    • name (basestring) – lstm layer name.
      • 
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • size (int) – lstm layer size.
      • 
 
    • reverse (bool) – whether to process the input data in a reverse order
      • 
-
    • mat_param_attr (paddle.v2.attr.ParameterAttribute) – mixed layer’s matrix projection parameter attribute.
      • 
-
    • bias_param_attr (paddle.v2.attr.ParameterAttribute|False) – bias parameter attribute. False means no bias, None
+
    • mat_param_attr (ParameterAttribute) – mixed layer’s matrix projection parameter attribute.
      • 
+
    • bias_param_attr (ParameterAttribute|False) – bias parameter attribute. False means no bias, None
 means default bias.
      • 
-
    • inner_param_attr (paddle.v2.attr.ParameterAttribute) – lstm cell parameter attribute.
      • 
-
    • act (paddle.v2.Activation.Base) – lstm final activiation type
      • 
-
    • gate_act (paddle.v2.Activation.Base) – lstm gate activiation type
      • 
-
    • state_act (paddle.v2.Activation.Base) – lstm state activiation type.
      • 
-
    • mixed_attr (paddle.v2.attr.ExtraAttribute) – mixed layer’s extra attribute.
      • 
-
    • lstm_cell_attr (paddle.v2.attr.ExtraAttribute) – lstm layer’s extra attribute.
      • 
+
    • inner_param_attr (ParameterAttribute) – lstm cell parameter attribute.
      • 
+
    • act (BaseActivation) – lstm final activiation type
      • 
+
    • gate_act (BaseActivation) – lstm gate activiation type
      • 
+
    • state_act (BaseActivation) – lstm state activiation type.
      • 
+
    • mixed_layer_attr (ExtraLayerAttribute) – mixed layer’s extra attribute.
      • 
+
    • lstm_cell_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
      • 
 
    
     		
 
    
 
    返回:
    lstm layer name.
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
     
 
    参数:
      
 
    • name (basestring) – bidirectional lstm layer name.
      • 
-
    • input (paddle.v2.config_base.Layer) – input layer.
      • 
+
    • input (LayerOutput) – input layer.
      • 
 
    • size (int) – lstm layer size.
      • 
 
    • return_seq (bool) – If set False, outputs of the last time step are
 concatenated and returned.
@@ -646,10 +646,10 @@ concatenated and returned.
      • 
 
    
     		
 
    返回:
    paddle.v2.config_base.Layer object accroding to the return_seq.
    
+
    返回:
    LayerOutput object accroding to the return_seq.
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
     
 
 
    参数:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – name of the gru group.
      • 
 
    • size (int) – hidden size of the gru.
      • 
-
    • act (paddle.v2.Activation.Base) – type of the activation
      • 
-
    • gate_act (paddle.v2.Activation.Base) – type of the gate activation
      • 
-
    • gru_attr (paddle.v2.attr.ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
+
    • act (BaseActivation) – type of the activation
      • 
+
    • gate_act (BaseActivation) – type of the gate activation
      • 
+
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
 
    
     		
 
    
 
    返回:
    the gru output layer.
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
 
    
@@ -720,21 +720,21 @@ to use the grumemory, which is relatively faster.
    
     
-
@@ -744,23 +744,23 @@ to use the grumemory, which is relatively faster.
    
 
 
    
 
    simple_gru
    
-
    
+
    
 
    
-class paddle.v2.networks.simple_gru(*args, **kwargs)
    
-
    You maybe see gru_step, grumemory in layers.py, gru_unit, gru_group,
+paddle.v2.networks.simple_gru(*args, **kwargs)
+
    You maybe see gru_step_layer, grumemory in layers.py, gru_unit, gru_group,
 simple_gru in network.py. The reason why there are so many interfaces is
 that we have two ways to implement recurrent neural network. One way is to
 use one complete layer to implement rnn (including simple rnn, gru and lstm)
-with multiple time steps, such as recurrent, lstmemory, grumemory. But,
+with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But,
 the multiplication operation \(W x_t\) is not computed in these layers.
 See details in their interfaces in layers.py.
 The other implementation is to use an recurrent group which can ensemble a
 series of layers to compute rnn step by step. This way is flexible for
 attenion mechanism or other complex connections.
    
 
      
-
    • gru_step: only compute rnn by one step. It needs an memory as input
+
    • gru_step_layer: only compute rnn by one step. It needs an memory as input
 and can be used in recurrent group.
      • 
-
    • gru_unit: a wrapper of gru_step with memory.
      • 
+
    • gru_unit: a wrapper of gru_step_layer with memory.
      • 
 
    • gru_group: a GRU cell implemented by a combination of multiple layers in
 recurrent group.
 But \(W x_t\) is not done in group.
      • 
@@ -781,21 +781,21 @@ gru_group, and gru_group is relatively better than simple_gru.
      
 
    
-
@@ -805,9 +805,9 @@ gru_group, and gru_group is relatively better than simple_gru.
    
 
 
    
 
    simple_gru2
    
-
    
+
    
 
    
-class paddle.v2.networks.simple_gru2(*args, **kwargs)
    
+paddle.v2.networks.simple_gru2(*args, **kwargs)
 
    simple_gru2 is the same with simple_gru, but using grumemory instead
 Please see grumemory in layers.py for more detail about the maths.
 simple_gru2 is faster than simple_gru.
    
@@ -820,21 +820,21 @@ simple_gru2 is faster than simple_gru.
    
 
    
-
@@ -844,9 +844,9 @@ simple_gru2 is faster than simple_gru.
    
 
 
    
 
    bidirectional_gru
    
-
    
+
    
 
    
-class paddle.v2.networks.bidirectional_gru(*args, **kwargs)
    
+paddle.v2.networks.bidirectional_gru(*args, **kwargs)
 
    A bidirectional_gru is a recurrent unit that iterates over the input
 sequence both in forward and bardward orders, and then concatenate two
 outputs to form a final output. However, concatenation of two outputs
@@ -862,7 +862,7 @@ just add them together.
    
 
    
-
-
@@ -886,9 +886,9 @@ concatenated and returned.
 
 
    
 
    simple_attention
    
-
    
+
    
 
    
-class paddle.v2.networks.simple_attention(*args, **kwargs)
    
+paddle.v2.networks.simple_attention(*args, **kwargs)
 
    Calculate and then return a context vector by attention machanism.
 Size of the context vector equals to size of the encoded_sequence.
    
 
    
@@ -912,18 +912,18 @@ Align and Translate for more details. The link is as follows:
 
    
 
 
    参数:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – name of the gru group.
      • 
 
    • size (int) – hidden size of the gru.
      • 
 
    • reverse (bool) – whether to process the input data in a reverse order
      • 
-
    • act (paddle.v2.Activation.Base) – type of the activiation
      • 
-
    • gate_act (paddle.v2.Activation.Base) – type of the gate activiation
      • 
-
    • gru_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
-
    • gru_attr (paddle.v2.attr.ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
+
    • act (BaseActivation) – type of the activiation
      • 
+
    • gate_act (BaseActivation) – type of the gate activiation
      • 
+
    • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
+
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
 
    
     		
 
    
 
    返回:
    the gru group.
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
     
 
 
    参数:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – name of the gru group.
      • 
 
    • size (int) – hidden size of the gru.
      • 
 
    • reverse (bool) – whether to process the input data in a reverse order
      • 
-
    • act (paddle.v2.Activation.Base) – type of the activiation
      • 
-
    • gate_act (paddle.v2.Activation.Base) – type of the gate activiation
      • 
-
    • gru_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
-
    • gru_attr (paddle.v2.attr.ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
+
    • act (BaseActivation) – type of the activiation
      • 
+
    • gate_act (BaseActivation) – type of the gate activiation
      • 
+
    • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
+
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
 
    
     		
 
    
 
    返回:
    the gru group.
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
     
 
 
    参数:
      
-
    • input (paddle.v2.config_base.Layer) – input layer name.
      • 
+
    • input (LayerOutput) – input layer name.
      • 
 
    • name (basestring) – name of the gru group.
      • 
 
    • size (int) – hidden size of the gru.
      • 
 
    • reverse (bool) – whether to process the input data in a reverse order
      • 
-
    • act (paddle.v2.Activation.Base) – type of the activiation
      • 
-
    • gate_act (paddle.v2.Activation.Base) – type of the gate activiation
      • 
-
    • gru_bias_attr (paddle.v2.attr.ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
-
    • gru_attr (paddle.v2.attr.ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
+
    • act (BaseActivation) – type of the activiation
      • 
+
    • gate_act (BaseActivation) – type of the gate activiation
      • 
+
    • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • 
+
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • 
 
    
     		
 
    
 
    返回:
    the gru group.
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
     
 
    参数:
      
 
    • name (basestring) – bidirectional gru layer name.
      • 
-
    • input (paddle.v2.config_base.Layer) – input layer.
      • 
+
    • input (LayerOutput) – input layer.
      • 
 
    • size (int) – gru layer size.
      • 
 
    • return_seq (bool) – If set False, outputs of the last time step are
 concatenated and returned.
@@ -872,10 +872,10 @@ concatenated and returned.
      • 
 
    
     		
 
    返回:
    paddle.v2.config_base.Layer object.
    
+
    返回:
    LayerOutput object.
    
     		
 
    返回类型:
    paddle.v2.config_base.Layer
    
+
    返回类型:
    LayerOutput
    
     		
 
    
     
 
    参数:
      
 
    • name (basestring) – name of the attention model.
      • 
-
    • softmax_param_attr (paddle.v2.attr.ParameterAttribute) – parameter attribute of sequence softmax
+
    • softmax_param_attr (ParameterAttribute) – parameter attribute of sequence softmax
 that is used to produce attention weight
      • 
 
    • weight_act (Activation) – activation of the attention model
      • 
-
    • encoded_sequence (paddle.v2.config_base.Layer) – output of the encoder
      • 
-
    • encoded_proj (paddle.v2.config_base.Layer) – attention weight is computed by a feed forward neural
+
    • encoded_sequence (LayerOutput) – output of the encoder
      • 
+
    • encoded_proj (LayerOutput) – attention weight is computed by a feed forward neural
 network which has two inputs : decoder’s hidden state
 of previous time step and encoder’s output.
 encoded_proj is output of the feed-forward network for
 encoder’s output. Here we pre-compute it outside
 simple_attention for speed consideration.
      • 
-
    • decoder_state (paddle.v2.config_base.Layer) – hidden state of decoder in previous time step
      • 
-
    • transform_param_attr (paddle.v2.attr.ParameterAttribute) – parameter attribute of the feed-forward
+
    • decoder_state (LayerOutput) – hidden state of decoder in previous time step
      • 
+
    • transform_param_attr (ParameterAttribute) – parameter attribute of the feed-forward
 network that takes decoder_state as inputs to
 compute attention weight.
      • 
 
    
@@ -942,9 +942,9 @@ compute attention weight.
 
    Miscs
    
 
    
 
    dropout_layer
    
-
    
+
    
 
    
-class paddle.v2.networks.dropout_layer(*args, **kwargs)
    
+paddle.v2.networks.dropout_layer(*args, **kwargs)
 
    @TODO(yuyang18): Add comments.
    
 diff --git a/develop/doc_cn/api/v2/data.html b/develop/doc_cn/api/v2/data.html
index 712d36f0062c829d8b7f46a6a059c10f3e584ed0..c0d4b4679f72728ffe16e8cba6340ac8d0aa356b 100644
--- a/develop/doc_cn/api/v2/data.html
+++ b/develop/doc_cn/api/v2/data.html
@@ -192,12 +192,50 @@
 
    Data Reader Interface and DataSets
    
 
    DataTypes
    
+
    
+
    
+paddle.v2.data_type.dense_array(dim, seq_type=0)
    
+
    Dense Array. It means the input feature is dense array with float type.
+For example, if the input is an image with 28*28 pixels, the input of
+Paddle neural network could be a dense vector with dimension 784 or a
+numpy array with shape (28, 28).
    
+
    For the 2-D convolution operation, each sample in one mini-batch must have
+the similarly size in PaddlePaddle now. But, it supports variable-dimension
+feature across mini-batch. For the variable-dimension, the param dim is not
+used. While the data reader must yield numpy array and the data feeder will
+set the data shape correctly.
    
+
    
 

 
    
+++
+
+
+
+
+
+
+
+
    


    参数:
      
+
    • dim (int) – dimension of this vector.
      • 
+
    • seq_type (int) – sequence type of input.
      • 
+
    
+
    返回:
    An input type object.
    
+
    返回类型:
    InputType
    
+
    
+
    
+
 
    
 
    
 paddle.v2.data_type.dense_vector(dim, seq_type=0)
    
-
    Dense Vector. It means the input feature is dense float vector. For example,
-if the input is an image with 28*28 pixels, the input of Paddle neural
-network should be a dense vector with dimension 784.
    
+
    Dense Array. It means the input feature is dense array with float type.
+For example, if the input is an image with 28*28 pixels, the input of
+Paddle neural network could be a dense vector with dimension 784 or a
+numpy array with shape (28, 28).
    
+
    For the 2-D convolution operation, each sample in one mini-batch must have
+the similarly size in PaddlePaddle now. But, it supports variable-dimension
+feature across mini-batch. For the variable-dimension, the param dim is not
+used. While the data reader must yield numpy array and the data feeder will
+set the data shape correctly.
    
 diff --git a/develop/doc_cn/design/cluster_train/master_server.html b/develop/doc_cn/design/cluster_train/master_server.html
index 5de2aa592da075128dedfdb4f8574d5bbd41f775..eda5a6d902facc25ea843c7e5a4bdd62c9f351be 100644
--- a/develop/doc_cn/design/cluster_train/master_server.html
+++ b/develop/doc_cn/design/cluster_train/master_server.html
@@ -193,7 +193,7 @@
 
    
 
    Task Queue
    
-
    As mentioned in distributed training design doc, a task is a data shard that the master server assigns to the trainer process to train on. A task consists of one or multiple blocks from one or multiple files. The master server maintains task queues to track the training progress.
    
+
    As mentioned in distributed training design doc, a task is a data shard that the master server assigns to the trainer process to train on. A task consists of one or multiple chunks from one or multiple files. The master server maintains task queues to track the training progress.
    
 
    
 
    Task Queue Creation
    
 
      
@@ -204,21 +204,21 @@
 
    
 
    
-
  • The master server will scan through each RecordIO file to generate the block index and know how many blocks does each file have. A block can be referenced by the file path and the index of the block within the file. The block index is in memory data structure that enables fast access to each block, and the index of the block with the file is an integer start from 0, representing the n-th block within the file.
    
-
    The definition of the block is:
    
-
    type Block struct {
-    Idx   int // index of the block within the file
+
  • The master server will scan through each RecordIO file to generate the chunk index and know how many chunks does each file have. A chunk can be referenced by the file path and the index of the chunk within the file. The chunk index is in memory data structure that enables fast access to each chunk, and the index of the chunk with the file is an integer start from 0, representing the n-th chunk within the file.
    
+
    The definition of the chunk is:
    
+
    type Chunk struct {
+    Idx   int // index of the chunk within the file
     Path  string
-    Index recordio.Index // block index
+    Index recordio.Index // chunk index
 }
 
    
 
    
 
    • 
-
  • Blocks are grouped into tasks, and tasks are filled into the todo queue. The pending queue and the done queue are initialized with no element.
    
+
  • Chunks are grouped into tasks, and tasks are filled into the todo queue. The pending queue and the done queue are initialized with no element.
    
 
    The definition of the task is:
    
 
    type Task struct {
     Index  int
-    Blocks []Block
+    Chunks []Chunk
 }
 
    
 
    
diff --git a/develop/doc_cn/design/cluster_train/pserver_client.html b/develop/doc_cn/design/cluster_train/pserver_client.html
index 1b8a76dc57996be490c3a161dd9056a47a2d15c5..eb0875767841946554ff8f99c05a924361995144 100644
--- a/develop/doc_cn/design/cluster_train/pserver_client.html
+++ b/develop/doc_cn/design/cluster_train/pserver_client.html
@@ -233,7 +233,7 @@ name:sparse-n-1
 
    int paddle_begin_init_params(paddle_pserver_client* client, const char* config_proto);
 
    
 
    
-
    The selected trainer’s call to paddle_begin_init_params will return with 1, and the other trainers’ call to paddle_begin_init_params will block until initialization is done, and return 0. As illustrated below:
    
+
    The selected trainer’s call to paddle_begin_init_params will return with 1, and the other trainers’ call to paddle_begin_init_params will return 0. paddle_get_params will be blocked until initialization is completed. As illustrated below:
    
 
    
 
    • 
 
    
@@ -266,16 +266,13 @@ name:sparse-n-1
  *
  * paddle_begin_init_params will be called from multiple trainers,
  * only one trainer will be selected to initialize the parameters on
- * parameter servers. Other trainers will be blocked until the
- * initialization is done, and they need to get the initialized
+ * parameter servers. Other trainers need to get the initialized
  * parameters from parameter servers using @paddle_get_params.
  *
- * @param pserver_config_proto serialized parameter server configuration in
- * Protocol Buffers format.
  * @return 1 if the trainer is selected to initialize parameter
  * servers, otherwise 0.
  */
-int paddle_begin_init_params(paddle_pserver_client* client, const char* pserver_config_proto);
+int paddle_begin_init_params(paddle_pserver_client* client);
 
 /**
  * @brief paddle_init_param initializes the parameter on parameter
@@ -283,12 +280,13 @@ name:sparse-n-1
  *
  * @param param the parameter to initialize.
  * @param param_config_proto the configuration for the parameter.
+ * @param config_len the length of param_config_proto
  * @return 0 if successful, otherwise -1. On failure, the trainer
  * needs to restart the entire initialization process (starting from
  * @paddle_begin_init_param). Or simply exit the program and wait for
  * the cluster management system to restart the trainer.
  */
-int paddle_init_param(paddle_pserver_client* client, paddle_parameter params, const char* param_config_proto);
+int paddle_init_param(paddle_pserver_client* client, paddle_parameter param, const unsigned char* param_config_proto, int config_len);
 
 /**
  * @brief paddle_finish_init_params tells parameter servers client has
@@ -315,6 +313,9 @@ name:sparse-n-1
 /**
  * @brief paddle_get_params gets parameters from parameter servers.
  *
+ * paddle_get_params will block until parameters are initialized on
+ * the parameter servers.
+ *
  * @param names the array of names of the parameters to get.
  * @param dst the destination array of parameters to save to.
  * @param len the length of the names array and the paddle_parameter
diff --git a/develop/doc_cn/design/parameters_in_cpp.html b/develop/doc_cn/design/parameters_in_cpp.html
new file mode 100644
index 0000000000000000000000000000000000000000..03ab64f137d4dbc82b4a0e40986b4da1127030ae
--- /dev/null
+++ b/develop/doc_cn/design/parameters_in_cpp.html
@@ -0,0 +1,282 @@
+
+
+
+
+  
+
+  
+  
+  
+  
+  Design Doc: The C++ Class Parameters — PaddlePaddle  文档
+  
+
+  
+  
+
+  
+
+  
+  
+    
+
+  
+
+  
+  
+    
+  
+
+  
+  
+        
+        
+     
+
+  
+  
+  
+
+  
+
+  
+  
+
+
+
+
+
+  
+  
    
+    
+    
    
+      
    
+        Folk me on Github
+        
    
+          
+            English
+            
+            
+          
+          
+        
    
+        
+      
    
+      
    
+        
+        
+
+        
+
    
+  
    
+    
+    
+    
+  
+
            
+      
    
+    
    
+  
    
+  
+  
    
+
+    
+    
+    
+    
    
+
+      
+
+ 
+
+
+
+
+
+
+
+
    
+  
      
+      
+    
    • Design Doc: The C++ Class Parameters
      • 
+  
    
+
    
+      
+      
    
+        
    
+          
    
+           
    
+            
+  
    
+
    Design Doc: The C++ Class Parameters
    
+
    Parameters is a concept we designed in Paddle V2 API. Parameters is a container of parameters, and make Paddle can shared parameter between topologies. We described usages of Parameter in api.md.
    
+
    We used Python to implement Parameters when designing V2 API before. There are several defects for current implementation:
    
+
      
+
    • We just use memcpy to share Parameters between topologies, but this is very inefficient.
      • 
+
    • We did not implement share Parameters while training. We just trigger memcpy when start training.
      • 
+
    
+
    It is necessary that we implement Parameters in CPP side. However, it could be a code refactoring for Paddle, because Paddle was designed for training only one topology before, i.e., each GradientMachine contains its Parameter as a data member. In current Paddle implementation, there are three concepts associated with Parameters:
    
+
      
+
    1. paddle::Parameter. A Parameters is a container for paddle::Parameter.
+It is evident that we should use paddle::Parameter when developing Parameters.
+However, the Parameter class contains many functions and does not have a clear interface.
+It contains create/store Parameter, serialize/deserialize, optimize(i.e SGD), randomize/zero.
+When we developing Parameters, we only use create/store Parameter functionality.
+We should extract functionalities of Parameter into many classes to clean Paddle CPP implementation.
      2. 
+
    2. paddle::GradientMachine and its sub-classes, e.g., paddle::MultiGradientMachine, paddle::NeuralNetwork.
+We should pass Parameters to paddle::GradientMachine when forward/backward to avoid memcpy between topologies.
+Also, we should handle multi-GPU/CPU training, because forward and backward would perform on multi-GPUs and multi-CPUs.
+Parameters should dispatch the parameter value to each device, and gather the parameter gradient from each device.
      3. 
+
    3. paddle::ParameterUpdater. The ParameterUpdater is used to update parameters in Paddle.
+So Parameters should be used by paddle::ParameterUpdater, and paddle::ParameterUpdater should optimize Parameters (by SGD).
      4. 
+
    
+
    The step by step approach for implementation Parameters in Paddle C++ core is listed below. Each step should be a PR and could be merged into Paddle one by one.
    
+
      
+
    1. Clean paddle::Parameter interface. Extract the functionalities of paddle::Parameter to prepare for the implementation of Parameters.
      2. 
+
    2. Implementation a Parameters class. It just stores the paddle::Parameter inside. Make GradientMachine uses Parameters as a class member.
      3. 
+
    3. Make Parameters support Multi-CPU and Multi-GPU training to prepare for sharing Parameter between topologies.
+Because we need share Parameters between topologies, it is Parameters‘s response to exchange Parameters between GPUs.
+GradientMachine should not handle how to exchange Parameters because GradientMachine only used to train one topology and we need to support train many topologies in Paddle, i.e., there could be many GradientMachines use one Parameters.
        
+
      • We should use a global function to exchange Parameters between GPUs, not a member function in Parameters. The MultiGradientMachine invoke this function, which uses Parameters as this function inputs.
        • 
+
      • The MultiGradientMachine contains many functionalities. Extracting the Parameters exchanging logic could make MultiGradientMachine clearer and simpler.
        • 
+
      
+
      4. 
+
    4. Make Parameters as an argument for forward/backward function, not a data member for GradientMachine. For example, forward could be forward(const Parameters& params, ...) and backward could be backward(Parameters* params, ...). After this step, Paddle could share Parameters between topologies.
      5. 
+
    5. ParameterUpdater is invoked by GradientMachine and Trainer, but it updates Parameters. In the end of this code refactoring, we could change ParameterUpdater directly uses Parameters to make ParameterUpdater‘s implementation clear.
      6. 
+
    
+
    
+
+
+           
    
+          
    
+          
    
+  
+
+  
    
+
+  
    
+    
    
+        © Copyright 2016, PaddlePaddle developers.
+
+    
    
+  
    
+  Built with Sphinx using a theme provided by Read the Docs. 
+
+
    
+
+        
    
+      
    
+
+    
    
+
+  
    
+  
+
+
+  
+
+    
+      
+      
+      
+      
+      
+       
+  
+
+  
+  
+    
+  
+  
+  
+  
+   
+
+
+
\ No newline at end of file
diff --git a/develop/doc_cn/objects.inv b/develop/doc_cn/objects.inv
index 3017ec20526bd951ea9ca77ce19924af732a5625..bab76d0a88896668886d62026dce12677932bbe3 100644
Binary files a/develop/doc_cn/objects.inv and b/develop/doc_cn/objects.inv differ
diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js
index 9b2741e9ebe181d27741c4fa513702d1c50a562e..ab8a2109a8b58b5ea2e94463df2a5555523dd2c1 100644
--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js
@@ -1 +1 @@
-Search.setIndex({docnames:["about/index_cn","api/index_cn","api/v1/data_provider/dataprovider_cn","api/v1/data_provider/pydataprovider2_cn","api/v1/index_cn","api/v1/predict/swig_py_paddle_cn","api/v2/config/activation","api/v2/config/attr","api/v2/config/evaluators","api/v2/config/layer","api/v2/config/networks","api/v2/config/optimizer","api/v2/config/pooling","api/v2/data","api/v2/model_configs","api/v2/run_logic","design/api","design/build_system/README","design/cluster_train/README","design/cluster_train/checkpointing","design/cluster_train/data_dispatch","design/cluster_train/master_server","design/cluster_train/pserver_client","design/cluster_train/submit-job","design/file_manager/README","design/file_manager/pfs/pfsclient","design/multi_language_interface/00.why_plain_c","design/multi_language_interface/01.inference_implementation","design/reader/README","design/releasing_process","faq/index_cn","getstarted/basic_usage/index_cn","getstarted/build_and_install/cmake/build_from_source_cn","getstarted/build_and_install/docker_install_cn","getstarted/build_and_install/index_cn","getstarted/build_and_install/ubuntu_install_cn","getstarted/concepts/use_concepts_cn","getstarted/index_cn","howto/cross_compiling/cross_compiling_for_android_cn","howto/cross_compiling/cross_compiling_for_raspberry_cn","howto/deep_model/rnn/hierarchical_layer_cn","howto/deep_model/rnn/hrnn_rnn_api_compare_cn","howto/deep_model/rnn/index_cn","howto/deep_model/rnn/recurrent_group_cn","howto/deep_model/rnn/rnn_config_cn","howto/dev/contribute_to_paddle_cn","howto/dev/new_layer_cn","howto/dev/write_docs_cn","howto/index_cn","howto/optimization/gpu_profiling_cn","howto/usage/cluster/cluster_train_cn","howto/usage/cmd_parameter/arguments_cn","howto/usage/cmd_parameter/detail_introduction_cn","howto/usage/cmd_parameter/index_cn","howto/usage/cmd_parameter/use_case_cn","howto/usage/k8s/k8s_basis_cn","howto/usage/k8s/k8s_cn","howto/usage/k8s/k8s_distributed_cn","howto/usage/k8s/src/k8s_data/README","howto/usage/k8s/src/k8s_train/README","index_cn","tutorials/embedding_model/index_cn","tutorials/image_classification/index_cn","tutorials/imagenet_model/resnet_model_cn","tutorials/index_cn","tutorials/quick_start/index_cn","tutorials/rec/ml_dataset_cn","tutorials/rec/ml_regression_cn","tutorials/semantic_role_labeling/index_cn","tutorials/sentiment_analysis/index_cn","tutorials/text_generation/index_cn"],envversion:50,filenames:["about/index_cn.md","api/index_cn.rst","api/v1/data_provider/dataprovider_cn.rst","api/v1/data_provider/pydataprovider2_cn.rst","api/v1/index_cn.rst","api/v1/predict/swig_py_paddle_cn.rst","api/v2/config/activation.rst","api/v2/config/attr.rst","api/v2/config/evaluators.rst","api/v2/config/layer.rst","api/v2/config/networks.rst","api/v2/config/optimizer.rst","api/v2/config/pooling.rst","api/v2/data.rst","api/v2/model_configs.rst","api/v2/run_logic.rst","design/api.md","design/build_system/README.md","design/cluster_train/README.md","design/cluster_train/checkpointing.md","design/cluster_train/data_dispatch.md","design/cluster_train/master_server.md","design/cluster_train/pserver_client.md","design/cluster_train/submit-job.md","design/file_manager/README.md","design/file_manager/pfs/pfsclient.md","design/multi_language_interface/00.why_plain_c.md","design/multi_language_interface/01.inference_implementation.md","design/reader/README.md","design/releasing_process.md","faq/index_cn.rst","getstarted/basic_usage/index_cn.rst","getstarted/build_and_install/cmake/build_from_source_cn.rst","getstarted/build_and_install/docker_install_cn.rst","getstarted/build_and_install/index_cn.rst","getstarted/build_and_install/ubuntu_install_cn.rst","getstarted/concepts/use_concepts_cn.rst","getstarted/index_cn.rst","howto/cross_compiling/cross_compiling_for_android_cn.md","howto/cross_compiling/cross_compiling_for_raspberry_cn.md","howto/deep_model/rnn/hierarchical_layer_cn.rst","howto/deep_model/rnn/hrnn_rnn_api_compare_cn.rst","howto/deep_model/rnn/index_cn.rst","howto/deep_model/rnn/recurrent_group_cn.md","howto/deep_model/rnn/rnn_config_cn.rst","howto/dev/contribute_to_paddle_cn.md","howto/dev/new_layer_cn.rst","howto/dev/write_docs_cn.rst","howto/index_cn.rst","howto/optimization/gpu_profiling_cn.rst","howto/usage/cluster/cluster_train_cn.md","howto/usage/cmd_parameter/arguments_cn.md","howto/usage/cmd_parameter/detail_introduction_cn.md","howto/usage/cmd_parameter/index_cn.rst","howto/usage/cmd_parameter/use_case_cn.md","howto/usage/k8s/k8s_basis_cn.md","howto/usage/k8s/k8s_cn.md","howto/usage/k8s/k8s_distributed_cn.md","howto/usage/k8s/src/k8s_data/README.md","howto/usage/k8s/src/k8s_train/README.md","index_cn.rst","tutorials/embedding_model/index_cn.md","tutorials/image_classification/index_cn.md","tutorials/imagenet_model/resnet_model_cn.md","tutorials/index_cn.md","tutorials/quick_start/index_cn.rst","tutorials/rec/ml_dataset_cn.md","tutorials/rec/ml_regression_cn.rst","tutorials/semantic_role_labeling/index_cn.md","tutorials/sentiment_analysis/index_cn.md","tutorials/text_generation/index_cn.md"],objects:{},objnames:{},objtypes:{},terms:{"00012\u7684\u6a21\u578b\u6709\u7740\u6700\u9ad8\u7684bleu\u503c27":70,"0005\u4e58\u4ee5batch":62,"000\u4e2a\u5df2\u6807\u6ce8\u8fc7\u7684\u9ad8\u6781\u6027\u7535\u5f71\u8bc4\u8bba\u7528\u4e8e\u8bad\u7ec3":69,"000\u4e2a\u7528\u4e8e\u6d4b\u8bd5":69,"000\u4e2atxt\u6587\u4ef6":69,"000\u4f4d\u7528\u6237\u5bf94":66,"000\u5e45\u56fe\u50cf\u4e0a\u6d4b\u8bd5\u4e86\u6a21\u578b\u7684\u5206\u7c7b\u9519\u8bef\u7387":63,"000\u5f20\u7070\u5ea6\u56fe\u7247\u7684\u6570\u5b57\u5206\u7c7b\u6570\u636e\u96c6":3,"000\u6761\u8bc4\u4ef7":66,"000\u90e8\u7535\u5f71\u76841":66,"00186201e":5,"00m":49,"02595v1":9,"03m":49,"0424m":49,"0473v3":10,"05d":62,"0630u":49,"06u":49,"0810u":49,"08823112e":5,"0957m":49,"0\u53f7\u8bad\u7ec3\u8282\u70b9\u662f\u4e3b\u8bad\u7ec3\u8282\u70b9":52,"0\u5c42\u5e8f\u5217":40,"0\u8868\u793a\u7b2c\u4e00\u6b21\u7ecf\u8fc7\u8bad\u7ec3\u96c6":69,"0ab":9,"0b1":35,"0rc1":[29,33],"0rc2":29,"10000\u5f20\u4f5c\u4e3a\u6d4b\u8bd5\u96c6":62,"10007_10":69,"10014_7":69,"100m":30,"101\u5c42\u548c152\u5c42\u7684\u7f51\u7edc\u7ed3\u6784\u4e2d":63,"101\u5c42\u548c152\u5c42\u7684\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6\u53ef\u53c2\u7167":63,"101\u5c42\u7f51\u7edc\u6a21\u578b":63,"10\u4e2d\u7684\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6":62,"10\u6570\u636e\u96c6":62,"10\u6570\u636e\u96c6\u5305\u542b60000\u5f2032x32\u7684\u5f69\u8272\u56fe\u7247":62,"10\u6570\u636e\u96c6\u7684\u5b98\u65b9\u7f51\u5740":62,"10\u6570\u636e\u96c6\u8bad\u7ec3\u4e00\u4e2a\u5377\u79ef\u795e\u7ecf\u7f51\u7edc":62,"10g":23,"1150u":49,"11\u5b9e\u73b0\u4e86c":27,"11e6":56,"12194102e":5,"124n":49,"128\u7ef4\u548c256\u7ef4":61,"13m":56,"1490u":49,"14\u4ee5\u4e0a":39,"14\u6570\u636e\u96c6":70,"14\u6570\u636e\u96c6\u4e0a\u5f97\u5230\u826f\u597d\u8868\u73b0\u7684\u8bad\u7ec3\u8fc7\u7a0b":70,"14\u8fd9\u79cd\u5199\u6cd5\u5c06\u4f1a\u6d4b\u8bd5\u6a21\u578b":54,"152\u5c42\u7f51\u7edc\u6a21\u578b":63,"15501715e":5,"1550u":49,"15\u884c":41,"1636k":70,"16u":49,"173m":63,"173n":49,"1770u":49,"18\u5c81\u4ee5\u4e0b":66,"18e457ce3d362ff5f3febf8e7f85ffec852f70f3b629add10aed84f930a68750":56,"197u":49,"1\u7684\u5c42\u4e4b\u5916":54,"1\u7a00\u758f\u6570\u636e":46,"1\u8f6e\u5b58\u50a8\u7684\u6240\u6709\u6a21\u578b":54,"1\u9664\u4ee5batch":62,"1m\u6570\u636e\u96c6\u4e2d":67,"1m\u7684\u5b57\u6bb5\u914d\u7f6e\u6587\u4ef6\u5728\u76ee\u5f55":67,"200\u6570\u636e\u96c6\u4e0a\u4f7f\u7528vgg\u6a21\u578b\u8bad\u7ec3\u4e00\u4e2a\u9e1f\u7c7b\u56fe\u7247\u5206\u7c7b\u6a21\u578b":62,"210u":49,"211839e770f7b538e2d8":10,"215n":49,"228u":49,"234m":63,"24\u5c81":66,"2520u":49,"25639710e":5,"25k":65,"2680u":49,"26\u884c":41,"27787406e":5,"279n":49,"27m":49,"285m":49,"2863m":49,"28\u7684\u56fe\u7247\u50cf\u7d20\u7070\u5ea6\u503c":3,"28\u7ef4\u7684\u7a20\u5bc6\u6d6e\u70b9\u6570\u5411\u91cf\u548c\u4e00\u4e2a":3,"28m":49,"2977m":49,"29997\u4e2a\u6700\u9ad8\u9891\u5355\u8bcd\u548c3\u4e2a\u7279\u6b8a\u7b26\u53f7":70,"2\u4e09\u7c7b\u7684\u6bd4\u4f8b\u4e3a":30,"2\u4e2a\u6d6e\u70b9\u6570":31,"2\u5206\u522b\u4ee3\u88683\u4e2a\u8282\u70b9\u7684trainer":57,"2\u610f\u5473\u77400\u53f7\u548c1\u53f7gpu\u5c06\u4f1a\u4f7f\u7528\u6570\u636e\u5e76\u884c\u6765\u8ba1\u7b97fc1\u548cfc2\u5c42":54,"2\u8fd9\u51e0\u4e2a\u76ee\u5f55\u8868\u793apaddlepaddle\u8282\u70b9\u4e0etrain":57,"2nd":9,"302n":49,"30u":49,"3206326\u4e2a\u8bcd\u548c4\u4e2a\u7279\u6b8a\u6807\u8bb0":61,"32777140e":5,"328n":49,"32\u7ef4":61,"32u":49,"32x32":13,"331n":49,"3320u":49,"34\u5c81":66,"35\u65f6":70,"36540484e":5,"36u":49,"3710m":49,"3768m":49,"387u":49,"38u":49,"3920u":49,"39u":49,"3\u4e2a\u7279\u6b8a\u7b26\u53f7":70,"3\u53f7gpu":30,"4035m":49,"4090u":49,"4096mb":52,"4279m":49,"43630644e":5,"43u":49,"448a5b355b84":56,"44\u5c81":66,"4560u":49,"4563m":49,"45u":49,"4650u":49,"4726m":49,"473m":56,"48565123e":5,"48684503e":5,"49316648e":5,"49\u5c81":66,"4gb":52,"500\u4e2atxt\u6587\u4ef6":69,"500m":30,"50\u5c42":63,"50\u5c42\u7f51\u7edc\u6a21\u578b":63,"51111044e":5,"514u":49,"525n":49,"526u":49,"53018653e":5,"536u":49,"5460u":49,"5470u":49,"54u":49,"55\u5c81":66,"55g":70,"5690m":49,"573u":49,"578n":49,"5798m":49,"586u":49,"58s":56,"5969m":49,"5\u4e2a\u6d4b\u8bd5\u6837\u4f8b\u548c2\u4e2a\u751f\u6210\u5f0f\u6837\u4f8b":61,"5\u5230\u672c\u5730\u73af\u5883\u4e2d":35,"6080u":49,"6082v4":9,"6140u":49,"6305m":49,"639u":49,"64\u5e73\u53f0\u4e3a\u4f8b":[38,39],"64\u73af\u5883":39,"64\u7ef4":61,"655u":49,"6780u":49,"6810u":49,"682u":49,"6970u":49,"6\u4e07\u4ebf\u6b21\u6d6e\u70b9\u8fd0\u7b97\u6bcf\u79d2":49,"6\u4e2a\u8282\u70b9":50,"6\u5143\u4e0a\u4e0b\u6587\u4f5c\u4e3a\u8f93\u5165\u5c42":61,"704u":49,"70634608e":5,"7090u":49,"72296313e":5,"72u":49,"73u":49,"75u":49,"760u":49,"767u":49,"783n":49,"784u":49,"78m":49,"7\u7248\u672c\u5f00\u59cb":38,"7eamaa":13,"7kb":56,"8250u":49,"8300u":49,"830n":49,"849m":49,"85625684e":5,"861u":49,"864k":70,"8661m":49,"877\u4e2a\u88ab\u5411\u91cf\u5316\u7684\u8bcd":61,"877\u884c":61,"892m":49,"901n":49,"90u":49,"918u":49,"9247m":49,"924n":49,"9261m":49,"93137714e":5,"9330m":49,"94u":49,"9530m":49,"96644767e":5,"983m":49,"988u":49,"997u":49,"99982715e":5,"99m":63,"99u":49,"9\u4e2d\u7684\u4e00\u4e2a\u6570\u5b57":3,"9f18":56,"\u0233":31,"\u03b5":31,"\u4e00":41,"\u4e00\u4e2a0\u5c42\u5e8f\u5217":40,"\u4e00\u4e2a0\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u6269\u5c55\u6210\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":40,"\u4e00\u4e2a\u4f8b\u5b50\u662f\u623f\u4ea7\u4f30\u503c":31,"\u4e00\u4e2a\u5178\u578b\u7684\u795e\u7ecf\u7f51\u7edc\u5982\u4e0b\u56fe\u6240\u793a":62,"\u4e00\u4e2a\u5178\u578b\u7684chunk\u5982\u4e0b\u6240\u793a":24,"\u4e00\u4e2a\u5206\u5e03\u5f0f\u7684\u5b58\u50a8\u7cfb\u7edf":55,"\u4e00\u4e2a\u5206\u5e03\u5f0fpaddle\u8bad\u7ec3\u4efb\u52a1\u4e2d\u7684\u6bcf\u4e2a\u8fdb\u7a0b\u90fd\u53ef\u4ee5\u4ececeph\u8bfb\u53d6\u6570\u636e":56,"\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":40,"\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217\u6216\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":40,"\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u6269\u5c55\u6210\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":40,"\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217\u8fdb\u5165":43,"\u4e00\u4e2a\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5305\u542b\u5982\u4e0b\u5c42":62,"\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":40,"\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217\u6216\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":40,"\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u53d8\u6210\u4e00\u4e2a0\u5c42\u5e8f\u5217":40,"\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u53d8\u6210\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":40,"\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217\u8fdb\u5165":43,"\u4e00\u4e2a\u53cc\u5c42rnn\u7531\u591a\u4e2a\u5355\u5c42rnn\u7ec4\u6210":43,"\u4e00\u4e2a\u53ef\u8c03\u7528\u7684\u51fd\u6570":43,"\u4e00\u4e2a\u57fa\u672c\u7684\u5e94\u7528\u573a\u666f\u662f\u533a\u5206\u7ed9\u5b9a\u6587\u672c\u7684\u8912\u8d2c\u4e24\u6781\u6027":69,"\u4e00\u4e2a\u6216\u591a\u4e2a":55,"\u4e00\u4e2a\u6570\u636e\u96c6\u5927\u90e8\u5206\u5e8f\u5217\u957f\u5ea6\u662f100":30,"\u4e00\u4e2a\u6587\u4ef6":3,"\u4e00\u4e2a\u662f\u6d6e\u70b9\u8ba1\u7b97\u91cf":49,"\u4e00\u4e2a\u662f\u76f4\u63a5\u628a\u4e00\u4e2a\u5bb9\u5668\u8f6c\u6362\u6210\u955c\u50cf":33,"\u4e00\u4e2a\u72ec\u7acb\u7684\u5143\u7d20":40,"\u4e00\u4e2a\u72ec\u7acb\u7684\u8bcd\u8bed":40,"\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc\u7684\u6a21\u578b\u7531\u5927\u91cf\u7684\u53c2\u6570\u7ec4\u6210":19,"\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\u5982":69,"\u4e00\u4e2a\u7ec8\u7aef\u8fd0\u884cvi":33,"\u4e00\u4e2a\u7f51\u7edc\u5c42\u7684\u524d\u5411\u4f20\u64ad\u90e8\u5206\u628a\u8f93\u5165\u8f6c\u5316\u4e3a\u76f8\u5e94\u7684\u8f93\u51fa":46,"\u4e00\u4e2a\u7f51\u7edc\u5c42\u7684\u53c2\u6570\u662f\u5728":46,"\u4e00\u4e2a\u7f51\u7edc\u5c42\u7684c":46,"\u4e00\u4e2a\u91cd\u8981\u7684\u95ee\u9898\u662f\u9009\u62e9\u6b63\u786e\u7684learning_r":30,"\u4e00\u4e2achunk\u7531\u6240\u5728\u7684\u6587\u4ef6\u504f\u79fb":24,"\u4e00\u4e2agpu\u8bbe\u5907\u4e0a\u4e0d\u5141\u8bb8\u914d\u7f6e\u591a\u4e2a\u6a21\u578b":52,"\u4e00\u4e2alabel":41,"\u4e00\u4e2alogging\u5bf9\u8c61":3,"\u4e00\u4e2amemory\u5305\u542b":44,"\u4e00\u4e2apass\u610f\u5473\u7740paddlepaddle\u8bad\u7ec3\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u6837\u672c\u88ab\u904d\u5386\u4e00\u6b21":68,"\u4e00\u4e2apass\u8868\u793a\u8fc7\u4e00\u904d\u6240\u6709\u8bad\u7ec3\u6837\u672c":65,"\u4e00\u4e2apod\u4e2d\u7684\u6240\u6709\u5bb9\u5668\u4f1a\u88ab\u8c03\u5ea6\u5230\u540c\u4e00\u4e2anode\u4e0a":55,"\u4e00\u4e2aposix\u517c\u5bb9\u7684\u6587\u4ef6\u7cfb\u7edf":24,"\u4e00\u4eba":41,"\u4e00\u53e5\u8bdd\u662f\u7531\u8bcd\u8bed\u6784\u6210\u7684\u5e8f\u5217":43,"\u4e00\u53f0\u673a\u5668\u4e0a\u9762\u7684\u7ebf\u7a0b\u6570\u91cf":67,"\u4e00\u65e9":41,"\u4e00\u662fbatch":30,"\u4e00\u6761\u6837\u672c":3,"\u4e00\u6837\u8bbe\u7f6e":50,"\u4e00\u6b21\u4f5c\u4e1a\u79f0\u4e3a\u4e00\u4e2ajob":55,"\u4e00\u6b21\u6027\u676f\u5b50":41,"\u4e00\u6b21yield\u8c03\u7528":3,"\u4e00\u79cd\u5e38\u7528\u7684\u505a\u6cd5\u662f\u7528\u5b66\u4e60\u7684\u6a21\u578b\u5bf9\u53e6\u5916\u4e00\u7ec4\u6d4b\u8bd5\u6570\u636e\u8fdb\u884c\u9884\u6d4b":31,"\u4e00\u79cd\u5e38\u7528\u7684cmake\u914d\u7f6e\u5982\u4e0b":38,"\u4e00\u7bc7\u8bba\u6587":70,"\u4e00\u7ea7\u76ee\u5f55":[69,70],"\u4e00\u81f4":[40,41],"\u4e00\u822c\u4e0d\u5141\u8bb8\u518d\u4ece":29,"\u4e00\u822c\u4ece":45,"\u4e00\u822c\u5728paddlepaddle\u4e2d":41,"\u4e00\u822c\u60c5\u51b5\u4e0b":[2,31],"\u4e00\u822c\u63a8\u8350\u8bbe\u7f6e\u6210true":3,"\u4e00\u822c\u662f\u7531\u4e8e\u76f4\u63a5\u4f20\u9012\u5927\u5b57\u5178\u5bfc\u81f4\u7684":30,"\u4e00\u822c\u6765\u8bf4":44,"\u4e00\u822c\u800c\u8a00":70,"\u4e00\u822c\u8868\u793a":41,"\u4e00\u884c\u4e3a\u4e00\u4e2a\u6837\u672c":65,"\u4e09\u79cd\u5e8f\u5217\u6a21\u5f0f":[3,36],"\u4e09\u7ea7\u76ee\u5f55":[69,70],"\u4e0a":45,"\u4e0a\u4e0b\u6587\u5927\u5c0f\u8bbe\u7f6e\u4e3a1\u7684\u4e00\u4e2a\u6837\u672c\u7684\u7279\u5f81\u5982\u4e0b":68,"\u4e0a\u4f20\u5230cloud\u6216\u8005\u4e0b\u8f7d\u5230\u672c\u5730\u7684\u65f6\u95f4\u53ef\u80fd\u6bd4\u8f83\u957f":24,"\u4e0a\u53d1\u8868\u7684\u8bc4\u8bba\u5206\u6210\u6b63\u9762\u8bc4\u8bba\u548c\u8d1f\u9762\u8bc4\u8bba\u4e24\u7c7b":69,"\u4e0a\u56fe\u4e2d\u865a\u7ebf\u7684\u8fde\u63a5":41,"\u4e0a\u56fe\u63cf\u8ff0\u4e86\u4e00\u4e2a3\u8282\u70b9\u7684\u5206\u5e03\u5f0f\u8bad\u7ec3\u573a\u666f":57,"\u4e0a\u6ce8\u518c\u4e00\u4e0b":24,"\u4e0a\u7f16\u8bd1android\u5e73\u53f0\u4e0a\u9002\u7528\u7684paddlepaddle\u5e93":38,"\u4e0a\u7f51":41,"\u4e0a\u8fd0\u884c":33,"\u4e0a\u8ff0\u4ee3\u7801\u5c06bias\u5168\u90e8\u521d\u59cb\u5316\u4e3a1":30,"\u4e0a\u8ff0\u7684\u4ee3\u7801\u7247\u6bb5\u5305\u542b\u4e86\u4e24\u79cd\u65b9\u6cd5":49,"\u4e0a\u8ff0\u811a\u672c\u4f7f\u7528":50,"\u4e0b":62,"\u4e0b\u56fe\u4e2d\u5c31\u5c55\u793a\u4e86\u4e00\u4e9b\u5173\u4e8e\u5185\u5b58\u6570\u636e\u8fc1\u5f99\u548c\u8ba1\u7b97\u8d44\u6e90\u5229\u7528\u7387\u7684\u5efa\u8bae":49,"\u4e0b\u56fe\u5c55\u793a\u4e86\u6240\u6709\u7684\u56fe\u7247\u7c7b\u522b":62,"\u4e0b\u56fe\u5c55\u793a\u4e86\u65f6\u95f4\u6269\u5c55\u76842\u5c42":68,"\u4e0b\u56fe\u5c55\u793a\u7684\u662f\u57fa\u4e8e\u6b8b\u5dee\u7684\u8fde\u63a5\u65b9\u5f0f":63,"\u4e0b\u56fe\u662f\u4e00\u4e2a\u5168\u8fde\u63a5\u5c42\u7684\u793a\u610f\u56fe":46,"\u4e0b\u5b58\u653e\u516c\u5171\u6570\u636e\u96c6\u5408":20,"\u4e0b\u6587\u4ee5nlp\u4efb\u52a1\u4e3a\u4f8b":43,"\u4e0b\u6587\u4f7f\u7528":57,"\u4e0b\u6587\u5c31\u662f\u7528job\u7c7b\u578b\u7684\u8d44\u6e90\u6765\u8fdb\u884c\u8bad\u7ec3":56,"\u4e0b\u6b21":41,"\u4e0b\u7684":57,"\u4e0b\u7684\u4f5c\u4e3a\u7f16\u8bd1\u5de5\u5177":39,"\u4e0b\u8868\u5c55\u793a\u4e86batch":63,"\u4e0b\u8f7d":24,"\u4e0b\u8f7d\u5230\u672c\u5730":24,"\u4e0b\u8f7d\u5b8c\u6570\u636e\u540e":56,"\u4e0b\u8f7d\u5b8c\u76f8\u5173\u5b89\u88c5\u5305\u540e":35,"\u4e0b\u8f7d\u6570\u636e\u96c6":62,"\u4e0b\u8f7draspberri":39,"\u4e0b\u8f7dwmt":70,"\u4e0b\u9762\u4e3e\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50":49,"\u4e0b\u9762\u4ecb\u7ecd\u9884\u5904\u7406\u8fc7\u7a0b\u5177\u4f53\u7684\u6b65\u9aa4":67,"\u4e0b\u9762\u5148\u7b80\u8981\u4ecb\u7ecd\u4e00\u4e0b\u672c\u6587\u7528\u5230\u7684\u51e0\u4e2akubernetes\u6982\u5ff5":55,"\u4e0b\u9762\u5206\u522b\u4ecb\u7ecd\u67d0\u4e00\u7c7b\u6587\u4ef6\u7684\u5b9e\u73b0\u65b9\u5f0f":27,"\u4e0b\u9762\u5217\u51fa\u4e86":44,"\u4e0b\u9762\u5217\u51fa\u4e86\u5168\u8fde\u63a5\u5c42\u7684\u68af\u5ea6\u68c0\u67e5\u5355\u5143\u6d4b\u8bd5":46,"\u4e0b\u9762\u5c31\u6839\u636e\u8fd9\u51e0\u4e2a\u6b65\u9aa4\u5206\u522b\u4ecb\u7ecd":57,"\u4e0b\u9762\u6211\u4eec\u7ed9\u51fa\u4e86\u4e00\u4e2a\u914d\u7f6e\u793a\u4f8b":62,"\u4e0b\u9762\u662f\u4e00\u4e2a\u8bef\u5dee\u66f2\u7ebf\u56fe\u7684\u793a\u4f8b":62,"\u4e0b\u9762\u662fcifar":62,"\u4e0b\u9762\u7684\u4ee3\u7801\u7247\u6bb5\u5b9e\u73b0\u4e86":46,"\u4e0b\u9762\u7684\u4f8b\u5b50\u4f7f\u7528\u4e86":63,"\u4e0b\u9762\u7684\u4f8b\u5b50\u540c\u6837\u4f7f\u7528\u4e86":63,"\u4e0b\u9762\u7ed9\u51fa\u4e86\u4e00\u4e2a\u4f8b\u5b50":46,"\u4e0b\u9762\u7ed9\u51fa\u5728\u4e09\u7ef4\u7a7a\u95f4\u4e2d\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u62df\u5408\u4e00\u6761\u76f4\u7ebf\u7684\u4f8b\u5b50":36,"\u4e0b\u9762\u811a\u672c\u7b26\u5408paddlepaddle\u671f\u5f85\u7684\u8bfb\u53d6\u6570\u636e\u7684python\u7a0b\u5e8f\u7684\u6a21\u5f0f":31,"\u4e0b\u9762\u8fd9\u4e9blayer\u80fd\u591f\u63a5\u53d7\u53cc\u5c42\u5e8f\u5217\u4f5c\u4e3a\u8f93\u5165":40,"\u4e0d":41,"\u4e0d\u4e00\u5b9a\u548c\u65f6\u95f4\u6709\u5173\u7cfb":3,"\u4e0d\u4e00\u81f4\u7684\u7531pfsclient\u4e0b\u8f7d\u6216\u8005\u4f20\u8f93chunk\u5b8c\u6210":24,"\u4e0d\u4f1a\u518d\u4ece":30,"\u4e0d\u4f7f\u7528\u9759\u6001\u5e93":26,"\u4e0d\u4f7f\u7528\u989d\u5916\u7a7a\u95f4":46,"\u4e0d\u4f7f\u7528c":26,"\u4e0d\u4f7f\u7528swig":26,"\u4e0d\u5305\u542b\u5728\u5b57\u5178\u4e2d\u7684\u5355\u8bcd":70,"\u4e0d\u540c":68,"\u4e0d\u540c\u4e3b\u673a":55,"\u4e0d\u540c\u4ea7\u54c1":69,"\u4e0d\u540c\u53c2\u6570\u670d\u52a1\u5668\u4e0a\u6570\u636e\u5927\u5c0f\u7684\u6700\u5927\u503c\u4e0e\u6700\u5c0f\u503c\u7684\u6bd4\u7387":52,"\u4e0d\u540c\u5c42\u7684\u7279\u5f81\u7531\u5206\u53f7":63,"\u4e0d\u540c\u65f6\u95f4\u6b65\u7684\u8f93\u5165\u662f\u4e0d\u540c\u7684":44,"\u4e0d\u540c\u7248\u672c\u7684\u7f16\u8bd1\u5668\u4e4b\u95f4":26,"\u4e0d\u540c\u7684\u4f18\u5316\u7b97\u6cd5\u9700\u8981\u4f7f\u7528\u4e0d\u540c\u5927\u5c0f\u7684\u5185\u5b58":30,"\u4e0d\u540c\u7684\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf":57,"\u4e0d\u540c\u7684\u6570\u636e\u7c7b\u578b\u548c\u5e8f\u5217\u6a21\u5f0f\u8fd4\u56de\u7684\u683c\u5f0f\u4e0d\u540c":[3,36],"\u4e0d\u540c\u7a7a\u95f4\u7684\u8d44\u6e90\u540d\u53ef\u4ee5\u91cd\u590d":55,"\u4e0d\u540c\u8bed\u8a00\u7684\u63a5\u53e3\u9002\u5e94\u4e0d\u540c\u8bed\u8a00\u7684\u7279\u6027":26,"\u4e0d\u540c\u8f93\u5165\u542b\u6709\u7684\u5b50\u53e5":43,"\u4e0d\u540c\u8f93\u5165\u5e8f\u5217\u542b\u6709\u7684\u8bcd\u8bed\u6570\u5fc5\u987b\u4e25\u683c\u76f8\u7b49":43,"\u4e0d\u540cdataprovider\u5bf9\u6bd4\u5982\u4e0b":41,"\u4e0d\u540cpod\u4e4b\u95f4\u53ef\u4ee5\u901a\u8fc7ip\u5730\u5740\u8bbf\u95ee":55,"\u4e0d\u5728":27,"\u4e0d\u5bb9\u6613\u51fa\u9519":24,"\u4e0d\u5c11":41,"\u4e0d\u5d4c\u5165\u5176\u4ed6\u8bed\u8a00\u89e3\u91ca\u5668":26,"\u4e0d\u5d4c\u5165python\u89e3\u91ca\u5668":26,"\u4e0d\u5e94\u8be5\u88ab\u62c6\u89e3":43,"\u4e0d\u5fc5\u518d\u5c06\u4efb\u610f\u957f\u5ea6\u6e90\u8bed\u53e5\u4e2d\u7684\u6240\u6709\u4fe1\u606f\u538b\u7f29\u81f3\u4e00\u4e2a\u5b9a\u957f\u7684\u5411\u91cf\u4e2d":70,"\u4e0d\u6307\u5b9a\u65f6":43,"\u4e0d\u63d0\u4f9b\u5206\u5e03\u5f0f\u5b58\u50a8":55,"\u4e0d\u652f\u6301":68,"\u4e0d\u662f\u4e00\u6761\u5e8f\u5217":[3,36],"\u4e0d\u663e\u793a\u7684\u5199\u6bcf\u4e2a\u7c7b\u5177\u4f53\u5305\u542b\u4ec0\u4e48":26,"\u4e0d\u6ee1\u8db3\u94a9\u5b50\u7684":45,"\u4e0d\u7528mount\u7684\u65b9\u5f0f\u6765\u8bbf\u95ee\u6570\u636e":20,"\u4e0d\u7f13\u5b58\u4efb\u4f55\u6570\u636e":3,"\u4e0d\u80fd\u592a\u968f\u610f":45,"\u4e0d\u80fd\u88ab\u63d0\u4ea4\u5230":45,"\u4e0d\u8fc7":41,"\u4e0d\u8fdc":41,"\u4e0d\u9519":41,"\u4e0d\u9700\u8981\u5bf9\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u4efb\u4f55\u9884\u5904\u7406":44,"\u4e0e":[57,61,70],"\u4e0e\u4e4b\u76f8\u5bf9\u7684\u662flocal":24,"\u4e0e\u529f\u80fd\u5206\u652f\u4e0d\u540c\u7684\u662f":29,"\u4e0e\u5355\u5c42rnn\u7684\u914d\u7f6e\u7c7b\u4f3c":41,"\u4e0e\u53ef\u80fd\u6709\u7684":29,"\u4e0e\u5728":68,"\u4e0e\u672c\u5730\u8bad\u7ec3\u76f8\u540c":50,"\u4e0e\u6b64\u4e0d\u540c\u7684\u662f":57,"\u4e0e\u7ffb\u8bd1":70,"\u4e0e\u8bad\u7ec3\u4e0d\u540c":69,"\u4e0e\u8bad\u7ec3\u6a21\u578b\u4e0d\u540c\u7684\u662f":70,"\u4e0e\u8fd9\u4e2a\u8bad\u7ec3\u6570\u636e\u4ea4\u4e92\u7684layer":30,"\u4e0eimdb\u7f51\u7ad9\u63d0\u4f9b\u7684\u4e00\u81f4":66,"\u4e0ejob":57,"\u4e0etime":66,"\u4e14":41,"\u4e14\u4e0d\u6392\u9664commit\u4e4b\u95f4\u7684\u4fee\u6539\u5b58\u5728\u76f8\u4e92\u8986\u76d6\u7684\u60c5\u51b5":45,"\u4e14\u589e\u52a0\u4e00\u4e2a\u7b2c\u4e09\u65b9\u8bed\u8a00":26,"\u4e14\u5e8f\u5217\u7684\u6bcf\u4e00\u4e2a\u5143\u7d20\u8fd8\u662f\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217":[3,36],"\u4e14\u652f\u6301\u90e8\u7f72\u5230":55,"\u4e14\u6bcf\u4e2a\u53e5\u5b50\u8868\u793a\u4e3a\u5bf9\u5e94\u7684\u8bcd\u8868\u7d22\u5f15\u6570\u7ec4":41,"\u4e14\u8c03\u7528\u65f6\u4e0d\u80fd\u629b\u51fa\u5f02\u5e38\u6216\u51fa\u73b0\u8fd0\u884c\u65f6\u9519\u8bef":27,"\u4e14\u9ed8\u8ba4\u5728\u8bad\u7ec3\u96c6\u4e0a\u6784\u5efa\u5b57\u5178":69,"\u4e14c99\u652f\u6301bool\u7c7b\u578b\u548c\u5b9a\u957f\u6574\u6570":26,"\u4e14c99\u76f8\u5bf9\u4e8ec11\u4f7f\u7528\u66f4\u52a0\u5e7f\u6cdb":26,"\u4e24":41,"\u4e24\u4e2a\u5217\u8868\u6587\u4ef6":50,"\u4e24\u4e2a\u5b50\u76ee\u5f55\u4e0b":47,"\u4e24\u4e2a\u5d4c\u5957\u7684":43,"\u4e24\u4e2a\u64cd\u4f5c":49,"\u4e24\u4e2a\u6587\u4ef6\u5939":62,"\u4e24\u4e2a\u8f93\u5165\u7279\u5f81\u5728\u8fd9\u4e2a\u6d41\u7a0b\u4e2d\u8d77\u7740\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528":68,"\u4e24\u4e2a\u8f93\u5165\u7684\u5b50\u5e8f\u5217\u957f\u5ea6\u4e5f\u5e76\u4e0d\u76f8\u540c":41,"\u4e24\u4e2a\u90e8\u5206":47,"\u4e24\u79cd\u7c7b\u522b":65,"\u4e24\u8005\u5747\u4e3a\u7eaf\u6587\u672c\u6587\u4ef6":2,"\u4e2a":65,"\u4e2a\u5185\u5b58\u6c60\u5b9e\u9645\u4e0a\u51b3\u5b9a\u4e86shuffle\u7684\u7c92\u5ea6":30,"\u4e2a\u5355\u8bcd":70,"\u4e2a\u6027\u5316\u63a8\u8350":[29,64],"\u4e2a\u6279\u6b21\u540e\u6253\u5370\u4e00\u4e2a":67,"\u4e2a\u6279\u6b21\u7684\u53c2\u6570\u5e73\u5747\u503c\u8fdb\u884c\u6d4b\u8bd5":52,"\u4e2a\u6a21\u578b\u6d4b\u8bd5\u6570\u636e":52,"\u4e2d":[26,27,30,46,57,62,65,67,68,69],"\u4e2d\u4e0d\u8981\u6dfb\u52a0\u5927\u6587\u4ef6\u7b49":45,"\u4e2d\u4ecb\u7ecd\u7684\u65b9\u6cd5":61,"\u4e2d\u4efb\u610f\u7b2ci\u884c\u7684\u53e5\u5b50\u4e4b\u95f4\u90fd\u5fc5\u987b\u6709\u7740\u4e00\u4e00\u5bf9\u5e94\u7684\u5173\u7cfb":70,"\u4e2d\u4efb\u610f\u7b2ci\u884c\u7684\u53e5\u5b50\u4e4b\u95f4\u90fd\u6709\u7740\u4e00\u4e00\u5bf9\u5e94\u7684\u5173\u7cfb":70,"\u4e2d\u5173\u4e8e\u65f6\u95f4\u9012\u5f52\u795e\u7ecf\u7f51\u7edc\u7684\u4ecb\u7ecd":41,"\u4e2d\u5199\u5165json\u5185\u5bb9":19,"\u4e2d\u5305\u542b\u4e00\u4e2aandroid\u7248\u672c\u7684\u5e93":38,"\u4e2d\u5305\u542b\u4e00\u4e2araspberri":39,"\u4e2d\u5305\u542b\u4e86\u8bad\u7ec3\u6a21\u578b\u7684\u57fa\u672c\u547d\u4ee4":65,"\u4e2d\u5305\u542b\u5982\u4e0b\u8868\u6240\u793a\u76843\u4e2a\u6587\u4ef6\u5939":70,"\u4e2d\u5305\u542bc":[38,39],"\u4e2d\u5355\u5143\u6d4b\u8bd5\u7684\u4e00\u90e8\u5206":45,"\u4e2d\u5355\u5143\u6d4b\u8bd5\u80fd\u987a\u5229\u901a\u8fc7":45,"\u4e2d\u5b89\u88c5":50,"\u4e2d\u5b8c\u5168\u4e00\u81f4":26,"\u4e2d\u5b8c\u6210":69,"\u4e2d\u5b9a\u4e49":44,"\u4e2d\u5b9a\u4e49\u4f7f\u7528\u54ea\u79cddataprovid":2,"\u4e2d\u5b9a\u4e49\u548c\u4f7f\u7528":43,"\u4e2d\u5b9e\u73b0\u7684\u7ed3\u6784\u4f53":27,"\u4e2d\u5bfc\u51fa\u9884\u5b9a\u4e49\u7684\u7f51\u7edc":69,"\u4e2d\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528python\u6765\u63d0\u53d6\u7279\u5f81":63,"\u4e2d\u6307\u5b9a":52,"\u4e2d\u6307\u5b9a\u7684\u540d\u5b57":54,"\u4e2d\u6307\u5b9a\u7684\u5c42\u987a\u5e8f\u4e00\u81f4":63,"\u4e2d\u63d0\u51fa\u7684resnet\u7f51\u7edc\u7ed3\u6784\u57282015\u5e74imagenet\u5927\u89c4\u6a21\u89c6\u89c9\u8bc6\u522b\u7ade\u8d5b":63,"\u4e2d\u641c\u7d22\u8fd9\u51e0\u4e2a\u5e93":32,"\u4e2d\u6587\u6587\u6863":47,"\u4e2d\u6587\u6587\u6863\u76ee\u5f55":47,"\u4e2d\u6587\u7ef4\u57fa\u767e\u79d1\u9875\u9762":41,"\u4e2d\u6709\u8bb8\u591a\u7684\u7279\u5f81":66,"\u4e2d\u6bcf\u4e2apod\u7684ip\u5730\u5740":57,"\u4e2d\u6bcf\u5c42\u7684\u6570\u503c\u7edf\u8ba1":52,"\u4e2d\u7684":63,"\u4e2d\u7684\u4e00\u884c":[3,45],"\u4e2d\u7684\u5185\u5bb9":68,"\u4e2d\u7684\u5bf9\u5e94\u5206\u652f\u5373\u53ef":45,"\u4e2d\u7684\u63a5\u53e3":67,"\u4e2d\u7684\u6570\u636e":63,"\u4e2d\u7684\u6570\u636e\u662f\u5426\u4e3a\u5e8f\u5217\u6a21\u5f0f":67,"\u4e2d\u7684\u6570\u636e\u8fdb\u884c\u9884\u6d4b":63,"\u4e2d\u7684\u6570\u6910\u96c6\u7684\u7ed3\u6784\u5982\u4e0b":69,"\u4e2d\u7684\u6bcf\u4e00\u884c\u547d\u4ee4":67,"\u4e2d\u7684\u751f\u6210\u7ed3\u679c\u5982\u4e0b\u6240\u793a":70,"\u4e2d\u7684\u7528\u6237\u8bc1\u4e66":55,"\u4e2d\u7684\u7b2ci\u884c":70,"\u4e2d\u7684\u8bf4\u660e":3,"\u4e2d\u7684\u8fd9\u4e9b\u6570\u636e\u6587\u4ef6":66,"\u4e2d\u770b\u5230\u4e0b\u9762\u7684\u6587\u4ef6":69,"\u4e2d\u83b7\u53d6":57,"\u4e2d\u8ba4\u771f\u8bbe\u7f6e":50,"\u4e2d\u8bbe\u7f6e":50,"\u4e2d\u8bbe\u7f6e\u7684\u6240\u6709\u8282\u70b9":50,"\u4e2d\u8be6\u7ec6\u4ecb\u7ecd":46,"\u4e2d\u8bfb\u53d6":3,"\u4e2d\u8fd0\u884c\u4efb\u52a1\u7684\u89d2\u5ea6":20,"\u4e2d\u914d\u7f6e\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":44,"\u4e2d\u914d\u7f6e\u7684\u6548\u679c\u4e00\u81f4":3,"\u4e34\u65f6\u53d8\u91cf\u7b49\u7b49":30,"\u4e3a":[3,38,39,44],"\u4e3a0":3,"\u4e3a\u4e86\u4f7f\u7528\u63d0\u524d\u7f16\u5199\u7684\u811a\u672c":69,"\u4e3a\u4e86\u4f7f\u8bc4\u5ba1\u4eba\u5728\u8bc4\u5ba1\u4ee3\u7801\u65f6\u66f4\u597d\u5730\u4e13\u6ce8\u4e8e\u4ee3\u7801\u672c\u8eab":45,"\u4e3a\u4e86\u4fdd\u8bc1\u6548\u7387":46,"\u4e3a\u4e86\u4fdd\u8bc1gpu\u9a71\u52a8\u80fd\u591f\u5728\u955c\u50cf\u91cc\u9762\u6b63\u5e38\u8fd0\u884c":33,"\u4e3a\u4e86\u5145\u5206\u7684\u968f\u673a\u6253\u4e71\u8bad\u7ec3\u96c6":69,"\u4e3a\u4e86\u5b8c\u6210\u5206\u5e03\u5f0f\u673a\u5668\u5b66\u4e60\u8bad\u7ec3\u4efb\u52a1":55,"\u4e3a\u4e86\u5c01\u88c5\u80fd\u591f\u6b63\u786e\u5de5\u4f5c":46,"\u4e3a\u4e86\u5e94\u5bf9\u4ee5\u4e0a\u7684\u95ee\u9898":24,"\u4e3a\u4e86\u63cf\u8ff0\u65b9\u4fbf":43,"\u4e3a\u4e86\u65b9\u4fbf\u5728\u56fd":33,"\u4e3a\u4e86\u65b9\u4fbf\u5927\u5bb6":45,"\u4e3a\u4e86\u65b9\u4fbf\u8d77\u89c1":50,"\u4e3a\u4e86\u66b4\u9732\u7684\u63a5\u53e3\u5c3d\u91cf\u7b80\u5355":27,"\u4e3a\u4e86\u6ee1\u8db3\u8bad\u7ec3":50,"\u4e3a\u4e86\u7b80\u5316cmake\u914d\u7f6e":[38,39],"\u4e3a\u4e86\u8fbe\u5230\u6027\u80fd\u6700\u4f18":49,"\u4e3a\u4e86\u8fd8\u539f":31,"\u4e3a\u4f8b":65,"\u4e3a\u4f8b\u521b\u5efa\u5206\u5e03\u5f0f\u7684\u5355\u8fdb\u7a0b\u8bad\u7ec3":50,"\u4e3a\u4f8b\u8fdb\u884c\u9884\u6d4b":65,"\u4e3a\u53c2\u6570\u77e9\u9635\u7684\u5bbd\u5ea6":30,"\u4e3a\u60a8\u505a\u6027\u80fd\u8c03\u4f18\u63d0\u4f9b\u4e86\u65b9\u5411":49,"\u4e3a\u60f3\u4fee\u6b63\u8bcd\u5411\u91cf\u6a21\u578b\u7684\u7528\u6237\u63d0\u4f9b\u4e86\u5c06\u6587\u672c\u8bcd\u5411\u91cf\u6a21\u578b\u8f6c\u6362\u4e3a\u4e8c\u8fdb\u5236\u6a21\u578b\u7684\u547d\u4ee4":61,"\u4e3a\u65b9\u4fbf\u4f5c\u4e1a\u542f\u52a8\u63d0\u4f9b\u4e86\u4e24\u4e2a\u72ec\u7279\u7684\u547d\u4ee4\u9009\u9879":50,"\u4e3a\u6b64":56,"\u4e3a\u8f93\u51fa\u5206\u914d\u5185\u5b58":46,"\u4e3a\u96c6\u7fa4\u4f5c\u4e1a\u8bbe\u7f6e\u989d\u5916\u7684":50,"\u4e3ajson\u6216yaml\u683c\u5f0f":67,"\u4e3aoutput_\u7533\u8bf7\u5185\u5b58":46,"\u4e3b\u8981\u4e3a\u5f00\u53d1\u8005\u4f7f\u7528":52,"\u4e3b\u8981\u529f\u80fd\u5305\u62ec":24,"\u4e3b\u8981\u5305\u62ec\u4ee5\u4e0b\u4e94\u4e2a\u6b65\u9aa4":5,"\u4e3b\u8981\u5305\u62ec\u56db\u79cd\u7c7b\u578b":36,"\u4e3b\u8981\u539f\u56e0":41,"\u4e3b\u8981\u539f\u56e0\u5305\u62ec\u4e24\u4e2a\u65b9\u9762":30,"\u4e3b\u8981\u539f\u56e0\u662f\u589e\u52a0\u4e86\u521d\u59cb\u5316\u673a\u5236":3,"\u4e3b\u8981\u6765\u81ea\u5317\u7f8e\u6d32":62,"\u4e3b\u8981\u7684\u89e3\u51b3\u529e\u6cd5\u662f\u51cf\u5c0f\u5b66\u4e60\u5f8b\u6216\u8005\u5bf9\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406":30,"\u4e3b\u8981\u804c\u8d23\u5728\u4e8e\u5c06\u8bad\u7ec3\u6570\u636e\u4f20\u5165\u5185\u5b58\u6216\u8005\u663e\u5b58":65,"\u4e3e\u4e00\u4e2a\u4f8b\u5b50":30,"\u4e3e\u4f8b":30,"\u4e3e\u4f8b\u8bf4\u660e":41,"\u4e4b\u524d":45,"\u4e4b\u524d\u914d\u7f6e\u6587\u4ef6\u4e2d":65,"\u4e4b\u540e":[31,36,46],"\u4e4b\u540e\u4f60\u4f1a\u5f97\u5230\u8bad\u7ec3":50,"\u4e4b\u540e\u4f7f\u7528":46,"\u4e4b\u540e\u4f7f\u7528\u77e9\u9635\u8fd0\u7b97\u51fd\u6570\u6765\u8ba1\u7b97":46,"\u4e4b\u540e\u521d\u59cb\u5316\u6240\u6709\u7684\u6743\u91cd\u77e9\u9635":46,"\u4e4b\u540e\u5b9a\u4e49\u7684":62,"\u4e4b\u5916\u7684\u6240\u6709\u5934\u6587\u4ef6":27,"\u4e4b\u95f4\u7684\u8ddd\u79bb":31,"\u4e4b\u95f4\u7684\u8fd0\u7b97\u662f\u72ec\u7acb\u7684":43,"\u4e58\u4e0a\u8f93\u51fa\u7684\u68af\u5ea6":46,"\u4e58\u9664\u7b49\u65f6\u5019":30,"\u4e5d\u4e2a":68,"\u4e5f":41,"\u4e5f\u4e0d\u4f7f\u7528\u5176\u4ed6\u52a8\u6001\u5e93":26,"\u4e5f\u4e0d\u5b58\u5728\u4e00\u4e2asubseq\u76f4\u63a5\u751f\u6210\u4e0b\u4e00\u4e2asubseq\u7684\u60c5\u51b5":43,"\u4e5f\u4e0d\u5e94\u8be5\u62a5\u9519":27,"\u4e5f\u4e0d\u751f\u6210":27,"\u4e5f\u53ef\u4ee5\u4f7f\u7528":45,"\u4e5f\u53ef\u4ee5\u53bb\u6389\u8fd9\u4e9b\u8bc1\u4e66\u7684\u914d\u7f6e":55,"\u4e5f\u53ef\u4ee5\u5728\u5f00\u53d1\u955c\u50cf\u4e2d\u542f\u52a8\u4e00\u4e2asshd\u670d\u52a1":33,"\u4e5f\u53ef\u4ee5\u662f\u4e00\u4e2a\u8bcd\u8bed":43,"\u4e5f\u53ef\u4ee5\u8bf4\u662f\u67d0\u4e9b\u7279\u5b9a\u6307\u4ee4\u7684\u4f7f\u7528\u60c5\u51b5":49,"\u4e5f\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539":57,"\u4e5f\u53ef\u4ee5\u901a\u8fc7saving_period_by_batches\u8bbe\u7f6e\u6bcf\u9694\u591a\u5c11batch\u4fdd\u5b58\u4e00\u6b21\u6a21\u578b":65,"\u4e5f\u53ef\u4ee5\u914d\u7f6e\u4e0d\u540c\u7684\u91cd\u8bd5\u673a\u5236":55,"\u4e5f\u53ef\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u83b7\u53d6":[38,39],"\u4e5f\u5c31\u662f":45,"\u4e5f\u5c31\u662f\u5c06\u8bcd\u5411\u91cf\u6a21\u578b\u8fdb\u4e00\u6b65\u6f14\u5316\u4e3a\u4e09\u4e2a\u65b0\u6b65\u9aa4":65,"\u4e5f\u5c31\u662f\u81ea\u5df1\u7528\u6237\u540d\u4e0b\u7684":45,"\u4e5f\u5c31\u662f\u8bf4":[52,54,61],"\u4e5f\u5f97\u5230\u4e00\u4e2a\u7528\u6237\u7279\u5f81":67,"\u4e5f\u63cf\u8ff0\u4e86\u5bb9\u5668\u9700\u8981\u4f7f\u7528\u7684\u5b58\u50a8\u5377\u6302\u8f7d\u7684\u60c5\u51b5":57,"\u4e5f\u652f\u6301cpu\u7684\u6027\u80fd\u5206\u6790":49,"\u4e5f\u662f\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217":41,"\u4e5f\u662f\u5bb9\u5668\u4e0enode\u4e4b\u95f4\u5171\u4eab\u6587\u4ef6\u7684\u65b9\u5f0f":55,"\u4e5f\u662fdecoder\u5faa\u73af\u5c55\u5f00\u7684\u4f9d\u636e":43,"\u4e5f\u662fpaddlepaddle\u6240\u80fd\u591f\u4fdd\u8bc1\u7684shuffle\u7c92\u5ea6":3,"\u4e5f\u6ca1\u7528":30,"\u4e5f\u79f0\u4e3arnn\u6a21\u578b":65,"\u4e5f\u8bb8\u662f\u56e0\u4e3a\u9700\u8981\u5b89\u88c5":62,"\u4e5f\u9700\u8981\u4e24\u6b21\u968f\u673a\u9009\u62e9\u5230\u76f8\u540cgenerator\u7684\u65f6\u5019":3,"\u4e66\u5199":26,"\u4e7e":41,"\u4e86":41,"\u4e86\u89e3\u60a8\u7684\u786c\u4ef6":49,"\u4e86\u89e3\u66f4\u591a\u7ec6\u8282":44,"\u4e86\u89e3\u66f4\u591a\u8be6\u7ec6\u4fe1\u606f":44,"\u4e8c\u7ea7\u76ee\u5f55":[69,70],"\u4e8c\u7ef4\u77e9\u9635":63,"\u4e8c\u8005\u8bed\u610f\u4e0a\u5b8c\u5168\u4e00\u81f4":41,"\u4e8c\u8fdb\u5236":61,"\u4e92\u76f8\u901a\u4fe1":55,"\u4e92\u8054\u7f51\u7535\u5f71\u6570\u636e\u5e93":69,"\u4e94\u661f\u7ea7":41,"\u4e9a\u9a6c\u900a":69,"\u4ea4\u53c9\u7f16\u8bd1\u5de5\u5177\u94fe":39,"\u4ea4\u53c9\u7f16\u8bd1\u5de5\u5177\u94fe\u4e3a":38,"\u4ea4\u53c9\u7f16\u8bd1android\u7248\u672c\u7684paddlepaddle\u5e93\u65f6":38,"\u4ea4\u53c9\u7f16\u8bd1raspberri":39,"\u4ea4\u7531cmake\u7cfb\u7edf\u672c\u8eab\u6765\u5904\u7406":38,"\u4ea4\u901a":41,"\u4ea4\u901a\u4fbf\u5229":41,"\u4ea6\u53ef\u4ee5\u901a\u8fc7\u624b\u52a8\u8bbe\u7f6e":[38,39],"\u4eab\u53d7\u60a8\u7684\u65c5\u7a0b":33,"\u4eba\u8138\u8bc6\u522b":20,"\u4ec0\u4e48":67,"\u4ec5\u4ec5\u4f7f\u7528":26,"\u4ec5\u4ec5\u662f\u4e00\u4e9b\u5173\u952e\u8bcd":69,"\u4ec5\u4ec5\u662f\u4e24\u4e2a\u5168\u8fde\u63a5\u5c42":67,"\u4ec5\u4ec5\u662f\u7b80\u5355\u7684\u5d4c\u5165":67,"\u4ec5\u5305\u542b\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6570\u6910\u96c6":69,"\u4ec5\u5728\u8fdc\u7a0b\u7a00\u758f\u8bad\u7ec3\u65f6\u6709\u6548":46,"\u4ec5\u5bf9\u7a00\u758f\u6570\u636e\u6709\u6548":46,"\u4ec5\u9700\u8981\u77e5\u9053\u5982\u4f55\u4ece":3,"\u4ecb\u7ecd\u4e86\u4e00\u79cd\u901a\u8fc7ssh\u8fdc\u7a0b\u5206\u53d1\u4efb\u52a1":57,"\u4ecb\u7ecd\u4ea4\u53c9\u7f16\u8bd1android\u5e73\u53f0\u4e0a\u9002\u7528\u7684paddlepaddle\u5e93\u7684\u65b9\u6cd5\u548c\u6b65\u9aa4":38,"\u4ecb\u7ecd\u4ea4\u53c9\u7f16\u8bd1raspberri":39,"\u4ecb\u7ecd\u5206\u5e03\u5f0f\u8bad\u7ec3\u4e4b\u524d":55,"\u4ecb\u7ecdpaddlepaddle\u7684\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5":65,"\u4ece":[29,49,68],"\u4ece0\u5230num":52,"\u4ece\u4e00\u4e2aword\u751f\u6210\u4e0b\u4e00\u4e2aword":43,"\u4ece\u5185\u6838\u51fd\u6570\u7684\u89d2\u5ea6":49,"\u4ece\u56fe\u4e2d\u53ef\u4ee5\u770b\u5230":31,"\u4ece\u5916\u90e8\u7f51\u7ad9\u4e0a\u4e0b\u8f7d\u7684\u539f\u59cb\u6570\u6910\u96c6":69,"\u4ece\u5927\u5230\u5c0f":70,"\u4ece\u6570\u636e\u63d0\u4f9b\u7a0b\u5e8f\u52a0\u8f7d\u5b9e\u4f8b":68,"\u4ece\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u6765\u770b":41,"\u4ece\u6bcf\u4e2a\u5355\u8bcd\u5de6\u53f3\u4e24\u7aef\u5206\u522b\u83b7\u53d6k\u4e2a\u76f8\u90bb\u7684\u5355\u8bcd":65,"\u4ece\u6e90\u7801\u4ea4\u53c9\u7f16\u8bd1paddlepaddl":[38,39],"\u4ece\u78c1\u76d8\u6587\u4ef6\u4e2d\u52a0\u8f7duuid\u6587\u4ef6\u540d\u7684\u68c0\u67e5\u70b9\u5feb\u7167\u6587\u4ef6":19,"\u4ece\u7b2c0\u4e2a\u8bc4\u4f30\u5230\u5f53\u524d\u8bc4\u4f30\u4e2d":70,"\u4ece\u7b2c0\u4e2abatch\u5230\u5f53\u524dbatch\u7684\u5e73\u5747\u635f\u5931":69,"\u4ece\u7b2c0\u4e2abatch\u5230\u5f53\u524dbatch\u7684\u5e73\u5747cost":70,"\u4ece\u800c\u53ef\u4ee5\u505a\u4e00\u4e9b\u4e0e\u8ba1\u7b97\u91cd\u53e0\u7684\u5de5\u4f5c":46,"\u4ece\u800c\u53ef\u4ee5\u62df\u5408\u4efb\u610f\u7684\u51fd\u6570\u6765\u5b66\u4e60\u590d\u6742\u7684\u6570\u636e\u5173\u7cfb":31,"\u4ece\u800c\u751f\u6210\u591a\u4e2agener":3,"\u4ece\u800c\u80fd\u591f\u88abpaddlepaddl":65,"\u4ece\u800c\u9632\u6b62\u8fc7\u62df\u5408":2,"\u4ece\u8be5\u94fe\u63a5":70,"\u4ece\u8bed\u4e49\u4e0a\u770b":43,"\u4ece\u8f93\u5165\u6570\u636e\u4e0a\u770b":41,"\u4ece\u8f93\u51fa\u65e5\u5fd7\u53ef\u4ee5\u770b\u5230":31,"\u4ece\u9884\u8bad\u7ec3\u6a21\u578b\u4e2d":61,"\u4ececmake":38,"\u4eceetcd\u4e2d\u8bfb\u53d6\u8282\u70b9":19,"\u4ecestart":52,"\u4ecetest":70,"\u4ed3\u5e93\u7684\u8fdc\u7a0b\u4e3b\u673a":45,"\u4ed4\u7ec6\u89c2\u5bdf":63,"\u4ed6\u4e3b\u8981\u5305\u542b\u4e86\u5b9e\u9645\u66b4\u9732\u7684\u7c7b\u578b\u7ed3\u6784":27,"\u4ed6\u4eec\u5206\u522b\u662f":41,"\u4ed6\u4eec\u5728paddle\u7684\u6587\u6863\u548capi\u4e2d\u662f\u4e00\u4e2a\u6982\u5ff5":41,"\u4ed6\u4eec\u63d0\u51fa\u6b8b\u5dee\u5b66\u4e60\u7684\u6846\u67b6\u6765\u7b80\u5316\u7f51\u7edc\u7684\u8bad\u7ec3":63,"\u4ed6\u662f\u5c06":27,"\u4ed6\u7684\u76ee\u6807\u662f\u4f7f\u7528c":26,"\u4ee3\u66ff":57,"\u4ee3\u7801":67,"\u4ee3\u7801\u4e2d9":41,"\u4ee3\u7801\u5982\u4e0b":44,"\u4ee3\u7801\u6ce8\u91ca\u8bf7\u9075\u5b88":45,"\u4ee3\u7801\u751f\u6210\u7684\u7b26\u53f7\u53ef\u80fd\u4e0d\u4e00\u81f4":26,"\u4ee3\u8868\u5bbf\u4e3b\u673a\u76ee\u5f55":57,"\u4ee3\u8868\u7f16\u53f7":67,"\u4ee3\u8868\u8fd9\u4e2ashard\u7684\u6700\u5927index":20,"\u4ee3\u8868shard\u7684index":20,"\u4ee5\u4e0a":45,"\u4ee5\u4e0a\u4ee3\u7801\u7684reader\u8f93\u51fa\u7684data":20,"\u4ee5\u4e0a\u547d\u4ee4\u4f1a\u5728\u5f53\u524d\u76ee\u5f55\u4e0b\u751f\u6210100\u4e2a\u6587\u4ef6":20,"\u4ee5\u4e0a\u547d\u4ee4\u4f1a\u6309\u7167\u751f\u6210\u7684dockerfile\u628a\u751f\u6210\u7684\u7a0b\u5e8f\u62f7\u8d1d\u5230\u751f\u4ea7\u955c\u50cf\u4e2d\u5e76\u505a\u76f8\u5e94\u7684\u914d\u7f6e":33,"\u4ee5\u4e0a\u547d\u4ee4\u4f1a\u7f16\u8bd1paddlepaddl":33,"\u4ee5\u4e0a\u65b9\u6cd5\u5728gpu\u955c\u50cf\u91cc\u4e5f\u80fd\u7528":33,"\u4ee5\u4e0b":[20,67],"\u4ee5\u4e0b\u4ee3\u7801\u6bb5\u5b9a\u4e49\u4e86\u4e09\u4e2a\u8f93\u5165":44,"\u4ee5\u4e0b\u4ee3\u7801\u7247\u6bb5\u5b9a\u4e49":44,"\u4ee5\u4e0b\u6211\u4eec\u7ffb\u8bd1\u6570\u636e\u96c6\u7f51\u7ad9\u4e2dreadme\u6587\u4ef6\u7684\u63cf\u8ff0":66,"\u4ee5\u4e0b\u6307\u4ee4\u80fd\u68c0\u67e5linux\u7535\u8111\u662f\u5426\u652f\u6301avx":33,"\u4ee5\u4e0b\u6559\u7a0b\u5c06\u6307\u5bfc\u60a8\u63d0\u4ea4\u4ee3\u7801":45,"\u4ee5\u4e0b\u662f\u5bf9\u4e0a\u8ff0\u6570\u636e\u52a0\u8f7d\u7684\u89e3\u91ca":65,"\u4ee5\u4e0b\u6b65\u9aa4\u57fa\u4e8e":50,"\u4ee5\u4e0b\u793a\u8303\u5982\u4f55\u4f7f\u7528\u9884\u8bad\u7ec3\u7684\u4e2d\u6587\u5b57\u5178\u548c\u8bcd\u5411\u91cf\u8fdb\u884c\u77ed\u8bed\u6539\u5199":61,"\u4ee5\u4e0b\u9009\u9879\u5fc5\u987b\u5728":50,"\u4ee5\u4ea4\u4e92\u5bb9\u5668\u65b9\u5f0f\u8fd0\u884c\u5f00\u53d1\u955c\u50cf":33,"\u4ee5\u4fbf\u6211\u4eec\u53ef\u4ee5\u628a\u66f4\u591a\u7684\u7cbe\u529b\u653e\u5230\u903b\u8f91\u672c\u8eab\u4e0a":24,"\u4ee5\u4fbf\u7528\u6237":50,"\u4ee5\u4fdd\u8bc1\u68af\u5ea6\u7684\u6b63\u786e\u8ba1\u7b97":46,"\u4ee5\u4fdd\u8bc1\u68af\u5ea6\u8ba1\u7b97\u7684\u6b63\u786e\u6027":46,"\u4ee5\u5206\u7c7b\u6765\u81ea":69,"\u4ee5\u53ca":46,"\u4ee5\u53ca\u4f7f\u7528\u5b50\u5e8f\u5217\u6765\u5b9a\u4e49\u5206\u7ea7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u67b6\u6784":44,"\u4ee5\u53ca\u53cc\u5c42\u5e8f\u5217":40,"\u4ee5\u53ca\u5728wmt":70,"\u4ee5\u53ca\u5982\u4f55\u5728\u5c42\u4e4b\u95f4\u8fdb\u884c\u8fde\u63a5":62,"\u4ee5\u53ca\u751f\u6210\u521b\u5efa\u751f\u4ea7\u955c\u50cf\u7684dockerfil":33,"\u4ee5\u53ca\u76ee\u6807\u673a\u7248openblas\u5e93":39,"\u4ee5\u53ca\u8ba1\u7b97\u903b\u8f91\u5728\u5e8f\u5217\u4e0a\u7684\u5faa\u73af\u5c55\u5f00":43,"\u4ee5\u53ca\u8f93\u5165\u7684\u68af\u5ea6":46,"\u4ee5\u53caandroid":38,"\u4ee5\u53caandroid\u6240\u9700":38,"\u4ee5\u53canumpi":20,"\u4ee5\u53capaddle\u5982\u4f55\u5904\u7406\u591a\u79cd\u7c7b\u578b\u7684\u8f93\u5165":67,"\u4ee5\u53carelu":46,"\u4ee5\u63d0\u4f9b\u4e00\u4e9b\u9ed8\u8ba4\u7684\u7f16\u8bd1\u5668\u548c\u7f16\u8bd1\u53c2\u6570\u76f8\u5173\u914d\u7f6e":[38,39],"\u4ee5\u76f8\u5bf9\u8def\u5f84\u5f15\u7528":2,"\u4ee5\u786e\u4fdd\u6240\u6709\u7684\u7b2c\u4e09\u65b9\u4f9d\u8d56\u5e93\u548cpaddlepaddle\u4ee3\u7801\u90fd\u662f\u9488\u5bf9\u65b0\u7684cmake\u914d\u7f6e\u91cd\u65b0\u7f16\u8bd1\u7684":[38,39],"\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u7f51\u7edc\u6027\u80fd":50,"\u4ee5\u9017\u53f7":61,"\u4ee5\u9017\u53f7\u95f4\u9694":52,"\u4ef7\u683c":41,"\u4efb\u52a1":67,"\u4efb\u52a1\u6765\u7ec8\u6b62\u96c6\u7fa4\u4f5c\u4e1a":50,"\u4efb\u610f\u5c06\u4e00\u4e9b\u6570\u636e\u7ec4\u5408\u6210\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217":41,"\u4efb\u610f\u65f6\u523b\u53ea\u53ef\u80fd\u540c\u65f6\u6709\u4e00\u53f0\u670d\u52a1\u5668\u6545\u969c":19,"\u4f18\u5316":69,"\u4f18\u5316\u5668\u5219\u7528\u94fe\u5f0f\u6cd5\u5219\u6765\u5bf9\u6bcf\u4e2a\u53c2\u6570\u8ba1\u7b97\u635f\u5931\u51fd\u6570\u7684\u68af\u5ea6":46,"\u4f1a\u5148\u8fdb\u884c\u53c2\u6570\u7684\u521d\u59cb\u5316\u4e0e\u89e3\u6790":57,"\u4f1a\u5171\u4eab\u53c2\u6570":30,"\u4f1a\u52a0\u8f7d\u4e0a\u4e00\u8f6e\u7684\u53c2\u6570":52,"\u4f1a\u53d8\u6210\u8bcd\u8868\u4e2d\u7684\u4f4d\u7f6e":41,"\u4f1a\u542f\u52a8pserver\u4e0etrainer\u8fdb\u7a0b":57,"\u4f1a\u5728\u5f53\u524d\u76ee\u5f55\u751f\u6210\u4e24\u4e2a\u5b50\u76ee\u5f55":47,"\u4f1a\u5bf9\u6bcf\u4e00\u4e2a\u6fc0\u6d3b\u6682\u5b58\u4e00\u4e9b\u6570\u636e":30,"\u4f1a\u5bf9\u8fd9\u7c7b\u8f93\u5165\u8fdb\u884c\u62c6\u89e3":43,"\u4f1a\u5bfc\u81f4\u4e0d\u540c\u7248\u672cpython\u5728\u4e00\u4e2a\u8fdb\u7a0b\u91cc\u7684bug":26,"\u4f1a\u5c06\u6bcf\u4e2a\u65f6\u95f4\u6b65\u7684\u8f93\u51fa\u62fc\u63a5":43,"\u4f1a\u5c06\u7b2c\u4e00\u4e2a":30,"\u4f1a\u6210\u4e3astep\u51fd\u6570\u7684\u8f93\u5165":43,"\u4f1a\u6254\u5230\u8fd9\u6761\u6570\u636e":3,"\u4f1a\u62a5\u9519":43,"\u4f1a\u6839\u636e\u547d\u4ee4\u884c\u53c2\u6570\u6307\u5b9a\u7684\u6d4b\u8bd5\u65b9\u5f0f":2,"\u4f1a\u6839\u636einput_types\u68c0\u67e5\u6570\u636e\u7684\u5408\u6cd5\u6027":3,"\u4f1a\u76f4\u63a5\u62a5\u9519\u9000\u51fa":26,"\u4f1a\u76f8\u5e94\u5730\u6539\u53d8\u8f93\u51fa\u7684\u5c3a\u5bf8":46,"\u4f1a\u81ea\u52a8\u5173\u95ed\u5bf9\u5e94\u7684issu":45,"\u4f1a\u81ea\u9002\u5e94\u5730\u4ece\u8fd9\u4e9b\u5411\u91cf\u4e2d\u9009\u62e9\u4e00\u4e2a\u5b50\u96c6\u51fa\u6765":70,"\u4f1a\u83b7\u53d6\u5f53\u524dnamespace\u4e0b\u7684\u6240\u6709pod":57,"\u4f1a\u88ab\u62c6\u89e3\u4e3a\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":43,"\u4f1a\u88ab\u62c6\u89e3\u4e3a\u975e\u5e8f\u5217":43,"\u4f1a\u88abpickle\u5e8f\u5217\u5316\u6210\u5b57\u7b26\u4e32":20,"\u4f1a\u9020\u6210\u90ae\u4ef6\u707e\u96be":45,"\u4f20\u5165":[3,20],"\u4f20\u5165\u4e0a\u4e00\u6b65\u89e3\u6790\u51fa\u6765\u7684\u6a21\u578b\u914d\u7f6e\u5c31\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a":5,"\u4f20\u5165\u9884\u6d4b\u6570\u636e":5,"\u4f20\u7ed9dataprovider\u7684\u67d0\u4e00\u4e2aargs\u8fc7\u5927":30,"\u4f20\u9012\u7ed9\u914d\u7f6e\u6587\u4ef6\u7684\u53c2\u6570":52,"\u4f46":27,"\u4f46\u4e0d\u66b4\u9732":27,"\u4f46\u4e0d\u7528\u4e8e\u8ba1\u7b97\u68af\u5ea6":46,"\u4f46\u4e0d\u9700\u8981\u63d0\u524d\u521b\u5efa":52,"\u4f46\u4e8e\u53cc\u5c42\u5e8f\u5217\u7684lstm\u6765\u8bf4":41,"\u4f46\u548c\u5355\u5c42rnn\u4e0d\u540c":41,"\u4f46\u5728\u8d77\u521d\u7684\u51e0\u8f6e\u8bad\u7ec3\u4e2d\u5b83\u4eec\u90fd\u5728\u5feb\u901f\u903c\u8fd1\u771f\u5b9e\u503c":31,"\u4f46\u5b50\u53e5\u542b\u6709\u7684\u8bcd\u8bed\u6570\u53ef\u4ee5\u4e0d\u76f8\u7b49":43,"\u4f46\u5c3d\u91cf\u8bf7\u4fdd\u6301\u7f16\u8bd1\u548c\u8fd0\u884c\u4f7f\u7528\u7684cudnn\u662f\u540c\u4e00\u4e2a\u7248\u672c":32,"\u4f46\u5e76\u6ca1\u6709\u7ecf\u8fc7\u56de\u5f52\u6d4b\u8bd5":29,"\u4f46\u5e8f\u5217\u8f93\u51fa\u65f6":41,"\u4f46\u5f53\u8c03\u7528\u8fc7\u4e00\u6b21\u540e":3,"\u4f46\u6240\u6709fork\u7684\u7248\u672c\u5e93\u7684\u6240\u6709\u5206\u652f\u90fd\u76f8\u5f53\u4e8e\u7279\u6027\u5206\u652f":29,"\u4f46\u662f":[30,41],"\u4f46\u662f2008\u5e74\u4e4b\u524d\u751f\u4ea7\u7684\u65e7\u7535\u8111\u4e0d\u652f\u6301avx":33,"\u4f46\u662f\u4e5f\u6ca1\u6709\u5fc5\u8981\u5220\u9664\u65e0\u7528\u7684\u6587\u4ef6":50,"\u4f46\u662f\u53c8\u8fc7\u4e8e\u7410\u788e":27,"\u4f46\u662f\u5927\u90e8\u5206\u53c2\u6570\u662f\u4e3a\u5f00\u53d1\u8005\u63d0\u4f9b\u7684":51,"\u4f46\u662f\u5b50\u5e8f\u5217\u7684\u6570\u76ee\u5fc5\u987b\u4e00\u6837":41,"\u4f46\u662f\u6211\u4eec\u5e76\u4e0d\u63a8\u8350\u8fd9\u79cd\u65b9\u6cd5":33,"\u4f46\u662f\u652f\u6301avx\u6307\u4ee4\u96c6":45,"\u4f46\u662f\u65b9\u4fbf\u8c03\u8bd5\u548c\u6d4bbenchmark":32,"\u4f46\u662f\u6bcf\u4e2a\u6837\u672c\u4ec5\u5305\u542b\u51e0\u4e2a\u8bcd":54,"\u4f46\u662f\u7a81\u7136\u6709\u4e00\u4e2a10000\u957f\u7684\u5e8f\u5217":30,"\u4f46\u662f\u89e3\u91ca\u6027\u8bed\u8a00":26,"\u4f46\u662f\u8fd9\u79cd\u65b9\u6cd5\u5728\u6bcf\u5c42\u53ea\u4fdd\u5b58\u9884\u8bbe\u6570\u91cf\u7684\u6700\u4f18\u72b6\u6001":70,"\u4f46\u662f\u8fdc\u672a\u5b8c\u5584":0,"\u4f46\u662f\u9690\u85cf\u5c42\u4e2d\u7684\u6bcf\u4e2a\u666e\u901a\u8282\u70b9\u88ab\u4e00\u4e2a\u8bb0\u5fc6\u5355\u5143\u66ff\u6362":69,"\u4f46\u662fbatch":30,"\u4f46\u6709\u503c\u7684\u5730\u65b9\u5fc5\u987b\u4e3a1":[3,36],"\u4f46\u6709\u503c\u7684\u90e8\u5206\u53ef\u4ee5\u662f\u4efb\u4f55\u6d6e\u70b9\u6570":[3,36],"\u4f46\u8fd9\u4e2a\u5173\u7cfb\u53ef\u80fd\u4e0d\u6b63\u786e":3,"\u4f4d\u7f6e":41,"\u4f4f":41,"\u4f53\u88c1\u5b57\u5178":67,"\u4f53\u88c1\u5b57\u6bb5":67,"\u4f59\u5f26\u76f8\u4f3c\u5ea6\u56de\u5f52":67,"\u4f59\u5f26\u76f8\u4f3c\u5ea6\u5c42":67,"\u4f5c\u4e3a\u4e0b\u4e00\u4e2a\u5b50\u53e5memory\u7684\u521d\u59cb\u72b6\u6001":41,"\u4f5c\u4e3a\u4f8b\u5b50\u6f14\u793a\u5982\u4f55\u914d\u7f6e\u590d\u6742\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u6a21\u578b":44,"\u4f5c\u4e3a\u53c2\u6570\u7684id":30,"\u4f5c\u4e3a\u5b58\u50a8\u7cfb\u7edf":20,"\u4f5c\u4e3a\u5f53\u524d\u65f6\u523b\u8f93\u5165":43,"\u4f5c\u4e3a\u793a\u4f8b\u6570\u636e":66,"\u4f5c\u4e3a\u7c7b\u53e5\u67c4":26,"\u4f5c\u4e3a\u7ebf\u6027\u56de\u5f52\u7684\u8f93\u5165":31,"\u4f5c\u4e3a\u8f93\u51fa":44,"\u4f5c\u4e3a\u96c6\u7fa4\u8bad\u7ec3\u7684\u5de5\u4f5c\u7a7a\u95f4":50,"\u4f5c\u4e3aboot_layer\u4f20\u7ed9\u4e0b\u4e00\u4e2a\u5b50\u53e5\u7684memori":41,"\u4f5c\u5bb6":66,"\u4f5c\u7528":40,"\u4f60\u4e5f\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e09\u4e2a\u503c":63,"\u4f60\u4e5f\u53ef\u4ee5\u5148\u8df3\u8fc7\u672c\u6587\u7684\u89e3\u91ca\u73af\u8282":65,"\u4f60\u4e5f\u53ef\u4ee5\u7b80\u5355\u7684\u8fd0\u884c\u4ee5\u4e0b\u7684\u547d\u4ee4":61,"\u4f60\u4e5f\u53ef\u4ee5\u901a\u8fc7\u5728\u547d\u4ee4\u884c\u53c2\u6570\u4e2d\u589e\u52a0\u4e00\u4e2a\u53c2\u6570\u5982":63,"\u4f60\u4e5f\u8bb8\u53ef\u4ee5\u5c1d\u8bd5\u66f4\u8001\u7684\u65b9\u6cd5":33,"\u4f60\u53ea\u9700\u5b8c\u6210":50,"\u4f60\u53ea\u9700\u8981\u5728\u547d\u4ee4\u884c\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4":65,"\u4f60\u53ea\u9700\u8981\u6309\u7167\u5982\u4e0b\u65b9\u5f0f\u7ec4\u7ec7\u6570\u636e":70,"\u4f60\u53ef\u4ee5\u4f7f\u7528":63,"\u4f60\u53ef\u4ee5\u4f7f\u7528\u4e0b\u9762\u7684\u811a\u672c\u4e0b\u8f7d":69,"\u4f60\u53ef\u4ee5\u4f7f\u7528\u5b83\u6765\u8bbe\u7f6e":50,"\u4f60\u53ef\u4ee5\u4f7f\u7528\u672c\u5730\u8bad\u7ec3\u4e2d\u7684\u76f8\u540c\u6a21\u578b\u6587\u4ef6\u8fdb\u884c\u96c6\u7fa4\u8bad\u7ec3":50,"\u4f60\u53ef\u4ee5\u5728\u4efb\u4f55\u65f6\u5019\u7528":67,"\u4f60\u53ef\u4ee5\u5bf9\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30":67,"\u4f60\u53ef\u4ee5\u5c06\u7f51\u7edc\u914d\u7f6e\u6210\u67d0\u4e9b\u5c42\u4f7f\u7528gpu\u8ba1\u7b97":54,"\u4f60\u53ef\u4ee5\u6267\u884c\u4e0a\u8ff0\u547d\u4ee4\u6765\u4e0b\u8f7d\u6240\u6709\u7684\u6a21\u578b\u548c\u5747\u503c\u6587\u4ef6":63,"\u4f60\u53ef\u4ee5\u901a\u8fc7\u6267\u884c\u4e0b\u9762\u7684\u547d\u4ee4\u6765\u5f97\u5230resnet\u7f51\u7edc\u7684\u7ed3\u6784\u53ef\u89c6\u5316\u56fe":63,"\u4f60\u53ef\u4ee5\u9884\u6d4b\u4efb\u4f55\u7528\u6237\u5bf9\u4e8e\u4efb\u4f55\u4e00\u90e8\u7535\u5f71\u7684\u8bc4\u4ef7":67,"\u4f60\u5c06\u4f1a\u770b\u5230\u4ee5\u4e0b\u7684\u6a21\u578b\u7ed3\u6784":61,"\u4f60\u5c06\u4f1a\u770b\u5230\u5982\u4e0b\u6d88\u606f":70,"\u4f60\u5c06\u4f1a\u770b\u5230\u5982\u4e0b\u7ed3\u679c":63,"\u4f60\u5c06\u4f1a\u770b\u5230\u7279\u5f81\u5b58\u50a8\u5728":63,"\u4f60\u5c06\u4f1a\u770b\u5230\u8fd9\u6837\u7684\u6d88\u606f":70,"\u4f60\u5c06\u5728\u76ee\u5f55":69,"\u4f60\u5c06\u770b\u5230\u5982\u4e0b\u7684\u4fe1\u606f":67,"\u4f60\u7684\u5de5\u4f5c\u7a7a\u95f4\u5e94\u5982\u4e0b\u6240\u793a":50,"\u4f60\u8fd8\u53ef\u4ee5\u5c06\u7528\u6237\u548c":50,"\u4f60\u9700\u8981\u4e00\u4e9b\u66f4\u590d\u6742\u7684\u5355\u5143\u6d4b\u8bd5\u6765\u4fdd\u8bc1\u4f60\u5b9e\u73b0\u7684\u7f51\u7edc\u5c42\u662f\u6b63\u786e\u7684":46,"\u4f60\u9700\u8981\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u6307\u5b9a\u8bbe\u5907\u7684id\u53f7":54,"\u4f60\u9700\u8981\u5728\u914d\u7f6ecmake\u65f6\u5c06":46,"\u4f60\u9700\u8981\u5b89\u88c5python\u7684\u7b2c\u4e09\u65b9\u5e93":67,"\u4f60\u9700\u8981\u628a\u8be5\u6587\u4ef6\u52a0\u5165":46,"\u4f7f\u5176\u8f6c\u53d8\u4e3a\u7ef4\u5ea6\u4e3ahidden_dim\u7684\u65b0\u5411\u91cf":65,"\u4f7f\u5f97":31,"\u4f7f\u5f97\u4e24\u4e2a\u5b57\u5178\u6709\u76f8\u540c\u7684\u4e0a\u4e0b\u6587":70,"\u4f7f\u5f97\u5355\u5143\u6d4b\u8bd5\u6709\u4e00\u4e2a\u5e72\u51c0\u7684\u73af\u5883":30,"\u4f7f\u5f97\u642d\u6a21\u578b\u65f6\u66f4\u65b9\u4fbf":46,"\u4f7f\u5f97\u6700\u7ec8\u5f97\u5230\u7684\u6a21\u578b\u51e0\u4e4e\u4e0e\u771f\u5b9e\u6a21\u578b\u4e00\u81f4":31,"\u4f7f\u7528":[27,29,30,33,41,43,44,46,49,52,65,68,69],"\u4f7f\u75280\u53f7\u548c1\u53f7gpu\u8ba1\u7b97fc2\u5c42":54,"\u4f7f\u75280\u53f7gpu\u8ba1\u7b97fc2\u5c42":54,"\u4f7f\u752810\u4e2a\u88c1\u526a\u56fe\u50cf\u5757":63,"\u4f7f\u75281\u53f7gpu\u8ba1\u7b97fc3\u5c42":54,"\u4f7f\u75282\u53f7\u548c3\u53f7gpu\u8ba1\u7b97fc3\u5c42":54,"\u4f7f\u7528\u4e00\u4e2a\u5c3a\u5ea6\u4e3a":46,"\u4f7f\u7528\u4e00\u4e2a\u8bcd\u524d\u4e24\u4e2a\u8bcd\u548c\u540e\u4e24\u4e2a\u8bcd":30,"\u4f7f\u7528\u4e0a\u6587\u521b\u5efa\u7684yaml\u6587\u4ef6\u521b\u5efakubernet":56,"\u4f7f\u7528\u4e0b\u9762\u547d\u4ee4":20,"\u4f7f\u7528\u4e86\u540c\u6837\u7684parameter\u548cbia":30,"\u4f7f\u7528\u4e86\u57fa\u4e8e\u53e5\u6cd5\u7ed3\u6784\u7684\u9884\u5b9a\u4e49\u7279\u5f81\u6a21\u677f":68,"\u4f7f\u7528\u4e86avx\u6307\u4ee4\u96c6":35,"\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3":61,"\u4f7f\u7528\u5177\u6709softmax\u6fc0\u6d3b\u7684\u5168\u8fde\u63a5\u524d\u9988\u5c42\u6765\u6267\u884c\u5206\u7c7b\u4efb\u52a1":69,"\u4f7f\u7528\u52a8\u6001\u5e93":26,"\u4f7f\u7528\u540c\u6837\u7684\u8bad\u7ec3\u6570\u636eblock":19,"\u4f7f\u7528\u591a\u5757\u663e\u5361\u8bad\u7ec3":30,"\u4f7f\u7528\u591a\u7ebf\u7a0b\u8bad\u7ec3":30,"\u4f7f\u7528\u5982\u4e0b\u53c2\u6570":62,"\u4f7f\u7528\u5982\u4e0b\u547d\u4ee4":61,"\u4f7f\u7528\u5b66\u4e60\u5b8c\u6210\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u751f\u6210\u5e8f\u5217":44,"\u4f7f\u7528\u5bb9\u5668\u65b9\u5f0f\u8fd0\u884c\u8bad\u7ec3\u4efb\u52a1\u7684kubernet":57,"\u4f7f\u7528\u6211\u4eec\u4e4b\u524d\u6784\u9020\u7684\u955c\u50cf":56,"\u4f7f\u7528\u663e\u5361\u8bad\u7ec3":30,"\u4f7f\u7528\u667a\u80fd\u6307\u9488\u7684\u539f\u56e0\u662f":27,"\u4f7f\u7528\u6848\u4f8b":53,"\u4f7f\u7528\u7684":30,"\u4f7f\u7528\u7684\u7f16\u8bd1\u5668\u4e3aarm":38,"\u4f7f\u7528\u76f8\u5bf9\u8def\u5f84\u7684\u5f15\u7528\u65b9\u5f0f":27,"\u4f7f\u7528\u8005\u4e0d\u9700\u8981\u5173\u5fc3":52,"\u4f7f\u7528\u8005\u53ea\u9700\u8981\u5173\u6ce8\u4e8e\u8bbe\u8ba1rnn\u5728\u4e00\u4e2a\u65f6\u95f4\u6b65\u4e4b\u5185\u5b8c\u6210\u7684\u8ba1\u7b97":43,"\u4f7f\u7528\u8005\u53ef\u4ee5\u4f7f\u7528\u4e0b\u9762\u7684python\u811a\u672c\u6765\u8bfb\u53d6\u53c2\u6570\u503c":63,"\u4f7f\u7528\u8005\u65e0\u9700\u5173\u5fc3\u8fd9\u4e2a\u53c2\u6570":52,"\u4f7f\u7528\u8005\u901a\u5e38\u65e0\u9700\u5173\u5fc3":52,"\u4f7f\u7528\u81ea\u52a8\u7684\u66ff\u8865\u6765\u66ff\u4ee3\u7ecf\u9a8c\u4e30\u5bcc\u7684\u4eba\u5de5\u8bc4\u5224":70,"\u4f7f\u7528\u8c13\u8bcd\u4e0a\u4e0b\u6587":68,"\u4f7f\u7528\u8fd9\u4e2a\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u5b8c\u6210\u5bf9\u65b0\u6570\u636e\u7684\u9884\u6d4b":19,"\u4f7f\u7528\u8fd9\u4e2a\u811a\u672c\u524d\u8bf7\u786e\u8ba4\u5df2\u7ecf\u5b89\u88c5\u4e86pillow\u53ca\u76f8\u5173\u4f9d\u8d56\u6a21\u5757":62,"\u4f7f\u7528\u8fd9\u79cd\u65b9\u5f0f":41,"\u4f7f\u7528\u8fdc\u7a0b\u7a00\u758f\u65b9\u5f0f\u8bad\u7ec3\u65f6":46,"\u4f7f\u7528\u968f\u673a\u68af\u5ea6\u4e0b\u964d":69,"\u4f7f\u7528\u9759\u6001\u5e93\u548c\u52a8\u6001\u5e93\u96be\u5ea6\u5dee\u4e0d\u591a":26,"\u4f7f\u7528\u9884\u8bad\u7ec3\u7684\u6807\u51c6\u683c\u5f0f\u8bcd\u5411\u91cf\u6a21\u578b":61,"\u4f7f\u7528args\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u8bbe\u7f6e":3,"\u4f7f\u7528c":27,"\u4f7f\u7528c99\u505a\u63a5\u53e3":26,"\u4f7f\u7528c99\u800c\u4e0d\u4f7f\u7528c11\u7684\u539f\u56e0\u662f":26,"\u4f7f\u7528c99\u800c\u4e0d\u4f7f\u7528c89":26,"\u4f7f\u7528checkgrad\u6a21\u5f0f\u65f6\u7684\u53c2\u6570\u53d8\u5316\u5927\u5c0f":52,"\u4f7f\u7528cpu\u4e24\u7ebf\u7a0b\u8ba1\u7b97fc4\u5c42":54,"\u4f7f\u7528cpu\u8ba1\u7b97fc4\u5c42":54,"\u4f7f\u7528cpu\u8bad\u7ec3":69,"\u4f7f\u7528init":54,"\u4f7f\u7528lstm\u4f5c\u4e3aencod":41,"\u4f7f\u7528max":62,"\u4f7f\u7528memory\u7684rnn\u5b9e\u73b0\u4fbf\u5982\u4e0b\u56fe\u6240\u793a":41,"\u4f7f\u7528model":54,"\u4f7f\u7528paddlepaddl":65,"\u4f7f\u7528python\u6570\u636e\u63d0\u4f9b\u5668":62,"\u4f7f\u7528rdma\u8fd8\u662ftcp\u4f20\u8f93\u534f\u8bae":52,"\u4f7f\u7528regress":29,"\u4f7f\u7528swig\u53ea\u652f\u6301cpython\u89e3\u91ca\u5668":26,"\u4f7f\u7528swig\u9700\u8981\u591a\u8bed\u8a00\u7ed1\u5b9a\u7684\u5f00\u53d1\u4eba\u5458\u719f\u7ec3\u638c\u63e1swig\u914d\u7f6e":26,"\u4f7f\u7528void":26,"\u4f7f\u8f93\u5165\u5c42\u5230\u9690\u85cf\u5c42\u7684\u795e\u7ecf\u5143\u662f\u5168\u90e8\u8fde\u63a5\u7684":62,"\u4f86":41,"\u4f8b\u5982":[3,20,26,27,29,30,32,36,41,44,46,49,50,51,52,54,57,63,65,67,69],"\u4f8b\u5982\u4e0a\u6587\u7684pod":55,"\u4f8b\u5982\u4e0a\u9762\u7684":31,"\u4f8b\u5982\u4ee5\u592a\u7f51\u7684":50,"\u4f8b\u5982\u4f7f\u7528":30,"\u4f8b\u5982\u586b\u5145":44,"\u4f8b\u5982\u5bf9\u4e8ejava\u6216\u8005python":26,"\u4f8b\u5982\u5bf9\u4e8ejava\u6765\u8bf4":26,"\u4f8b\u5982\u5bf9\u4e8epython":26,"\u4f8b\u5982\u5c06\u7b2c\u4e00\u6761\u6570\u636e\u8f6c\u5316\u4e3a":41,"\u4f8b\u5982\u6587\u672c\u5206\u7c7b\u4e2d":41,"\u4f8b\u5982\u672c\u4f8b\u4e2d\u7684\u4e24\u4e2a\u7279\u5f81":41,"\u4f8b\u5982\u673a\u5668\u4e0a\u67094\u5757gpu":30,"\u4f8b\u5982\u7b2c300\u4e2apass\u7684\u6a21\u578b\u4f1a\u88ab\u4fdd\u5b58\u5728":62,"\u4f8b\u5982c":26,"\u4f8b\u5982hostpath":55,"\u4f8b\u5982java\u4e0epython\u7684\u9519\u8bef\u5904\u7406\u662f\u76f4\u63a5\u6254\u51fa\u6765except":26,"\u4f8b\u5982output\u76ee\u5f55\u4e0b\u5c31\u5b58\u653e\u4e86\u8f93\u51fa\u7ed3\u679c":57,"\u4f8b\u5982python\u53ef\u4ee5\u4f7f\u7528":26,"\u4f8b\u5982python\u7684":26,"\u4f8b\u5982sigmoid":46,"\u4f8b\u5982sigmoid\u53d8\u6362":65,"\u4f8b\u5b50\u4e2d\u662f":46,"\u4f8b\u5b50\u4e2d\u662f0":46,"\u4f8b\u5b50\u4e2d\u662f100":46,"\u4f8b\u5b50\u4e2d\u662f4096":46,"\u4f8b\u5b50\u4e2d\u662f8192":46,"\u4f8b\u5b50\u4e2d\u662ffc":46,"\u4f8b\u5b50\u4e2d\u662fsoftmax":46,"\u4f8b\u5b50\u4f7f\u7528":55,"\u4f9bpaddlepaddle\u52a0\u8f7d":52,"\u4f9d\u636e\u5206\u7c7b\u9519\u8bef\u7387\u83b7\u5f97\u6700\u4f73\u6a21\u578b\u8fdb\u884c\u6d4b\u8bd5":69,"\u4f9d\u6b21\u7c7b\u63a8":29,"\u4f9d\u8d56\u4e8epython\u7684":62,"\u4fbf\u4e8e\u5b58\u50a8\u8d44\u6e90\u7ba1\u7406\u548cpod\u5f15\u7528":55,"\u4fbf\u4e8e\u672c\u5730\u9a8c\u8bc1\u548c\u6d4b\u8bd5":55,"\u4fbf\u4e8e\u7528\u6237\u6d4f\u89c8c":33,"\u4fbf\u5229":41,"\u4fbf\u548c\u5355\u5c42rnn\u914d\u7f6e\u4e2d\u7684":41,"\u4fbf\u5b9c":41,"\u4fbf\u662f\u5c06\u9759\u6001\u5e93\u52a0\u5165jvm\u4e2d":26,"\u4fdd\u5b58\u6a21\u578b\u53c2\u6570\u7684\u76ee\u5f55":52,"\u4fdd\u5b58\u751f\u6210\u7ed3\u679c\u7684\u6587\u4ef6":70,"\u4fdd\u5b58\u7f51\u7edc\u5c42\u8f93\u51fa\u7ed3\u679c\u7684\u76ee\u5f55":52,"\u4fdd\u5b58\u9884\u6d4b\u7ed3\u679c\u7684\u6587\u4ef6\u540d":52,"\u4fdd\u6301\u5bbd\u9ad8\u6bd4\u7f29\u653e\u5230\u77ed\u8fb9\u4e3a256":63,"\u4fdd\u6301\u5c3d\u91cf\u5c11\u7684commit":45,"\u4fe1\u53f7\u6765\u81ea\u52a8\u7ec8\u6b62\u5b83\u542f\u52a8\u7684\u6240\u6709\u8fdb\u7a0b":50,"\u4fee\u590d\u6240\u6709bug\u540e":29,"\u4fee\u590ddocker\u7f16\u8bd1\u955c\u50cf\u95ee\u9898":29,"\u4fee\u590dubuntu":29,"\u4fee\u6539":[55,56],"\u4fee\u6539\u542f\u52a8\u811a\u672c\u540e":56,"\u4fee\u6539\u6210\u66f4\u5feb\u7684\u7248\u672c":49,"\u4fee\u6539\u6587\u6863":48,"\u503c\u5f97\u6ce8\u610f\u7684\u662f":[41,45],"\u503c\u5f97\u6df1\u5165\u5206\u6790":49,"\u503c\u7c7b\u578b":54,"\u5047\u5982\u6211\u4eec\u662f\u4e09\u5206\u7c7b\u95ee\u9898":30,"\u5047\u8bbe":46,"\u5047\u8bbe\u53d8\u91cf":31,"\u5047\u8bbe\u60a8\u5df2\u7ecf\u5b8c\u6210\u4e86\u4e00\u4e2aai\u8bad\u7ec3\u7684python\u7a0b\u5e8f":33,"\u5047\u8bbe\u635f\u5931\u51fd\u6570\u662f":46,"\u5047\u8bbe\u8bcd\u5411\u91cf\u7ef4\u5ea6\u4e3a32":61,"\u504f\u7f6e\u53c2\u6570":63,"\u504f\u7f6e\u53c2\u6570\u7684\u5927\u5c0f":46,"\u505a\u53ea\u8bfb\u6302\u8f7d":20,"\u505a\u5982\u4e0b\u51e0\u4e2a\u64cd\u4f5c":29,"\u505a\u63a5\u53e3":26,"\u505c\u6b62\u4fdd\u5b58\u68c0\u67e5\u70b9\u7684\u7ebf\u7a0b":19,"\u505c\u6b62\u52a0\u8f7d\u6570\u636e":52,"\u505c\u7535":41,"\u50cf":33,"\u513f\u7ae5\u7247":66,"\u5143\u7d20":40,"\u5143\u7d20\u4e4b\u95f4\u7684\u987a\u5e8f\u662f\u91cd\u8981\u7684\u8f93\u5165\u4fe1\u606f":40,"\u5148\u5b9e\u73b0\u6a21\u578b\u63a8\u65ad\u7684api":27,"\u5148\u8c03\u7528initializer\u51fd\u6570":65,"\u5168\u5bb6":41,"\u5168\u8fde\u63a5\u5c42":[31,61,62,67],"\u5168\u8fde\u63a5\u5c42\u4ee5\u4e00\u4e2a\u7ef4\u5ea6\u4e3a":46,"\u5168\u8fde\u63a5\u5c42\u5c06\u7535\u5f71\u7684\u6bcf\u4e2a\u7279\u5f81\u7ed3\u5408\u6210\u4e00\u4e2a\u7535\u5f71\u7279\u5f81":67,"\u5168\u8fde\u63a5\u5c42\u6743\u91cd":63,"\u5168\u8fde\u63a5\u5c42\u6ca1\u6709\u7f51\u7edc\u5c42\u914d\u7f6e\u7684\u8d85\u53c2\u6570":46,"\u5168\u8fde\u63a5\u5c42\u7684\u5b9e\u73b0\u4f4d\u4e8e":46,"\u5168\u8fde\u63a5\u5c42\u7684\u6bcf\u4e2a\u8f93\u51fa\u90fd\u8fde\u63a5\u5230\u4e0a\u4e00\u5c42\u7684\u6240\u6709\u7684\u795e\u7ecf\u5143\u4e0a":46,"\u5168\u8fde\u63a5\u5c42python\u5c01\u88c5\u7684\u4f8b\u5b50\u4e2d\u5305\u542b\u4e0b\u9762\u51e0\u6b65":46,"\u516b\u4e2a\u7279\u5f81\u5206\u522b\u8f6c\u6362\u4e3a\u5411\u91cf":68,"\u516c\u5f0f":33,"\u516c\u94a5\u5199\u5165":50,"\u516d\u4e2a\u7279\u5f81\u548c\u6807\u7b7e\u90fd\u662f\u7d22\u5f15\u69fd":68,"\u5171\u4eab\u4efb\u52a1\u4e2d\u8bbe\u7f6e\u7684\u6570\u636e\u8fdb\u884c\u8bad\u7ec3\u548c\u6d4b\u8bd5":68,"\u5171\u4eab\u5b58\u50a8\u6302\u5728\u7684\u8def\u5f84":57,"\u5171\u670932":61,"\u5173\u4e8e\u5982\u4f55\u5b9a\u4e49\u7f51\u7edc\u4e2d\u7684\u5c42":62,"\u5173\u4e8e\u65f6\u95f4\u5e8f\u5217":41,"\u5173\u4e8e\u6784\u5efa\u548c\u6d4b\u8bd5\u7684\u66f4\u591a\u4fe1\u606f":45,"\u5173\u4e8epaddlepaddle\u7684\u5206\u5e03\u5f0f\u8bad\u7ec3":57,"\u5173\u4e8epaddlepaddle\u7684\u66f4\u591a\u4f7f\u7528\u65b9\u6cd5\u8bf7\u53c2\u8003":36,"\u5173\u4e8eunbound":43,"\u5173\u4e8evgg\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u63cf\u8ff0\u53ef\u4ee5\u53c2\u8003":62,"\u5173\u952e\u8bcd\u5305\u62ec":45,"\u5176\u4e0b\u5b50\u6587\u4ef6\u5939\u7684\u7ed3\u6784\u5982\u4e0b":62,"\u5176\u4e2d":[3,26,29,30,31,36,38,39,44,46,61,62,63],"\u5176\u4e2d156\u548c285\u662f\u8fd9\u4e9b\u56fe\u50cf\u7684\u5206\u7c7b\u6807\u7b7e":63,"\u5176\u4e2d50000\u5f20\u56fe\u7247\u4f5c\u4e3a\u8bad\u7ec3\u96c6":62,"\u5176\u4e2d\u5206\u522b\u5305\u542b\u4e86cifar":62,"\u5176\u4e2d\u5305\u542b6":66,"\u5176\u4e2d\u5305\u542b\u4e86200\u79cd\u9e1f\u7c7b\u7684\u7167\u7247":62,"\u5176\u4e2d\u5305\u542b\u7b97\u6cd5\u548c\u7f51\u7edc\u914d\u7f6e":69,"\u5176\u4e2d\u5305\u62ec\u51fd\u6570":68,"\u5176\u4e2d\u5b9a\u4e49\u4e86\u6a21\u578b\u67b6\u6784\u548csolver\u914d\u7f6e":70,"\u5176\u4e2d\u6587\u672c\u8f93\u5165\u7c7b\u578b\u5b9a\u4e49\u4e3a\u6574\u6570\u65f6\u5e8f\u7c7b\u578binteger_value_sequ":65,"\u5176\u4e2d\u6bcf\u4e00\u884c\u5bf9\u5e94\u4e00\u4e2a\u6570\u636e\u6587\u4ef6\u5730\u5740":2,"\u5176\u4e2d\u6bcf\u4e2a\u5143\u7d20\u662f\u53cc\u5c42\u5e8f\u5217\u4e2d\u6bcf\u4e2asubseq\u6700\u540e\u4e00\u4e2a":40,"\u5176\u4e2d\u6bcf\u4e2a\u5411\u91cf\u5bf9\u5e94\u8f93\u5165\u8bed\u53e5\u4e2d\u7684\u4e00\u4e2a\u5143\u7d20":70,"\u5176\u4e2d\u6bcf\u6761pass\u82b1\u8d39\u4e867\u4e2a\u5c0f\u65f6":70,"\u5176\u4e2d\u6bcf\u884c\u6570\u636e\u4ee3\u8868\u4e00\u5f20\u56fe\u7247":3,"\u5176\u4e2d\u8be6\u7ec6\u8bf4\u660e\u4e86\u6a21\u578b\u67b6\u6784":70,"\u5176\u4e2d\u8f93\u5165\u56fe\u50cf\u7684\u989c\u8272\u901a\u9053\u987a\u5e8f\u4e3a":63,"\u5176\u4e2dbeam":70,"\u5176\u4e2dcheckgrad\u4e3b\u8981\u4e3a\u5f00\u53d1\u8005\u4f7f\u7528":52,"\u5176\u4e2dmean\u548cstd\u662f\u8bad\u7ec3\u914d\u7f6e\u4e2d\u7684\u53c2\u6570":52,"\u5176\u4e2dvalue\u5373\u4e3asoftmax\u5c42\u7684\u8f93\u51fa":5,"\u5176\u4e2dx\u8868\u793a\u8f93\u5165\u6570\u636e\u662f\u4e00\u4e2a\u7ef4\u5ea6\u4e3a2\u7684\u7a20\u5bc6\u5411\u91cf":36,"\u5176\u4ed6":66,"\u5176\u4ed6\u4eba\u5f88\u5bb9\u6613\u770b\u61c2\u955c\u50cf\u751f\u6210\u8fc7\u7a0b":33,"\u5176\u4ed6\u516d\u884c\u5217\u51fa\u4e86\u96c6\u675f\u641c\u7d22\u7684\u7ed3\u679c":70,"\u5176\u4ed6\u5185\u5b58\u6742\u9879":30,"\u5176\u4ed6\u5185\u5b58\u6742\u9879\u662f\u6307paddlepaddle\u672c\u8eab\u6240\u7528\u7684\u4e00\u4e9b\u5185\u5b58":30,"\u5176\u4ed6\u51fd\u6570\u5747\u8fd4\u56de":27,"\u5176\u4ed6\u53c2\u6570\u4f7f\u7528":3,"\u5176\u4ed6\u53c2\u6570\u8bf7\u53c2\u8003":65,"\u5176\u4ed6\u6240\u6709\u5c42\u90fd\u4f1a\u4f7f\u7528gpu\u8ba1\u7b97":54,"\u5176\u4ed6\u7528\u6237\u7684fork\u7248\u672c\u5e93\u5e76\u4e0d\u9700\u8981\u4e25\u683c\u9075\u5b88":29,"\u5176\u4ed6\u914d\u7f6e\u53c2\u6570":[38,39],"\u5176\u4ed6\u9ad8\u7ea7\u529f\u80fd\u5305\u62ec\u5b9a\u4e49\u591a\u4e2amemori":44,"\u5176\u4f1a\u81ea\u52a8\u88ab\u52a0\u5165\u7f16\u8bd1\u5217\u8868":46,"\u5176\u4f59\u884c\u662f":61,"\u5176\u4f5c\u7528\u662f\u5c06\u6570\u636e\u4f20\u5165\u5185\u5b58\u6216\u663e\u5b58":2,"\u5176\u5177\u4f53\u8bf4\u660e\u4e86\u5b57\u6bb5\u7c7b\u578b\u548c\u6587\u4ef6\u540d\u79f0":67,"\u5176\u5185\u90e8\u7684\u6587\u4ef6\u4e5f\u4f1a\u968f\u4e4b\u6d88\u5931":55,"\u5176\u5305\u62ec\u4e24\u4e2a\u51fd\u6570":65,"\u5176\u53c2\u6570\u5982\u4e0b":3,"\u5176\u5b83\u90e8\u5206\u548c\u903b\u8f91\u56de\u5f52\u7f51\u7edc\u7ed3\u6784\u4e00\u81f4":65,"\u5176\u5b83layer\u7684\u8f93\u51fa":43,"\u5176\u5b9e\u4e5f\u662f\u548c\u6bcf\u4e2amini":30,"\u5176\u63d0\u4f9b\u5e94\u7528\u90e8\u7f72":55,"\u5176\u6b21":[3,41,65],"\u5176\u76ee\u7684\u662f\u5728\u7ed9\u5b9a\u7684\u8f93\u5165\u53e5\u5b50\u4e2d\u53d1\u73b0\u6bcf\u4e2a\u8c13\u8bcd\u7684\u8c13\u8bcd\u8bba\u5143\u7ed3\u6784":68,"\u5176\u8bf4\u660e\u5982\u4e0b":41,"\u5176\u8f93\u5165\u53c2\u6570\u5982\u4e0b":70,"\u5176\u8f93\u51fa\u88ab\u7528\u4f5cmemory\u7684\u521d\u59cb\u503c":44,"\u5177\u4f53\u4f7f\u7528\u65b9\u6cd5\u4e3a":[27,30],"\u5177\u4f53\u539f\u56e0\u53c2\u8003":27,"\u5177\u4f53\u53ef\u4ee5\u53c2\u8003":[3,46],"\u5177\u4f53\u53ef\u53c2\u8003\u6587\u6863":43,"\u5177\u4f53\u5982\u4e0b":33,"\u5177\u4f53\u60c5\u51b5\u56e0\u4eba\u800c\u5f02":49,"\u5177\u4f53\u64cd\u4f5c\u5982\u4e0b":30,"\u5177\u4f53\u6d41\u7a0b\u5982\u4e0b":65,"\u5177\u4f53\u7684\u683c\u5f0f\u8bf4\u660e":3,"\u5177\u4f53\u7684\u89e3\u51b3\u65b9\u6cd5\u662f":30,"\u5177\u4f53\u8bf7\u53c2\u7167\u793a\u4f8b":63,"\u5177\u4f53\u8bf7\u53c2\u8003":[3,27,45],"\u5177\u4f53\u8bf7\u89c1":45,"\u5177\u6709\u76f8\u540c\u7684\u7ed3\u679c\u4e86":41,"\u5177\u6709\u81ea\u5faa\u73af\u8fde\u63a5\u7684\u795e\u7ecf\u5143":69,"\u517c\u5907\u6613\u7528\u6027":0,"\u5185":44,"\u5185\u5b58":49,"\u5185\u5b58\u5bb9\u9650\u9608\u503c":52,"\u5185\u5bb9":65,"\u5185\u5bb9\u5982\u4e0b":56,"\u5185\u5c42inner_step\u7684recurrent_group\u548c\u5355\u5c42\u5e8f\u5217\u7684\u51e0\u4e4e\u4e00\u6837":41,"\u5185\u5df2\u7ecf\u5305\u542bpaddlepaddle\u7684\u6267\u884c\u7a0b\u5e8f\u4f46\u662f\u8fd8\u6ca1\u4e0a\u8ff0\u529f\u80fd":57,"\u5185\u7684\u5f00\u53d1\u8005\u4e0b\u8f7ddocker\u955c\u50cf":33,"\u5185\u90e8":57,"\u5185\u90e8\u9a71\u52a8python\u89e3\u91ca\u5668\u8fdb\u884c\u6a21\u578b\u914d\u7f6e\u89e3\u6790\u548c\u6570\u636e\u8bfb\u53d6":26,"\u518d\u4e3apaddle\u7684\u8bad\u7ec3\u8fc7\u7a0b\u63d0\u4f9b\u6587\u4ef6\u5217\u8868":67,"\u518d\u5728\u6bcf\u4e00\u4e2aapi\u4e2d\u81ea\u5df1\u68c0\u67e5\u7c7b\u578b":26,"\u518d\u57fa\u4e8e":29,"\u518d\u5bf9\u6bcf\u4e00\u4e2a\u5355\u5c42\u65f6\u95f4\u5e8f\u5217\u8fdb\u884c\u5904\u7406":41,"\u518d\u5bf9\u6bcf\u4e00\u53e5\u8bdd\u7684\u7f16\u7801\u5411\u91cf\u7528lstm\u7f16\u7801\u6210\u4e00\u4e2a\u6bb5\u843d\u7684\u5411\u91cf":41,"\u518d\u5bf9\u8fd9\u4e2a\u6bb5\u843d\u5411\u91cf\u8fdb\u884c\u5206\u7c7b":41,"\u518d\u6307\u5b9a":32,"\u518d\u6b21\u5bf9\u4ee3\u7801\u8fdb\u884c\u6027\u80fd\u5206\u6790":49,"\u518d\u7528\u8fd9\u4e2a\u68af\u5ea6\u53bb\u548c":46,"\u518d\u901a\u8fc7\u51fd\u6570":57,"\u5192\u9669\u7247":66,"\u5197\u4f59\u7b49\u529f\u80fd":55,"\u5199\u4ee3\u7801":26,"\u5199\u5165\u5feb\u7167\u6570\u636e":19,"\u5199\u5b8c\u6a21\u578b\u914d\u7f6e\u4e4b\u540e":70,"\u5199\u68af\u5ea6\u68c0\u67e5\u5355\u5143\u6d4b\u8bd5\u662f\u4e00\u4e2a\u9a8c\u8bc1\u65b0\u5b9e\u73b0\u7684\u5c42\u662f\u5426\u6b63\u786e\u7684\u76f8\u5bf9\u7b80\u5355\u7684\u529e\u6cd5":46,"\u519c\u6c11":66,"\u51c6\u5907":41,"\u51c6\u5907\u597d\u6570\u636e":67,"\u51c6\u5907\u7528\u6765\u5b66\u4e60\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u5e8f\u5217\u6570\u636e":44,"\u51c6\u5907\u9884\u6d4b\u6570\u636e":5,"\u51cf\u5c0f\u5e8f\u5217\u7684\u957f\u5ea6":30,"\u51cf\u5c0f\u8fd9\u4e2a\u5185\u5b58\u6c60\u5373\u53ef\u51cf\u5c0f\u5185\u5b58\u5360\u7528":30,"\u51cf\u5c0fbatch":30,"\u51fa\u53bb\u73a9":41,"\u51fa\u5dee":41,"\u51fa\u6765":41,"\u51fa\u73b0":30,"\u51fa\u73b0\u4ee5\u4e0b\u9519\u8bef":30,"\u51fa\u73b0\u8fd9\u4e2a\u95ee\u9898\u7684\u4e3b\u8981\u539f\u56e0\u662f":30,"\u51fd\u6570":[3,31,44,46,49,68,69],"\u51fd\u6570\u4e2d":44,"\u51fd\u6570\u4e2d\u4f7f\u7528":3,"\u51fd\u6570\u4e2d\u8bbe\u7f6e\u7684":50,"\u51fd\u6570\u5047\u8bbe":44,"\u51fd\u6570\u52a0\u5230\u4ee3\u7801\u4e2d":49,"\u51fd\u6570\u5373\u53ef\u5b8c\u6210\u8f6c\u6362":20,"\u51fd\u6570\u53ea\u5173\u6ce8\u4e8ernn\u4e00\u4e2a\u65f6\u95f4\u6b65\u4e4b\u5185\u7684\u8ba1\u7b97":43,"\u51fd\u6570\u540d\u4e3a":27,"\u51fd\u6570\u547d\u540d":26,"\u51fd\u6570\u5c06\u8fd4\u56de\u4e09\u4e2a\u6574\u6570\u5217\u8868":44,"\u51fd\u6570\u5c31\u662f\u6839\u636e\u8be5\u673a\u5236\u914d\u7f6e\u7684":3,"\u51fd\u6570\u5f97\u5230\u7684\u68af\u5ea6\u53bb\u5bf9\u6bd4":46,"\u51fd\u6570\u5fc5\u987b\u5148\u8c03\u7528\u57fa\u7c7b\u4e2d\u7684\u51fd\u6570":46,"\u51fd\u6570\u5fc5\u987b\u8fd4\u56de\u4e00\u4e2a\u6216\u591a\u4e2alayer\u7684\u8f93\u51fa":43,"\u51fd\u6570\u6307\u51fa\u4e86\u5728\u8bad\u7ec3\u65f6\u9700\u8981\u4ece\u53c2\u6570\u670d\u52a1\u5668\u53d6\u51fa\u7684\u884c":46,"\u51fd\u6570\u6765\u5c06\u4fe1\u606f\u8f93\u51fa\u5230\u754c\u9762\u4e2d":49,"\u51fd\u6570\u67e5\u8be2\u8f6f\u4ef6\u5305\u76f8\u5173api\u8bf4\u660e":5,"\u51fd\u6570\u7684":3,"\u51fd\u6570\u7684\u5b9e\u73b0\u662f\u6b63\u786e\u7684":46,"\u51fd\u6570\u7684\u5f00\u5934\u5fc5\u987b\u8c03\u7528":46,"\u5206\u4e3a\u597d\u8bc4":65,"\u5206\u522b\u4e3a":61,"\u5206\u522b\u4e3atrain":70,"\u5206\u522b\u4ece\u8bcd\u8bed\u548c\u53e5\u5b50\u7ea7\u522b\u7f16\u7801\u8f93\u5165\u6570\u636e":43,"\u5206\u522b\u4f7f\u7528\u5355\u53cc\u5c42rnn\u4f5c\u4e3a\u7f51\u7edc\u914d\u7f6e\u7684\u6a21\u578b":41,"\u5206\u522b\u5305\u542b\u4e86\u6cd5\u8bed\u5230\u82f1\u8bed\u7684\u5e73\u884c\u8bed\u6599\u5e93\u7684\u8bad\u7ec3\u6570\u636e":70,"\u5206\u522b\u5b9a\u4e49\u5b50\u53e5\u7ea7\u522b\u548c\u8bcd\u8bed\u7ea7\u522b\u4e0a\u9700\u8981\u5b8c\u6210\u7684\u8fd0\u7b97":43,"\u5206\u522b\u5bf9\u5e94\u4e8e\u53d8\u91cf":31,"\u5206\u522b\u662f":40,"\u5206\u522b\u662frnn\u72b6\u6001\u548c\u8f93\u5165\u7684\u53d8\u6362\u77e9\u9635":44,"\u5206\u522b\u662fsentences\u548clabel":41,"\u5206\u522b\u662fwords\u548clabel":41,"\u5206\u522b\u8ba1\u7b97\u6bcf\u4e2a\u53c2\u6570\u7684\u68af\u5ea6":46,"\u5206\u522b\u8fdb\u884c\u5e8f\u5217\u64cd\u4f5c":41,"\u5206\u5272":[66,68],"\u5206\u5272\u6587\u4ef6\u7684\u65b9\u6cd5\u662f":67,"\u5206\u5e03\u5f0f\u5b58\u50a8\u670d\u52a1":19,"\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf":55,"\u5206\u6210\u4e24\u90e8\u5206":3,"\u5206\u652f":[29,45],"\u5206\u652f\u4e00\u65e6\u5efa\u7acb":29,"\u5206\u652f\u4e0a":45,"\u5206\u652f\u4e0a\u521b\u5efa\u65b0\u5206\u652f":45,"\u5206\u652f\u4e2d":29,"\u5206\u652f\u4e3a\u5f00\u53d1":29,"\u5206\u652f\u4e3a\u6bcf\u4e00\u6b21release\u65f6\u5efa\u7acb\u7684\u4e34\u65f6\u5206\u652f":29,"\u5206\u652f\u4e3a\u7a33\u5b9a":29,"\u5206\u652f\u529f\u80fd\u7684\u5c01\u95ed":29,"\u5206\u652f\u5408\u5165":29,"\u5206\u652f\u5408\u5165master\u5206\u652f":29,"\u5206\u652f\u540c\u6b65\u4e3b\u7248\u672c\u5e93\u7684":29,"\u5206\u652f\u540d":45,"\u5206\u652f\u540d\u4e3a":29,"\u5206\u652f\u5b58\u5728\u7684\u65f6\u5019":29,"\u5206\u652f\u6d3e\u751f\u51fa\u65b0\u7684\u5206\u652f":29,"\u5206\u652f\u7684\u7248\u672c\u90fd\u662f\u7ecf\u8fc7\u5355\u5143\u6d4b\u8bd5\u548c\u56de\u5f52\u6d4b\u8bd5\u7684\u7248\u672c":29,"\u5206\u652f\u7684\u7248\u672c\u90fd\u7ecf\u8fc7\u5355\u5143\u6d4b\u8bd5":29,"\u5206\u652f\u7684\u76ee\u6807\u673a\u7248openblas\u5e93":38,"\u5206\u652f\u89c4\u8303":45,"\u5206\u6790\u5f97\u5230\u7684\u4fe1\u606f\u7528\u4e8e\u534f\u52a9\u8fdb\u884c\u7a0b\u5e8f\u7684\u4f18\u5316":49,"\u5206\u7247":19,"\u5206\u7c7b\u6210\u6b63\u9762\u60c5\u7eea\u548c\u8d1f\u9762\u60c5\u7eea\u4e24\u7c7b":3,"\u5206\u7c7b\u8bef\u5dee\u662f0":69,"\u5206\u7c7b\u9519\u8bef\u7387\u548c\u6a21\u578b\u5927\u5c0f\u7531\u4e0b\u8868\u7ed9\u51fa":63,"\u5206\u8bcd\u5e8f\u5217\u7684\u5f00\u59cb":61,"\u5206\u8bcd\u5e8f\u5217\u7684\u7ed3\u675f":61,"\u5206\u8bcd\u98ce\u683c\u5982\u4e0b":61,"\u5206\u914d\u5230\u5f53\u524d\u6570\u636e\u5757\u6837\u672c\u6570\u7684\u56db\u5206\u4e4b\u4e00":52,"\u5206\u9694":[61,67],"\u5206\u9694\u7b26\u4e3a":66,"\u5207\u6362\u5230":45,"\u5207\u6362\u5230\u6240\u5efa\u5206\u652f":45,"\u5217\u8868":67,"\u5217\u8868\u5982\u4e0b":[3,36],"\u5219\u4e0d\u5728\u4e4e\u5185\u5b58\u6682\u5b58\u591a\u5c11\u6761\u6570\u636e":3,"\u5219\u4e0d\u9700\u8981\u91cd\u5199\u8be5\u51fd\u6570":46,"\u5219\u4f1a\u9884\u5148\u8bfb\u53d6\u5168\u90e8\u6570\u636e\u5230\u5185\u5b58\u4e2d":3,"\u5219\u4f7f\u7528\u533a\u57df\u6807\u8bb0":68,"\u5219\u4f7f\u7528\u540c\u6b65\u8bad\u7ec3":52,"\u5219\u4f7f\u7528\u542f\u52a8\u53c2\u6570\u5b9a\u4e49\u7684\u521d\u59cb\u5316\u65b9\u6cd5\u521d\u59cb\u5316\u53c2\u6570":19,"\u5219\u4f7f\u7528\u8be5\u53c2\u6570\u4f5c\u4e3a\u9ed8\u8ba4\u503c":52,"\u5219\u5148\u505a\u5d4c\u5165":67,"\u5219\u53ef\u4ee5\u50cf":50,"\u5219\u53ef\u8bbe\u7f6e":[38,39],"\u5219\u5b57\u4e0e\u5b57\u4e4b\u95f4\u7528\u7a7a\u683c\u5206\u9694":65,"\u5219\u5ffd\u7565":19,"\u5219\u603b\u4f1a\u663e\u793a\u963b\u9694\u6458\u8981\u4fe1\u606f":52,"\u5219\u628a\u53e6\u4e00\u4e2a\u6162\u901f\u7684kill\u6389":19,"\u5219\u63a8\u8350\u5927\u4e8e\u8bad\u7ec3\u65f6batch":3,"\u5219\u662f\u5e26gui\u7684nvidia\u53ef\u89c6\u5316\u6027\u80fd\u5206\u6790\u5de5\u5177":49,"\u5219\u663e\u793a\u963b\u9694\u6027\u80fd\u7684\u6458\u8981\u4fe1\u606f":52,"\u5219\u76f4\u63a5\u5f15\u5165\u53e6\u4e00\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5219\u8bbe\u7f6e\u6210":[38,39],"\u5219\u9700\u8981\u4f7f\u7528\u7b49\u4e8e\u6743\u91cd\u53c2\u6570\u89c4\u6a21\u5927\u7ea65\u500d\u7684\u5185\u5b58":30,"\u5219\u9700\u8981\u56de\u6eda\u5230\u4e0a\u4e00\u4e2a\u68c0\u67e5\u70b9":19,"\u5219\u9700\u8981\u914d\u7f6e":55,"\u5219\u9700\u9009\u7528":39,"\u521b\u5efa\u4e00\u4e2akubernet":57,"\u521b\u5efa\u548c\u53d1\u5e03\u81ea\u5df1\u7684ai\u7a0b\u5e8f\u955c\u50cf":33,"\u521b\u5efa\u5e76\u5207\u6362\u5230\u65b0\u5206\u652f":45,"\u521b\u5efa\u6210\u529f\u540e":57,"\u521b\u5efa\u8bad\u7ec3\u6570\u636e\u7684":70,"\u521b\u5efagener":3,"\u521d\u59cb\u5316\u4e4b\u540e":5,"\u521d\u59cb\u5316\u504f\u7f6e\u5411\u91cf":46,"\u521d\u59cb\u5316\u65f6\u8c03\u7528\u7684\u51fd\u6570":3,"\u521d\u59cb\u5316\u6743\u91cd\u8868":46,"\u521d\u59cb\u5316\u6a21\u578b\u7684\u8def\u5f84":52,"\u521d\u59cb\u5316\u6a21\u578b\u7684\u8def\u5f84\u914d\u7f6e\u4e3a":61,"\u521d\u59cb\u5316\u7236\u7c7b":46,"\u521d\u59cb\u5316biases_":46,"\u521d\u59cb\u5316paddlepaddle\u73af\u5883":5,"\u521d\u59cb\u72b6\u6001":43,"\u5220\u9664":45,"\u5220\u9664\u78c1\u76d8\u76ee\u5f55\u4e2d\u4e0d\u662f\u5f53\u524duuid\u7684\u5feb\u7167\u6587\u4ef6":19,"\u5229\u7528\u5206\u5e03\u5f0f\u8bad\u7ec3\u9a7e\u9a6d\u66f4\u591a\u7684\u8ba1\u7b97\u8d44\u6e90":30,"\u5229\u7528\u5355\u8bcdid\u67e5\u627e\u8be5\u5355\u8bcd\u5bf9\u5e94\u7684\u8fde\u7eed\u5411\u91cf":65,"\u5229\u7528\u66f4\u591a\u7684\u8ba1\u7b97\u8d44\u6e90\u53ef\u4ee5\u5206\u4e3a\u4e00\u4e0b\u51e0\u4e2a\u65b9\u5f0f\u6765\u8fdb\u884c":30,"\u5229\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u6765\u89e3\u6790\u8be5\u7279\u5f81":67,"\u5229\u7528\u8bad\u7ec3\u96c6\u751f\u6210\u7684\u5b57\u5178":69,"\u5229\u7528\u8fd9\u79cd\u7279\u6027":43,"\u5229\u7528\u903b\u8f91\u56de\u5f52\u6a21\u578b\u5bf9\u8be5\u5411\u91cf\u8fdb\u884c\u5206\u7c7b":65,"\u5229\u7528kubernetes\u80fd\u65b9\u4fbf\u5730\u7ba1\u7406\u8de8\u673a\u5668\u8fd0\u884c\u5bb9\u5668\u5316\u7684\u5e94\u7528":55,"\u5229\u843d":41,"\u522b\u4eba\u5e2e\u4e86\u5fd9":45,"\u5230":[19,30,44],"\u5230\u6240\u6709\u8282\u70b9\u800c\u4e0d\u7528\u5bc6\u7801":50,"\u5230\u672c\u5730":45,"\u5230\u6b64":33,"\u5230\u76ee\u524d\u4e3a\u6b62":68,"\u5236\u4f5c\u65b0\u955c\u50cf\u6765\u5b8c\u6210\u4ee5\u4e0a\u7684\u5de5\u4f5c":57,"\u5236\u4f5cpaddlepaddle\u5f00\u53d1\u955c\u50cf":33,"\u5236\u4f5cpaddlepaddle\u751f\u4ea7\u955c\u50cf":33,"\u5236\u4f5cpaddlepaddle\u955c\u50cf":57,"\u5237\u7259":41,"\u524d\u4e00\u7bc7\u6587\u7ae0\u4ecb\u7ecd\u4e86\u5982\u4f55\u5728kubernetes\u96c6\u7fa4\u4e0a\u542f\u52a8\u4e00\u4e2a\u5355\u673apaddlepaddle\u8bad\u7ec3\u4f5c\u4e1a":57,"\u524d\u4e09\u884cimport\u4e86\u5b9a\u4e49network":70,"\u524d\u53f0":41,"\u524d\u5411\u4f20\u64ad":46,"\u524d\u5411\u4f20\u64ad\u7ed9\u5b9a\u8f93\u5165":46,"\u524d\u5411\u548c\u540e\u5411":46,"\u524d\u81ea\u52a8\u68c0\u67e5\u4e00\u4e9b\u57fa\u672c\u4e8b\u5b9c":45,"\u5269\u4e0b\u7684pass\u4f1a\u76f4\u63a5\u4ece\u5185\u5b58\u91cc":3,"\u529f\u80fd":24,"\u529f\u80fd\u7684\u6b63\u786e\u6027\u5305\u62ec\u9a8c\u8bc1paddle\u76ee\u524d\u7684":29,"\u52a0\u4e0a\u504f\u7f6e\u5411\u91cf":46,"\u52a0\u4e86l2\u6b63\u5219\u548c\u68af\u5ea6\u622a\u65ad":65,"\u52a0\u5165":49,"\u52a0\u6743\u548c\u7528\u6765\u751f\u6210":44,"\u52a0\u6743\u7f16\u7801\u5411\u91cf":44,"\u52a0\u8f7d\u6570\u636e":68,"\u52a0\u8f7d\u6a21\u578b":68,"\u52a0\u8f7d\u6a21\u578b\u53c2\u6570":70,"\u52a0\u8f7dtest":52,"\u52a0\u901fpaddlepaddle\u8bad\u7ec3\u53ef\u4ee5\u8003\u8651\u4ece\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762":30,"\u52a8\u4f5c\u7247":66,"\u52a8\u6001\u5e93":26,"\u52a8\u753b\u7247":66,"\u52a8\u8bcd":68,"\u52a9\u624b":46,"\u5305\u542b12":69,"\u5305\u542b20\u4e2a\u8bad\u7ec3\u6837\u4f8b":61,"\u5305\u542b3\u4e2a\u5c5e\u6027":61,"\u5305\u542b50":69,"\u5305\u542b\u4e86\u67d0\u79cd\u7c7b\u578b\u7684\u7c7b\u578b\u5b9a\u4e49\u548c\u66b4\u9732\u7684\u5168\u90e8\u51fd\u6570":27,"\u5305\u542b\u7684\u6240\u6709\u4f9d\u8d56\u5047\u8bbe\u90fd\u53ef\u4ee5\u5728paddle\u7684\u8fd0\u884c\u5bb9\u5668\u4e2d":33,"\u5305\u5e76\u91cd\u65b0\u7f16\u8bd1paddlepaddl":30,"\u5305\u62ec":[20,52,65,68,70],"\u5305\u62ec\u4ee5\u4e0b\u4e24\u79cd":3,"\u5305\u62ec\u53d1\u884c\u65f6\u95f4":66,"\u5305\u62ec\u5b57\u7b26\u4e32\u5206\u914d":30,"\u5305\u62ec\u6570\u636e\u8f93\u5165":31,"\u5305\u62ec\u6743\u91cdw\u548c\u504f\u7f6eb":19,"\u5305\u62ec\u751f\u6210cpu":32,"\u5305\u62ec\u795e\u7ecf\u7f51\u7edc\u62d3\u6251\u7ed3\u6784":36,"\u5305\u62ec\u7b80\u5355\u7684":65,"\u5305\u62ecbool":54,"\u5305\u62ecdocker\u955c\u50cf":34,"\u5305\u62eclinux":33,"\u5305\u662f\u6700\u65b0\u7684":30,"\u5305\u6bd4\u8f83\u8001":30,"\u5305\u7684\u65b9\u6cd5\u662f":30,"\u533a\u522b\u662f\u540c\u65f6\u5904\u7406\u4e86\u4e24\u4e2a\u8f93\u5165":41,"\u533a\u522b\u662frnn\u4f7f\u7528\u4e24\u5c42\u5e8f\u5217\u6a21\u578b":41,"\u533b\u751f":66,"\u533b\u7597\u4fdd\u5065":66,"\u5341\u4e00":41,"\u5347\u5e8f\u6392\u5217":70,"\u534e\u6da6\u4e07\u5bb6":41,"\u534f\u540c\u5b8c\u6210releas":29,"\u5355\u4e2a\u503c":20,"\u5355\u4f4d\u662fmb":52,"\u5355\u5143\u6d4b\u8bd5\u4f1a\u5f15\u7528site":30,"\u5355\u5143\u6d4b\u8bd5checkgrad_ep":51,"\u5355\u53cc\u5c42\u5e8f\u5217\u7684\u53e5\u5b50\u662f\u4e00\u6837\u7684":41,"\u5355\u53cc\u5c42rnn":42,"\u5355\u53d8\u91cf\u7684\u7ebf\u6027\u56de\u5f52":31,"\u5355\u5c42":43,"\u5355\u5c42\u4e0d\u7b49\u957frnn":41,"\u5355\u5c42\u548c\u53cc\u5c42\u5e8f\u5217\u7684\u4f7f\u7528\u548c\u793a\u4f8b2\u4e2d\u7684\u793a\u4f8b\u7c7b\u4f3c":41,"\u5355\u5c42\u5e8f\u5217":40,"\u5355\u5c42\u5e8f\u5217\u7684\u6bcf\u4e2a\u5143\u7d20":40,"\u5355\u5c42\u5e8f\u5217\u7b2ci\u4e2a\u5143\u7d20":40,"\u5355\u5c42\u6216\u53cc\u5c42":40,"\u5355\u5c42\u65f6\u95f4\u5e8f\u5217":41,"\u5355\u5c42rnn":[41,43],"\u5355\u5c42rnn\u548c\u53cc\u5c42rnn\u7684\u7f51\u7edc\u914d\u7f6e":41,"\u5355\u673acpu\u8bad\u7ec3":30,"\u5355\u673agpu\u8bad\u7ec3":30,"\u5355\u6b65\u51fd\u6570":44,"\u5355\u6b65\u51fd\u6570\u548c\u8f93\u51fa\u51fd\u6570\u5728":44,"\u5355\u6b65\u51fd\u6570\u548c\u8f93\u51fa\u51fd\u6570\u90fd\u975e\u5e38\u7b80\u5355":44,"\u5355\u6b65\u51fd\u6570\u7684\u5b9e\u73b0\u5982\u4e0b\u6240\u793a":44,"\u5355\u70b9\u6545\u969c":19,"\u5355\u8fdb\u5355\u51fa":43,"\u5360\u4f4d\u7b26":61,"\u536b\u751f":41,"\u5373":[27,30,31,47,57,65,69],"\u5373\u4e00\u4e2a\u5c06\u5355\u8bcd\u5b57\u7b26\u4e32\u6620\u5c04\u5230\u5355\u8bcdid\u7684\u5b57\u5178":3,"\u5373\u4e0a\u8ff0\u4ee3\u7801\u4e2d\u7684\u7b2c19\u884c":41,"\u5373\u4e0d\u8981\u5c06\u6bcf\u4e00\u4e2a\u6837\u672c\u90fd\u653e\u5165train":3,"\u5373\u4e0d\u9700\u8981\u4f7f\u7528memori":41,"\u5373\u4e3a\u4e00\u4e2a\u65f6\u95f4\u6b65":41,"\u5373\u4e3a\u5355\u5c42rnn\u5e8f\u5217\u7684\u4f7f\u7528\u4ee3\u7801":41,"\u5373\u4e3a\u65f6\u95f4\u5e8f\u5217\u7684\u8f93\u5165":41,"\u5373\u4e3a\u8fd9\u4e2a\u53cc\u5c42rnn\u7684\u7f51\u7edc\u7ed3\u6784":41,"\u5373\u4e3a\u8fd9\u4e2a\u6570\u636e\u6587\u4ef6\u7684\u540d\u5b57":3,"\u5373\u4e8c\u7ef4\u6570\u7ec4":41,"\u5373\u4f7f\u7528":27,"\u5373\u4f7f\u7528\u6237\u76f4\u63a5\u5f15\u7528\u67d0\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5373\u4f7f\u95f4\u9694\u5f88\u5c0f":52,"\u5373\u4f7fc":27,"\u5373\u4f7fprocess\u51fd\u6570\u91cc\u9762\u53ea\u6709\u4e00\u4e2ayield":3,"\u5373\u4f8b\u5982":27,"\u5373\u4fbf\u8bbe\u7f6e":30,"\u5373\u4fbfpaddl":27,"\u5373\u521d\u59cb\u72b6\u6001\u4e3a0":43,"\u5373\u5305\u542b\u65f6\u95f4\u6b65\u4fe1\u606f":3,"\u5373\u5355\u65f6\u95f4\u6b65\u6267\u884c\u7684\u51fd\u6570":44,"\u5373\u53cc\u5411lstm\u548c\u4e09\u5c42\u5806\u53e0lstm":69,"\u5373\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217":41,"\u5373\u53cc\u5c42rnn\u7684\u6bcf\u4e2a\u72b6\u6001":43,"\u5373\u53ef":[31,45],"\u5373\u53ef\u4ee5\u6781\u5927\u7684\u52a0\u901f\u6570\u636e\u8f7d\u5165\u6d41\u7a0b":30,"\u5373\u53ef\u8fdb\u884c\u4e0b\u4e00\u6b65\u64cd\u4f5c":47,"\u5373\u5728\u53cc\u5c42\u5e8f\u5217\u7684\u539f\u59cb\u6570\u636e\u4e2d":41,"\u5373\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d":30,"\u5373\u5927\u90e8\u5206\u503c\u4e3a0":[3,36],"\u5373\u5b8c\u6210\u67d0\u4e00\u4e2a\u4efb\u52a1\u7684\u6700\u5c11\u51fd\u6570":27,"\u5373\u5bf9\u7b2c\u4e09\u6b65\u8fdb\u884c\u66ff\u6362":65,"\u5373\u5c06\u4e00\u6bb5\u82f1\u6587\u6587\u672c\u6570\u636e":3,"\u5373\u5c06\u4e00\u6bb5\u8bdd\u8fdb\u884c\u5206\u7c7b":41,"\u5373\u5f53\u524d\u65f6\u95f4\u6b65\u4e0b\u7684\u795e\u7ecf\u7f51\u7edc\u4f9d\u8d56\u524d\u4e00\u4e2a\u65f6\u95f4\u6b65\u795e\u7ecf\u7f51\u7edc\u4e2d\u67d0\u4e00\u4e2a\u795e\u7ecf\u5143\u8f93\u51fa":41,"\u5373\u6211\u4eec\u7684\u8bad\u7ec3\u76ee\u6807":31,"\u5373\u628a\u5355\u5c42rnn\u751f\u6210\u540e\u7684subseq\u7ed9\u62fc\u63a5\u6210\u4e00\u4e2a\u65b0\u7684\u53cc\u5c42seq":43,"\u5373\u6574\u4e2a\u53cc\u5c42group\u662f\u5c06\u524d\u4e00\u4e2a\u5b50\u53e5\u7684\u6700\u540e\u4e00\u4e2a\u5411\u91cf":41,"\u5373\u6574\u4e2a\u8f93\u5165\u5e8f\u5217":40,"\u5373\u6574\u6570\u6570\u7ec4":41,"\u5373\u65f6\u95f4\u9012\u5f52\u795e\u7ecf\u7f51\u7edc":41,"\u5373\u662f\u8de8\u8d8a\u65f6\u95f4\u6b65\u7684\u7f51\u7edc\u8fde\u63a5":41,"\u5373\u66b4\u9732":27,"\u5373\u6b63\u9762\u548c\u8d1f\u9762":69,"\u5373\u6b63\u9762\u8bc4\u4ef7\u6807\u7b7e\u548c\u8d1f\u9762\u8bc4\u4ef7\u6807\u7b7e":69,"\u5373\u7279\u5f81\u7684\u6570\u7ec4":41,"\u5373\u7f51\u5361\u540d":57,"\u5373\u82e5\u5e72\u6570\u636e\u6587\u4ef6\u8def\u5f84\u7684\u67d0\u4e00\u4e2a":3,"\u5373\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u51fa\u73b0nan\u6216\u8005inf":30,"\u5373\u8bbe\u7f6e":30,"\u5373\u8fd9\u4e2a\u52a8\u6001\u5e93\u662f\u4e0d\u4f9d\u8d56\u4e8e\u5176\u4ed6\u4efb\u4f55\u6587\u4ef6\u7684":26,"\u5373define_py_data_sources2\u5e94\u6539\u4e3a":30,"\u5373input":43,"\u5373rnn\u4e4b\u95f4\u6709\u4e00\u6b21\u5d4c\u5957\u5173\u7cfb":41,"\u5377\u79ef\u5c42":62,"\u5377\u79ef\u5c42\u6743\u91cd":63,"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u8fa8\u8bc6\u56fe\u7247\u4e2d\u7684\u4e3b\u4f53":62,"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5728\u56fe\u7247\u5206\u7c7b\u4e0a\u6709\u7740\u60ca\u4eba\u7684\u6027\u80fd":62,"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u662f\u4e00\u79cd\u4f7f\u7528\u5377\u79ef\u5c42\u7684\u524d\u5411\u795e\u7ecf\u7f51\u7edc":62,"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u80fd\u591f\u5f88\u597d\u7684\u8868\u793a\u8fd9\u4e24\u7c7b\u4fe1\u606f":62,"\u5377\u79ef\u7f51\u7edc\u662f\u4e00\u79cd\u7279\u6b8a\u7684\u4ece\u8bcd\u5411\u91cf\u8868\u793a\u5230\u53e5\u5b50\u8868\u793a\u7684\u65b9\u6cd5":65,"\u5378\u8f7dpaddlepaddle\u5305":30,"\u538b\u7f29\u6210\u4e00\u4e2a\u5411\u91cf":41,"\u539f\u56e0":45,"\u539f\u56e0\u5728\u4e8e\u6ca1\u6709\u628a\u673a\u5668\u4e0acuda\u76f8\u5173\u7684\u9a71\u52a8\u548c\u5e93\u6620\u5c04\u5230\u5bb9\u5668\u5185\u90e8":30,"\u539f\u56e0\u662f\u672a\u8bbe\u7f6ecuda\u8fd0\u884c\u65f6\u73af\u5883\u53d8\u91cf":35,"\u539f\u56e0\u662f\u6bcf\u4e2a\u56de\u590d\u90fd\u4f1a\u53d1\u9001\u4e00\u5c01\u90ae\u4ef6":45,"\u53bb\u8fc7":41,"\u53c2\u6570":[3,7,8,9,10,11,12,13,15,26,46,51,57,61,63,69],"\u53c2\u6570\u5171\u4eab\u7684\u914d\u7f6e\u793a\u4f8b\u4e3a":30,"\u53c2\u6570\u521d\u59cb\u5316\u8def\u5f84":68,"\u53c2\u6570\u5373\u53ef":69,"\u53c2\u6570\u540d":63,"\u53c2\u6570\u6570\u91cf":65,"\u53c2\u6570\u670d\u52a1\u5668":51,"\u53c2\u6570\u670d\u52a1\u5668\u7684\u53c2\u6570\u5206\u5757\u5927\u5c0f":52,"\u53c2\u6570\u670d\u52a1\u5668\u7684\u76d1\u542c\u7aef\u53e3":52,"\u53c2\u6570\u670d\u52a1\u5668\u7684\u7f51\u7edc\u8bbe\u5907\u540d\u79f0":52,"\u53c2\u6570\u670d\u52a1\u5668\u7684ip\u5730\u5740":52,"\u53c2\u6570\u670d\u52a1\u5668\u7a00\u758f\u66f4\u65b0\u7684\u53c2\u6570\u5206\u5757\u5927\u5c0f":52,"\u53c2\u6570\u6765\u63a7\u5236\u7f13\u5b58\u65b9\u6cd5":30,"\u53c2\u6570\u6982\u8ff0":53,"\u53c2\u6570\u7684\u89e3\u6790":57,"\u53c2\u6570\u7ef4\u5ea6":61,"\u53c2\u6570\u884c":61,"\u53c2\u6570\u8bbe\u7f6e\u4e86\u5916\u5c42":41,"\u53c2\u6570\u9700\u8981\u5b9e\u73b0":44,"\u53c2\u7167\u4e0a\u8ff0\u6b65\u9aa4\u66f4\u65b0":45,"\u53c2\u8003":[24,26,55],"\u53c2\u8003\u5f3a\u8c03\u90e8\u5206":49,"\u53c2\u8003\u6587\u732e":70,"\u53c2\u8003\u65f6\u95f4\u5e8f\u5217":41,"\u53c2\u8003\u955c\u50cf\u7684":57,"\u53c8":41,"\u53c8\u662f\u4e00\u4e2a\u5355\u5c42\u7684\u5e8f\u5217":40,"\u53c8\u8981\u4fdd\u8bc1\u6570\u636e\u662f\u968f\u673a\u7684":30,"\u53ca":46,"\u53cc\u5411\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u9690\u85cf\u72b6\u6001":44,"\u53cc\u5411\u9a8c\u8bc1":24,"\u53cc\u5c42":43,"\u53cc\u5c42\u4e0d\u7b49\u957frnn":41,"\u53cc\u5c42\u5e8f\u5217":40,"\u53cc\u5c42\u5e8f\u5217\u6216\u5355\u5c42\u5e8f\u5217":40,"\u53cc\u5c42\u5e8f\u5217\u6570\u636e\u4e00\u5171\u67094\u4e2a\u6837\u672c":41,"\u53cc\u5c42\u5e8f\u5217\u662f\u4e00\u4e2a\u5d4c\u5957\u7684\u5e8f\u5217":40,"\u53cc\u5c42\u5e8f\u5217\u662fpaddlepaddle\u652f\u6301\u7684\u4e00\u79cd\u975e\u5e38\u7075\u6d3b\u7684\u6570\u636e\u7ec4\u7ec7\u65b9\u5f0f":43,"\u53cc\u5c42\u5e8f\u5217\u6bcf\u4e2asubseq\u4e2d\u6bcf\u4e2a\u5143\u7d20":40,"\u53cc\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u53d8\u6210\u4e00\u4e2a0\u5c42\u5e8f\u5217":40,"\u53cc\u5c42\u6216\u8005\u5355\u5c42":40,"\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217\u7684dataprovider\u7684\u4ee3\u7801":41,"\u53cc\u5c42rnn":43,"\u53cc\u5c42rnn\u6570\u636e\u968f\u610f\u52a0\u4e86\u4e00\u4e9b\u9694\u65ad":41,"\u53cc\u5c42rnn\u987e\u540d\u601d\u4e49":41,"\u53cc\u8fdb\u5355\u51fa":43,"\u53cc\u8fdb\u53cc\u51fa":43,"\u53cd\u4e4b\u5219":68,"\u53cd\u5411\u4f20\u64ad":46,"\u53cd\u5411\u4f20\u64ad\u6839\u636e\u8f93\u51fa\u7684\u68af\u5ea6":46,"\u53d1\u5e03":33,"\u53d1\u5e03\u5230dockerhub":29,"\u53d1\u5e03\u5230github":29,"\u53d1\u6563\u5230\u4e86\u4e00\u4e2a\u6570\u503c\u7279\u522b\u5927\u7684\u5730\u65b9":30,"\u53d1\u884c\u548c\u7ef4\u62a4":45,"\u53d1\u9001\u53c2\u6570\u7684\u7aef\u53e3\u53f7":52,"\u53d6\u51b3\u4e8e\u662f\u5426\u5bfb\u627e\u5230cuda\u5de5\u5177\u94fe":32,"\u53d6\u51b3\u4e8e\u662f\u5426\u5bfb\u627e\u5230gtest":32,"\u53d6\u51b3\u4e8e\u662f\u5426\u5bfb\u627e\u5230swig":32,"\u53d8\u6362\u77e9\u9635":46,"\u53d8\u91cf\u6765\u8bbe\u7f6e\u5185\u5b58\u4e2d\u6682\u5b58\u7684\u6570\u636e\u6761":3,"\u53e3\u5934":41,"\u53e3\u7edf\u8ba1\u5b66\u4fe1\u606f\u7684\u7528\u6237\u624d\u88ab\u5305\u542b\u5728\u6570\u636e\u96c6\u4e2d":66,"\u53e5\u5b50":69,"\u53e5\u5b50\u4e2d\u7684\u7ec4\u5757\u5c06\u4f1a\u626e\u6f14\u67d0\u4e9b\u8bed\u4e49\u89d2\u8272":68,"\u53e5\u5b50\u8868\u793a\u7684\u8ba1\u7b97\u66f4\u65b0\u4e3a\u4e24\u6b65":65,"\u53e6\u4e00\u4e2a\u4f8b\u5b50\u662f\u901a\u8fc7\u5206\u6790\u6bcf\u65e5twitter\u535a\u5ba2\u7684\u6587\u672c\u5185\u5bb9\u6765\u9884\u6d4b\u80a1\u7968\u53d8\u52a8":69,"\u53e6\u4e00\u4e2a\u597d\u5904\u662f\u6211\u4eec\u53ef\u4ee5\u628apaddlepaddle\u5bb9\u5668\u8fd0\u884c\u5728\u8fdc\u7a0b\u670d\u52a1\u5668\u4e0a":33,"\u53e6\u4e00\u4e2a\u662f\u5185\u5b58\u64cd\u4f5c\u91cf":49,"\u53e6\u4e00\u4e2a\u662f\u521b\u5efadockerfile\u5e76\u8fd0\u884cdock":33,"\u53e6\u4e00\u4e2a\u662f\u6bcf\u6761\u5e8f\u5217":30,"\u53e6\u4e00\u4e2a\u7ec8\u7aef\u8fd0\u884cpython":33,"\u53e6\u4e00\u65b9\u9762":69,"\u53e6\u4e00\u79cd\u65b9\u5f0f\u662f\u5c06\u7f51\u7edc\u5c42\u5212\u5206\u5230\u4e0d\u540c\u7684gpu\u4e0a\u53bb\u8ba1\u7b97":54,"\u53e6\u5916":41,"\u53e6\u5916\u4e24\u4e2a\u5206\u522b\u662f\u6ed1\u52a8\u5747\u503c\u548c\u65b9\u5dee":63,"\u53ea\u4f5c\u4e3aread":43,"\u53ea\u4fdd\u5b58\u6700\u540e\u4e00\u8f6e\u7684\u53c2\u6570":52,"\u53ea\u5141\u8bb8\u6574\u6570\u7684\u661f\u7ea7":66,"\u53ea\u5728\u7b2c\u4e00\u6b21cmake\u7684\u65f6\u5019\u6709\u6548":32,"\u53ea\u5bf9\u7279\u6b8a\u5728\u7ebf\u7cfb\u7edf\u8003\u8651\u4e24\u53f0\u4ee5\u4e0a\u540c\u65f6\u6545\u969c\u7684\u5bb9\u707e":19,"\u53ea\u622a\u53d6\u4e2d\u5fc3\u65b9\u5f62\u7684\u56fe\u50cf\u533a\u57df":63,"\u53ea\u662f\u53cc\u5c42\u5e8f\u5217\u5c06\u5176\u53c8\u505a\u4e86\u5b50\u5e8f\u5217\u5212\u5206":41,"\u53ea\u662f\u5c06\u53e5\u5b50\u7528\u8fde\u7eed\u5411\u91cf\u8868\u793a\u66ff\u6362\u4e3a\u7528\u7a00\u758f\u5411\u91cf\u8868\u793a":65,"\u53ea\u662f\u8bf4\u660e\u6570\u636e\u7684\u987a\u5e8f\u662f\u91cd\u8981\u7684":3,"\u53ea\u662f\u8bf7\u4e0d\u8981\u5fd8\u8bb0\u63d0\u524d\u5728\u7269\u7406\u673a\u4e0a\u5b89\u88c5gpu\u6700\u65b0\u9a71\u52a8":33,"\u53ea\u66b4\u9732\u6982\u5ff5\u7684\u63a5\u53e3":27,"\u53ea\u6709":41,"\u53ea\u67092\u4e2a\u914d\u7f6e\u4e0d\u4e00\u6837":61,"\u53ea\u6709\u542b\u6709\u4eba":66,"\u53ea\u6709\u5f53\u8bbe\u7f6e\u4e86spars":52,"\u53ea\u7528\u4e8e\u5728\u5e8f\u5217\u751f\u6210\u4efb\u52a1\u4e2d\u6307\u5b9a\u8f93\u5165\u6570\u636e":43,"\u53ea\u80fd\u6d4b\u8bd5\u5355\u4e2a\u6a21\u578b":54,"\u53ea\u80fd\u8c03\u7528paddle\u7684\u52a8\u6001\u5e93":26,"\u53ea\u8981\u4e00\u7cfb\u5217\u7279\u5f81\u6570\u636e\u4e2d\u7684":41,"\u53ea\u8981\u51fa\u73b0\u6d6e\u70b9\u6570\u5f02\u5e38":30,"\u53ea\u8981\u5728docker\u91cc\u542f\u52a8paddlepaddle\u7684\u65f6\u5019\u7ed9\u5b83\u4e00\u4e2a\u540d\u5b57":33,"\u53ea\u8bfbmemory\u8f93\u5165":43,"\u53ea\u9488\u5bf9\u5185\u5b58":30,"\u53ea\u9700\u4e2d\u65ad":50,"\u53ea\u9700\u4f7f\u7528":50,"\u53ea\u9700\u5220\u9664\u6700\u540e\u4e00\u884c\u4e2d\u7684\u6ce8\u91ca\u5e76\u628a":69,"\u53ea\u9700\u5728linux\u4e0b\u8fd0\u884c\u5982\u4e0b\u547d\u4ee4":70,"\u53ea\u9700\u7528\u4f60\u5b9a\u4e49\u7684\u76ee\u5f55\u4fee\u6539":50,"\u53ea\u9700\u77e5\u9053\u8fd9\u662f\u4e00\u4e2a\u6807\u8bb0\u5c5e\u6027\u7684\u65b9\u6cd5\u5c31\u53ef\u4ee5\u4e86":3,"\u53ea\u9700\u8981":44,"\u53ea\u9700\u8981\u4e00\u884c\u4ee3\u7801\u5c31\u53ef\u4ee5\u8c03\u7528\u8fd9\u4e2apydataprovider2":3,"\u53ea\u9700\u8981\u5728\u51fd\u6570\u4e2d\u8c03\u7528\u591a\u6b21yield\u5373\u53ef":3,"\u53ea\u9700\u8981\u6062\u590d\u8fd9\u53f0\u8282\u70b9":19,"\u53ea\u9700\u8981\u7b80\u5355\u5730\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4":70,"\u53ea\u9700\u8981\u7b80\u5355\u7684\u8fd0\u884c\u4e0b\u9762\u7684\u547d\u4ee4\u5373\u53ef":67,"\u53ea\u9700\u8981\u8fd0\u884c":67,"\u53ef\u4ee5":[41,45,50],"\u53ef\u4ee5\u4ee5\u540e\u53f0\u8fdb\u7a0b\u65b9\u5f0f\u8fd0\u884c\u5bb9\u5668":33,"\u53ef\u4ee5\u4f20\u5165\u4e00\u4e2a\u51fd\u6570":3,"\u53ef\u4ee5\u4f30\u8ba1\u51fa\u5982\u679c\u6a21\u578b\u91c7\u7528\u4e0d\u53d8\u7684\u8f93\u51fa\u6700\u5c0f\u7684cost0\u662f\u591a\u5c11":30,"\u53ef\u4ee5\u4f7f\u7528":30,"\u53ef\u4ee5\u4f7f\u7528\u547d\u4ee4":35,"\u53ef\u4ee5\u4f7f\u7528\u5982\u4e0b\u4ee3\u7801":30,"\u53ef\u4ee5\u4f7f\u7528\u8be5\u53c2\u6570":52,"\u53ef\u4ee5\u4f7f\u7528kubernetes\u7684\u547d\u4ee4\u884c\u5de5\u5177\u521b\u5efajob":57,"\u53ef\u4ee5\u4f7f\u7528python\u7684":5,"\u53ef\u4ee5\u51cf\u5c0f\u7cfb\u7edf\u590d\u6742\u6027":19,"\u53ef\u4ee5\u51cf\u5c11\u7f13\u5b58\u6c60\u7684\u5927\u5c0f":30,"\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a":56,"\u53ef\u4ee5\u53c2\u7167\u4e0b\u9762\u7684\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5":62,"\u53ef\u4ee5\u53c2\u8003":[41,44,45,55,57,70],"\u53ef\u4ee5\u53c2\u8003\u4fdd\u5b58\u5728":61,"\u53ef\u4ee5\u53c2\u8003paddlepaddl":36,"\u53ef\u4ee5\u544a\u8bc9\u60a8\u67d0\u4e2a\u64cd\u4f5c\u5230\u5e95\u82b1\u4e86\u591a\u957f\u65f6\u95f4":49,"\u53ef\u4ee5\u5728":33,"\u53ef\u4ee5\u5728\u4efb\u4f55\u673a\u5668\u4e0a\u6267\u884c\u7684":26,"\u53ef\u4ee5\u5728\u5171\u4eab\u5b58\u50a8\u4e0a\u67e5\u770b\u8f93\u51fa\u7684\u65e5\u5fd7\u548c\u6a21\u578b":57,"\u53ef\u4ee5\u5728\u5f88\u5927\u7a0b\u5ea6\u4e0a\u6d88\u9664\u6b67\u4e49":68,"\u53ef\u4ee5\u5728\u7f51\u7ad9\u4e0a\u627e\u5230":68,"\u53ef\u4ee5\u5728\u8fd9\u4e2a":45,"\u53ef\u4ee5\u5728kubernetes\u4e2d\u6309\u7167":55,"\u53ef\u4ee5\u5c06\u67d0\u4e00\u4e2a\u51fd\u6570\u6807\u8bb0\u6210\u4e00\u4e2apydataprovider2":3,"\u53ef\u4ee5\u5c06\u78c1\u76d8\u4e0a\u67d0\u4e2a\u76ee\u5f55\u5171\u4eab\u7ed9\u7f51\u7edc\u4e2d\u5176\u4ed6\u673a\u5668\u8bbf\u95ee":55,"\u53ef\u4ee5\u5c06memory\u7406\u89e3\u4e3a\u4e00\u4e2a\u65f6\u5ef6\u64cd\u4f5c":43,"\u53ef\u4ee5\u5e2e\u60a8\u63d0\u4f9b\u4e00\u4e9b\u5b9a\u4f4d\u6027\u80fd\u74f6\u9888\u7684\u5efa\u8bae":49,"\u53ef\u4ee5\u5feb\u901f\u8fed\u4ee3":33,"\u53ef\u4ee5\u6267\u884c\u4ee5\u4e0b\u547d\u4ee4\u7f16\u8bd1\u751f\u6210\u6587\u6863":47,"\u53ef\u4ee5\u628a\u672c\u5730\u7684\u6570\u636e\u4e0a\u4f20\u5230\u5b58\u50a8\u96c6\u7fa4\u4e2d":20,"\u53ef\u4ee5\u6307\u5b9a\u54ea\u4e00\u4e2a\u8f93\u5165\u548c\u8f93\u51fa\u5e8f\u5217\u4fe1\u606f\u4e00\u81f4":41,"\u53ef\u4ee5\u6309\u5982\u4e0b\u7684\u7ed3\u6784\u6765\u51c6\u5907\u6570\u6910":69,"\u53ef\u4ee5\u662f\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":[40,43],"\u53ef\u4ee5\u662f\u4e00\u4e2a\u975e\u5e8f\u5217":43,"\u53ef\u4ee5\u662f\u4ee5\u4e0b\u51e0\u79cd":46,"\u53ef\u4ee5\u663e\u793a\u5730\u6307\u5b9a\u4e00\u4e2alayer\u7684\u8f93\u51fa\u7528\u4e8e\u521d\u59cb\u5316memori":43,"\u53ef\u4ee5\u6709\u4ee5\u4e0b\u4e24\u79cd":43,"\u53ef\u4ee5\u6709\u6548\u51cf\u5c0f\u7f51\u7edc\u7684\u963b\u585e":52,"\u53ef\u4ee5\u6709\u6548\u7684\u907f\u514dparamet":19,"\u53ef\u4ee5\u67e5\u770b":57,"\u53ef\u4ee5\u67e5\u770b\u6b64pod\u8fd0\u884c\u7684\u5bbf\u4e3b\u673a":56,"\u53ef\u4ee5\u6d4b\u8bd5\u591a\u4e2a\u6a21\u578b":54,"\u53ef\u4ee5\u7528":24,"\u53ef\u4ee5\u7528\u4e8e\u4ece\u5b98\u65b9\u7f51\u7ad9\u4e0a\u4e0b\u8f7dcifar":62,"\u53ef\u4ee5\u7528\u4e8e\u5c0f\u91cf\u6570\u636e\u7684\u9a8c\u8bc1":55,"\u53ef\u4ee5\u7528\u4e8e\u63a5\u6536\u548cpydataprovider2\u4e00\u6837\u7684\u8f93\u5165\u6570\u636e\u5e76\u8f6c\u6362\u6210\u9884\u6d4b\u63a5\u53e3\u6240\u9700\u7684\u6570\u636e\u7c7b\u578b":5,"\u53ef\u4ee5\u7528\u4ee5\u4e0b\u6307\u4ee4":20,"\u53ef\u4ee5\u7528\u5982\u4e0b\u547d\u4ee4":45,"\u53ef\u4ee5\u7528\u6765\u8ba1\u7b97cpu\u51fd\u6570\u6216cuda\u5185\u6838\u7684\u65f6\u95f4\u6d88\u8017":49,"\u53ef\u4ee5\u770b\u4f5c\u662f\u4e00\u4e2a\u975e\u5e8f\u5217\u8f93\u5165":40,"\u53ef\u4ee5\u7cbe\u786e\u8bf4\u660e\u4e00\u4e2a\u957f\u8017\u65f6\u64cd\u4f5c\u7684\u5177\u4f53\u539f\u56e0":49,"\u53ef\u4ee5\u7ee7\u7eed\u5728\u81ea\u5df1\u7684\u529f\u80fd\u5206\u652f\u63d0\u4ea4\u4ee3\u7801":29,"\u53ef\u4ee5\u7f16\u5199":33,"\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u4e00\u4e9b\u4f18\u5316\u7b97\u6cd5":30,"\u53ef\u4ee5\u8bbe\u7f6e":[38,39,62],"\u53ef\u4ee5\u8f7b\u677e\u5730\u5b8c\u6210\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e":36,"\u53ef\u4ee5\u8fd0\u884c\u4e0b\u9762\u7684\u547d\u4ee4\u6765\u751f\u6210":67,"\u53ef\u4ee5\u8fd0\u884c\u811a\u672ctrain":62,"\u53ef\u4ee5\u9009\u62e9\u662f\u5426\u4f7f\u7528\u53c2\u6570":54,"\u53ef\u4ee5\u901a\u8fc7":[45,55],"\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539\u8fd9\u4e24\u4e2a\u51fd\u6570\u6765\u5b9e\u73b0\u590d\u6742\u7684\u7f51\u7edc\u914d\u7f6e":44,"\u53ef\u4ee5\u901a\u8fc7\u8c03\u7528":5,"\u53ef\u4ee5\u901a\u8fc7\u9636\u6bb5\u6027\u7684\u4fdd\u5b58\u6bcf\u4e2aparamet":19,"\u53ef\u4ee5\u901a\u8fc7show_parameter_stats_period\u8bbe\u7f6e\u6253\u5370\u53c2\u6570\u4fe1\u606f\u7b49":65,"\u53ef\u7528\u4e8e\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u89e3\u6790\u8fd9\u4e9b\u53c2\u6570":54,"\u53ef\u7528\u5728\u6d4b\u8bd5\u6216\u8bad\u7ec3\u65f6\u6307\u5b9a\u521d\u59cb\u5316\u6a21\u578b":65,"\u53ef\u80fd\u4f1a\u5bfc\u81f4\u51fa\u9519":57,"\u53ef\u80fd\u4f1a\u9020\u6210\u7f51\u7edc\u62e5\u585e":19,"\u53ef\u80fd\u7684\u4ee3\u7801\u4e3a":30,"\u53ef\u80fd\u7684\u539f\u56e0\u662f":30,"\u53ef\u80fd\u7684\u60c5\u51b5\u4e0b":49,"\u53ef\u8bbe\u7f6e":38,"\u53ef\u9009":[3,46],"\u53f3\u8fb9\u662f":63,"\u5403":41,"\u5403\u996d":41,"\u5404\u65b9\u9762":41,"\u5404\u9879\u53c2\u6570\u7684\u8be6\u7ec6\u8bf4\u660e\u53ef\u4ee5\u5728\u547d\u4ee4\u884c\u53c2\u6570\u76f8\u5173\u6587\u6863\u4e2d\u627e\u5230":62,"\u5408":41,"\u5408\u5e76":70,"\u5408\u5e76\u6bcf\u4e2a":70,"\u5408\u7406":41,"\u540c\u65f6":[30,49],"\u540c\u65f6\u4e5f\u4f1a\u8bfb\u53d6\u76f8\u5173\u8def\u5f84\u53d8\u91cf\u6765\u8fdb\u884c\u641c\u7d22":32,"\u540c\u65f6\u4e5f\u53ef\u4ee5\u52a0\u901f\u5f00\u59cb\u8bad\u7ec3\u524d\u6570\u636e\u8f7d\u5165\u7684\u8fc7\u7a0b":30,"\u540c\u65f6\u4e5f\u53ef\u4ee5\u901a\u8fc7":45,"\u540c\u65f6\u4e5f\u80fd\u591f\u5f15\u5165\u66f4\u52a0\u590d\u6742\u7684\u8bb0\u5fc6\u673a\u5236":43,"\u540c\u65f6\u4f1a\u8ba1\u7b97\u5206\u7c7b\u51c6\u786e\u7387":65,"\u540c\u65f6\u4f60\u53ef\u4ee5\u4f7f\u7528":63,"\u540c\u65f6\u4f7f\u7528\u4e86l2\u6b63\u5219":65,"\u540c\u65f6\u5176\u5185\u90e8\u5b9e\u73b0\u53ef\u4ee5\u907f\u514d\u7eafcpu\u7248\u672cpaddlepaddle\u5728\u6267\u884c\u672c\u8bed\u53e5\u65f6\u53d1\u751f\u5d29\u6e83":49,"\u540c\u65f6\u518d\u5c06":29,"\u540c\u65f6\u53ef\u4ee5\u4f7f\u7528\u6237\u53ea\u5173\u6ce8\u5982\u4f55\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6bcf\u4e00\u6761\u6570\u636e":3,"\u540c\u65f6\u5728\u5185\u5b58\u91cc\u76f4\u63a5\u968f\u5373\u9009\u53d6\u6570\u636e\u6765\u505ashuffl":30,"\u540c\u65f6\u5c06\u53c2\u6570\u521d\u59cb\u5316\u4e3a":30,"\u540c\u65f6\u6211\u4eec\u5e0c\u671b\u5e7f\u5927\u5f00\u53d1\u8005\u79ef\u6781\u63d0\u4f9b\u53cd\u9988\u548c\u8d21\u732e\u6e90\u4ee3\u7801":0,"\u540c\u65f6\u63d0\u8d77":29,"\u540c\u65f6\u6b22\u8fce\u8d21\u732e\u66f4\u591a\u7684\u5b89\u88c5\u5305":34,"\u540c\u65f6\u7528\u6237\u9700\u8981\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u6307\u5b9a":54,"\u540c\u65f6\u8bbe\u7f6e\u5185\u5b58\u7f13\u5b58\u529f\u80fd":30,"\u540c\u65f6\u8bbe\u7f6e\u5b83\u7684input_types\u5c5e\u6027":3,"\u540c\u65f6\u9884\u6d4b\u7f51\u7edc\u901a\u5e38\u76f4\u63a5\u8f93\u51fa\u6700\u540e\u4e00\u5c42\u7684\u7ed3\u679c\u800c\u4e0d\u662f\u50cf\u8bad\u7ec3\u7f51\u7edc\u4e00\u6837\u518d\u63a5\u4e00\u5c42cost":5,"\u540c\u6837":36,"\u540c\u6837\u4e5f\u53ef\u4ee5\u5728\u6d4b\u8bd5\u6a21\u5f0f\u4e2d\u6307\u5b9a\u6a21\u578b\u8def\u5f84":52,"\u540c\u6837\u53ef\u4ee5\u6269\u5c55\u5230\u53cc\u5c42\u5e8f\u5217\u7684\u5904\u7406\u4e0a":43,"\u540c\u6b65\u6267\u884c\u64cd\u4f5c\u7684\u7ebf\u7a0b\u6570":52,"\u540d\u5b57\u4fee\u9970":26,"\u540d\u79f0":65,"\u540e":[30,32,38,39,45,57,69],"\u540e\u5411\u4f20\u64ad":46,"\u540e\u5411\u4f20\u64ad\u7ed9\u5b9a\u8f93\u51fa\u7684\u68af\u5ea6":46,"\u540e\u9762\u8fde\u5168\u8fde\u63a5\u5c42\u548csoftmax\u5c42":69,"\u5411\u6307\u5b9a\u7684\u76ee\u5f55\u4e2d\u4e00\u4e2a\u65b0\u7684\u6587\u4ef6":19,"\u5411\u91cfenable_parallel_vector":51,"\u5411paddle\u7684\u4e3b\u7248\u672c\u5e93\u63d0\u4ea4":29,"\u5426":32,"\u5426\u5219":[2,38,39,50,67],"\u5426\u5219\u4f1a\u628a":45,"\u5426\u5219\u4f60\u9700\u8981\u81ea\u5df1\u4e0b\u8f7d":70,"\u5426\u5219\u4f7f\u7528\u591a\u673a\u8bad\u7ec3":52,"\u5426\u5219\u4f7f\u7528cpu\u6a21\u5f0f":52,"\u5426\u5219\u4f7f\u7528gpu":54,"\u5426\u5219\u5b83\u4ee5\u4e00\u4e2a\u5e8f\u5217\u8f93\u5165":44,"\u5426\u5219\u5f97\u628apaddle\u9759\u6001\u5e93\u94fe\u63a5\u5230\u89e3\u91ca\u5668\u91cc":26,"\u5426\u5219\u9891\u7e41\u7684\u591a\u8282\u70b9\u5de5\u4f5c\u7a7a\u95f4\u90e8\u7f72\u53ef\u80fd\u4f1a\u5f88\u9ebb\u70e6":50,"\u5426\u5b9a":68,"\u542b\u4e49":[63,69],"\u542b\u6709":66,"\u542b\u6709\u5e8f\u5217\u4fe1\u606f\u548c\u5b50\u5e8f\u5217\u4fe1\u606f\u7684\u7a20\u5bc6\u5411\u91cf":46,"\u542b\u6709\u5e8f\u5217\u4fe1\u606f\u7684\u6574\u6570":46,"\u542b\u6709\u5e8f\u5217\u4fe1\u606f\u7684\u7a20\u5bc6\u5411\u91cf":46,"\u542f\u52a8\u4e00\u4e2a\u65b0\u7684\u7ebf\u7a0b\u5f00\u59cb\u4fdd\u5b58\u68c0\u67e5\u70b9":19,"\u542f\u52a8\u5bb9\u5668\u5f00\u59cb\u8bad\u7ec3":57,"\u542f\u52a8\u5e76\u884c\u5411\u91cf\u7684\u9608\u503c":52,"\u542f\u52a8\u5feb\u901f\u5e94\u7b54":52,"\u542f\u7528\u68af\u5ea6\u53c2\u6570\u7684\u9608\u503c":52,"\u5440":41,"\u544a\u8bc9paddle\u54ea\u4e2a\u6587\u4ef6\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u914d\u7f6e\u6587\u4ef6":67,"\u544a\u8bc9paddle\u5c06\u6a21\u578b\u4fdd\u5b58\u5728":67,"\u5468\u56f4":41,"\u547d\u4ee4":50,"\u547d\u4ee4\u4e3a":[30,56],"\u547d\u4ee4\u521b\u5efa\u65b0\u955c\u50cf":56,"\u547d\u4ee4\u5220\u9664":[38,39],"\u547d\u4ee4\u53ef\u4ee5\u8bbe\u7f6e":32,"\u547d\u4ee4\u6307\u5b9a\u7684\u53c2\u6570\u4f1a\u4f20\u5165\u7f51\u7edc\u914d\u7f6e\u4e2d":65,"\u547d\u4ee4\u884c\u53c2\u6570\u6587\u6863":65,"\u547d\u4ee4\u8bbe\u7f6e\u8be5\u7c7b\u7f16\u8bd1\u9009\u9879":32,"\u547d\u4ee4\u9009\u9879\u5e76\u4e14":50,"\u547d\u540d\u4e3a":45,"\u547d\u540d\u7a7a\u95f4":55,"\u547d\u540d\u7a7a\u95f4\u4e3b\u8981\u4e3a\u4e86\u5bf9\u8c61\u8fdb\u884c\u903b\u8f91\u4e0a\u7684\u5206\u7ec4\u4fbf\u4e8e\u7ba1\u7406":55,"\u548c":[20,26,27,29,30,31,32,38,39,41,44,45,46,47,49,50,54,55,61,62,65,67,70],"\u548c\u4e00\u4e2a\u5df2\u7ecf\u5206\u8bcd\u540e\u7684\u53e5\u5b50":41,"\u548c\u4e09\u79cd\u5e8f\u5217\u6a21\u5f0f":[3,36],"\u548c\u4e2d\u6587\u6587\u6863":47,"\u548c\u4e4b\u524d\u51cf\u5c0f\u901a\u8fc7\u51cf\u5c0f\u7f13\u5b58\u6c60\u6765\u51cf\u5c0f\u5185\u5b58\u5360\u7528\u7684\u539f\u7406\u4e00\u81f4":30,"\u548c\u504f\u7f6e\u5411\u91cf":46,"\u548c\u533a\u57df\u6807\u8bb0":68,"\u548c\u53cc\u5c42\u5e8f\u5217\u542b\u6709subseq":40,"\u548c\u5728":3,"\u548c\u5bf9\u8c61\u5b58\u50a8api":55,"\u548c\u5dee\u8bc4":65,"\u548c\u5e8f\u5217\u4e2d\u542b\u6709\u5143\u7d20\u7684\u6570\u76ee\u540c":40,"\u548c\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u8f93\u5165":44,"\u548c\u68af\u5ea6\u622a\u65ad":65,"\u548c\u6a21\u578b\u8def\u5f84":69,"\u548c\u771f\u5b9e":31,"\u548c\u793a\u4f8b2\u4e2d\u7684\u914d\u7f6e\u7c7b\u4f3c":41,"\u548c\u79bb\u7ebf\u6570\u636e\u7684\u65b9\u5f0f":20,"\u548c\u7b2c6\u884c\u7684":70,"\u548c\u90e8\u5206layer":43,"\u548cadam\u5b66\u4e60\u65b9\u6cd5":70,"\u548cargument":68,"\u548ccudnn":35,"\u548cpool":40,"\u548cpython\u63a5\u53e3\u6765\u63d0\u53d6\u7279\u5f81":63,"\u54c1\u8d28":41,"\u54ea\u4e2atrainer\u5148\u5b8c\u6210block\u7684\u8bad\u7ec3":19,"\u54ea\u4e9b\u4e0d\u662f":41,"\u552f\u4e00\u9700\u8981\u505a\u7684\u662f\u5c06\u76f8\u5e94\u7c7b\u578b\u8bbe\u7f6e\u4e3a\u8f93\u5165":44,"\u5546\u52a1":41,"\u554a":41,"\u559c\u5267\u7247":66,"\u5668":65,"\u56db\u79cd\u6570\u636e\u7c7b\u578b":[3,36],"\u56de\u5f52\u8bef\u5dee\u4ee3\u4ef7\u5c42":31,"\u56e0\u4e3a\u5168\u8fde\u63a5\u5c42\u7684\u6fc0\u6d3b\u53ef\u4ee5\u662fsoftmax":46,"\u56e0\u4e3a\u5355\u4e2a\u8c13\u8bcd\u4e0d\u80fd\u7cbe\u786e\u5730\u63cf\u8ff0\u8c13\u8bcd\u4fe1\u606f":68,"\u56e0\u4e3a\u53c2\u6570":54,"\u56e0\u4e3a\u5b83\u4eec\u7684\u8ba1\u7b97\u6548\u7387\u6bd4":44,"\u56e0\u4e3a\u5b83\u6bd4":44,"\u56e0\u4e3a\u5b98\u65b9\u955c\u50cf":57,"\u56e0\u4e3a\u5bb9\u5668\u5185\u7684\u6587\u4ef6\u90fd\u662f\u6682\u65f6\u5b58\u5728\u7684":55,"\u56e0\u4e3a\u8be5\u6587\u4ef6\u53ef\u9002\u7528\u4e8e\u9884\u6d4b":62,"\u56e0\u4e3a\u8fd9\u6837\u505a\u4e5f\u6ca1\u6cd5\u4fdd\u8bc1\u6d88\u9664\u968f\u673a\u6027":19,"\u56e0\u4e3adocker\u80fd\u5728\u6240\u6709\u4e3b\u8981\u64cd\u4f5c\u7cfb\u7edf":33,"\u56e0\u4e3apaddlepaddle\u7684v2":47,"\u56e0\u4e3apython\u7684\u641c\u7d22\u8def\u5f84\u662f\u4f18\u5148\u5df2\u7ecf\u5b89\u88c5\u7684python\u5305":30,"\u56e0\u4e3aswig\u5728\u7b2c\u4e09\u65b9\u8bed\u8a00\u4e2d\u66b4\u9732\u7684\u51fd\u6570\u540d":26,"\u56e0\u6b64":[2,3,41,43,46],"\u56e0\u6b64\u4f7f\u7528":3,"\u56e0\u6b64\u53cc\u5c42\u5e8f\u5217\u7684\u914d\u7f6e\u4e2d":41,"\u56e0\u6b64\u53ef\u4ee5\u4f7f\u7528\u8be5\u9009\u9879":61,"\u56e0\u6b64\u53ef\u80fd\u4f1a\u6709\u4e00\u4e9b\u9519\u8bef\u548c\u4e0d\u4e00\u81f4\u53d1\u751f":66,"\u56e0\u6b64\u5982\u679c\u8fd9\u4e2a\u811a\u672c\u8fd0\u884c\u5931\u8d25":62,"\u56e0\u6b64\u5b83\u662finteger_value_sub_sequ":41,"\u56e0\u6b64\u6211\u4eec\u91c7\u7528\u8f93\u51fa\u7684\u52a0\u6743\u548c":46,"\u56e0\u6b64\u6709\u4e24\u79cd\u89e3\u51b3\u65b9\u6848":3,"\u56e0\u6b64\u7528\u6237\u5e76\u4e0d\u9700\u8981\u5173\u5fc3\u5b83\u4eec":51,"\u56e0\u6b64\u8be5\u5c42\u4e2d\u6ca1\u6709\u504f\u7f6e":63,"\u56e0\u6b64\u9519\u8bef\u7684\u4f7f\u7528\u4e8c\u8fdb\u5236\u53d1\u884c\u7248\u53ef\u80fd\u4f1a\u5bfc\u81f4\u8fd9\u79cd\u9519\u8bef":30,"\u56e0\u6b64init_hook\u5c3d\u91cf\u4f7f\u7528":3,"\u56e2\u8d2d\u7f51\u7ad9":69,"\u56fe":[63,69],"\u56fe2\u662f\u53cc\u5411lstm\u7f51\u7edc":69,"\u56fe3\u662f\u4e09\u5c42lstm\u7ed3\u6784":69,"\u56fe\u50cf\u5206\u7c7b":[29,64],"\u56fe\u50cf\u5927\u5c0f\u4e3a3":63,"\u56fe\u50cf\u63cf\u8ff0":70,"\u56fe\u7247\u5206\u4e3a10\u7c7b":62,"\u56fe\u7684\u5e95\u90e8\u662fword":69,"\u56fe\u8868":[33,69],"\u5728":[3,27,29,35,40,41,44,45,50,63,65,66,68],"\u5728\u4e00\u4e2a\u4e0d\u53ef\u4e2d\u65ad\u5e76\u7f3a\u5c11\u5907\u4efd\u7684\u8bad\u7ec3\u4efb\u52a1\u4e2d":19,"\u5728\u4e00\u4e2a\u529f\u80fd\u9f50\u5168\u7684kubernetes\u673a\u7fa4\u91cc":56,"\u5728\u4e00\u4e2a\u53c2\u6570\u7684\u68af\u5ea6\u88ab\u66f4\u65b0\u540e":46,"\u5728\u4e00\u4e2a\u5468\u671f\u5185\u6d4b\u8bd5\u6240\u6709\u6570\u636e":68,"\u5728\u4e00\u8f6e\u4e2d\u6bcfsave":52,"\u5728\u4e0a\u56fe\u4e2d\u663e\u793a\u4e86\u5728\u4e00\u4e2a\u5b9e\u9645\u751f\u4ea7\u73af\u5883\u4e2d\u7684\u5e94\u7528":20,"\u5728\u4e0a\u9762\u4ee3\u7801\u4e2d":41,"\u5728\u4e0b\u4e00\u7bc7\u4e2d":56,"\u5728\u4e0b\u9762\u4f8b\u5b50\u91cc":65,"\u5728\u4e0b\u9762\u7684\u4f8b\u5b50\u4e2d":62,"\u5728\u4e0d\u540c\u64cd\u4f5c\u7cfb\u7edf":55,"\u5728\u4e4b\u540e\u7684":30,"\u5728\u4efb\u610f\u957f\u5ea6\u8bed\u53e5\u7ffb\u8bd1\u7684\u573a\u666f\u4e0b\u90fd\u53ef\u4ee5\u89c2\u5bdf\u5230\u5176\u6548\u679c\u7684\u63d0\u5347":70,"\u5728\u4f7f\u7528\u5b83\u4e4b\u524d\u8bf7\u5b89\u88c5paddlepaddle\u7684python":69,"\u5728\u4f7f\u7528\u8be5\u6587\u6863\u4e4b\u524d":36,"\u5728\u5168\u8fde\u63a5\u5c42\u4e2d":46,"\u5728\u51c6\u5907\u53d1\u8d77":45,"\u5728\u51fa\u73b0\u5355\u70b9\u6545\u969c\u65f6":19,"\u5728\u51fd\u6570":57,"\u5728\u5206\u5e03\u5f0f\u73af\u5883\u4e2d\u6d4b\u8bd5":52,"\u5728\u5206\u5e03\u5f0f\u8bad\u7ec3\u4e2d":52,"\u5728\u5355\u5c42\u6570\u636e\u7684\u57fa\u7840\u4e0a":41,"\u5728\u53c2\u6570\u670d\u52a1\u5668\u4e0a\u52a0\u8f7d\u548c\u4fdd\u5b58\u53c2\u6570":52,"\u5728\u53c2\u6570\u670d\u52a1\u5668\u7ec8\u7aef\u6bcflog":52,"\u5728\u53cc\u5c42rnn\u4e2d\u7684\u7ecf\u5178\u60c5\u51b5\u662f\u5c06\u5185\u5c42\u7684\u6bcf\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u6570\u636e":41,"\u5728\u53cd\u5411\u4f20\u9012\u7684\u65f6\u5019":30,"\u5728\u53d8\u6362\u65f6\u9700\u8981\u5c06\u8f93\u5165\u5e8f\u5217\u4f20\u5165":41,"\u5728\u540c\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4\u4e2d":55,"\u5728\u542f\u52a8job\u4e4b\u524d":57,"\u5728\u56de\u590d\u8bc4\u5ba1\u4eba\u610f\u89c1\u65f6":45,"\u5728\u56fd\u5185":33,"\u5728\u58f0\u660edataprovider\u7684\u65f6\u5019\u4f20\u5165dictionary\u4f5c\u4e3a\u53c2\u6570":3,"\u5728\u591acpu\u8bad\u7ec3\u65f6\u5171\u4eab\u8be5\u53c2\u6570":52,"\u5728\u5b8c\u6210\u795e\u7ecf\u7f51\u7edc\u7684\u642d\u5efa\u4e4b\u540e":36,"\u5728\u5b9a\u4e49\u8f93\u5165layer\u4e4b\u540e":36,"\u5728\u5b9e\u73b0\u8fc7\u7a0b\u4e2d":27,"\u5728\u5bb9\u5668\u521b\u5efa\u540e":57,"\u5728\u5bf9\u5bb9\u5668\u7684\u63cf\u8ff0":57,"\u5728\u5c42\u4e2d\u6307\u5b9a":54,"\u5728\u5e8f\u5217\u751f\u6210\u4efb\u52a1\u4e2d":43,"\u5728\u5f00\u59cb\u8bad\u7ec3\u4e4b\u524d":[20,62],"\u5728\u5f02\u6784\u96c6\u7fa4\u4e2d":19,"\u5728\u5f15\u5165\u5176\u4ed6\u7c7b\u578b\u7684\u5934\u6587\u4ef6\u65f6":27,"\u5728\u5f53\u524d\u7684\u5b9e\u73b0\u65b9\u5f0f\u4e0b":46,"\u5728\u5f97\u5230":57,"\u5728\u5feb\u7167\u5199\u5165\u5b8c\u6210\u540e":19,"\u5728\u60a8\u7684\u5b9e\u9645\u73af\u5883\u4e2d":19,"\u5728\u6211\u4eec\u7684\u4f8b\u5b50\u4e2d":44,"\u5728\u6211\u4eec\u7684\u6d4b\u8bd5\u4e2d":69,"\u5728\u63d0\u4ea4":45,"\u5728\u642d\u5efa\u795e\u7ecf\u7f51\u7edc\u7684\u8fc7\u7a0b\u4e2d":36,"\u5728\u6570\u636e\u52a0\u8f7d\u548c\u7f51\u7edc\u914d\u7f6e\u5b8c\u6210\u4e4b\u540e":65,"\u5728\u6587\u4ef6":67,"\u5728\u6709\u65b0\u7684\u5355\u8bcd\u6765\u4e34\u7684\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u9aa4\u5185":69,"\u5728\u672c\u4f8b\u4e2d":[41,45,54],"\u5728\u672c\u4f8b\u4e2d\u6ca1\u6709\u4f7f\u7528":3,"\u5728\u672c\u6559\u7a0b\u4e2d":[44,62],"\u5728\u672c\u6587\u4e2d":50,"\u5728\u672c\u6587\u4e2d\u4f7f\u7528\u7684":50,"\u5728\u672c\u6587\u6863\u4e2d":24,"\u5728\u672c\u6f14\u793a\u4e2d":69,"\u5728\u672c\u793a\u4f8b\u4e2d":[41,69],"\u5728\u672c\u8282\u4e2d":44,"\u5728\u673a\u7fa4\u4e0a\u8fd0\u884c\u8f6c\u6362\u7a0b\u5e8f":20,"\u5728\u6811\u7684\u6bcf\u4e00\u5c42\u4e0a":52,"\u5728\u6837\u4f8b\u4e2d":27,"\u5728\u6a21\u578b\u6587\u4ef6\u7684":50,"\u5728\u6a21\u578b\u914d\u7f6e\u4e2d\u901a\u8fc7":65,"\u5728\u6b64":[0,51,54],"\u5728\u6b64\u4e3a\u65b9\u4fbf\u5bf9\u6bd4\u4e0d\u540c\u7f51\u7edc\u7ed3\u6784":65,"\u5728\u6b64\u611f\u8c22":61,"\u5728\u6bcf\u4e2a\u65f6\u95f4\u6b65\u4e2d":44,"\u5728\u6bcf\u4e2a\u65f6\u95f4\u6b65\u7684\u5b50\u5e8f\u5217\u957f\u5ea6\u53ef\u4ee5\u4e0d\u76f8\u7b49":41,"\u5728\u6bcf\u4e2a\u65f6\u95f4\u6b65\u957f":44,"\u5728\u6bcf\u4e2apod\u4e0a\u90fd\u901a\u8fc7volume\u65b9\u5f0f\u6302\u8f7d\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf\u7684\u4e00\u4e2a\u76ee\u5f55\u7528\u4e8e\u4fdd\u5b58\u8bad\u7ec3\u6570\u636e\u548c\u8f93\u51fa\u7ed3\u679c":57,"\u5728\u6bcf\u8bad\u7ec3":67,"\u5728\u6d4b\u8bd5\u9636\u6bb5":52,"\u5728\u6d4b\u8bd5\u9636\u6bb5\u5b83\u4eec\u5c06\u4f1a\u88ab\u52a0\u8f7d\u5230\u6a21\u578b\u4e2d":63,"\u5728\u6e90\u7801\u76ee\u5f55\u6811\u7684\u6839\u76ee\u5f55\u4e2d\u8fd0\u884c":45,"\u5728\u6f14\u793a\u4e2d":68,"\u5728\u7269\u7406\u673a\u4e0a\u624b\u52a8\u90e8\u7f72":55,"\u5728\u751f\u6210\u65f6":44,"\u5728\u751f\u6210\u8fc7\u7a0b\u4e2d":70,"\u5728\u751f\u6210\u8fc7\u7a0b\u4e2d\u6211\u4eec\u4f7f\u7528sgd\u8bad\u7ec3\u7b97\u6cd5":70,"\u5728\u7528\u6237\u4f7f\u7528c":27,"\u5728\u7528\u6237\u6587\u4ef6user":67,"\u5728\u7528\u6237\u719f\u6089\u7684\u5f00\u53d1\u5e73\u53f0":38,"\u5728\u7535\u5f71\u6587\u4ef6movi":67,"\u5728\u793a\u4f8b\u4e2d\u6211\u4eec\u4f7f\u7528attention\u7248\u672c\u7684gru\u7f16\u89e3\u7801\u7f51\u7edc":70,"\u5728\u793a\u4f8b\u4e2d\u6211\u4eec\u4f7f\u7528sgd\u8bad\u7ec3\u7b97\u6cd5":70,"\u5728\u793a\u4f8b\u4e2d\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u5e8f\u5217\u5230\u5e8f\u5217\u7684\u751f\u6210\u6570\u636e":70,"\u5728\u793a\u4f8b\u4e2d\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u5e8f\u5217\u5230\u5e8f\u5217\u7684\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6570\u636e":70,"\u5728\u7a0b\u5e8f\u5f00\u59cb\u9636\u6bb5":5,"\u5728\u7b2c\u4e00\u884c\u4e2d\u6211\u4eec\u8f7d\u5165\u7528\u4e8e\u5b9a\u4e49\u7f51\u7edc\u7684\u51fd\u6570":62,"\u5728\u7ebf\u6a21\u578b\u9884\u6d4b\u670d\u52a1":20,"\u5728\u7ec4\u5408\u65f6":36,"\u5728\u7f16\u8bd1\u5b89\u88c5\u6b63\u786e\u4e4b\u540e":47,"\u5728\u7f16\u8bd1\u5bbf\u4e3b\u673a\u7248protoc\u53ef\u6267\u884c\u6587\u4ef6\u548c\u76ee\u6807\u673a\u7248openblas\u5e93\u65f6\u9700\u8981\u7528\u5230":[38,39],"\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d":46,"\u5728\u7f51\u7edc\u914d\u7f6e\u91cc":3,"\u5728\u7ffb\u8bd1\u6cd5\u8bed\u53e5\u5b50\u4e4b\u524d":70,"\u5728\u811a\u672c":67,"\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u4e2d":40,"\u5728\u8bad\u7ec3\u4e2d":44,"\u5728\u8bad\u7ec3\u4e4b\u524d":57,"\u5728\u8bad\u7ec3\u4e86":67,"\u5728\u8bad\u7ec3\u4e86\u51e0\u4e2a\u8f6e\u6b21\u4ee5\u540e":67,"\u5728\u8bad\u7ec3\u5b8c\u6210\u540e":62,"\u5728\u8bad\u7ec3\u6570\u96c6\u4e0a\u8bad\u7ec3\u751f\u6210\u8bcd\u5411\u91cf\u5b57\u5178":61,"\u5728\u8bad\u7ec3\u65f6":56,"\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d":[57,70],"\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u6bcfshow":52,"\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u8fdb\u884c\u6d4b\u8bd5":2,"\u5728\u8bbe\u7f6e":[38,39],"\u5728\u8bc4\u5ba1\u8fc7\u7a0b\u4e2d":29,"\u5728\u8be5\u914d\u7f6e\u76847":41,"\u5728\u8bed\u8a00\u751f\u6210\u9886\u57df\u4e2d":70,"\u5728\u8d2d\u7269\u7f51\u7ad9\u4e0a":65,"\u5728\u8f6f\u4ef6\u5de5\u7a0b\u7684\u8303\u7574\u91cc":49,"\u5728\u8f93\u51fa\u7684\u8fc7\u7a0b\u4e2d":43,"\u5728\u8fd0\u884c":69,"\u5728\u8fd9\u4e2a":29,"\u5728\u8fd9\u4e2a\u4efb\u52a1\u4e2d":70,"\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d":[31,69],"\u5728\u8fd9\u4e2a\u4f8b\u5b50\u91cc":[46,56],"\u5728\u8fd9\u4e2a\u51fd\u6570\u4e2d":41,"\u5728\u8fd9\u4e2a\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165\u4efb\u4f55\u5176\u4ed6\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u5728\u8fd9\u4e2a\u6559\u7a0b\u4e2d":49,"\u5728\u8fd9\u4e2a\u6a21\u578b\u4e2d":44,"\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d":68,"\u5728\u8fd9\u4e2a\u9636\u6bb5\u7684\u4ee3\u7801\u6b63\u5728\u7ecf\u5386\u56de\u5f52\u6d4b\u8bd5":29,"\u5728\u8fd9\u4e9b\u5934\u6587\u4ef6\u4e2d":27,"\u5728\u8fd9\u4e9b\u6587\u4ef6\u4e2d":27,"\u5728\u8fd9\u4e9b\u7f51\u7edc\u4e2d":67,"\u5728\u8fd9\u4e9blayer\u4e2d":41,"\u5728\u8fd9\u6b65\u4efb\u52a1\u4e2d":69,"\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b":[44,46],"\u5728\u8fd9\u79cd\u7ed3\u6784\u4e2d":43,"\u5728\u8fd9\u7bc7\u6587\u6863\u91cc":56,"\u5728\u8fd9\u7bc7\u6587\u7ae0\u91cc":57,"\u5728\u8fd9\u91cc":43,"\u5728\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528\u5168\u8fde\u63a5\u5c42\u4f5c\u4e3a\u4f8b\u5b50\u6765\u5c55\u793a\u5b9e\u73b0\u65b0\u7f51\u7edc\u5c42\u6240\u9700\u8981\u7684\u56db\u4e2a\u6b65\u9aa4":46,"\u5728\u8fdb\u884c\u7f51\u7edc\u914d\u7f6e\u4e4b\u524d":36,"\u5728\u914d\u7f6e\u4e2d\u9700\u8981\u8bfb\u53d6\u5916\u90e8\u5b57\u5178":3,"\u5728\u914d\u7f6e\u6587\u4ef6\u4e2d\u7684":63,"\u5728\u91c7\u7528sgd":30,"\u5728\u96c6\u7fa4\u4e0a\u8bad\u7ec3\u4e00\u4e2a\u7a00\u758f\u6a21\u578b\u9700\u8981\u52a0\u4e0a\u4e0b\u9762\u7684\u53c2\u6570":54,"\u5728\u9884\u5904\u7406\u542b\u6709\u591a\u884c\u6570\u6910\u7684\u6587\u672c\u6587\u4ef6\u65f6\u53c2\u6570\u8bbe\u7f6e\u7a0d\u6709\u4e0d\u540c":69,"\u5728\u9884\u6d4b\u5e8f\u5217\u6216\u6bb5\u843d\u7684\u60c5\u611f\u4e2d\u8d77\u4e3b\u8981\u4f5c\u7528":69,"\u5728android\u5e73\u53f0\u4e0a\u4e0d\u652f\u6301\u901a\u8fc7swig\u8c03\u7528\u6765\u8bad\u7ec3\u6216\u8005\u9884\u6d4b":38,"\u5728android\u5e73\u53f0\u4e0a\u53ea\u652f\u6301\u4f7f\u7528c":38,"\u5728aws\u4e0a\u5feb\u901f\u90e8\u7f72\u96c6\u7fa4":55,"\u5728c":26,"\u5728c\u7684\u5934\u6587\u4ef6":26,"\u5728cub":62,"\u5728generator\u7684\u4e0a\u4e0b\u6587\u4e2d\u5c3d\u91cf\u7559\u4e0b\u975e\u5e38\u5c11\u7684\u53d8\u91cf\u5f15\u7528":3,"\u5728kubernetes\u4e2d\u521b\u5efa\u7684\u6240\u6709\u8d44\u6e90\u5bf9\u8c61":55,"\u5728linux\u4e0b":70,"\u5728meta\u6587\u4ef6\u4e2d\u6709\u4e24\u79cd\u7279\u5f81":67,"\u5728movielen":67,"\u5728paddl":57,"\u5728paddle\u4e2d":54,"\u5728paddle\u4e4b\u4e0a\u8fd0\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u8f93\u51fa\u7684\u6a21\u578b\u4f1a\u63d0\u4f9b\u7ed9\u5728\u7ebf\u4eba\u8138\u8bc6\u522b\u7684\u5e94\u7528\u4f7f\u7528":20,"\u5728paddlepaddle\u4e2d":[36,43],"\u5728paddlepaddle\u7684\u6587\u6863\u4e2d":41,"\u5728paddlepaddle\u91cc":31,"\u5728paramet":19,"\u5728step\u51fd\u6570\u4e2d\u5b9a\u4e49":43,"\u5728step\u51fd\u6570\u4e2d\u5b9a\u4e49memori":43,"\u5728trainer":54,"\u5730\u5740\u4e5f\u53ef\u4ee5\u4e3ahdfs\u6587\u4ef6\u8def\u5f84":2,"\u5730\u6bb5":41,"\u5730\u7406\u4f4d\u7f6e":41,"\u5730\u94c1\u7ad9":41,"\u5747\u4f1a\u88ab\u5b89\u88c5\u5230includ":27,"\u5747\u503c\u56fe\u50cf\u6587\u4ef6":63,"\u5747\u5300\u5206\u5e03":30,"\u5747\u5300\u5206\u5e03\u7684\u8303\u56f4\u662f":52,"\u5747\u662f\u5728":27,"\u5747\u6709\u4e09\u4e2a\u5b50\u5e8f\u5217":41,"\u5747\u6709\u4e24\u7ec4\u7279\u5f81":41,"\u57fa\u4e8e\u53cc\u5c42\u5e8f\u5217\u8f93\u5165":43,"\u57fa\u4e8e\u5b57\u6bcd\u7684\u8bcd\u5d4c\u5165\u7279\u5f81":67,"\u57fa\u4e8e\u7c98\u6027\u4f1a\u8bdd\u7684\u8d1f\u8f7d\u5747\u8861\u529f\u80fd":24,"\u57fa\u4e8epython\u7684\u6a21\u578b\u9884\u6d4b":5,"\u57fa\u4e8epython\u7684\u9884\u6d4b":[4,65],"\u57fa\u672c\u4e0a\u548cmnist\u6837\u4f8b\u4e00\u81f4":3,"\u57fa\u672c\u4f7f\u7528\u6982\u5ff5":37,"\u57fa\u672c\u76f8\u540c":61,"\u586b\u5199":45,"\u589e\u52a0\u4e86\u4e00\u6761cd\u547d\u4ee4":56,"\u589e\u52a0\u5982\u4e0b\u53c2\u6570":54,"\u589e\u52a0\u68af\u5ea6\u68c0\u6d4b\u7684\u5355\u5143\u6d4b\u8bd5":46,"\u58f0\u660epython\u6570\u636e\u6e90":67,"\u5904\u7406\u5668\u6709\u4e24\u4e2a\u5173\u952e\u6027\u80fd\u9650\u5236":49,"\u5904\u7406\u6570\u636e\u7684python\u811a\u672c\u6587\u4ef6":65,"\u5904\u7406\u7684\u8f93\u5165\u5e8f\u5217\u4e3b\u8981\u5206\u4e3a\u4ee5\u4e0b\u4e09\u79cd\u7c7b\u578b":43,"\u5904\u7406\u76f8\u4f3c\u5ea6\u56de\u5f52":67,"\u5904\u7406\u8fc7\u7a0b\u4e2d\u6570\u636e\u5b58\u50a8\u683c\u5f0f":62,"\u5907\u6ce8":49,"\u590d\u6742\u5ea6\u6216\u65f6\u95f4\u590d\u6742\u5ea6":49,"\u5916\u5c42memory\u662f\u4e00\u4e2a\u5143\u7d20":41,"\u5916\u5c42outer_step\u4e2d":41,"\u591a\u4e2a\u503c":20,"\u591a\u4e2ainput\u4ee5list\u65b9\u5f0f\u8f93\u5165":65,"\u591a\u4e2aparamet":19,"\u591a\u53e5\u8bdd\u8fdb\u4e00\u6b65\u6784\u6210\u4e86\u6bb5\u843d":43,"\u591a\u673a\u8bad\u7ec3":30,"\u591a\u7ebf\u7a0b\u7684\u6570\u636e\u8bfb\u53d6":3,"\u591a\u8f6e\u5bf9\u8bdd\u7b49\u66f4\u4e3a\u590d\u6742\u7684\u8bed\u8a00\u6570\u636e":43,"\u5927\u578b\u7535\u5f71\u8bc4\u8bba\u6570\u636e\u96c6":69,"\u5927\u591a\u6570\u5c42\u4e0d\u9700\u8981\u8fdc\u7a0b\u7a00\u758f\u8bad\u7ec3\u51fd\u6570":46,"\u5927\u591a\u6570\u5c42\u9700\u8981\u8bbe\u7f6e\u4e3a":46,"\u5927\u591a\u6570\u6210\u529f\u7684srl\u7cfb\u7edf\u662f\u5efa\u7acb\u5728\u67d0\u79cd\u5f62\u5f0f\u7684\u53e5\u6cd5\u5206\u6790\u7ed3\u679c\u4e4b\u4e0a\u7684":68,"\u5927\u591a\u6570\u7f51\u7edc\u5c42\u4e0d\u9700\u8981\u652f\u6301\u8fdc\u7a0b\u7a00\u758f\u66f4\u65b0":46,"\u5927\u591a\u6570\u8bed\u8a00\u90fd\u652f\u6301\u4f7f\u7528c\u8bed\u8a00api":26,"\u5927\u5b66\u751f":66,"\u5927\u5bb6\u53ef\u4ee5\u901a\u8fc7\u5b83\u5236\u4f5c\u548c\u5206\u4eab\u5e26\u6709\u4ee3\u7801":33,"\u5927\u5c0f":50,"\u5929":41,"\u5929\u4e00\u5e7f\u573a":41,"\u5929\u4e00\u9601":41,"\u5929\u732b":69,"\u5934\u6587\u4ef6\u4e2d\u628a\u53c2\u6570\u5b9a\u4e49\u4e3a\u7c7b\u7684\u6210\u5458\u53d8\u91cf":46,"\u5934\u6587\u4ef6\u5982\u4e0b":46,"\u5947\u5e7b\u7247":66,"\u597d":41,"\u597d\u5403":41,"\u597d\u8bc4":65,"\u5982":[3,44,45,50,54],"\u59822":50,"\u5982\u4e0b":[3,67,69],"\u5982\u4e0b\u56fe\u6240\u793a":[41,49,62],"\u5982\u4e0b\u6240\u793a":[54,63,67],"\u5982\u4e0b\u662f\u4e00\u6bb5\u4f7f\u7528mnist":5,"\u5982\u4e0b\u8868\u683c":65,"\u5982\u4f55":67,"\u5982\u4f55\u5b58\u50a8\u7b49\u7b49":3,"\u5982\u4f55\u89e3\u6790\u8be5\u5730\u5740\u4e5f\u662f\u7528\u6237\u81ea\u5b9a\u4e49dataprovider\u65f6\u9700\u8981\u8003\u8651\u7684\u5730\u65b9":2,"\u5982\u4f55\u8d21\u732e":48,"\u5982\u4f55\u8d21\u732e\u4ee3\u7801":48,"\u5982\u4f55\u8fdb\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3":65,"\u5982\u4fe1\u606f\u63d0\u53d6":68,"\u5982\u56fe2\u6240\u793a":69,"\u5982\u56fe\u4e2dtrainer":19,"\u5982\u5f62\u5bb9\u8bcd\u548c\u526f\u8bcd":69,"\u5982\u60f3\u4e86\u89e3\u66f4\u591a\u8be6\u7ec6\u7684\u89e3\u91ca":70,"\u5982\u672c\u4f8b\u4e2d":3,"\u5982\u672c\u4f8b\u7684":3,"\u5982\u679c\u4e00\u4e2a\u7f51\u7edc\u5c42\u9700\u8981\u914d\u7f6e\u7684\u8bdd":46,"\u5982\u679c\u4e0a\u9762\u4e24\u6b65\u51fa\u73b0\u9519\u8bef":19,"\u5982\u679c\u4e0b\u8f7d\u6210\u529f":63,"\u5982\u679c\u4e0d\u4e3a0":52,"\u5982\u679c\u4e0d\u4e86\u89e3":3,"\u5982\u679c\u4e0d\u5207\u8bcd":65,"\u5982\u679c\u4e0d\u6536\u655b":30,"\u5982\u679c\u4e0d\u662f\u5e8f\u5217":67,"\u5982\u679c\u4e3a":3,"\u5982\u679c\u4e3a0":52,"\u5982\u679c\u4e3afals":52,"\u5982\u679c\u4e3atrue":[3,52],"\u5982\u679c\u4e4b\u540e\u60f3\u8981\u91cd\u65b0\u8bbe\u7f6e":32,"\u5982\u679c\u4ec5\u4ec5\u4fee\u6539\u4e00\u4e2a\u6587\u4ef6\u4f46\u63d0\u4ea4\u4e86\u5341\u51e0\u4e2acommit":45,"\u5982\u679c\u4ed4\u7ec6\u8bbe\u7f6e\u7684\u8bdd":52,"\u5982\u679c\u4f60\u4e0d\u9700\u8981\u8fd9\u4e2a\u64cd\u4f5c":69,"\u5982\u679c\u4f60\u53ea\u9700\u8981\u4f7f\u7528\u7b80\u5355\u7684rnn":44,"\u5982\u679c\u4f60\u5b89\u88c5gpu\u7248\u672c\u7684paddlepaddl":69,"\u5982\u679c\u4f60\u60f3\u4f7f\u7528\u8fd9\u4e9b\u7279\u6027":54,"\u5982\u679c\u4f60\u60f3\u8981\u4fdd\u5b58\u67d0\u4e9b\u5c42\u7684\u7279\u5f81\u56fe":52,"\u5982\u679c\u4f60\u60f3\u8fdb\u884c\u8bf8\u5982\u8bed\u4e49\u8f6c\u8ff0":70,"\u5982\u679c\u4f60\u6267\u884c\u5176\u5b83\u7684\u7528\u60c5\u611f\u5206\u6790\u6765\u5206\u7c7b\u6587\u672c\u7684\u4efb\u52a1":69,"\u5982\u679c\u4f60\u66fe\u7ecf\u5728\u6e90\u7801\u76ee\u5f55\u4e0b\u7f16\u8bd1\u8fc7\u5176\u4ed6\u5e73\u53f0\u7684paddlepaddle\u5e93":[38,39],"\u5982\u679c\u4f60\u6b63\u5728\u5904\u7406\u5e8f\u5217\u6807\u8bb0\u4efb\u52a1":44,"\u5982\u679c\u4f60\u6ca1\u6709gpu\u73af\u5883":62,"\u5982\u679c\u4f60\u8981\u4e3a\u4e86\u6d4b\u8bd5\u800c\u589e\u52a0\u65b0\u7684\u6587\u4ef6":46,"\u5982\u679c\u4f7f\u7528":[50,61],"\u5982\u679c\u4f7f\u7528gpu\u7248\u672c\u7684paddlepaddl":35,"\u5982\u679c\u4f7f\u7528nvidia":33,"\u5982\u679c\u4f7f\u7528ssl\u8ba4\u8bc1":55,"\u5982\u679c\u4f7f\u7528swig\u6211\u4eec\u9700\u8981\u5c06\u5728interface\u6587\u4ef6\u91cc":26,"\u5982\u679c\u51fa\u73b0\u4ee5\u4e0bpython\u76f8\u5173\u7684\u5355\u5143\u6d4b\u8bd5\u90fd\u8fc7\u4e0d\u4e86\u7684\u60c5\u51b5":30,"\u5982\u679c\u53c2\u6570\u4fdd\u5b58\u4e0b\u6765\u7684\u6a21\u578b\u76ee\u5f55":30,"\u5982\u679c\u53c2\u6570\u6a21\u578b\u6587\u4ef6\u7f3a\u5931":61,"\u5982\u679c\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u672a\u8bbe\u7f6easync":52,"\u5982\u679c\u5728\u8bad\u7ec3\u671f\u95f4\u540c\u65f6\u53d1\u8d77\u53e6\u5916\u4e00\u4e2a\u8fdb\u7a0b\u8fdb\u884c\u6d4b\u8bd5":52,"\u5982\u679c\u5728\u8bad\u7ec3\u914d\u7f6e\u4e2d\u8bbe\u7f6ebatch":52,"\u5982\u679c\u5728\u8bad\u7ec3nlp\u76f8\u5173\u6a21\u578b\u65f6":30,"\u5982\u679c\u5931\u8d25":29,"\u5982\u679c\u5b58\u5728\u67d0\u4e9btrainer\u6267\u884c\u901f\u5ea6\u8fc7\u6162\u4f1a\u5f71\u54cd\u6574\u4f53\u96c6\u7fa4\u7684\u901f\u5ea6":19,"\u5982\u679c\u5b83\u4f4d\u4e8e\u8c13\u8bcd\u4e0a\u4e0b\u6587\u533a\u57df\u4e2d":68,"\u5982\u679c\u5c06\u8fd9\u4e2a\u5185\u5b58\u6c60\u51cf\u5c0f":30,"\u5982\u679c\u5df2\u5b89\u88c5":68,"\u5982\u679c\u5df2\u7ecf\u6709pod\u8fd0\u884c":57,"\u5982\u679c\u5df2\u7ecf\u6b63\u5728\u6267\u884c\u4fdd\u5b58\u68c0\u67e5\u70b9\u7684\u7ebf\u7a0b":19,"\u5982\u679c\u5f00\u542f\u4f1a\u5bfc\u81f4\u8fd0\u884c\u7565\u6162":32,"\u5982\u679c\u60a8":33,"\u5982\u679c\u60a8\u60f3\u8981\u66f4\u6df1\u5165\u4e86\u89e3deep":33,"\u5982\u679c\u60a8\u6709\u597d\u7684\u5efa\u8bae\u6765":67,"\u5982\u679c\u60a8\u7684gpu\u7406\u8bba\u53ef\u4ee5\u8fbe\u52306":49,"\u5982\u679c\u60f3\u4e3a\u4e00\u4e2a\u6570\u636e\u6587\u4ef6\u8fd4\u56de\u591a\u6761\u6837\u672c":3,"\u5982\u679c\u60f3\u4f7f\u7528\u53ef\u89c6\u5316\u7684\u5206\u6790\u5668":49,"\u5982\u679c\u60f3\u5f88\u597d\u7684\u7406\u89e3\u7a0b\u5e8f\u7684\u884c\u4e3a":49,"\u5982\u679c\u60f3\u8981\u4e86\u89e3\u53cc\u5c42rnn\u5728\u5177\u4f53\u95ee\u9898\u4e2d\u7684\u4f7f\u7528":41,"\u5982\u679c\u60f3\u8981\u542f\u7528paddlepaddle\u7684\u5185\u7f6e\u5b9a\u65f6\u5668":49,"\u5982\u679c\u6211\u77e5\u9053\u5185\u6838\u82b1\u4e8610ms\u6765\u79fb\u52a81gb\u6570\u636e":49,"\u5982\u679c\u6267\u884c\u5931\u8d25":55,"\u5982\u679c\u6267\u884c\u6210\u529f":63,"\u5982\u679c\u63d0\u793a\u6b63\u786e":47,"\u5982\u679c\u63d0\u793a\u9519\u8bef":47,"\u5982\u679c\u6570\u636e\u6587\u4ef6\u5b58\u4e8e\u672c\u5730\u78c1\u76d8":2,"\u5982\u679c\u6570\u6910\u83b7\u53d6\u6210\u529f":69,"\u5982\u679c\u662f\u4f7f\u7528\u975essl\u65b9\u5f0f\u8bbf\u95ee":55,"\u5982\u679c\u662f\u5176\u5b83\u7c7b\u578b":20,"\u5982\u679c\u662f\u5e8f\u5217":67,"\u5982\u679c\u6709\u591a\u4e2a\u8f93\u5165":43,"\u5982\u679c\u6709\u591a\u4e2a\u8f93\u5165\u5e8f\u5217":43,"\u5982\u679c\u6709\u66f4\u590d\u6742\u7684\u4f7f\u7528":2,"\u5982\u679c\u6709\u9700\u8981\u4fee\u6539\u7684\u5730\u65b9":45,"\u5982\u679c\u6709bugfix\u7684\u884c\u4e3a":29,"\u5982\u679c\u672a\u8bbe\u7f6e":52,"\u5982\u679c\u672a\u8bbe\u7f6egpu":54,"\u5982\u679c\u67d0\u4e00\u4e2a\u7c7b\u578b\u9700\u8981\u5f15\u7528\u53e6\u4e00\u4e2a\u7c7b\u578b":27,"\u5982\u679c\u67d0\u4e00\u4e2apaddl":27,"\u5982\u679c\u67d0\u4e00\u4e2apaddle\u6982\u5ff5\u5fc5\u987b\u8981\u66b4\u9732":27,"\u5982\u679c\u67d0\u4e00\u5757\u6839\u672c\u5c31\u4e0d\u600e\u4e48\u8017\u65f6":49,"\u5982\u679c\u68c0\u67e5\u5230\u5206\u914d\u5728\u4e0d\u540c\u53c2\u6570\u670d\u52a1\u5668\u4e0a\u7684\u53c2\u6570\u7684\u5206\u5e03\u4e0d\u5747\u5300\u6b21\u6570\u5927\u4e8echeck":52,"\u5982\u679c\u6ca1\u6709\u5b9a\u4e49memori":43,"\u5982\u679c\u6ca1\u6709\u8bbe\u7f6e":70,"\u5982\u679c\u6ca1\u6709\u8bbe\u7f6etest":2,"\u5982\u679c\u6ca1\u8fc7":45,"\u5982\u679c\u6d88\u606f\u6570\u636e\u592a\u5c0f":52,"\u5982\u679c\u6ee1\u8db3\u6761\u4ef6":19,"\u5982\u679c\u7528\u6237\u4e0d\u663e\u793a\u6307\u5b9a\u8fd4\u56de\u6570\u636e\u7684\u5bf9\u5e94\u5173\u7cfb":3,"\u5982\u679c\u7528\u6237\u60f3\u8981\u4e86\u89e3\u8be6\u7ec6\u7684\u6570\u636e\u96c6\u7684\u683c\u5f0f":61,"\u5982\u679c\u7528\u6237\u60f3\u8981\u81ea\u5b9a\u4e49\u521d\u59cb\u5316\u65b9\u5f0f":30,"\u5982\u679c\u7528\u6237\u8981\u628apaddle\u7684\u9759\u6001\u5e93":26,"\u5982\u679c\u771f\u60f3\u6316\u6398\u5185\u6838\u6df1\u5904\u7684\u67d0\u4e2a\u79d8\u5bc6":49,"\u5982\u679c\u7a0b\u5e8f\u5d29\u6e83\u4f60\u4e5f\u53ef\u4ee5\u624b\u52a8\u7ec8\u6b62":50,"\u5982\u679c\u7cfb\u7edf\u5b89\u88c5\u4e86\u591a\u4e2apython\u7248\u672c":30,"\u5982\u679c\u7f51\u7edc\u5c42\u4e0d\u9700\u8981\u8fdc\u7a0b\u7a00\u758f\u66f4\u65b0":46,"\u5982\u679c\u7f51\u7edc\u67b6\u6784\u7b80\u5355":44,"\u5982\u679c\u8981\u4f7f\u7528\u53cc\u5411lstm":69,"\u5982\u679c\u8981\u4f7f\u7528gpu":33,"\u5982\u679c\u8981\u542f\u7528gpu":50,"\u5982\u679c\u8981\u8fd0\u884c\u6240\u6709\u7684\u5355\u5143\u6d4b\u8bd5":45,"\u5982\u679c\u89e3\u51b3\u4e86\u67d0\u4e2aissue\u7684\u95ee\u9898":45,"\u5982\u679c\u8bad\u7ec3\u4e00\u4e2apass":30,"\u5982\u679c\u8bad\u7ec3\u8fc7\u7a0b\u542f\u52a8\u6210\u529f\u7684\u8bdd":67,"\u5982\u679c\u8bad\u7ec3\u8fc7\u7a0b\u7684\u7684cost\u660e\u663e\u9ad8\u4e8e\u8fd9\u4e2a\u5e38\u6570\u8f93\u51fa\u7684cost":30,"\u5982\u679c\u8bbe\u7f6e":3,"\u5982\u679c\u8bbe\u7f6e\u8be5\u53c2\u6570":52,"\u5982\u679c\u8bc4\u5ba1\u610f\u89c1\u6bd4\u8f83\u591a":45,"\u5982\u679c\u8c03\u7528\u9759\u6001\u5e93\u53ea\u80fd\u5c06\u9759\u6001\u5e93\u4e0e\u89e3\u91ca\u5668\u94fe\u63a5":26,"\u5982\u679c\u8f93\u51fa\u662fno":33,"\u5982\u679c\u8fd0\u884c":30,"\u5982\u679c\u8fd0\u884c\u6210\u529f":[63,69],"\u5982\u679c\u96c6\u7fa4\u8282\u70b9\u6570\u91cf\u5c11":50,"\u5982\u679c\u9700\u8981\u5305\u542b\u66f4\u591a\u7684\u4f9d\u8d56":33,"\u5982\u679c\u9700\u8981\u6269\u5927\u77e9\u9635":46,"\u5982\u679c\u9700\u8981\u7f29\u51cf\u77e9\u9635":46,"\u5982\u679clearning_rate\u592a\u5927":30,"\u5982\u679clearning_rate\u592a\u5c0f":30,"\u5982\u679cpaddlepaddle\u5305\u5df2\u7ecf\u5728python\u7684sit":30,"\u5982\u679cparamet":19,"\u5982\u6bcf\u4e2a\u6587\u4ef6\u53ea\u6709\u4e00\u4e2a":45,"\u5982\u795e\u7ecf\u5143\u6fc0\u6d3b\u503c\u7b49":30,"\u5982\u8981build\u8fd9\u4e2a\u5f00\u53d1\u955c\u50cf":45,"\u5982\u9ad8\u4eae\u90e8\u5206":49,"\u5b50":41,"\u5b50\u53e5":43,"\u5b50\u53e5\u7684\u5355\u8bcd\u6570\u548c\u6307\u5b9a\u7684\u4e00\u4e2a\u8f93\u5165\u5e8f\u5217\u4e00\u81f4":43,"\u5b57\u5178":70,"\u5b57\u5178\u4f1a\u5305\u542b\u8f93\u5165\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u5355\u8bcd":70,"\u5b57\u5178\u5171\u5305\u542b":61,"\u5b57\u5178\u6587\u4ef6":[68,69],"\u5b57\u5178\u91c7\u7528utf8\u7f16\u7801":61,"\u5b57\u5178imdb":69,"\u5b57\u6bb5\u4e2d":57,"\u5b57\u6bb5\u8868\u793a\u5bb9\u5668\u7684\u73af\u5883\u53d8\u91cf":57,"\u5b57\u6bb5\u8868\u793a\u8fd9\u4e2ajob\u4f1a\u540c\u65f6\u5f00\u542f3\u4e2apaddlepaddle\u8282\u70b9":57,"\u5b57\u7b26\u4e32":20,"\u5b58\u50a8":20,"\u5b58\u50a8\u5377":55,"\u5b58\u50a8\u5728\u8bb0\u5fc6\u5355\u5143\u533a\u5757\u7684\u5386\u53f2\u4fe1\u606f\u88ab\u66f4\u65b0\u7528\u6765\u8fed\u4ee3\u7684\u5b66\u4e60\u5355\u8bcd\u4ee5\u5408\u7406\u7684\u5e8f\u5217\u7a0b\u73b0":69,"\u5b58\u50a8\u6a21\u578b\u7684\u8def\u5f84":70,"\u5b58\u50a8\u7740\u7535\u5f71\u6216\u7528\u6237\u4fe1\u606f":67,"\u5b58\u5165settings\u5bf9\u8c61":3,"\u5b58\u5728\u6216\u66f4\u6539\u4e3a\u5176\u5b83\u6a21\u578b\u8def\u5f84":69,"\u5b66\u4e60\u6210\u672c\u9ad8":26,"\u5b66\u4e60\u7b97\u6cd5":31,"\u5b66\u672f":66,"\u5b81\u6ce2":41,"\u5b83\u4e0d\u4ec5\u80fd\u591f\u5904\u7406imdb\u6570\u636e":69,"\u5b83\u4eec\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4f5c\u4e3a\u7f51\u7edc\u7684\u51fa\u53e3":31,"\u5b83\u4eec\u7684\u5927\u5c0f\u662f":44,"\u5b83\u4eec\u7684\u6587\u4ef6\u540d\u662f":20,"\u5b83\u4eec\u8fd8\u53ef\u4ee5\u4f9b\u90a3\u4e9b\u8fd0\u884c\u66f4\u590d\u6742\u7684\u96c6\u7fa4\u7ba1\u7406\u7cfb\u7edf":50,"\u5b83\u4eec\u90fd\u662f\u5e8f\u5217":44,"\u5b83\u4f1a\u5728dataprovider\u521b\u5efa\u7684\u65f6\u5019\u6267\u884c":3,"\u5b83\u4f7f\u752850\u5c42\u7684resnet\u6a21\u578b\u6765\u5bf9":63,"\u5b83\u5305\u542b\u4ee5\u4e0b\u51e0\u6b65":46,"\u5b83\u5305\u542b\u4ee5\u4e0b\u53c2\u6570":46,"\u5b83\u5305\u542b\u56db\u4e2a\u7248\u672c":35,"\u5b83\u5305\u542b\u7684\u5c5e\u6027\u53c2\u6570\u5982\u4e0b":3,"\u5b83\u5305\u62ec\u4e86\u4e00\u4e2a\u53cc\u5411\u7684gru\u4f5c\u4e3a\u7f16\u7801\u5668\u548c\u89e3\u7801\u5668":70,"\u5b83\u53eb\u505a":44,"\u5b83\u53ef\u4ee5\u5728\u53e5\u5b50\u7ea7\u522b\u5229\u7528\u53ef\u6269\u5c55\u7684\u4e0a\u4e0b\u6587":69,"\u5b83\u53ef\u4ee5\u5e2e\u52a9\u51cf\u5c11\u5206\u53d1\u5ef6\u8fdf":50,"\u5b83\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u683c\u5f0f\u5316\u6e90\u4ee3\u7801":45,"\u5b83\u53ef\u4ee5\u6307\u6d4b\u91cf\u4e00\u4e2a\u7a0b\u5e8f\u7684\u7a7a\u95f4":49,"\u5b83\u53ef\u4ee5\u88ab\u5e94\u7528\u4e8e\u8fdb\u884c\u673a\u5668\u7ffb\u8bd1":70,"\u5b83\u53ef\u80fd\u6709\u4e0d\u6b62\u4e00\u4e2a\u6743\u91cd":46,"\u5b83\u540c\u65f6\u5b66\u4e60\u6392\u5217":70,"\u5b83\u548c\u6570\u636e\u4f20\u5165\u51fd\u6570\u7684\u7b2c\u4e00\u4e2a\u53c2\u6570":3,"\u5b83\u5b58\u50a8\u5f53\u524d\u8282\u70b9\u6240\u6709\u8bad\u7ec3":50,"\u5b83\u5b9a\u4e49\u4e86":44,"\u5b83\u5b9a\u4e49\u4e86\u6a21\u578b\u53c2\u6570\u6539\u53d8\u7684\u89c4\u5219":31,"\u5b83\u5b9a\u4e49\u89e3\u7801\u7f51\u7edc\u7684":44,"\u5b83\u5c06\u88ab\u5206\u53d1\u5230":50,"\u5b83\u5c06\u8f93\u5165\u8bed\u53e5\u7f16\u7801\u4e3a\u5411\u91cf\u7684\u5e8f\u5217":70,"\u5b83\u5c06\u8fd4\u56de\u5982\u4e0b\u7684\u5b57\u5178":63,"\u5b83\u5c31\u4f1a\u5728\u6e90\u8bed\u53e5\u4e2d\u641c\u7d22\u51fa\u6700\u76f8\u5173\u4fe1\u606f\u7684\u4f4d\u7f6e\u7684\u96c6\u5408":70,"\u5b83\u652f\u6301\u591a\u7ebf\u7a0b\u66f4\u65b0":46,"\u5b83\u662finteger_value\u7c7b\u578b\u7684":41,"\u5b83\u662finteger_value_sequence\u7c7b\u578b\u7684":41,"\u5b83\u6709\u52a9\u4e8e\u5e2e\u52a9\u9891\u7e41\u4fee\u6539\u548c\u8bbf\u95ee\u5de5\u4f5c\u533a\u6587\u4ef6\u7684\u7528\u6237\u51cf\u5c11\u8d1f\u62c5":50,"\u5b83\u6a21\u62df\u4e86\u89e3\u7801\u7ffb\u8bd1\u8fc7\u7a0b\u4e2d\u5728\u6e90\u8bed\u53e5\u4e2d\u7684\u641c\u7d22":70,"\u5b83\u7684":44,"\u5b83\u7684\u6536\u655b\u901f\u5ea6\u6bd4":69,"\u5b83\u7684\u6bcf\u4e00\u4e2a\u5143\u7d20":40,"\u5b83\u7684\u76ee\u7684\u662f\u9884\u6d4b\u5728\u4e00\u4e2a\u5e8f\u5217\u4e2d\u8868\u8fbe\u7684\u60c5\u611f\u6001\u5ea6":69,"\u5b83\u7684\u8f93\u5165\u4e0e\u7ecf\u8fc7\u5b66\u4e60\u7684\u53c2\u6570\u505a\u5185\u79ef\u5e76\u52a0\u4e0a\u504f\u7f6e":46,"\u5b83\u76f4\u63a5\u5b66\u4e60\u6bb5\u843d\u8868\u793a":69,"\u5b83\u80fd\u591f\u4ece\u8bcd\u7ea7\u5230\u5177\u6709\u53ef\u53d8\u4e0a\u4e0b\u6587\u957f\u5ea6\u7684\u4e0a\u4e0b\u6587\u7ea7\u522b\u6765\u603b\u7ed3\u8868\u793a":69,"\u5b83\u8bfb\u5165\u6570\u636e\u5e76\u5c06\u5b83\u4eec\u4f20\u8f93\u5230\u63a5\u4e0b\u6765\u7684\u7f51\u7edc\u5c42":31,"\u5b83\u8fd4\u56degen":70,"\u5b83\u8fd4\u56detrain":70,"\u5b83\u9700\u8981\u5728\u8fd9\u91cc\u6307\u5b9a":69,"\u5b83\u9996\u5148\u8c03\u7528\u57fa\u6784\u9020\u51fd\u6570":46,"\u5b89\u6392":41,"\u5b89\u88c5\u4e0e\u7f16\u8bd1":37,"\u5b89\u88c5\u5305\u7684\u4e0b\u8f7d\u5730\u5740\u662f":35,"\u5b89\u88c5\u540e\u7684\u76ee\u5f55\u7ed3\u6784\u4e3a":27,"\u5b89\u88c5\u597ddocker\u4e4b\u540e\u53ef\u4ee5\u4f7f\u7528\u6e90\u7801\u76ee\u5f55\u4e0b\u7684\u811a\u672c\u6784\u5efa\u6587\u6863":47,"\u5b89\u88c5\u5b8c\u6210\u540e":35,"\u5b89\u88c5\u6587\u6863":36,"\u5b89\u88c5\u6d41\u7a0b":65,"\u5b89\u88c5\u8be5\u8f6f\u4ef6\u5305\u5c31\u53ef\u4ee5\u5728python\u73af\u5883\u4e0b\u5b9e\u73b0\u6a21\u578b\u9884\u6d4b":5,"\u5b89\u88c5paddlepaddl":65,"\u5b89\u88c5pillow":62,"\u5b89\u9759":41,"\u5b8c\u6210":45,"\u5b8c\u6210\u4e00\u4e2a\u4f20\u8f93\u52a8\u4f5c\u5b8c\u6210\u7684\u65f6\u95f4\u4e5f\u6bd4\u8f83\u77ed":24,"\u5b8c\u6210\u4efb\u610f\u7684\u8fd0\u7b97\u903b\u8f91":43,"\u5b8c\u6210\u540evolume\u4e2d\u7684\u6587\u4ef6\u5185\u5bb9\u5927\u81f4\u5982\u4e0b":57,"\u5b8c\u6210\u5f00\u53d1":33,"\u5b8c\u6210\u6570\u636e\u7684\u9884\u5904\u7406":20,"\u5b8c\u6210\u76f8\u5e94\u7684\u8ba1\u7b97":40,"\u5b8c\u6210paddlepaddle\u7684\u5b89\u88c5":36,"\u5b8c\u6574\u6e90\u7801\u53ef\u53c2\u8003":30,"\u5b8c\u6574\u7684\u4ee3\u7801\u89c1":5,"\u5b8c\u6574\u7684\u53c2\u6570\u77e9\u9635\u88ab\u5206\u5e03\u5728\u4e0d\u540c\u7684\u53c2\u6570\u670d\u52a1\u5668\u4e0a":46,"\u5b8c\u6574\u7684\u6570\u636e\u63d0\u4f9b\u6587\u4ef6\u5728":44,"\u5b8c\u6574\u7684\u914d\u7f6e\u6587\u4ef6\u5728":44,"\u5b98\u65b9\u6587\u6863":32,"\u5b9a\u4e49\u4e00\u4e2a\u65f6\u95f4\u6b65\u4e4b\u5185rnn\u5355\u5143\u5b8c\u6210\u7684\u8ba1\u7b97":43,"\u5b9a\u4e49\u4e00\u4e2apython\u7684":3,"\u5b9a\u4e49\u4e86\u4e00\u4e2a\u53ea\u8bfb\u7684memori":43,"\u5b9a\u4e49\u4e86\u7f51\u7edc\u7684\u6570\u636e\u69fd":68,"\u5b9a\u4e49\u4e86\u7f51\u7edc\u7ed3\u6784":62,"\u5b9a\u4e49\u4e86\u7f51\u7edc\u7ed3\u6784\u5e76\u4fdd\u5b58\u4e3a":31,"\u5b9a\u4e49\u5728\u5916\u5c42":43,"\u5b9a\u4e49\u5f02\u6b65\u8bad\u7ec3\u7684\u957f\u5ea6":52,"\u5b9a\u4e49\u6570\u636e\u6765\u6e90":31,"\u5b9a\u4e49\u6e90\u8bed\u53e5\u7684\u6570\u636e\u5c42":44,"\u5b9a\u4e49\u89e3\u7801\u5668\u7684memori":44,"\u5b9a\u4e49\u8bad\u7ec3\u6570\u6910\u548c\u6d4b\u8bd5\u6570\u6910\u63d0\u4f9b\u8005":69,"\u5b9a\u4e49\u8f93\u5165\u6570\u636e\u7684\u7c7b\u578b":31,"\u5b9a\u4e49\u8f93\u51fa\u51fd\u6570":44,"\u5b9a\u4e49\u95e8\u63a7\u5faa\u73af\u5355\u5143\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u5355\u6b65\u51fd\u6570":44,"\u5b9d\u5854\u7684\u5e95\u7aef\u9700\u8981\u575a\u5b9e\u7684\u57fa\u5ea7\u6765\u652f\u6491":36,"\u5b9e\u4f8b\u5982\u4e0b":68,"\u5b9e\u73b0\u4e24\u4e2a\u5b8c\u5168\u7b49\u4ef7\u7684\u5168\u8fde\u63a5rnn":41,"\u5b9e\u73b0\u524d\u5411\u4f20\u64ad\u7684\u90e8\u5206\u6709\u4e0b\u9762\u51e0\u4e2a\u6b65\u9aa4":46,"\u5b9e\u73b0\u5355\u6b65\u51fd\u6570":44,"\u5b9e\u73b0\u540e\u5411\u4f20\u64ad\u7684\u90e8\u5206\u6709\u4e0b\u9762\u51e0\u4e2a\u6b65\u9aa4":46,"\u5b9e\u73b0\u6570\u636e\u8f93\u5165\u51fd\u6570":3,"\u5b9e\u73b0\u6784\u9020\u51fd\u6570":46,"\u5b9e\u73b0\u7b80\u5355":26,"\u5b9e\u73b0\u7ec6\u8282":46,"\u5b9e\u73b0\u7f51\u7edc\u5c42\u7684\u524d\u5411\u4f20\u64ad":46,"\u5b9e\u73b0\u7f51\u7edc\u5c42\u7684\u540e\u5411\u4f20\u64ad":46,"\u5b9e\u73b0\u8bcd\u8bed\u548c\u53e5\u5b50\u4e24\u4e2a\u7ea7\u522b\u7684\u53cc\u5c42rnn\u7ed3\u6784":43,"\u5b9e\u73b0\u8be5\u5c42\u7684c":46,"\u5b9e\u9645\u4e0a\u53ea\u6709":63,"\u5b9e\u9645\u4e0a\u662fcsv\u6587\u4ef6":66,"\u5ba2\u6237":41,"\u5ba2\u6237\u670d\u52a1":66,"\u5bb6":41,"\u5bb9\u5668":55,"\u5bb9\u5668\u4e0d\u4f1a\u4fdd\u7559\u5728\u8fd0\u884c\u65f6\u751f\u6210\u7684\u6570\u636e":55,"\u5bb9\u5668\u8fd0\u884c\u90fd\u8fd0\u884c":57,"\u5bbf\u4e3b\u673a\u662flinux":39,"\u5bbf\u4e3b\u673a\u7684c":[38,39],"\u5bbf\u4e3b\u673a\u76ee\u5f55":55,"\u5bc4\u5b58\u5668\u4f7f\u7528\u60c5\u51b5\u548c\u5171\u4eab\u5185\u5b58\u4f7f\u7528\u60c5\u51b5\u80fd\u8ba9\u6211\u4eec\u5bf9gpu\u7684\u6574\u4f53\u4f7f\u7528\u6709\u66f4\u597d\u7684\u7406\u89e3":49,"\u5bf9":41,"\u5bf9\u4e00\u4e2a5\u7ef4\u975e\u5e8f\u5217\u7684\u7a00\u758f01\u5411\u91cf":[3,36],"\u5bf9\u4e00\u4e2a5\u7ef4\u975e\u5e8f\u5217\u7684\u7a00\u758f\u6d6e\u70b9\u5411\u91cf":[3,36],"\u5bf9\u4e8e":44,"\u5bf9\u4e8e\u4e0d\u540c\u8bed\u8a00":26,"\u5bf9\u4e8e\u4e24\u79cd\u4e0d\u540c\u7684\u8f93\u5165\u6570\u636e\u7c7b\u578b":41,"\u5bf9\u4e8e\u5185\u5b58\u8f83\u5c0f\u7684\u673a\u5668":3,"\u5bf9\u4e8e\u5355\u5c42rnn":41,"\u5bf9\u4e8e\u5355\u5c42rnn\u7684\u6570\u636e\u4e00\u5171\u6709\u4e24\u4e2a\u6837\u672c":41,"\u5bf9\u4e8e\u53cc\u5c42rnn":41,"\u5bf9\u4e8e\u540c\u4e00\u6bb5c":26,"\u5bf9\u4e8e\u540c\u6837\u7684\u6570\u636e":41,"\u5bf9\u4e8e\u591a\u8bed\u8a00\u63a5\u53e3":26,"\u5bf9\u4e8e\u5927\u591a\u6570\u8bed\u8a00":26,"\u5bf9\u4e8e\u6211\u4eec\u652f\u6301\u7684\u5168\u90e8\u77e9\u9635\u64cd\u4f5c":46,"\u5bf9\u4e8e\u6811\u7684\u6bcf\u4e00\u5c42":70,"\u5bf9\u4e8e\u6bb5\u843d\u7684\u6587\u672c\u5206\u7c7b":41,"\u5bf9\u4e8e\u6bcf\u4e00\u4e2a\u5355\u5c42rnn\u7684\u6570\u636e":41,"\u5bf9\u4e8e\u6bcf\u4f4d\u7528\u6237":67,"\u5bf9\u4e8e\u6bcf\u79cd\u7c7b\u578b":27,"\u5bf9\u4e8e\u6bcf\u79cdc":27,"\u5bf9\u4e8e\u7ed9\u5b9a\u7684\u4e00\u6761\u6587\u672c":65,"\u5bf9\u4e8e\u914d\u5907\u6709\u6ce8\u610f\u529b\u673a\u5236\u7684\u89e3\u7801\u5668":44,"\u5bf9\u4e8eamazon":65,"\u5bf9\u4e8erasspberri":39,"\u5bf9\u4ee3\u7801\u8fdb\u884c\u6027\u80fd\u5206\u6790":49,"\u5bf9\u5168\u8fde\u63a5\u5c42\u6765\u8bf4":46,"\u5bf9\u56fe\u7247\u8fdb\u884c\u9884\u5904\u7406":62,"\u5bf9\u5df2\u7ecfpush\u5230\u8fdc\u7a0b\u4ed3\u5e93\u7684\u591a\u4e2acommit":45,"\u5bf9\u5e94\u4e00\u4e2a\u5b50\u53e5":43,"\u5bf9\u5e94\u4e00\u4e2a\u8bcd":43,"\u5bf9\u5e94\u4e8e\u5b57\u5178":61,"\u5bf9\u5e94\u7684":3,"\u5bf9\u6570\u636e\u96c6\u8fdb\u884c\u9884\u5904\u7406\u7684\u57fa\u672c\u547d\u4ee4\u662f":70,"\u5bf9\u6574\u4e2a\u65b0\u5411\u91cf\u96c6\u5408\u7684\u6bcf\u4e00\u4e2a\u7ef4\u5ea6\u53d6\u6700\u5927\u503c\u6765\u8868\u793a\u6700\u540e\u7684\u53e5\u5b50":65,"\u5bf9\u6587\u6863\u5904\u7406\u540e\u5f62\u6210\u7684\u5355\u8bcd\u5411\u91cf":69,"\u5bf9\u673a\u5668\u7ffb\u8bd1\u7684\u4eba\u5de5\u8bc4\u4f30\u5de5\u4f5c\u5f88\u5e7f\u6cdb\u4f46\u4e5f\u5f88\u6602\u8d35":70,"\u5bf9\u6bcf\u4e2a\u8f93\u5165":46,"\u5bf9\u6bcf\u4e2a\u8f93\u5165\u4e58\u4e0a\u53d8\u6362\u77e9\u9635":46,"\u5bf9\u6bd4":26,"\u5bf9\u6fc0\u6d3b\u6c42\u5bfc":46,"\u5bf9\u7528\u6237\u6765\u8bf4":3,"\u5bf9\u8bad\u7ec3\u6570\u636e\u8fdb\u884cshuffl":3,"\u5bf9\u8bc4\u5ba1\u610f\u89c1\u4e0d\u540c\u610f\u7684":45,"\u5bf9\u8bc4\u5ba1\u610f\u89c1\u540c\u610f\u4e14\u6309\u5176\u4fee\u6539\u5b8c\u7684":45,"\u5bf9\u8be5\u5411\u91cf\u8fdb\u884c\u975e\u7ebf\u6027\u53d8\u6362":65,"\u5bf9\u8c61":30,"\u5bf9\u8c61\u5b58\u50a8\u4e3a\u6587\u4ef6":67,"\u5bf9\u8f93\u5165\u53c2\u6570\u7684\u5b89\u5168\u6027\u8fdb\u884c\u4e86\u5fc5\u8981\u7684\u5224\u65ad":27,"\u5bf9\u8f93\u51fa\u7684\u5408\u5e76":43,"\u5bf9\u8fd9\u4e2a\u7248\u672c\u7684\u63d0\u4ea4":29,"\u5bf9\u9762":41,"\u5bf9check":3,"\u5bf9sparse_binary_vector\u548csparse_float_vector":[3,36],"\u5bfc\u51fa\u8fd9\u4e9b\u63a5\u53e3":27,"\u5bfc\u81f4\u4e86\u6d6e\u70b9\u6570\u6ea2\u51fa":30,"\u5bfc\u81f4\u53c2\u6570\u6536\u655b\u5230\u4e86\u4e00\u4e9b\u5947\u5f02\u7684\u60c5\u51b5":30,"\u5bfc\u81f4\u53c2\u6570\u7d2f\u52a0":30,"\u5bfc\u81f4\u7f16\u8bd1paddlepaddle\u5931\u8d25":30,"\u5bfc\u81f4\u8bad\u7ec3\u65f6\u95f4\u8fc7\u957f":30,"\u5c01\u88c5\u4e86":49,"\u5c01\u88c5\u8be5\u5c42\u7684python\u63a5\u53e3":46,"\u5c06":[3,29,30,49,65],"\u5c06\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc\u53c2\u6570\u62c6\u5206\u6210\u591a\u4efd":19,"\u5c06\u4e0a\u4e00\u65f6\u95f4\u6b65\u6240\u751f\u6210\u7684\u8bcd\u7684\u5411\u91cf\u6765\u4f5c\u4e3a\u5f53\u524d\u65f6\u95f4\u6b65\u7684\u8f93\u5165":44,"\u5c06\u4ed6\u4eec\u79fb\u52a8\u5230\u76ee\u5f55":67,"\u5c06\u4f1a\u5c06\u7528\u6237\u4f20\u8fdb\u6765\u7684\u914d\u7f6e\u53c2\u6570\u4f20\u9012cmake\u7cfb\u7edf":38,"\u5c06\u4f1a\u81ea\u52a8\u8ba1\u7b97\u51fa\u4e00\u4e2a\u5408\u9002\u7684\u503c":52,"\u5c06\u5176\u8bbe\u7f6e\u6210":30,"\u5c06\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5148\u53d8\u6362\u6210\u5355\u5c42\u65f6\u95f4\u5e8f\u5217\u6570\u636e":41,"\u5c06\u542b\u6709\u5b50\u53e5":43,"\u5c06\u542b\u6709\u8bcd\u8bed\u7684\u53e5\u5b50\u5b9a\u4e49\u4e3a\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":43,"\u5c06\u56fe\u7247\u6309\u7167\u4e0a\u8ff0\u7ed3\u6784\u5b58\u50a8\u597d\u4e4b\u540e":62,"\u5c06\u5728":62,"\u5c06\u5728\u8fd0\u884c\u65f6\u62a5\u9519":50,"\u5c06\u5916\u90e8\u7684\u5b58\u50a8\u670d\u52a1\u5728kubernetes\u4e2d\u63cf\u8ff0\u6210\u4e3a\u7edf\u4e00\u7684\u8d44\u6e90\u5f62\u5f0f":55,"\u5c06\u591a\u53e5\u8bdd\u770b\u6210\u4e00\u4e2a\u6574\u4f53\u540c\u65f6\u4f7f\u7528encoder\u538b\u7f29":41,"\u5c06\u591a\u53f0\u673a\u5668\u7684\u6d4b\u8bd5\u7ed3\u679c\u5408\u5e76":52,"\u5c06\u5927\u91cf\u7684":26,"\u5c06\u5b57\u5178\u7684\u5730\u5740\u4f5c\u4e3aargs\u4f20\u7ed9dataprovid":30,"\u5c06\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u524d\u5411\u548c\u53cd\u5411\u90e8\u5206\u6df7\u5408\u5728\u4e00\u8d77":44,"\u5c06\u6570\u636e\u5904\u7406\u6210\u89c4\u8303\u683c\u5f0f":61,"\u5c06\u6570\u636e\u7ec4\u5408\u6210batch\u8fdb\u884c\u8bad\u7ec3":3,"\u5c06\u6570\u636e\u8f6c\u6362\u4e3apaddle\u7684\u683c\u5f0f":62,"\u5c06\u65b0\u5206\u652f\u7684\u7248\u672c\u6253\u4e0atag":29,"\u5c06\u65b0\u5efa\u7684\u6743\u91cd\u52a0\u5165\u6743\u91cd\u8868":46,"\u5c06\u65e5\u5fd7\u5199\u5165\u6587\u4ef6":67,"\u5c06\u672c\u5730\u7684\u4fee\u6539\u63a8\u9001\u5230":45,"\u5c06\u6837\u672c\u7684\u5730\u5740\u653e\u5165\u53e6\u4e00\u4e2a\u6587\u672c\u6587\u4ef6":3,"\u5c06\u6b64\u76ee\u5f55\u6302\u8f7d\u4e3a\u5bb9\u5668\u7684":57,"\u5c06\u6bcf\u4e2a\u6e90\u8bed\u8a00\u5230\u76ee\u6807\u8bed\u8a00\u7684\u5e73\u884c\u8bed\u6599\u5e93\u6587\u4ef6\u5408\u5e76\u4e3a\u4e00\u4e2a\u6587\u4ef6":70,"\u5c06\u73af\u5883\u53d8\u91cf\u8f6c\u6362\u6210paddle\u7684\u547d\u4ee4\u884c\u53c2\u6570":57,"\u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u6b64\u76ee\u5f55\u91cc":57,"\u5c06\u884c\u4e2d\u7684\u6570\u636e\u8f6c\u6362\u6210\u4e0einput_types\u4e00\u81f4\u7684\u683c\u5f0f":3,"\u5c06\u88ab\u5206\u4e3a":61,"\u5c06\u8bad\u7ec3\u6587\u4ef6\u4e0e\u5207\u5206\u597d\u7684\u6570\u636e\u4e0a\u4f20\u5230\u5171\u4eab\u5b58\u50a8":57,"\u5c06\u8be5\u53e5\u8bdd\u5305\u542b\u7684\u6240\u6709\u5355\u8bcd\u5411\u91cf\u6c42\u5e73\u5747":65,"\u5c06\u8df3\u8fc7\u5206\u53d1\u9636\u6bb5\u76f4\u63a5\u542f\u52a8\u6240\u6709\u8282\u70b9\u7684\u96c6\u7fa4\u4f5c\u4e1a":50,"\u5c06\u8fd9\u79cd\u8de8\u8d8a\u65f6\u95f4\u6b65\u7684\u8fde\u63a5\u7528\u4e00\u4e2a\u7279\u6b8a\u7684\u795e\u7ecf\u7f51\u7edc\u5355\u5143\u5b9e\u73b0":41,"\u5c06\u8fdc\u7a0b\u4ed3\u5e93":45,"\u5c06\u900f\u660e":50,"\u5c06ip\u6392\u5e8f\u751f\u6210\u7684\u5e8f\u53f7\u4f5c\u4e3atrain":57,"\u5c06master\u5206\u652f\u7684\u5408\u5165commit\u6253\u4e0atag":29,"\u5c11\u4e8e5":50,"\u5c1a\u53ef":41,"\u5c31":41,"\u5c31\u4f1a\u751f\u6210\u975e\u5e38\u591a\u7684gener":3,"\u5c31\u53ef\u4ee5\u518d\u8fd0\u884c\u53e6\u4e00\u4e2anginx":33,"\u5c31\u53ef\u4ee5\u5c06\u6570\u636e\u4f20\u9001\u7ed9paddlepaddle\u4e86":3,"\u5c31\u53ef\u4ee5\u5c06\u8fd9\u4e9b\u6587\u4ef6\u6301\u4e45\u5316\u5b58\u50a8":55,"\u5c31\u5f88\u5bb9\u6613\u5bfc\u81f4\u5185\u5b58\u8d85\u9650":30,"\u5c31\u662f":41,"\u5c31\u662f\u6a21\u578b\u7684\u53c2\u6570":31,"\u5c31\u662f\u7528\u4e8e\u5c55\u793a\u4e0a\u8ff0\u5206\u6790\u5de5\u5177\u7684\u7528\u6cd5":49,"\u5c31\u80fd\u591f\u5f88\u65b9\u4fbf\u7684\u5b8c\u6210\u6570\u636e\u4e0b\u8f7d\u548c\u76f8\u5e94\u7684\u9884\u5904\u7406\u5de5\u4f5c":65,"\u5c31\u8fd9\u4e48\u7b80\u5355":33,"\u5c31\u901a\u5e38\u7684gpu\u6027\u80fd\u5206\u6790\u6765\u8bf4":49,"\u5c31\u9700\u8981\u5bf9\u8fd9\u4e2a\u7b2c\u4e09\u65b9\u8bed\u8a00\u589e\u52a0\u4e00\u4e9b\u5b9a\u4e49":26,"\u5c31\u9700\u8981\u9009\u62e9\u4f7f\u7528no":33,"\u5c3a\u5bf8":63,"\u5c40\u90e8\u5173\u8054\u6027\u8d28\u548c\u7a7a\u95f4\u4e0d\u53d8\u6027\u8d28":62,"\u5c42\u540e\u5f97\u5230\u6df1\u5ea6":68,"\u5c42\u548c\u8f93\u5165\u7684\u914d\u7f6e":46,"\u5c42\u6743\u91cd":63,"\u5c42\u6b21\u5316\u7684rnn":43,"\u5c42\u7279\u5f81":63,"\u5c42\u7684\u540d\u79f0\u4e0e":44,"\u5c42\u7684\u5927\u5c0f":46,"\u5c42\u7684\u7279\u5f81":63,"\u5c42\u7684\u7c7b\u578b":46,"\u5c42\u7684\u8f93\u5165":68,"\u5c42\u7684\u8f93\u5165\u548c\u8f93\u51fa\u4f5c\u4e3a\u4e0b\u4e00\u4e2a":68,"\u5c42\u7684\u8f93\u51fa\u88ab\u7528\u4f5c\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684":44,"\u5c42\u7ec4\u6210\u4e00\u5bf9":68,"\u5c45\u7136":41,"\u5c55\u793a\u4e86\u4e00\u79cd\u65b9\u6cd5":70,"\u5c55\u793a\u4e86\u4e0a\u8ff0\u7f51\u7edc\u6a21\u578b\u7684\u8bad\u7ec3\u6548\u679c":65,"\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u6bcf\u4e2a\u7279\u5f81\u6620\u5c04\u5230\u4e00\u4e2a\u5411\u91cf":67,"\u5c5e\u6027":68,"\u5d4c\u5165\u5c42":67,"\u5d4c\u5165\u7279\u5f81\u5b57\u5178":67,"\u5d4c\u5165\u7f16\u53f7\u4f1a\u6839\u636e\u5355\u8bcd\u6392\u5e8f":67,"\u5de5\u4f5c\u6a21\u5f0f":52,"\u5de5\u4f5c\u7a7a\u95f4":50,"\u5de5\u4f5c\u7a7a\u95f4\u4e2d\u7684":50,"\u5de5\u4f5c\u7a7a\u95f4\u6839\u76ee\u5f55":50,"\u5de5\u4f5c\u7a7a\u95f4\u76ee\u5f55\u7684\u5de5\u4f5c\u7a7a\u95f4":50,"\u5de5\u4f5c\u7a7a\u95f4\u914d\u7f6e":50,"\u5de5\u5177":69,"\u5de5\u5177\u4e2d\u7684\u811a\u672c":69,"\u5de5\u5177\u6765\u7ba1\u7406":45,"\u5de5\u5177\u94fe\u7684android":38,"\u5de5\u7a0b\u5e08":66,"\u5de6\u56fe\u6784\u9020\u7f51\u7edc\u6a21\u5757\u7684\u65b9\u5f0f\u88ab\u7528\u4e8e34\u5c42\u7684\u7f51\u7edc\u4e2d":63,"\u5de6\u8fb9\u662f":63,"\u5dee\u8bc4":65,"\u5df2\u6253\u5f00":45,"\u5df2\u7ecf\u5728\u96c6\u7fa4\u63d0\u4ea4\u73af\u5883\u4e2d\u5b8c\u6210\u8bbe\u7f6e":52,"\u5df2\u7ecf\u63d0\u4f9b\u4e86\u811a\u672c\u6765\u5e2e\u52a9\u60a8\u521b\u5efa\u8fd9\u4e24\u4e2a\u6587\u4ef6":50,"\u5e02\u573a":66,"\u5e02\u9762\u4e0a\u5df2\u7ecf\u6709nvidia\u6216\u7b2c\u4e09\u65b9\u63d0\u4f9b\u7684\u4f17\u591a\u5de5\u5177":49,"\u5e0c\u671b\u80fd\u8ba9\u6211\u4eec\u77e5\u6653":67,"\u5e2e\u52a9\u6211\u4eec\u5b8c\u6210\u5bf9\u8f93\u5165\u5e8f\u5217\u7684\u62c6\u5206":43,"\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u63cf\u8ff0\u6bb5\u843d":43,"\u5e2e\u52a9\u6211\u4eec\u6784\u9020\u4e00\u4e9b\u590d\u6742\u7684\u8f93\u5165\u4fe1\u606f":40,"\u5e38\u5e38\u51fa\u73b0":30,"\u5e38\u7528\u4f18\u5316\u7b97\u6cd5\u5305\u62ecmomentum":65,"\u5e38\u89c1\u7684\u53ef\u9009\u5b58\u50a8\u670d\u52a1\u5305\u62ec":55,"\u5e72\u51c0":41,"\u5e73\u53f0\u4e3a\u60f3\u89c2\u6d4b\u8bcd\u5411\u91cf\u7684\u7528\u6237\u63d0\u4f9b\u4e86\u5c06\u4e8c\u8fdb\u5236\u8bcd\u5411\u91cf\u6a21\u578b\u8f6c\u6362\u4e3a\u6587\u672c\u6a21\u578b\u7684\u529f\u80fd":61,"\u5e73\u5747\u6545\u969c\u4fee\u590d\u65f6\u95f4":19,"\u5e73\u5747\u6545\u969c\u7387":19,"\u5e73\u5747\u7279\u5f81\u56fe\u7684\u9ad8\u5ea6\u53ca\u5bbd\u5ea6":62,"\u5e74\u9f84":66,"\u5e74\u9f84\u4ece\u4e0b\u5217\u5217\u8868\u8303\u56f4\u4e2d\u9009\u53d6":66,"\u5e74\u9f84\u548c\u804c\u4e1a":67,"\u5e76\u4e0d\u4fdd\u8bc1":46,"\u5e76\u4e0d\u662f\u4f7f\u7528\u53cc\u5c42rnn\u89e3\u51b3\u5b9e\u9645\u7684\u95ee\u9898":41,"\u5e76\u4e0d\u662fkubernetes\u4e2d\u7684node\u6982\u5ff5":57,"\u5e76\u4e0d\u771f\u6b63\u7684\u548c":41,"\u5e76\u4e14":[3,44],"\u5e76\u4e14\u4f7f\u7528":27,"\u5e76\u4e14\u5185\u5c42\u7684\u5e8f\u5217\u64cd\u4f5c\u4e4b\u95f4\u72ec\u7acb\u65e0\u4f9d\u8d56":41,"\u5e76\u4e14\u5206\u522b\u91cd\u547d\u540d\u6587\u4ef6\u540e\u7f00":70,"\u5e76\u4e14\u52a0\u4e0a\u4e0b\u9762\u7684\u547d\u4ee4\u884c\u53c2\u6570":54,"\u5e76\u4e14\u5305\u62ecunit":45,"\u5e76\u4e14\u53ea\u6709\u4e00\u4e2a\u6743\u91cd":63,"\u5e76\u4e14\u53ef\u80fd\u4f1a\u52a0\u901f\u8bad\u7ec3\u8fc7\u7a0b":30,"\u5e76\u4e14\u540e\u7eed\u4ecd\u5728\u4e0d\u65ad\u6539\u8fdb":31,"\u5e76\u4e14\u542f\u52a8\u8bad\u7ec3":57,"\u5e76\u4e14\u5728\u5185\u5b58\u8db3\u591f\u7684\u60c5\u51b5\u4e0b\u8d8a\u5927\u8d8a\u597d":3,"\u5e76\u4e14\u5728\u5e38\u89c1\u7684\u5e73\u53f0\u4e0a":26,"\u5e76\u4e14\u5728\u968f\u540e\u7684\u8bfb\u53d6\u6570\u636e\u8fc7\u7a0b\u4e2d\u586b\u5145\u8bcd\u8868":65,"\u5e76\u4e14\u5728dataprovider\u4e2d\u5b9e\u73b0\u5982\u4f55\u8bbf\u95ee\u8bad\u7ec3\u6587\u4ef6\u5217\u8868":2,"\u5e76\u4e14\u5b83\u4eec\u7684\u987a\u5e8f\u4e0e":63,"\u5e76\u4e14\u5bf9\u7528\u6237\u7684\u7279\u5f81\u505a\u540c\u6837\u7684\u64cd\u4f5c":67,"\u5e76\u4e14\u5c06\u9884\u5904\u7406\u597d\u7684\u6570\u636e\u96c6\u5b58\u653e\u5728":70,"\u5e76\u4e14\u5c55\u793a\u4e86\u5982\u4f55\u5229\u7528paddlepaddle\u6765\u89e3\u51b3\u4e00\u4e2a\u7ecf\u5178\u7684\u7ebf\u6027\u56de\u5f52\u95ee\u9898":36,"\u5e76\u4e14\u628a\u7cfb\u7edf\u751f\u6210\u7684ca":24,"\u5e76\u4e14\u628a\u7ed3\u679c\u8fd4\u56depfsclient\u7aef":24,"\u5e76\u4e14\u67e5\u8be2paddlepaddle\u5355\u5143\u6d4b\u8bd5\u7684\u65e5\u5fd7":30,"\u5e76\u4e14\u7b2c\u4e8c\u4e2a\u662f\u53cd\u5411lstm":69,"\u5e76\u4e14\u7f16\u8bd1\u80fd\u901a\u8fc7\u4ee3\u7801\u6837\u5f0f\u68c0\u67e5":45,"\u5e76\u4e14\u8ba9\u63a5\u53e3\u8131\u79bb\u5b9e\u73b0\u7ec6\u8282":26,"\u5e76\u4e14\u8f93\u51fa\u4e00\u4e2a":45,"\u5e76\u4e14\u901a\u8fc7\u7ed9\u51fa\u5f53\u524d\u76ee\u6807\u5355\u8bcd\u6765\u9884\u6d4b\u4e0b\u4e00\u4e2a\u76ee\u6807\u5355\u8bcd":70,"\u5e76\u4e14\u96c6\u7fa4\u4f5c\u4e1a\u4e2d\u7684\u6240\u6709\u8282\u70b9\u5c06\u5728\u6b63\u5e38\u60c5\u51b5\u4e0b\u5904\u7406\u5177\u6709\u76f8\u540c\u903b\u8f91\u4ee3\u7801\u7684\u6587\u4ef6":50,"\u5e76\u4e14\u9700\u8981\u91cd\u5199\u57fa\u7c7b\u4e2d\u7684\u4ee5\u4e0b\u51e0\u4e2a\u865a\u51fd\u6570":46,"\u5e76\u4e14softmax\u5c42\u7684\u4e24\u4e2a\u8f93\u5165\u4e5f\u4f7f\u7528\u4e86\u540c\u6837\u7684\u53c2\u6570":30,"\u5e76\u4f20\u5165\u76f8\u5e94\u7684\u547d\u4ee4\u884c\u53c2\u6570\u521d\u59cb\u5316paddlepaddl":5,"\u5e76\u4f7f\u7528":68,"\u5e76\u4f7f\u7528\u4e86dropout":65,"\u5e76\u4f7f\u7528\u8fd9\u4e2a\u795e\u7ecf\u7f51\u7edc\u6765\u5bf9\u56fe\u7247\u8fdb\u884c\u5206\u7c7b":62,"\u5e76\u521b\u5efa\u4e86\u4e00\u4e2a\u65b0\u6587\u4ef6":45,"\u5e76\u521b\u5efaoptim":36,"\u5e76\u5220\u9664":29,"\u5e76\u5220\u9664\u66f4\u65e9\u7684\u5feb\u7167":19,"\u5e76\u52a0\u8f7d\u5176\u4e2d\u7684\u53c2\u6570":19,"\u5e76\u5728\u4e58\u79ef\u7ed3\u679c\u4e0a\u518d\u52a0\u4e0a\u7ef4\u5ea6\u4e3a":46,"\u5e76\u5728\u6700\u5f00\u59cb\u521d\u59cb\u5316\u4e3a\u8d77\u59cb\u8bcd":44,"\u5e76\u5728\u7b14\u8bb0\u672c\u4e0a\u901a\u8fc7ssh\u4e0e\u5176\u8fde\u63a5":33,"\u5e76\u5728\u7c7b\u6784\u5efa\u51fd\u6570\u4e2d\u628a\u5b83\u653e\u5165\u4e00\u4e2a\u7c7b\u6210\u5458\u53d8\u91cf\u91cc":46,"\u5e76\u5728\u96c6\u7fa4\u4e2d\u8fd0\u884c\u591a\u4e2a\u5206\u5e03\u5f0f\u6570\u636e\u5904\u7406\u4efb\u52a1":20,"\u5e76\u5bf9\u76f8\u5e94\u7684\u53c2\u6570\u8c03\u7528":46,"\u5e76\u5c06\u5176\u6295\u5c04\u5230":44,"\u5e76\u5c06\u5b83\u4eec\u6309\u7167\u542f\u53d1\u4ee3\u4ef7":70,"\u5e76\u5c06\u5b83\u4eec\u653e\u5728":70,"\u5e76\u5c06\u6bcf\u8f6e\u7684\u6a21\u578b\u7ed3\u679c\u5b58\u653e\u5728":31,"\u5e76\u5c06c":27,"\u5e76\u5c06develop\u548ctest\u6570\u636e\u5206\u522b\u653e\u5165\u4e0d\u540c\u7684\u6587\u4ef6\u5939":70,"\u5e76\u60f3\u4f7f\u7528gpu\u6765\u8bad\u7ec3\u8bbe\u7f6e\u4e3atru":69,"\u5e76\u628a\u5feb\u7167\u4fdd\u5b58\u5230\u8fd9\u4e2a\u76ee\u5f55\u4e0b":19,"\u5e76\u6307\u5b9abatch":70,"\u5e76\u63d0\u4f9b\u4e86\u7b80\u5355\u7684cache\u529f\u80fd":3,"\u5e76\u6b22\u8fce\u60a8\u6765\u53c2\u4e0e\u8d21\u732e":69,"\u5e76\u6ca1\u6709paddle\u7279\u522b\u9700\u8981\u7684\u7279\u6027":26,"\u5e76\u7531":68,"\u5e76\u7ed9\u51fa\u5206\u7c7b\u7ed3\u679c":62,"\u5e76\u7ed9\u51fa\u7684\u76f8\u5173\u6a21\u578b\u683c\u5f0f\u7684\u5b9a\u4e49":61,"\u5e76\u81ea\u52a8\u7f16\u8bd1\u5bbf\u4e3b\u673a\u7248protoc\u53ef\u6267\u884c\u6587\u4ef6":[38,39],"\u5e76\u88ab\u53cd\u5411\u5904\u7406":68,"\u5e76\u88ab\u5b58\u50a8\u5728\u8bf8\u5982hadoop":20,"\u5e76\u89c2\u5bdf\u7ed3\u679c":49,"\u5e76\u8bb0\u5f55\u5b83\u7684\u7f16\u53f7":45,"\u5e76\u8bbe\u7f6e":[35,50],"\u5e76\u8fdb\u884c\u521d\u59cb\u5316\u64cd\u4f5c":36,"\u5e76\u9002\u5e94github\u7684\u7279\u6027\u505a\u4e86\u4e00\u4e9b\u533a\u522b":29,"\u5e76\u9010\u6e10\u5c55\u793a\u66f4\u52a0\u6df1\u5165\u7684\u529f\u80fd":65,"\u5e8a\u4e0a\u7528\u54c1":41,"\u5e8a\u57ab":41,"\u5e8f\u5217\u4e2d\u542b\u6709\u5143\u7d20\u7684\u6570\u76ee\u540c":40,"\u5e8f\u5217\u5230\u5e8f\u5217":70,"\u5e8f\u5217\u6570\u636e\u662f\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u9762\u5bf9\u7684\u4e00\u79cd\u4e3b\u8981\u8f93\u5165\u6570\u636e\u7c7b\u578b":43,"\u5e8f\u5217\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u6570\u636e\u7c7b\u578b":40,"\u5e8f\u5217\u751f\u6210\u4efb\u52a1\u5927\u591a\u9075\u5faaencod":43,"\u5e8f\u5217\u751f\u6210\u4efb\u52a1\u7684\u8f93\u5165":43,"\u5e8f\u5217\u7684\u5f00\u59cb":70,"\u5e8f\u5217\u7684\u6bcf\u4e2a\u5143\u7d20\u662f\u539f\u6765\u53cc\u5c42\u5e8f\u5217\u6bcf\u4e2asubseq\u5143\u7d20\u7684\u5e73\u5747\u503c":40,"\u5e8f\u5217\u7684\u7ed3\u5c3e":70,"\u5e8f\u5217\u7684\u7ed3\u675f":70,"\u5e93":50,"\u5e93\u7684\u8def\u5f84":50,"\u5e94\u7528\u524d\u5411\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":44,"\u5e94\u7528\u53cd\u5411\u9012\u5f52\u795e\u7ecf\u7f51\u7edc":44,"\u5e94\u7528\u6a21\u578b":65,"\u5e94\u80fd\u53cd\u6620\u5f53\u524dcommit\u7684\u5185\u5bb9":45,"\u5e94\u8be5":41,"\u5e94\u8be5\u4e0e\u5b83\u7684memory\u540d\u5b57\u76f8\u540c":44,"\u5e94\u8be5\u8bf4\u8c22\u8c22":45,"\u5e94\u8be5\u964d\u4f4e\u5b66\u4e60\u7387":30,"\u5e95\u5c42\u8fdb\u7a0b":50,"\u5efa\u7acb\u4e00\u4e2a":45,"\u5efa\u7acb\u4e00\u4e2a\u6d3b\u8dc3\u7684\u5f00\u6e90\u793e\u533a":0,"\u5efa\u8bae":[29,45],"\u5efa\u8bae\u5c06\u5176\u8bbe\u7f6e\u4e3a\u8f83\u5927":50,"\u5efa\u8bae\u5c06\u8be5\u53c2\u6570\u8bbe\u4e3atrue":52,"\u5f00\u53d1\u4e86\u6a21\u578b\u9884\u6d4b\u7684\u6837\u4f8b\u4ee3\u7801":27,"\u5f00\u53d1\u4eba\u5458\u4f7f\u7528":45,"\u5f00\u53d1\u4eba\u5458\u53ef\u4ee5\u5728docker\u5f00\u53d1\u955c\u50cf\u4e2d\u5f00\u53d1paddlepaddl":33,"\u5f00\u53d1\u8005\u4fee\u6539\u81ea\u5df1\u7684\u4ee3\u7801":29,"\u5f00\u53d1\u8005\u7ed9paddlepaddle\u4ee3\u7801\u589e\u52a0\u7684\u6ce8\u91ca\u4ee5pr\u7684\u5f62\u5f0f\u63d0\u4ea4\u5230github\u4e2d":47,"\u5f00\u53d1\u8005fork\u7684\u7248\u672c\u5e93\u4e2d":29,"\u5f00\u53d1\u8005fork\u7684\u7248\u672c\u5e93\u4f7f\u7528":29,"\u5f00\u53d1\u955c\u50cf":[33,45],"\u5f00\u53d1\u955c\u50cf\u5305\u542b\u4e86\u4ee5\u4e0b\u5de5\u5177":33,"\u5f00\u59cb":31,"\u5f00\u59cb\u63d0\u4f9b\u670d\u52a1":19,"\u5f00\u59cb\u6807\u8bb0":44,"\u5f00\u59cb\u8bad\u7ec3\u6a21\u578b":65,"\u5f00\u59cb\u9636\u6bb5":49,"\u5f02\u6b65\u968f\u673a\u68af\u5ea6\u4e0b\u964d":51,"\u5f15\u5165\u4e86\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5f15\u5165lstm\u6a21\u578b\u4e3b\u8981\u662f\u4e3a\u4e86\u514b\u670d\u6d88\u5931\u68af\u5ea6\u7684\u95ee\u9898":69,"\u5f15\u5165paddlepaddle\u7684pydataprovider2\u5305":3,"\u5f15\u53d1":13,"\u5f15\u5bfc\u5c42":44,"\u5f15\u7528":50,"\u5f15\u7528memory\u5f97\u5230\u8fd9layer\u4e0a\u4e00\u65f6\u523b\u8f93\u51fa":43,"\u5f3a\u70c8\u63a8\u8350":41,"\u5f3a\u70c8\u63a8\u8350\u4f7f\u7528virtualenv\u6765\u521b\u9020\u4e00\u4e2a\u5e72\u51c0\u7684python\u73af\u5883":67,"\u5f52\u4e00\u5316\u6982\u7387\u5411\u91cf":44,"\u5f53":54,"\u5f53\u4f20\u9012\u76f8\u540c\u7684\u6837\u672c\u6570\u65f6":69,"\u5f53\u4f60\u6267\u884c\u547d\u4ee4":46,"\u5f53\u51fd\u6570\u8fd4\u56de\u7684\u65f6\u5019":3,"\u5f53\u524d\u5355\u8bcd\u5728\u76f8\u6bd4\u4e4b\u4e0b\u603b\u662f\u88ab\u5f53\u4f5c\u771f\u503c":70,"\u5f53\u524d\u5355\u8bcd\u662f\u89e3\u7801\u5668\u6700\u540e\u4e00\u6b65\u7684\u8f93\u51fa":70,"\u5f53\u524d\u65f6\u95f4\u6b65\u5904\u7684memory\u7684\u8f93\u51fa\u4f5c\u4e3a\u4e0b\u4e00\u65f6\u95f4\u6b65memory\u7684\u8f93\u5165":44,"\u5f53\u524d\u7684\u5b9e\u73b0\u65b9\u5f0f\u4e0b":46,"\u5f53\u524d\u7684\u8f93\u5165y\u548c\u4e0a\u4e00\u4e2a\u65f6\u95f4\u6b65\u7684\u8f93\u51farnn_state\u505a\u4e86\u4e00\u4e2a\u5168\u94fe\u63a5":41,"\u5f53\u524d\u8bc4\u4f30\u4e2d":70,"\u5f53\u524dbatch\u7684cost":70,"\u5f53\u524dlog_period\u4e2abatch\u6240\u6709\u6837\u672c\u7684\u5e73\u5747\u5206\u7c7b\u9519\u8bef\u7387":65,"\u5f53\u524dlog_period\u4e2abatch\u6240\u6709\u6837\u672c\u7684\u5e73\u5747cost":65,"\u5f53\u529f\u80fd\u5206\u652f\u5f00\u53d1\u5b8c\u6bd5\u540e":29,"\u5f53\u5728\u7f51\u7edc\u5c42\u914d\u7f6e\u4e2d\u8bbe\u7f6e":52,"\u5f53\u5728\u8bad\u7ec3\u914d\u7f6e\u4e2d\u8bbe\u7f6e":52,"\u5f53\u5bb9\u5668\u56e0\u4e3a\u5404\u79cd\u539f\u56e0\u88ab\u9500\u6bc1\u65f6":55,"\u5f53\u6240\u6709pod\u90fd\u5904\u4e8erunning\u72b6\u6001":57,"\u5f53\u6a21\u578b\u53c2\u6570\u4e0d\u5b58\u5728\u65f6":52,"\u5f53\u6a21\u578b\u8bad\u7ec3\u597d\u4e86\u4e4b\u540e":65,"\u5f53\u6a21\u5f0f\u4e3a":52,"\u5f53\u7136":49,"\u5f53\u7528\u6237\u4f7f\u7528\u5b8c\u8fd9\u4e2a\u53c2\u6570\u540e":27,"\u5f53\u7f51\u7edc\u5c42\u7528\u4e00\u4e2a\u6279\u6b21\u505a\u8bad\u7ec3\u65f6":46,"\u5f53\u89e3\u8bfb\u6bcf\u4e00\u4e2a":44,"\u5f53\u8bad\u7ec3\u6570\u636e\u975e\u5e38\u591a\u65f6":3,"\u5f53\u8d85\u8fc7\u8be5\u9608\u503c\u65f6":52,"\u5f53\u8f93\u5165\u662f\u7ef4\u5ea6\u5f88\u9ad8\u7684\u7a00\u758f\u6570\u636e\u65f6":54,"\u5f53\u9700\u8981\u5feb\u901f\u6216\u8005\u9891\u7e41\u7684\u8bc4\u4f30\u65f6":70,"\u5f53classif":70,"\u5f53destination\u6587\u4ef6\u4e0d\u5b58\u5728\u6216\u8005\u5927\u5c0f\u548csource\u6587\u4ef6\u4e0d\u4e00\u81f4\u65f6":24,"\u5f62\u6210recurr":43,"\u5f62\u6210recurrent\u8fde\u63a5":43,"\u5f62\u72b6":63,"\u5f88":[41,65],"\u5f88\u591a":41,"\u5f88\u591a\u5f00\u53d1\u8005\u4f1a\u4f7f\u7528\u8fdc\u7a0b\u7684\u5b89\u88c5\u6709gpu\u7684\u670d\u52a1\u5668\u5de5\u4f5c":33,"\u5f88\u5b89\u9759":41,"\u5f88\u5e72\u51c0":41,"\u5f88\u65b9\u4fbf":41,"\u5f88\u6709\u53ef\u80fd\u5b9e\u9645\u5e94\u7528\u5c31\u662f\u6ca1\u6709\u6309\u7167\u60a8\u7684\u9884\u671f\u60c5\u51b5\u8fd0\u884c":49,"\u5f88\u9002\u5408\u6784\u5efa\u7528\u4e8e\u7406\u89e3\u56fe\u7247\u5185\u5bb9\u7684\u6a21\u578b":62,"\u5f88\u96be\u4fdd\u8bc1\u591a\u8bed\u8a00\u4ee3\u7801\u98ce\u683c\u7684\u4e00\u81f4\u6027":26,"\u5f88\u96be\u6574\u4f53\u4fee\u6b63":3,"\u5f8b\u5e08":66,"\u5f97":41,"\u5f97\u4f7f\u7528":26,"\u5f97\u5230\u53e5\u5b50\u7684\u8868\u793a":65,"\u5f97\u5230\u6700\u597d\u8f6e\u6b21\u4e0b\u7684\u6a21\u578b":67,"\u5faa\u73af\u5c55\u5f00\u7684\u6bcf\u4e2a\u65f6\u95f4\u6b65\u603b\u662f\u80fd\u591f\u5f15\u7528\u6240\u6709\u8f93\u5165":43,"\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u4e2d":44,"\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u4f5c\u4e3a\u4f7f\u7528":44,"\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u548c":44,"\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u5728\u6bcf\u4e2a\u65f6\u95f4\u6b65\u9aa4\u987a\u5e8f\u5730\u5904\u7406\u5e8f\u5217":44,"\u5faa\u73af\u7f51\u7edc\u4ece":44,"\u5fc5\u8981":27,"\u5fc5\u987b":46,"\u5fc5\u987b\u4e00\u81f4":3,"\u5fc5\u987b\u4f7f\u7528python\u5173\u952e\u8bcd":3,"\u5fc5\u987b\u5c06\u524d\u4e00\u4e2a\u5b50\u53e5\u7684\u6700\u540e\u4e00\u4e2a\u5143\u7d20":41,"\u5fc5\u987b\u6307\u5411\u4e00\u4e2apaddlepaddle\u5b9a\u4e49\u7684lay":43,"\u5fc5\u987b\u662f\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":43,"\u5fc5\u987b\u662f\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":40,"\u5fc5\u987b\u7531\u53ea\u8bfbmemory\u7684":44,"\u5fc5\u987b\u8bbe\u7f6e\u4e3a":38,"\u5fc5\u987b\u914d\u7f6e\u4e3a":39,"\u5fd8\u8bb0\u95e8\u548c\u8f93\u51fa\u95e8":69,"\u5feb":[41,69],"\u5feb\u901f\u5165\u95e8":64,"\u5feb\u901f\u5728\u672c\u5730\u542f\u52a8\u4e00\u4e2a\u5355\u673a\u7684kubernetes\u670d\u52a1\u5668":55,"\u5feb\u901f\u90e8\u7f72\u96c6\u7fa4":55,"\u6027\u4ef7\u6bd4":41,"\u6027\u522b":[66,67],"\u6027\u80fd\u5206\u6790":49,"\u6027\u80fd\u5206\u6790\u5de5\u5177\u662f\u7528\u4e8e\u7ed9\u5e94\u7528\u7a0b\u5e8f\u7684\u6027\u80fd\u505a\u5b9a\u91cf\u5206\u6790\u7684":49,"\u6027\u80fd\u5206\u6790\u662f\u6027\u80fd\u4f18\u5316\u7684\u5173\u952e\u4e00\u6b65":49,"\u6027\u80fd\u8c03\u4f18":51,"\u603b\u4f53\u6765\u8bf4":41,"\u603b\u8ba1\u7684\u53c2\u6570\u4e2a\u6570":61,"\u603b\u8bc4\u520610\u5206":69,"\u6050\u6016\u7247":66,"\u60a8\u4f1a\u5728\u63a5\u4e0b\u6765\u7684\u90e8\u5206\u4e2d\u83b7\u5f97\u66f4\u591a\u7684\u7ec6\u8282\u4ecb\u7ecd":49,"\u60a8\u53ef\u4ee5\u4efb\u610f\u4f7f\u7528\u4e00\u4e2a\u6216\u4e24\u4e2a\u6765\u5bf9\u611f\u5174\u8da3\u7684\u4ee3\u7801\u6bb5\u505a\u6027\u80fd\u5206\u6790":49,"\u60a8\u53ef\u4ee5\u5bfc\u5165":49,"\u60a8\u53ef\u4ee5\u91c7\u7528\u4e0b\u9762\u4e94\u4e2a\u6b65\u9aa4":49,"\u60a8\u5c06\u4e86\u89e3\u5982\u4f55":44,"\u60a8\u5c31\u80fd\u83b7\u5f97\u5982\u4e0b\u7684\u5206\u6790\u7ed3\u679c":49,"\u60a8\u6309\u5982\u4e0b\u6b65\u9aa4\u64cd\u4f5c\u5373\u53ef":49,"\u60a8\u6700\u597d\u5148\u786e\u8ba4":49,"\u60a8\u9700\u8981\u66f4\u6539":33,"\u60a8\u9996\u5148\u9700\u8981\u5728\u76f8\u5173\u4ee3\u7801\u6bb5\u4e2d\u52a0\u5165":49,"\u60ac\u7591\u7247":66,"\u60c5\u6001\u52a8\u8bcd":68,"\u60c5\u611f\u5206\u6790":[29,64],"\u60c5\u611f\u5206\u6790\u4e5f\u5e38\u7528\u4e8e\u57fa\u4e8e\u5927\u91cf\u8bc4\u8bba\u548c\u4e2a\u4eba\u535a\u5ba2\u6765\u76d1\u63a7\u793e\u4f1a\u5a92\u4f53":69,"\u60c5\u611f\u5206\u6790\u662f\u81ea\u7136\u8bed\u8a00\u7406\u89e3\u4e2d\u6700\u5178\u578b\u7684\u95ee\u9898\u4e4b\u4e00":69,"\u60c5\u611f\u5206\u6790\u6709\u8bb8\u591a\u5e94\u7528\u573a\u666f":69,"\u60ca\u9669\u7535\u5f71":66,"\u60f3\u4e86\u89e3\u66f4\u591a\u7ec6\u8282\u53ef\u4ee5\u53c2\u8003pydataprovider\u90e8\u5206\u7684\u6587\u6863":69,"\u610f\u5473\u7740\u4e0d\u540c\u65f6\u95f4\u6b65\u7684\u8f93\u5165\u90fd\u662f\u76f8\u540c\u7684\u503c":44,"\u610f\u601d\u662f\u4e0d\u4f7f\u7528\u5e73\u5747\u53c2\u6570\u6267\u884c\u6d4b\u8bd5":52,"\u610f\u601d\u662f\u4e0d\u4fdd\u5b58\u7ed3\u679c":52,"\u610f\u601d\u662f\u4f7f\u7528\u7b2ctest":52,"\u610f\u601d\u662f\u5728gpu\u6a21\u5f0f\u4e0b\u4f7f\u75284\u4e2agpu":52,"\u611f\u89c9":41,"\u620f\u5267":66,"\u6210\u529f\u8bad\u7ec3\u4e14\u9000\u51fa\u7684pod\u6570\u76ee\u4e3a3\u65f6":57,"\u6211\u4eec\u4e0d\u80fd\u901a\u8fc7\u5e38\u89c4\u7684\u68af\u5ea6\u68c0\u67e5\u7684\u65b9\u5f0f\u6765\u8ba1\u7b97\u68af\u5ea6":46,"\u6211\u4eec\u4e3b\u8981\u4f1a\u4ecb\u7ecdnvprof\u548cnvvp":49,"\u6211\u4eec\u4e5f\u53ef\u4ee5\u786e\u5b9a\u6bcf\u4e00\u4e2a\u53c2\u6570\u7684\u7c7b\u578b":27,"\u6211\u4eec\u4ec5\u4ec5\u5bf9\u795e\u7ecf\u7f51\u7edc\u7684\u8f93\u5165\u8fdb\u884c\u4e86\u63cf\u8ff0":36,"\u6211\u4eec\u4ec5\u4ec5\u662f\u5c06\u6bcf\u4e2a\u7279\u5f81\u79cd\u7c7b\u6620\u5c04\u5230\u4e00\u4e2a\u7279\u5f81\u5411\u91cf\u4e2d":67,"\u6211\u4eec\u4ec5\u6709\u4e00\u4e2a\u8f93\u5165":46,"\u6211\u4eec\u4ec5\u7528":67,"\u6211\u4eec\u4ecb\u7ecd\u5982\u4f55\u5728":56,"\u6211\u4eec\u4ecb\u7ecd\u5982\u4f55\u5728kubernetes\u96c6\u7fa4\u4e0a\u8fdb\u884c\u5206\u5e03\u5f0fpaddlepaddle\u8bad\u7ec3\u4f5c\u4e1a":57,"\u6211\u4eec\u4ece\u63d0\u524d\u7ed9\u5b9a\u7684\u7c7b\u522b\u96c6\u5408\u4e2d\u9009\u62e9\u5176\u6240\u5c5e\u7c7b\u522b":65,"\u6211\u4eec\u4ee5mnist\u624b\u5199\u8bc6\u522b\u4e3a\u4f8b":3,"\u6211\u4eec\u4f1a\u53d1\u73b0\u6570\u636e\u96c6":70,"\u6211\u4eec\u4f1a\u5728":33,"\u6211\u4eec\u4f1a\u7ee7\u7eed\u4f7f\u7528\u73b0\u6709\u7684\u5185\u5b58\u5757":46,"\u6211\u4eec\u4f1a\u91cd\u65b0\u5206\u914d\u5185\u5b58":46,"\u6211\u4eec\u4f7f\u7528":[46,50,69],"\u6211\u4eec\u4f7f\u7528\u4e0d\u540c\u7684layer\u8fdb\u884c\u7ec4\u5408":36,"\u6211\u4eec\u4f7f\u7528\u4e86":41,"\u6211\u4eec\u4f7f\u7528\u4e86\u4e00\u4e2a\u7f16\u89e3\u7801\u6a21\u578b\u7684\u6269\u5c55":70,"\u6211\u4eec\u4f7f\u7528\u4e86\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":69,"\u6211\u4eec\u4f7f\u7528\u5176\u4e2d\u7684":70,"\u6211\u4eec\u4f7f\u7528\u52a8\u6001\u5e93\u6765\u5206\u53d1paddl":26,"\u6211\u4eec\u4f7f\u7528\u6700\u5927\u6982\u7387\u7684\u6807\u7b7e\u4f5c\u4e3a\u7ed3\u679c":68,"\u6211\u4eec\u4f7f\u7528\u96c6\u675f\u641c\u7d22":70,"\u6211\u4eec\u4f7f\u7528paddlepaddle\u5728ilsvrc\u7684\u9a8c\u8bc1\u96c6\u517150":63,"\u6211\u4eec\u5047\u8bbe\u4e00\u53f0\u673a\u5668\u4e0a\u67094\u4e2agpu":54,"\u6211\u4eec\u5047\u8bbe\u623f\u4ea7\u7684\u4ef7\u683c":31,"\u6211\u4eec\u5148\u4ece\u4e00\u6761\u968f\u673a\u7684\u76f4\u7ebf":31,"\u6211\u4eec\u51c6\u5907\u4e86\u4e00\u4e2a\u811a\u672c":62,"\u6211\u4eec\u5373\u53ef\u5b8c\u6210\u795e\u7ecf\u7f51\u7edc\u7684\u642d\u5efa":36,"\u6211\u4eec\u53ea\u4f7f\u7528\u5df2\u7ecf\u6807\u6ce8\u8fc7\u7684\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6":69,"\u6211\u4eec\u53ea\u6240\u4ee5\u4f7f\u7528lstm\u6765\u6267\u884c\u8fd9\u4e2a\u4efb\u52a1\u662f\u56e0\u4e3a\u5176\u6539\u8fdb\u7684\u8bbe\u8ba1\u5e76\u4e14\u5177\u6709\u95e8\u673a\u5236":69,"\u6211\u4eec\u53ea\u6f14\u793a\u4e00\u4e2a\u5355\u673a\u4f5c\u4e1a":56,"\u6211\u4eec\u53ea\u9700\u8981\u4f7f\u7528lstm":41,"\u6211\u4eec\u53ea\u9700\u8981\u8fd0\u884c":65,"\u6211\u4eec\u53ea\u9700\u8981\u8fd0\u884c\u4e0b\u9762\u547d\u4ee4\u628a\u7f16\u8bd1\u597d\u7684paddlepaddle\u6253\u5305\u6210\u4e00\u4e2a":45,"\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528":62,"\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5176\u4ed6layer\u8fdb\u884c\u7ec4\u5408":36,"\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5b83\u6765\u751f\u6210\u5e8f\u5217":44,"\u6211\u4eec\u53ef\u4ee5\u521b\u5efatrainer\u6765\u5bf9\u7f51\u7edc\u8fdb\u884c\u8bad\u7ec3":36,"\u6211\u4eec\u53ef\u4ee5\u5728":45,"\u6211\u4eec\u53ef\u4ee5\u5b9a\u4e49\u5982\u4e0b\u7684layer\u7ec4\u5408":36,"\u6211\u4eec\u53ef\u4ee5\u5b9a\u4e49\u5982\u4e0blayer\u6765\u63cf\u8ff0\u795e\u7ecf\u7f51\u7edc\u7684\u8f93\u5165":36,"\u6211\u4eec\u53ef\u4ee5\u5c06":50,"\u6211\u4eec\u53ef\u4ee5\u6309\u7167\u5982\u4e0b\u5c42\u6b21\u5b9a\u4e49\u975e\u5e8f\u5217":40,"\u6211\u4eec\u53ef\u4ee5\u751f\u6210":67,"\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u89c2\u5bdf\u6a21\u578b\u7684\u53c2\u6570\u662f\u5426\u7b26\u5408\u9884\u671f\u6765\u8fdb\u884c\u68c0\u9a8c":31,"\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u5728\u76ee\u5f55":69,"\u6211\u4eec\u53ef\u4ee5\u8bbe\u8ba1\u642d\u5efa\u4e00\u4e2a\u7075\u6d3b\u7684":43,"\u6211\u4eec\u53ef\u4ee5\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\u6765\u505ableu\u8bc4\u4f30":70,"\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u6765\u8bad\u7ec3\u6a21\u578b":70,"\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u6765\u8fdb\u884c\u4ece\u6cd5\u8bed\u5230\u82f1\u8bed\u7684\u6587\u672c\u7ffb\u8bd1":70,"\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5982\u4e0b\u547d\u4ee4\u8fdb\u884c\u9884\u5904\u7406\u5de5\u4f5c":62,"\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u65e5\u5fd7\u67e5\u770b\u5bb9\u5668\u8bad\u7ec3\u7684\u60c5\u51b5":57,"\u6211\u4eec\u57285\u5929\u91cc\u8bad\u7ec3\u4e8616\u4e2apass":70,"\u6211\u4eec\u5728\u51fd\u6570\u7684\u7ed3\u5c3e\u8fd4\u56de":44,"\u6211\u4eec\u5728\u62e5\u670950\u4e2a\u8282\u70b9\u7684\u96c6\u7fa4\u4e2d\u8bad\u7ec3\u6a21\u578b":70,"\u6211\u4eec\u5728\u8bad\u7ec3\u4e4b\u524d\u9700\u8981\u5e38\u89c1\u4e00\u4e2a\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":70,"\u6211\u4eec\u5728initialzier\u51fd\u6570\u91cc\u521d\u59cb\u5316\u8bcd\u8868":65,"\u6211\u4eec\u5bf9\u6a21\u578b\u8fdb\u884c\u4e86\u4ee5\u4e0b\u66f4\u6539":44,"\u6211\u4eec\u5c06":[57,67],"\u6211\u4eec\u5c06\u4e00\u6bb5\u8bdd\u770b\u6210\u53e5\u5b50\u7684\u6570\u7ec4":41,"\u6211\u4eec\u5c06\u4ecb\u7ecd\u5982\u4f55\u542f\u52a8\u5206\u5e03\u5f0f\u8bad\u7ec3\u4f5c\u4e1a":56,"\u6211\u4eec\u5c06\u4ee5":[50,65],"\u6211\u4eec\u5c06\u4ee5\u6700\u57fa\u672c\u7684\u903b\u8f91\u56de\u5f52\u7f51\u7edc\u4f5c\u4e3a\u8d77\u70b9":65,"\u6211\u4eec\u5c06\u4f7f\u7528":44,"\u6211\u4eec\u5c06\u4f7f\u7528\u7b80\u5355\u7684":44,"\u6211\u4eec\u5c06\u4f7f\u7528cifar":62,"\u6211\u4eec\u5c06\u539f\u59cb\u6570\u636e\u7684\u6bcf\u4e00\u7ec4":41,"\u6211\u4eec\u5c06\u5728\u540e\u9762\u4ecb\u7ecd\u8bad\u7ec3\u548c\u9884\u6d4b\u6d41\u7a0b\u7684\u811a\u672c":65,"\u6211\u4eec\u5c06\u5b83\u4eec\u5212\u5206\u4e3a\u4e0d\u540c\u7684\u7c7b\u522b":51,"\u6211\u4eec\u5c06\u5bf9\u8fd9\u4e24\u4e2a\u6b65\u9aa4\u7ed9\u51fa\u4e86\u8be6\u7ec6\u7684\u89e3\u91ca":65,"\u6211\u4eec\u5c06\u653e\u7f6e\u4f9d\u8d56\u5e93":50,"\u6211\u4eec\u5c06\u8bc4\u5206\u5206\u6210\u4e24\u90e8\u5206":67,"\u6211\u4eec\u5c06\u9610\u91ca\u5982\u4f55\u5728\u96c6\u7fa4\u4e0a\u8fd0\u884c\u5206\u5e03\u5f0f":50,"\u6211\u4eec\u5c31\u53ef\u4ee5\u7740\u624b\u5bf9\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u4e86":62,"\u6211\u4eec\u5c31\u53ef\u4ee5\u8bad\u7ec3\u6a21\u578b\u4e86":65,"\u6211\u4eec\u5c31\u53ef\u4ee5\u8fdb\u884c\u9884\u6d4b\u4e86":65,"\u6211\u4eec\u5c31\u5b8c\u6210\u4e86\u4e00\u6b21\u4ee3\u7801\u8d21\u732e\u7684\u8fc7\u7a0b":45,"\u6211\u4eec\u5c55\u793a\u5982\u4f55\u7528paddlepaddle\u89e3\u51b3":31,"\u6211\u4eec\u5df2\u7ecf\u5b9e\u73b0\u4e86\u5927\u591a\u6570\u5e38\u7528\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u67b6\u6784":44,"\u6211\u4eec\u5e0c\u671b\u80fd\u5728\u8fd9\u4e2a\u57fa\u7840\u4e0a\u4e0d\u65ad\u7684\u6539\u8fdb":0,"\u6211\u4eec\u5e0c\u671b\u80fd\u591f\u68c0\u9a8c\u6a21\u578b\u7684\u597d\u574f":31,"\u6211\u4eec\u5e94\u5f53\u4f1a\u5f97\u5230\u4e00\u4e2a\u540d\u4e3acifar":62,"\u6211\u4eec\u5efa\u8bae\u4f60\u4e3a\u4f60\u7684python\u5c01\u88c5\u5b9e\u73b0\u4e00\u4e2a":46,"\u6211\u4eec\u5efa\u8bae\u4f60\u5728\u5199\u65b0\u7f51\u7edc\u5c42\u65f6\u628a\u6d4b\u8bd5\u4ee3\u7801\u653e\u5165\u65b0\u7684\u6587\u4ef6\u4e2d":46,"\u6211\u4eec\u603b\u7ed3\u4e86\u5404\u4e2a\u7f51\u7edc\u7684\u590d\u6742\u5ea6\u548c\u6548\u679c":65,"\u6211\u4eec\u611f\u8c22":70,"\u6211\u4eec\u628apaddlepaddle\u7684\u7f16\u8bd1\u73af\u5883\u6253\u5305\u6210\u4e00\u4e2a\u955c\u50cf":33,"\u6211\u4eec\u63a8\u8350\u4f7f\u7528":33,"\u6211\u4eec\u63a8\u8350\u4f7f\u7528\u6700\u65b0\u7248\u672c\u7684cudnn":32,"\u6211\u4eec\u63a8\u8350\u4f7f\u7528docker\u955c\u50cf\u6765\u90e8\u7f72\u73af\u5883":34,"\u6211\u4eec\u63d0\u4f9b\u4e24\u4e2a\u7f51\u7edc":69,"\u6211\u4eec\u63d0\u4f9b\u4e24\u4e2a\u8f6c\u6362\u65b9\u5f0f":20,"\u6211\u4eec\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6570\u636e\u9884\u5904\u7406\u811a\u672c":69,"\u6211\u4eec\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6784\u5efa\u811a\u672cbuild_doc":47,"\u6211\u4eec\u63d0\u4f9b\u4e86\u4e00\u4e2a\u793a\u4f8b\u811a\u672c":63,"\u6211\u4eec\u63d0\u4f9b\u4e86\u56fd\u5185\u7684\u955c\u50cf\u670d\u52a1\u5668\u4f9b\u5927\u5bb6\u4f7f\u7528":33,"\u6211\u4eec\u63d0\u4f9b\u4e86\u811a\u672c\u6765\u6784\u5efa\u5b57\u5178\u548c\u9884\u5904\u7406\u6570\u6910":69,"\u6211\u4eec\u63d0\u4f9b\u4e86c":63,"\u6211\u4eec\u63d0\u4f9b\u53ef\u4ee5\u76f4\u63a5\u8fd0\u884cpaddlepaddl":33,"\u6211\u4eec\u63d0\u51fa\u4e86chunk\u7684\u6982\u5ff5":24,"\u6211\u4eec\u662f\u5bf9\u6bcf\u4e00\u4e2a\u5b50\u5e8f\u5217\u53d6\u6700\u540e\u4e00\u4e2a\u5143\u7d20":41,"\u6211\u4eec\u6700\u7ec8\u7684\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165python\u6216\u8005\u5176\u4ed6\u4efb\u4f55\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u6211\u4eec\u6709\u4e00\u4e2a\u5e8f\u5217\u4f5c\u4e3a\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u72b6\u6001":44,"\u6211\u4eec\u7528":66,"\u6211\u4eec\u7528\u4ee5\u4e0b\u7684\u4e00\u4e9b":67,"\u6211\u4eec\u7528\u7f16\u53f7\u4f5c\u4e3akei":67,"\u6211\u4eec\u7528paddlepaddle\u89e3\u51b3\u4e86\u5355\u53d8\u91cf\u7ebf\u6027\u56de\u5f52\u95ee\u9898":31,"\u6211\u4eec\u7684\u5b57\u5178\u4f7f\u7528\u5185\u90e8\u7684\u5206\u8bcd\u5de5\u5177\u5bf9\u767e\u5ea6\u77e5\u9053\u548c\u767e\u5ea6\u767e\u79d1\u7684\u8bed\u6599\u8fdb\u884c\u5206\u8bcd\u540e\u4ea7\u751f":61,"\u6211\u4eec\u7684\u6807\u51c6\u5f00\u53d1\u6d41\u7a0b\u662f\u628a\u8fd9\u4e9b\u5de5\u5177\u90fd\u88c5\u8fdb\u4e00\u4e2adocker":45,"\u6211\u4eec\u7684\u8bad\u7ec3\u66f2\u7ebf\u5982\u4e0b":68,"\u6211\u4eec\u770b\u4e00\u4e0b\u5355\u5c42rnn\u7684\u914d\u7f6e":41,"\u6211\u4eec\u770b\u4e00\u4e0b\u8bed\u4e49\u76f8\u540c\u7684\u53cc\u5c42rnn\u7684\u7f51\u7edc\u914d\u7f6e":41,"\u6211\u4eec\u771f\u8bda\u5730\u611f\u8c22\u60a8\u7684\u5173\u6ce8":69,"\u6211\u4eec\u771f\u8bda\u5730\u611f\u8c22\u60a8\u7684\u8d21\u732e":45,"\u6211\u4eec\u79f0\u4e4b\u4e3a\u4e00\u4e2a0\u5c42\u7684\u5e8f\u5217":40,"\u6211\u4eec\u8bbe\u8ba1\u8bf4\u660e\u4e86\u540d\u4e3afilemanager\u7cfb\u7edf":24,"\u6211\u4eec\u8fd8\u53ef\u4ee5\u767b\u5f55\u5230\u5bbf\u4e3b\u673a\u4e0a\u67e5\u770b\u8bad\u7ec3\u7ed3\u679c":56,"\u6211\u4eec\u8fd8\u5c06\u7f16\u7801\u5411\u91cf\u6295\u5c04\u5230":44,"\u6211\u4eec\u9009\u53d6\u5355\u53cc\u5c42\u5e8f\u5217\u914d\u7f6e\u4e2d\u7684\u4e0d\u540c\u90e8\u5206":41,"\u6211\u4eec\u9009\u62e9":20,"\u6211\u4eec\u901a\u5e38\u5728\u6240\u6709\u8282\u70b9\u4e0a\u521b\u5efa\u4e00\u4e2a":50,"\u6211\u4eec\u901a\u5e38\u5c06\u4e00\u53e5\u8bdd\u7406\u89e3\u6210\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217":41,"\u6211\u4eec\u901a\u8fc7\u8bfb\u53d6":57,"\u6211\u4eec\u9075\u5faa":70,"\u6211\u4eec\u90fd\u63d0\u4f9bpython\u7684\u8f6c\u6362\u5e93":20,"\u6211\u4eec\u91c7\u7528\u4e0a\u9762\u7684\u7279\u5f81\u6a21\u677f":68,"\u6211\u4eec\u91c7\u7528\u5355\u5c42lstm\u6a21\u578b":65,"\u6211\u4eec\u91c7\u7528\u7b2c\u4e8c\u4e2a\u65b9\u6cd5":33,"\u6211\u4eec\u9700\u8981\u5148\u521b\u5efa\u4e00\u4e2a\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":62,"\u6211\u4eec\u9700\u8981\u521b\u5efa\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":70,"\u6211\u4eec\u9700\u8981\u5236\u4f5c\u4e00\u4e2a\u5305\u542b\u8bad\u7ec3\u6570\u636e\u7684paddle\u955c\u50cf":56,"\u6211\u4eec\u9700\u8981\u5728\u96c6\u7fa4\u7684\u6240\u6709\u8282\u70b9\u4e0a\u5b89\u88c5":50,"\u6211\u4eec\u9700\u8981\u8ba1\u7b97":46,"\u6211\u4eec\u9700\u8981\u8bbe\u7f6e":67,"\u6211\u4eec\u9700\u8981\u9884\u5904\u7406\u6570\u6910\u5e76\u6784\u5efa\u4e00\u4e2a\u5b57\u5178":69,"\u6211\u4eec\u975e\u5e38\u6b22\u8fce\u60a8\u7528paddlepaddle\u6784\u5efa\u66f4\u597d\u7684\u793a\u4f8b":67,"\u6211\u4eec\u9884\u8bad\u7ec3\u5f97\u52304\u79cd\u4e0d\u540c\u7ef4\u5ea6\u7684\u8bcd\u5411\u91cf":61,"\u6211\u4eec\u9996\u5148\u5904\u7406\u7535\u5f71\u6216\u7528\u6237\u7684\u7279\u5f81\u6587\u4ef6":67,"\u6211\u4eec\u9996\u5148\u9700\u8981\u6839\u636e\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u6765\u521b\u5efa\u6240\u9700\u8981\u4f18\u5316\u7684paramet":36,"\u6211\u4eec\u9ed8\u8ba4\u4f7f\u7528imdb\u7684\u6d4b\u8bd5\u6570\u636e\u96c6\u4f5c\u4e3a\u9a8c\u8bc1":69,"\u6211\u5220\u9664\u4e86":45,"\u6216":[3,49,62,68],"\u6216\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":40,"\u6216\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u53d8\u6210\u4e00\u4e2a0\u5c42\u5e8f\u5217":40,"\u6216\u4e00\u4e2a\u5411\u91cf":43,"\u6216\u4e0d\u786e\u5b9a":66,"\u6216\u5355\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u53d8\u6210\u4e00\u4e2a0\u5c42\u5e8f\u5217":40,"\u6216\u662f\u624b\u52a8\u7f16\u8f91\u751f\u6210":67,"\u6216\u6700\u5927\u503c":40,"\u6216\u6d4b\u8bd5\u6587\u4ef6\u5217\u8868":2,"\u6216\u7b2c\u4e00\u4e2a":40,"\u6216\u7b2c\u4e00\u4e2a\u5143\u7d20":40,"\u6216\u8005":[26,27,30,33,35,40,41,49],"\u6216\u8005\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":43,"\u6216\u8005\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":[40,43],"\u6216\u8005\u4ece\u5de5\u5177\u7684\u754c\u9762\u91cc\u8fd0\u884c\u60a8\u7684\u5e94\u7528":49,"\u6216\u8005\u53cd\u5411\u5730\u4ece":44,"\u6216\u8005\u5728cpu\u6a21\u5f0f\u4e0b\u4f7f\u75284\u4e2a\u7ebf\u7a0b":52,"\u6216\u8005\u5c06\u8fd9\u53f0\u8282\u70b9\u8fc1\u79fb\u5230\u53e6\u4e00\u4e2a\u8282\u70b9\u5e76\u542f\u52a8\u5373\u53ef\u6062\u590d\u8bad\u7ec3\u4efb\u52a1":19,"\u6216\u8005\u5df2\u7ecf\u5728\u96c6\u7fa4\u63d0\u4ea4\u73af\u5883\u4e2d\u81ea\u52a8\u8bbe\u7f6e":51,"\u6216\u8005\u6570\u636e\u5e93\u8fde\u63a5\u8def\u5f84\u7b49":2,"\u6216\u8005\u6570\u7ec4\u7684\u6570\u7ec4\u8fd9\u4e2a\u6982\u5ff5":41,"\u6216\u8005\u662f\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":40,"\u6216\u8005\u662f\u4e00\u4e2a\u5c0f\u7684\u6587\u672c\u7247\u6bb5":69,"\u6216\u8005\u662f\u51fd\u6570\u8c03\u7528\u7684\u9891\u7387\u548c\u8017\u65f6\u7b49":49,"\u6216\u8005\u66f4\u65e9":30,"\u6216\u8005\u6bcf\u4e00\u4e2a\u7cfb\u5217\u91cc\u7684\u7279\u5f81\u6570\u636e":41,"\u6216\u8005\u7528tuple\u8868\u793a\u7684\u591a\u4e2a\u503c":20,"\u6216\u8005\u7531\u5b83\u4eec\u7ec4\u6210\u7684list":20,"\u6216\u8005\u76f4\u63a5\u4f7f\u7528\u4e0b\u9762\u7684shell\u547d\u4ee4":63,"\u6216\u8005\u76f4\u63a5\u6254\u6389\u975e\u5e38\u957f\u7684\u5e8f\u5217":30,"\u6216\u8005\u76f8\u5bf9\u4e8e\u6784\u5efa\u76ee\u5f55\u7684\u76f8\u5bf9\u8def\u5f84":[38,39],"\u6216\u8005\u8f93\u5165\u6570\u636e\u5c3a\u5ea6\u8fc7\u5927":30,"\u6216\u8005\u8fd0\u884c":30,"\u6216\u8005\u91c7\u7528\u5e76\u884c\u8ba1\u7b97\u6765\u52a0\u901f\u67d0\u4e9b\u5c42\u7684\u66f4\u65b0":54,"\u6216\u8005\u9700\u8981\u53d1\u5e03\u60a8\u7684\u5e94\u7528\u7684\u955c\u50cf":33,"\u6216\u8005\u9700\u8981\u66f4\u9ad8\u7684\u6548\u7387":2,"\u6216\u8bbe\u7f6e\u4e3anone":2,"\u6216gpu":52,"\u6216gpu\u4e2a\u6570":69,"\u6218\u4e89\u7247":66,"\u623f":41,"\u623f\u95f4":41,"\u6240\u4ee5":[3,30],"\u6240\u4ee5\u4e00\u822c\u9700\u8981\u5bf9\u8bad\u7ec3\u7528\u7684\u6a21\u578b\u914d\u7f6e\u6587\u4ef6\u7a0d\u4f5c\u76f8\u5e94\u4fee\u6539\u624d\u80fd\u5728\u9884\u6d4b\u65f6\u4f7f\u7528":5,"\u6240\u4ee5\u4f60\u53ea\u7528\u6309\u4e0b\u9762\u7684\u7ed3\u6784\u6765\u7ec4\u7ec7\u6570\u6910\u5c31\u884c\u4e86":69,"\u6240\u4ee5\u505a\u6cd5\u53ef\u4ee5\u6709\u4e24\u79cd":30,"\u6240\u4ee5\u53ef\u4ee5\u5229\u7528\u5982\u4e0b\u65b9\u6cd5\u8bfb\u53d6\u6a21\u578b\u7684\u53c2\u6570":31,"\u6240\u4ee5\u53ef\u4ee5\u7b80\u5316\u5bf9\u73af\u5883\u7684\u8981\u6c42":56,"\u6240\u4ee5\u5728\u5199\u5165\u5feb\u7167\u7684\u8fc7\u7a0b\u4e2d":19,"\u6240\u4ee5\u5916\u5c42\u8f93\u51fa\u7684\u5e8f\u5217\u5f62\u72b6":41,"\u6240\u4ee5\u5982\u679c\u9700\u8981\u81ea\u884c\u914d\u7f6e\u5f00\u53d1\u73af\u5883\u9700\u8981\u8003\u8651\u7248\u672c\u7684\u56e0\u7d20":33,"\u6240\u4ee5\u5b83\u4eec\u4f7f\u7528\u540c\u4e00\u4e2aip\u5730\u5740":55,"\u6240\u4ee5\u5bf9":41,"\u6240\u4ee5\u5f88\u591a\u65f6\u5019\u4f60\u9700\u8981\u505a\u7684\u53ea\u662f\u5b9a\u4e49\u6b63\u786e\u7684\u7f51\u7edc\u5c42\u5e76\u628a\u5b83\u4eec\u8fde\u63a5\u8d77\u6765":31,"\u6240\u4ee5\u6027\u80fd\u4e5f\u5c31\u9010\u6b65\u53d8\u6210\u4e86\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u6700\u91cd\u8981\u7684\u6307\u6807":49,"\u6240\u4ee5\u6211\u4eec\u4f7f\u7528\u8fd9\u4e2a\u955c\u50cf\u6765\u4e0b\u8f7d\u8bad\u7ec3\u6570\u636e\u5230docker":56,"\u6240\u4ee5\u6211\u4eec\u53ef\u4ee5\u5728\u8fd9\u4e2a\u57fa\u7840\u4e0a":57,"\u6240\u4ee5\u6211\u4eec\u63a8\u8350\u4f7f\u7528\u57fa\u4e8edocker\u6765\u6784\u5efapaddlepaddle\u7684\u6587\u6863":47,"\u6240\u4ee5\u6211\u4eec\u9700\u8981\u5c06\u8f93\u5165\u6570\u636e\u6807\u8bb0\u6210":41,"\u6240\u4ee5\u63a8\u8350\u4f7f\u7528\u663e\u5f0f\u6307\u5b9a\u7684\u65b9\u5f0f\u6765\u8bbe\u7f6einput_typ":3,"\u6240\u4ee5\u653e\u4e00\u4e2a\u7a7a\u5217\u8868":31,"\u6240\u4ee5\u7528\u6237\u9700\u8981\u9996\u5148\u5728":24,"\u6240\u4ee5\u8bad\u7ec3":50,"\u6240\u4ee5\u8f93\u51fa\u7684value\u5305\u542b\u4e24\u4e2a\u5411\u91cf":5,"\u6240\u4ee5\u8fd9\u4e00\u6b65\u662f\u5fc5\u8981\u7684":46,"\u6240\u4f7f\u7528\u7684\u7f16\u8bd1\u5668\u4e3aarm":39,"\u6240\u5bf9\u5e94\u7684\u8bcd\u8868index\u6570\u7ec4":41,"\u6240\u6709\u4e0e\u7c7b\u578b\u76f8\u5173\u7684\u51fd\u6570":27,"\u6240\u6709\u4ee3\u7801\u5fc5\u987b\u5177\u6709\u5355\u5143\u6d4b\u8bd5":45,"\u6240\u6709\u53c2\u6570\u7f6e\u4e3a\u96f6":52,"\u6240\u6709\u540c\u76ee\u5f55\u4e0b\u7684\u6587\u672c\u5b9e\u4f8b\u6587\u4ef6\u90fd\u662f\u540c\u7ea7\u522b\u7684":69,"\u6240\u6709\u547d\u4ee4\u884c\u9009\u9879\u53ef\u4ee5\u8bbe\u7f6e\u4e3a":50,"\u6240\u6709\u6587\u4ef6\u5217\u8868":3,"\u6240\u6709\u672c\u5730\u8bad\u7ec3":50,"\u6240\u6709\u67b6\u6784":38,"\u6240\u6709\u6807\u8bb0\u7684\u6d4b\u8bd5\u96c6\u548c\u8bad\u7ec3\u96c6":69,"\u6240\u6709\u751f\u6210\u7684\u7684\u6587\u4ef6\u90fd\u5728build\u76ee\u5f55\u4e0b":33,"\u6240\u6709\u7684":[45,46],"\u6240\u6709\u7684\u4eba\u53e3\u7edf\u8ba1\u5b66\u4fe1\u606f\u7531\u7528\u6237\u81ea\u613f\u63d0\u4f9b":66,"\u6240\u6709\u7684\u5355\u6d4b\u90fd\u4f1a\u88ab\u6267\u884c\u4e00\u6b21":46,"\u6240\u6709\u7684\u63a5\u53e3\u5747\u4e3ac\u63a5\u53e3":27,"\u6240\u6709\u7684\u64cd\u4f5c\u90fd\u662f\u9488\u5bf9\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u6765\u8fdb\u884c\u7684":41,"\u6240\u6709\u7684\u7528\u6237\u4fe1\u606f\u90fd\u5305\u542b\u5728":66,"\u6240\u6709\u7684\u7535\u5f71\u4fe1\u606f\u90fd\u5305\u542b\u5728":66,"\u6240\u6709\u7684\u8bc4\u5206\u6570\u636e\u90fd\u5305\u542b\u5728":66,"\u6240\u6709\u7684python\u5c01\u88c5\u90fd\u4f7f\u7528":46,"\u6240\u6709\u7684python\u5c01\u88c5\u90fd\u5728":46,"\u6240\u6709\u7c7b\u578b\u540d\u4e3a":27,"\u6240\u6709\u7f51\u7edc\u5c42\u7684\u68af\u5ea6\u68c0\u67e5\u5355\u6d4b\u90fd\u4f4d\u4e8e":46,"\u6240\u6709\u8282\u70b9\u8fd0\u884c\u96c6\u7fa4\u4f5c\u4e1a\u7684\u4e3b\u673a\u540d\u6216":50,"\u6240\u6709\u8d21\u732e\u8005":0,"\u6240\u6709\u8f93\u5165\u5e8f\u5217\u5e94\u8be5\u6709\u76f8\u540c\u7684\u957f\u5ea6":44,"\u6240\u6709\u914d\u7f6e\u90fd\u80fd\u5728":65,"\u6240\u6784\u5efa\u7f51\u7edc\u7ed3\u6784\u7684\u7684\u6df1\u5ea6\u6bd4\u4e4b\u524d\u4f7f\u7528\u7684\u7f51\u7edc\u6709\u5927\u5e45\u5ea6\u7684\u63d0\u9ad8":63,"\u6240\u793a":68,"\u6240\u8c13\u65f6\u95f4\u6b65\u4fe1\u606f":3,"\u6240\u9700\u652f\u6301\u7684\u6700\u4f4eandroid":38,"\u624b\u5199\u591a\u8bed\u8a00\u7ed1\u5b9a":26,"\u624b\u5de5\u827a\u8005":66,"\u624d\u4f1a\u91ca\u653e\u8be5\u6bb5\u5185\u5b58":3,"\u624d\u4f1astop":3,"\u624d\u80fd\u4fdd\u8bc1\u548c\u5355\u5c42\u5e8f\u5217\u7684\u914d\u7f6e\u4e2d":41,"\u624d\u80fd\u5145\u5206\u5229\u7528mac":33,"\u624d\u80fd\u53d1\u6325\u5176\u5168\u90e8\u80fd\u529b":49,"\u6253\u5370\u5728\u5c4f\u5e55\u4e0a":67,"\u6253\u5370\u7684\u65e5\u5fd7\u53d8\u591a":32,"\u6253\u5f00":49,"\u6253\u5f00\u6587\u672c\u6587\u4ef6":3,"\u6253\u5f00\u6d4f\u89c8\u5668\u8bbf\u95ee\u5bf9\u5e94\u76ee\u5f55\u4e0b\u7684index":47,"\u6253\u5f00\u8fd9\u4e2a\u7f16\u8bd1\u9009\u9879":27,"\u6267\u884c":[35,68,69],"\u6267\u884c\u4e0b\u9762\u7684\u547d\u4ee4\u5c31\u53ef\u4ee5\u9884\u5904\u7406\u6570\u6910":69,"\u6267\u884c\u4ee5\u4e0b\u547d\u4ee4":[38,39],"\u6267\u884c\u4ee5\u4e0b\u64cd\u4f5c":44,"\u6267\u884c\u5b8c\u5b89\u88c5\u547d\u4ee4\u540e":[38,39],"\u6267\u884c\u60a8\u7684\u4ee3\u7801":49,"\u6267\u884c\u7684\u547d\u4ee4\u5982\u4e0b":63,"\u6269\u5c55\u548c\u5ef6\u4f38":0,"\u6269\u5c55\u673a\u5236\u7b49\u529f\u80fd":55,"\u6279\u6b21\u540e\u6253\u5370\u65e5\u5fd7":67,"\u6279\u6b21\u5bf9\u5e73\u5747\u53c2\u6570\u8fdb\u884c\u6d4b\u8bd5":68,"\u6279\u6b21\u7684\u6570\u636e":67,"\u627e\u5230":44,"\u627e\u5230\u8fd0\u884c\u6162\u7684\u539f\u56e0":49,"\u627e\u5230\u8fd0\u884c\u6162\u7684\u90e8\u5206":49,"\u6280\u672f\u5458":66,"\u628a":[20,46],"\u628a\u4e4b\u524d\u793a\u4f8b\u4e2d\u8f6c\u6362\u5b8c\u6bd5\u7684random":20,"\u628a\u7528\u6237\u5728\u8d2d\u7269\u7f51\u7ad9":69,"\u628a\u7f16\u8bd1\u51fa\u6765\u7684paddlepaddle\u4e5f\u6253\u5305\u6210\u4e00\u4e2a\u955c":33,"\u628a\u8bad\u7ec3\u6570\u636e\u76f4\u63a5\u653e\u5728":56,"\u6293\u53d6\u4ea7\u54c1\u7684\u7528\u6237\u8bc4\u8bba\u5e76\u5206\u6790\u4ed6\u4eec\u7684\u60c5\u611f":69,"\u6295\u5c04\u53cd\u5411rnn\u7684\u7b2c\u4e00\u4e2a\u5b9e\u4f8b\u5230":44,"\u6295\u5c04\u7f16\u7801\u5411\u91cf\u5230":44,"\u62a5\u9519":35,"\u62bd\u53d6\u51fa\u7684\u65b0\u8bcd\u8868\u7684\u4fdd\u5b58\u8def\u5f84":61,"\u62bd\u53d6\u5bf9\u5e94\u7684\u8bcd\u5411\u91cf\u6784\u6210\u65b0\u7684\u8bcd\u8868":61,"\u62c6\u5206\u5230\u4e0d\u540c\u6587\u4ef6\u5939":70,"\u62c6\u89e3":43,"\u62c6\u89e3\u6210\u7684\u6bcf\u4e00\u53e5\u8bdd\u518d\u901a\u8fc7\u4e00\u4e2alstm\u7f51\u7edc":41,"\u62f7\u8d1d\u8bad\u7ec3\u6587\u4ef6\u5230\u5bb9\u5668\u5185":57,"\u62fc\u63a5\u6210\u4e00\u4e2a\u65b0\u7684\u5411\u91cf":65,"\u6301\u7eed\u96c6\u6210\u7cfb\u7edf\u4e5f\u53ef\u4ee5\u7b80\u5355\u5730\u590d\u73b0\u8fd9\u4e2a\u8fc7\u7a0b":33,"\u6307\u5411\u4e00\u4e2alayer":43,"\u6307\u5b9a":[30,43,44],"\u6307\u5b9a\u4e00\u53f0\u673a\u5668\u4e0a\u4f7f\u7528\u7684\u7ebf\u7a0b\u6570":52,"\u6307\u5b9a\u4f7f\u75282":30,"\u6307\u5b9a\u521d\u59cb\u5316\u6a21\u578b\u8def\u5f84":65,"\u6307\u5b9a\u52a0\u8f7d\u7684\u65b9\u5f0f":52,"\u6307\u5b9a\u5de5\u4f5c\u6a21\u578b\u8fdb\u884c\u9884\u6d4b":63,"\u6307\u5b9a\u5de5\u4f5c\u6a21\u5f0f\u6765\u63d0\u53d6\u7279\u5f81":63,"\u6307\u5b9a\u63d0\u53d6\u7279\u5f81\u7684\u5c42":63,"\u6307\u5b9a\u662f\u5426\u4f7f\u7528gpu":63,"\u6307\u5b9a\u751f\u6210\u6570\u636e\u7684\u51fd\u6570":65,"\u6307\u5b9a\u7684\u5b57\u5178\u5355\u8bcd\u6570":70,"\u6307\u5b9a\u7684\u6570\u636e\u5c06\u4f1a\u88ab\u6d4b\u8bd5":65,"\u6307\u5b9a\u7684\u8f93\u5165\u4e0d\u4f1a\u88ab":43,"\u6307\u5b9a\u8bad\u7ec3\u6570\u636e\u548c\u6d4b\u8bd5\u6570\u636e":65,"\u6307\u5b9abatch":70,"\u6307\u5b9acudnn\u7684\u6700\u5927\u5de5\u4f5c\u7a7a\u95f4\u5bb9\u9650":52,"\u6307\u5bf9\u4e8e\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u8f93\u5165\u6570\u636e":41,"\u6307\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u4e4b\u540e\u5f97\u5230\u7684\u6240\u6709\u53c2\u6570":19,"\u6307\u793a\u4f7f\u7528\u54ea\u4e2agpu\u6838":52,"\u6307\u793a\u5728\u7b80\u5355\u7684recurrentlayer\u5c42\u7684\u8ba1\u7b97\u4e2d\u662f\u5426\u4f7f\u7528\u6279\u5904\u7406\u65b9\u6cd5":52,"\u6307\u793a\u5f53\u6307\u5b9a\u8f6e\u7684\u6d4b\u8bd5\u6a21\u578b\u4e0d\u5b58\u5728\u65f6":52,"\u6307\u793a\u662f\u5426\u4f7f\u7528\u591a\u7ebf\u7a0b\u6765\u8ba1\u7b97\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc":52,"\u6307\u793a\u662f\u5426\u5f00\u542f\u53c2\u6570\u670d\u52a1\u5668":52,"\u6307\u793a\u662f\u5426\u663e\u793a\u53c2\u6570\u670d\u52a1\u5668\u4e0a\u7684\u7a00\u758f\u53c2\u6570\u5206\u5e03\u7684\u65e5\u5fd7\u7ec6\u8282":52,"\u6307\u793a\u662f\u5426\u68c0\u67e5\u6240\u6709\u53c2\u6570\u670d\u52a1\u5668\u4e0a\u7684\u7a00\u758f\u53c2\u6570\u7684\u5206\u5e03\u662f\u5747\u5300\u7684":52,"\u6307\u793a\u6d4b\u8bd5\u4efb\u52a1":68,"\u6307\u793a\u6d4b\u8bd5\u4efb\u52a1\u7684\u6807\u8bb0":68,"\u6309\u542f\u53d1\u5f0f\u635f\u5931\u7684\u5927\u5c0f\u9012\u589e\u6392\u5e8f":52,"\u6309\u7167":39,"\u6309\u7167\u4e0b\u9762\u6b65\u9aa4\u5373\u53ef":57,"\u6309\u94ae":45,"\u633a":41,"\u633a\u597d":41,"\u6355\u83b7":65,"\u6362":41,"\u6392\u6210\u4e00\u5217\u7684\u591a\u4e2a\u5143\u7d20":40,"\u63a5\u4e0a\u4e00\u4e2a\u5168\u8fde\u63a5\u5c42":36,"\u63a5\u4e0a\u5747\u65b9\u8bef\u5dee\u5c42":36,"\u63a5\u4e0b\u6765":[65,69],"\u63a5\u4e0b\u6765\u53ef\u4ee5\u8003\u8651\u4e0b\u65f6\u95f4\u7ebf\u7684\u5206\u6790":49,"\u63a5\u4e0b\u6765\u5c31\u53ef\u4ee5\u4f7f\u7528":49,"\u63a5\u4e0b\u6765\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u539f\u59cb":45,"\u63a5\u4e0b\u6765\u6211\u4eec\u53d6\u6d88\u5bf9":45,"\u63a5\u4e0b\u6765\u6211\u4eec\u5c06\u5c55\u793a\u5982\u4f55\u7528paddlepaddle\u8bad\u7ec3\u4e00\u4e2a\u6587\u672c\u5206\u7c7b\u6a21\u578b":65,"\u63a5\u4e0b\u6765\u7b49\u5f85":45,"\u63a5\u53d7":68,"\u63a5\u53d7\u7684\u4e1c\u897f":68,"\u63a5\u53d7\u8005":68,"\u63a5\u53e3":[26,27],"\u63a5\u53e3\u540d\u79f0":50,"\u63a5\u53e3\u5c42\u505a\u8fc7\u591a\u5c01\u88c5":27,"\u63a5\u53e3\u63d0\u53d6\u7684\u7ed3\u679c\u662f\u4e00\u81f4\u7684":63,"\u63a5\u53e3\u662f":20,"\u63a5\u53e3\u6709\u4e00\u4e2a":30,"\u63a5\u53e3\u6765\u52a0\u8f7d\u6570\u636e":65,"\u63a5\u53e3\u6765\u52a0\u8f7d\u8be5\u6587\u4ef6":63,"\u63a5\u53e3\u6765\u6253\u5f00\u8be5\u6587\u4ef6":63,"\u63a5\u6536\u5904\u7406pfsclient\u7aef\u7684\u6587\u4ef6\u7ba1\u7406\u8bf7\u6c42":24,"\u63a5\u7740\u6211\u4eec\u5c31\u80fd\u591f\u6253\u5f00\u6d4f\u89c8\u5668\u5728":33,"\u63a7\u5236":52,"\u63a7\u5236\u5982\u4f55\u6539\u53d8\u6a21\u578b\u53c2\u6570":31,"\u63a7\u5236\u662f\u5426\u751f\u6210\u5355\u5143\u6d4b\u8bd5":33,"\u63a7\u5236\u751f\u6210\u7684\u751f\u4ea7\u955c\u50cf\u662f\u5426\u652f\u6301avx":33,"\u63a7\u5236\u751f\u6210\u7684\u751f\u4ea7\u955c\u50cf\u662f\u5426\u652f\u6301gpu":33,"\u63a7\u5236\u7528\u6237\u6743\u9650":20,"\u63a8\u5bfc\u8be5\u5c42\u524d\u5411\u548c\u540e\u5411\u4f20\u9012\u7684\u65b9\u7a0b":46,"\u63a8\u8350":41,"\u63a8\u8350\u4f7f\u7528":3,"\u63a8\u8350\u6e05\u7406\u6574\u4e2a\u7f16\u8bd1\u76ee\u5f55":32,"\u63a8\u8350\u76f4\u63a5\u5b58\u653e\u5230\u8bad\u7ec3\u76ee\u5f55":2,"\u63a8\u8350\u7cfb\u7edf":50,"\u63a8\u9001\u5230\u8fdc\u7a0b\u4ed3\u5e93":45,"\u63a8\u9500\u5458":66,"\u63cf\u8ff0":32,"\u63cf\u8ff0\u7f51\u7edc\u7ed3\u6784\u548c\u4f18\u5316\u7b97\u6cd5":65,"\u63cf\u8ff0\u95ee\u9898":45,"\u63cf\u8ff0kubernetes\u4e0a\u8fd0\u884c\u7684\u4f5c\u4e1a":55,"\u63d0\u4ea4\u65b9\u5f0f\u53ef\u53c2\u89c1":47,"\u63d0\u4ea4pull":45,"\u63d0\u4f9b":50,"\u63d0\u4f9b\u4e03\u5c42\u534f\u8bae\u7684\u53cd\u5411\u4ee3\u7406":24,"\u63d0\u4f9b\u4e86\u4e00\u4e2a\u542f\u52a8\u811a\u672c":57,"\u63d0\u4f9b\u4e86\u547d\u4ee4\u6837\u4f8b\u6765\u8fd0\u884c":50,"\u63d0\u4f9b\u4e86\u81ea\u52a8\u5316\u811a\u672c\u6765\u542f\u52a8\u4e0d\u540c\u8282\u70b9\u4e2d\u7684\u6240\u6709":50,"\u63d0\u4f9b\u51e0\u4e4e\u6240\u6709\u8bad\u7ec3\u7684\u5185\u90e8\u8f93\u51fa\u65e5\u5fd7":50,"\u63d0\u4f9b\u5e38\u7528\u7684\u547d\u4ee4\u884c\u7ba1\u7406\u547d\u4ee4\u7ba1\u7406\u6587\u4ef6\u548c\u76ee\u5f55":24,"\u63d0\u4f9b\u6269\u5c55\u7684\u957f\u5ea6\u4fe1\u606f":40,"\u63d0\u4f9b\u6700\u65b0":33,"\u63d0\u4f9b\u7528\u6237\u7ba1\u7406\u6587\u4ef6\u7684\u547d\u4ee4":24,"\u63d0\u4f9b\u7ed9paddle\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e":20,"\u63d0\u4f9b\u8bad\u7ec3\u8fc7\u7a0b\u7684":50,"\u63d0\u51fa\u7684\u4ee3\u7801\u9700\u6c42":61,"\u63d0\u793a":30,"\u642d\u5efa\u795e\u7ecf\u7f51\u7edc\u5c31\u50cf\u4f7f\u7528\u79ef\u6728\u642d\u5efa\u5b9d\u5854\u4e00\u6837":36,"\u64cd\u4f5c":41,"\u64cd\u6301\u5bb6\u52a1\u8005":66,"\u652f\u6301\u4e3b\u6d41x86\u5904\u7406\u5668\u5e73\u53f0":35,"\u652f\u6301\u53cc\u5c42\u5e8f\u5217\u4f5c\u4e3a\u8f93\u5165\u7684layer":[42,43],"\u652f\u6301\u5927\u6587\u4ef6\u7684\u65ad\u70b9\u4e0a\u4f20":24,"\u652f\u6301\u7684\u6700\u5c0f\u7684android":38,"\u652f\u6301nvidia":35,"\u652f\u6301rbd":55,"\u653e\u5728\u8fd9\u4e2a\u76ee\u5f55\u91cc\u7684\u6587\u4ef6\u5176\u5b9e\u662f\u4fdd\u5b58\u5230\u4e86mfs\u4e0a":57,"\u653e\u5fc3":41,"\u6545\u800c\u662f\u4e00\u4e2a\u5355\u5c42\u65f6\u95f4\u5e8f\u5217":41,"\u6548\u679c\u603b\u7ed3":65,"\u6559\u7a0b\u6587\u6863\u5230":50,"\u6559\u80b2\u5de5\u4f5c\u8005":66,"\u6570":[43,68],"\u6570\u5fc5\u987b\u4e25\u683c\u76f8\u7b49":43,"\u6570\u636e":[24,70],"\u6570\u636e\u4e0b\u8f7d\u4e4b\u540e":62,"\u6570\u636e\u4e2d0":30,"\u6570\u636e\u5217\u8868":63,"\u6570\u636e\u5c06\u4fdd\u5b58\u5728":61,"\u6570\u636e\u5c42":[31,67],"\u6570\u636e\u5e94\u8be5\u5728\u542f\u52a8\u96c6\u7fa4\u4f5c\u4e1a\u4e4b\u524d\u51c6\u5907\u597d":50,"\u6570\u636e\u63d0\u4f9b\u5668":51,"\u6570\u636e\u63d0\u4f9b\u811a\u672c\u4ec5\u4ec5\u662f\u8bfb\u53d6meta":67,"\u6570\u636e\u63d0\u4f9b\u811a\u672c\u7684\u7ec6\u8282\u6587\u6863\u53ef\u4ee5\u53c2\u8003":67,"\u6570\u636e\u7684\u6574\u6570id":44,"\u6570\u636e\u76ee\u5f55\u4e2d\u7684\u6240\u6709\u6587\u4ef6\u88ab":50,"\u6570\u636e\u7c7b\u578b":5,"\u6570\u636e\u7f13\u5b58\u7684\u7b56\u7565":3,"\u6570\u636e\u8bbf\u95ee":1,"\u6570\u636e\u8bfb\u53d6\u5747\u4ea4\u7531\u5176\u4ed6\u8bed\u8a00\u5b8c\u6210":26,"\u6570\u636e\u8f93\u5165":43,"\u6570\u636e\u8f93\u5165\u683c\u5f0f":3,"\u6570\u636e\u957f\u5ea6\u53ca\u6821\u9a8c\u503c\u7ec4\u6210":24,"\u6570\u636e\u96c6":66,"\u6570\u636e\u96c6\u63cf\u8ff0":67,"\u6570\u636e\u96c6\u6587\u4ef6\u5939\u540d\u79f0":70,"\u6570\u636e\u96c6\u9700\u8981\u9884\u5148\u88ab\u8f6c\u6362\u6210paddlepaddle\u5206\u5e03\u5f0f\u8bad\u7ec3\u4f7f\u7528\u7684\u5b58\u50a8\u683c":20,"\u6570\u636e\u9884\u5904\u7406\u4efb\u52a1":20,"\u6570\u636e\u9884\u5904\u7406\u5b8c\u6210\u4e4b\u540e":65,"\u6570\u636e\u9884\u6d4b":68,"\u6570\u6910\u5b9a\u4e49":69,"\u6570\u6910\u8bf4\u660e\u6587\u6863":69,"\u6570\u6910\u96c6\u548c":69,"\u6570\u76ee":54,"\u6574\u4f53":41,"\u6574\u4f53\u6570\u636e\u548c\u539f\u59cb\u6570\u636e\u5b8c\u5168\u4e00\u6837":41,"\u6574\u4f53\u7684\u7ed3\u6784\u56fe\u5982\u4e0b":57,"\u6574\u6570":46,"\u6574\u6570\u6807\u7b7e":[3,36],"\u6574\u6d01":41,"\u6587\u4e66\u5de5\u4f5c":66,"\u6587\u4ef6":[26,45,56,68],"\u6587\u4ef6\u4e2d":[57,63,66,68],"\u6587\u4ef6\u4e2d\u6307\u5b9a\u6a21\u578b\u8def\u5f84\u548c\u8f93\u51fa\u7684\u76ee\u5f55":63,"\u6587\u4ef6\u4e2d\u6307\u5b9a\u8981\u63d0\u53d6\u7279\u5f81\u7684\u7f51\u7edc\u5c42\u7684\u540d\u5b57":63,"\u6587\u4ef6\u4e2d\u7684":63,"\u6587\u4ef6\u4e2d\u7684\u6bcf\u884c\u90fd\u5fc5\u987b\u662f\u4e00\u4e2a\u53e5\u5b50":70,"\u6587\u4ef6\u4e3a":[30,70],"\u6587\u4ef6\u4e4b\u5916":45,"\u6587\u4ef6\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5bf9\u6837\u672c\u8fdb\u884c\u9884\u6d4b":63,"\u6587\u4ef6\u4f20\u8f93\u7684\u7684\u5173\u952e\u5728\u4e8e\u9700\u8981pfsclient\u7aef\u5bf9\u6bd4source\u548cdestination\u7684\u6587\u4ef6chunks\u7684checksum\u662f\u5426\u4fdd\u6301\u4e00\u81f4":24,"\u6587\u4ef6\u5185\u5bb9\u4e3a":26,"\u6587\u4ef6\u5206\u5272\u4e3a\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6587\u4ef6":67,"\u6587\u4ef6\u540d\u4e3a\u6b64uuid":19,"\u6587\u4ef6\u540d\u79f0\u4e3a":67,"\u6587\u4ef6\u59390":57,"\u6587\u4ef6\u5939\u4e2d\u7684\u6bcf\u4e2a\u6587\u4ef6\u7684\u6bcf\u4e00\u884c\u5305\u542b\u4e24\u90e8\u5206":70,"\u6587\u4ef6\u5bf9\u5e94\u7684data":20,"\u6587\u4ef6\u7684\u4e0a\u4f20\u548c\u4e0b\u8f7d\u90fd\u662f\u901a\u8fc7\u5bf9chunk\u7684\u64cd\u4f5c\u6765\u5b9e\u73b0\u7684":24,"\u6587\u4ef6\u7684\u5206\u9694\u7b26\u4e3a":67,"\u6587\u4ef6\u7684\u6539\u53d8":45,"\u6587\u4ef6\u7684\u683c\u5f0f\u53ef\u4ee5":67,"\u6587\u4ef6\u7a0d\u6709\u5dee\u522b":62,"\u6587\u4ef6\u7d22\u5f15":50,"\u6587\u4ef6\u7ed9\u51fa\u4e86\u5b8c\u6574\u4f8b\u5b50":65,"\u6587\u4ef6model":54,"\u6587\u5b57\u7684\u4ea4\u4e92\u5f0f\u6587\u6863":33,"\u6587\u672c\u4e2d\u7684\u5355\u8bcd\u7528\u7a7a\u683c\u5206\u9694":65,"\u6587\u672c\u4fe1\u606f\u5c31\u662f\u4e00\u4e2a\u5e8f\u5217\u6570\u636e":3,"\u6587\u672c\u5206\u7c7b\u95ee\u9898":65,"\u6587\u672c\u5377\u79ef\u5206\u53ef\u4e3a\u4e09\u4e2a\u6b65\u9aa4":65,"\u6587\u672c\u5377\u79ef\u91c7\u6837\u5c42":67,"\u6587\u672c\u6295\u5f71\u5c42":67,"\u6587\u672c\u683c\u5f0f\u7684\u5b9e\u4f8b\u6587\u4ef6":69,"\u6587\u6863":30,"\u6587\u6863\u7f16\u5199\u7b49\u5de5\u4f5c":33,"\u6587\u6863\u81ea\u52a8\u5206\u7c7b\u548c\u95ee\u7b54":68,"\u6587\u6863\u90fd\u662f\u901a\u8fc7":47,"\u6587\u7ae0":57,"\u65b0":41,"\u65b0\u5efa\u4e00\u4e2a\u6743\u91cd":46,"\u65b0\u624b\u5165\u95e8":60,"\u65b0\u624b\u5165\u95e8\u7ae0\u8282":29,"\u65b9\u4fbf":41,"\u65b9\u4fbf\u4eca\u540e\u7684\u5d4c\u5165\u5f0f\u79fb\u690d\u5de5\u4f5c":32,"\u65b9\u4fbf\u5f00\u53d1\u8005\u76f4\u63a5\u767b\u5f55\u5230\u955c\u50cf\u4e2d\u8fdb\u884c\u5f00\u53d1":33,"\u65b9\u4fbf\u6d4b\u8bd5\u4eba\u5458\u6d4b\u8bd5paddle\u7684\u884c\u4e3a":29,"\u65b9\u4fbf\u7528\u6237\u4e0a\u4f20\u81ea\u5df1\u7684\u8bad\u7ec3\u6570\u636e\u4ee5\u8fdb\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3":24,"\u65b9\u5f0f1":30,"\u65b9\u5f0f2":30,"\u65b9\u6848\u6765\u6807\u8bb0\u6bcf\u4e2a\u53c2\u6570":68,"\u65b9\u6cd5\u4e00":54,"\u65b9\u6cd5\u4e09":54,"\u65b9\u6cd5\u4e8c":54,"\u65c1\u8fb9":41,"\u65c5\u6e38\u7f51\u7ad9":69,"\u65e0":41,"\u65e0\u4e1a\u4eba\u58eb":66,"\u65e0\u5ef6\u8fdf":52,"\u65e0\u6cd5\u505a\u5230\u5bf9\u4e8e\u5404\u79cd\u8bed\u8a00\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u7684\u9002\u914d":26,"\u65e0\u8bba\u5728\u672c\u5730\u8fd8\u662f\u5728\u4e91\u7aef":20,"\u65e0\u8bba\u662f\u4ece":20,"\u65e0\u8bba\u662f\u5728\u672c\u5730\u6216\u662f\u4e91\u7aef\u8f6c\u6362":20,"\u65e0\u9ed8\u8ba4\u503c":[38,39],"\u65e5\u5fd7\u5c06\u4fdd\u5b58\u5728":69,"\u65e8\u5728\u5efa\u7acb\u4e00\u4e2a\u53ef\u4ee5\u88ab\u534f\u540c\u8c03\u81f3\u6700\u4f18\u7ffb\u8bd1\u6548\u679c\u7684\u5355\u795e\u7ecf\u5143\u7f51\u7edc":70,"\u65e9\u9910":41,"\u65f6":[19,30,40,44,46,52,57],"\u65f6\u5019":41,"\u65f6\u52a0\u4e0a":69,"\u65f6\u5e8f\u6a21\u578b\u5747\u4f7f\u7528\u8be5\u811a\u672c":65,"\u65f6\u5e8f\u6a21\u578b\u662f\u6307\u6570\u636e\u7684\u67d0\u4e00\u7ef4\u5ea6\u662f\u4e00\u4e2a\u5e8f\u5217\u5f62\u5f0f":3,"\u65f6\u76ee\u6807\u8bed\u8a00\u7684\u6587\u4ef6":70,"\u65f6\u88ab\u8bad\u7ec3\u7684":46,"\u65f6\u8bbe\u5907id\u53f7\u7684\u5206\u914d":54,"\u65f6\u95f4":41,"\u65f6\u95f4\u6233":66,"\u65f6\u95f4\u6233\u8868\u793a\u4e3a\u4ece1970":66,"\u65f6\u95f4\u6b65\u7684\u6982\u5ff5":41,"\u6620\u5c04\u5230\u4e00\u4e2a\u7ef4\u5ea6\u4e3a":46,"\u662f":[24,32,41],"\u662f\u4e00\u4e2a\u51681\u7684\u5411\u91cf":46,"\u662f\u4e00\u4e2a\u5185\u7f6e\u7684\u5b9a\u65f6\u5668\u5c01\u88c5":49,"\u662f\u4e00\u4e2a\u52a8\u6001\u7a0b\u5e8f\u5206\u6790\u7684\u672f\u8bed":49,"\u662f\u4e00\u4e2a\u5305\u88c5\u6570\u636e\u7684":68,"\u662f\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":40,"\u662f\u4e00\u4e2a\u53cc\u5c42\u7684\u5e8f\u5217":40,"\u662f\u4e00\u4e2a\u591a\u8bed\u8a00\u63a5\u53e3\u7684\u4ee3\u7801\u751f\u6210\u5668":26,"\u662f\u4e00\u4e2a\u5c01\u88c5\u5bf9\u8c61":49,"\u662f\u4e00\u4e2a\u5f88\u6709\u7528\u7684\u53c2\u6570":54,"\u662f\u4e00\u4e2a\u7b26\u5408\u9ad8\u65af\u5206\u5e03\u7684\u968f\u673a\u53d8\u91cf":31,"\u662f\u4e00\u4e2a\u7c7b\u578b\u7684\u6807\u5fd7":27,"\u662f\u4e00\u4e2a\u7edf\u8ba1\u5b66\u7684\u673a\u5668\u7ffb\u8bd1\u7cfb\u7edf":70,"\u662f\u4e00\u4e2a\u914d\u7f6e\u6587\u4ef6\u7684\u4f8b\u5b50":69,"\u662f\u4e00\u4e2a\u975e\u7ebf\u6027\u7684":46,"\u662f\u4e00\u4e2apython\u7684":3,"\u662f\u4e00\u4e2aunbound":43,"\u662f\u4e00\u6761\u65f6\u95f4\u5e8f\u5217":[3,36],"\u662f\u4e00\u79cd\u4efb\u610f\u590d\u6742\u7684rnn\u5355\u5143":43,"\u662f\u4e00\u7ec4":55,"\u662f\u4e0d\u5305\u62ec\u6e90\u7801\u7684":56,"\u662f\u4e0d\u5e38\u89c1\u7684\u505a\u6cd5":26,"\u662f\u4e0d\u662f\u5f88\u7b80\u5355\u5462":3,"\u662f\u4e0d\u662f\u8981\u5bf9\u6570\u636e\u505ashuffl":3,"\u662f\u4e3b\u8981\u7684\u53ef\u6267\u884cpython\u811a\u672c":68,"\u662f\u4ec0\u4e48\u4e5f\u6ca1\u5173\u7cfb":3,"\u662f\u4f17\u591a\u8bef\u5dee\u4ee3\u4ef7\u51fd\u6570\u5c42\u7684\u4e00\u79cd":31,"\u662f\u4f7f\u5f97\u8981\u5171\u4eab\u7684\u53c2\u6570\u4f7f\u7528\u540c\u6837\u7684":30,"\u662f\u504f\u5dee":44,"\u662f\u5176\u5927\u5c0f":31,"\u662f\u51e0\u4e4e\u4e0d\u5360\u5185\u5b58\u7684":3,"\u662f\u539f\u59cb\u6cd5\u8bed\u6587\u4ef6":70,"\u662f\u5404\u4e2a\u5b9e\u73b0\u4e2d\u5171\u4eab\u7684\u5934\u6587\u4ef6":27,"\u662f\u5411\u91cf":46,"\u662f\u5426\u4ee5\u9006\u5e8f\u5904\u7406\u8f93\u5165\u5e8f\u5217":43,"\u662f\u5426\u4f7f\u7528\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u6570":32,"\u662f\u5426\u4f7f\u7528\u65e7\u7684remoteparameterupdat":52,"\u662f\u5426\u4f7f\u7528\u6743\u91cd":46,"\u662f\u5426\u4f7f\u7528arm\u6a21\u5f0f":38,"\u662f\u5426\u4f7f\u7528gpu":67,"\u662f\u5426\u4f7f\u7528gpu\u8bad\u7ec3":70,"\u662f\u5426\u4f7f\u7528neon\u6307\u4ee4":[38,39],"\u662f\u5426\u5141\u8bb8\u6682\u5b58\u7565\u5fae\u591a\u4f59pool_size\u7684\u6570\u636e":3,"\u662f\u5426\u5185\u5d4cpython\u89e3\u91ca\u5668":32,"\u662f\u5426\u5c06\u5168\u5c40\u79cd\u5b50\u5e94\u7528\u4e8e\u672c\u5730\u7ebf\u7a0b\u7684\u968f\u673a\u6570":52,"\u662f\u5426\u5f00\u542f\u5355\u5143\u6d4b\u8bd5":32,"\u662f\u5426\u5f00\u542f\u8ba1\u65f6\u529f\u80fd":32,"\u662f\u5426\u5f00\u542frdma":32,"\u662f\u5426\u6253\u5370\u7248\u672c\u4fe1\u606f":52,"\u662f\u5426\u652f\u6301gpu":32,"\u662f\u5426\u663e\u793a":52,"\u662f\u5426\u7a00\u758f":46,"\u662f\u5426\u7f16\u8bd1\u4e2d\u82f1\u6587\u6587\u6863":32,"\u662f\u5426\u7f16\u8bd1\u542b\u6709avx\u6307\u4ee4\u96c6\u7684paddlepaddle\u4e8c\u8fdb\u5236\u6587\u4ef6":32,"\u662f\u5426\u7f16\u8bd1\u65f6\u8fdb\u884c\u4ee3\u7801\u98ce\u683c\u68c0\u67e5":32,"\u662f\u5426\u7f16\u8bd1python\u7684swig\u63a5\u53e3":32,"\u662f\u5426\u8fd0\u884c\u65f6\u52a8\u6001\u52a0\u8f7dcuda\u52a8\u6001\u5e93":32,"\u662f\u5426\u9700\u8981\u7b49\u5f85\u8be5\u8f6e\u6a21\u578b\u53c2\u6570":52,"\u662f\u56e0\u4e3ac99\u652f\u6301":26,"\u662f\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u52a0\u8f7d\u5b57\u5178\u5e76\u5b9a\u4e49\u6570\u636e\u63d0\u4f9b\u7a0b\u5e8f\u6a21\u5757\u548c\u7f51\u7edc\u67b6\u6784\u7684\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6":68,"\u662f\u5728paddlepaddle\u4e2d\u6784\u9020\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u65f6\u6700\u91cd\u8981\u7684\u6982\u5ff5":44,"\u662f\u57fa\u7840\u7684\u8ba1\u7b97\u5355\u5143":31,"\u662f\u5b58\u6709\u4e00\u7cfb\u5217\u53d8\u6362\u77e9\u9635\u7684\u6743\u91cd":46,"\u662f\u5b58\u6709\u504f\u7f6e\u5411\u91cf\u7684\u6743\u91cd":46,"\u662f\u5bf9\u7528\u6237\u6587\u4ef6\u5b58\u50a8\u7a7a\u95f4\u7684\u62bd\u8c61":24,"\u662f\u5e8f\u5217":67,"\u662f\u5f85\u6269\u5c55\u7684\u6570\u636e":40,"\u662f\u6211\u4eec":45,"\u662f\u6307":27,"\u662f\u6307\u4e00\u7cfb\u5217\u7684\u7279\u5f81\u6570\u636e":41,"\u662f\u6307recurrent_group\u7684\u591a\u4e2a\u8f93\u5165\u5e8f\u5217":41,"\u662f\u6570\u636e\u8f93\u5165":44,"\u662f\u6574\u4e2a\u7684\u8bcd\u5d4c\u5165":67,"\u662f\u6709\u610f\u4e49\u7684":41,"\u662f\u6784\u5efa\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u6700\u91cd\u8981\u7684\u5de5\u5177":44,"\u662f\u6a21\u578b\u53c2\u6570\u4f18\u5316\u7684\u76ee\u6807\u51fd\u6570":31,"\u662f\u6d45\u5c42\u8bed\u4e49\u89e3\u6790\u7684\u4e00\u79cd\u5f62\u5f0f":68,"\u662f\u6e90\u8bed\u8a00\u7684\u6587\u4ef6":70,"\u662f\u7528\u6237\u4f7f\u7528c":27,"\u662f\u76ee\u6807\u82f1\u8bed\u6587\u4ef6":70,"\u662f\u77e9\u9635":46,"\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u5165\u53e3":31,"\u662f\u7f51\u7edc\u548c\u6570\u636e\u914d\u7f6e\u6587\u4ef6":62,"\u662f\u7f51\u7edc\u5c42\u5b9e\u4f8b\u7684\u540d\u5b57\u6807\u8bc6\u7b26":46,"\u662f\u7f51\u7edc\u5c42\u7684\u6807\u8bc6\u7b26":46,"\u662f\u7f51\u7edc\u5c42\u7684\u7c7b\u578b":46,"\u662f\u7f51\u7edc\u5c42\u8f93\u51fa\u7684\u5927\u5c0f":46,"\u662f\u8be5\u5c42\u7684\u6807\u8bc6\u7b26":46,"\u662f\u8be5\u5c42\u7684\u7c7b\u540d":46,"\u662f\u8be5\u7f51\u7edc\u5c42\u7684":46,"\u662f\u8f93\u5165":44,"\u662f\u901a\u7528\u7269\u4f53\u5206\u7c7b\u9886\u57df\u4e00\u4e2a\u4f17\u6240\u5468\u77e5\u7684\u6570\u636e\u5e93":63,"\u662f\u9700\u8981\u4e86\u89e3\u54ea\u4e9b\u6b65\u9aa4\u62d6\u6162\u4e86\u6574\u4f53":49,"\u662fc":27,"\u662fdecoder\u7684\u6570\u636e\u8f93\u5165":43,"\u662fgoogle\u5f00\u6e90\u7684\u5bb9\u5668\u96c6\u7fa4\u7ba1\u7406\u7cfb\u7edf":55,"\u662fnvidia\u6027\u80fd\u5206\u6790\u5de5\u5177":49,"\u662fpaddlepaddle\u652f\u6301\u7684\u4e00\u79cd\u4efb\u610f\u590d\u6742\u7684rnn\u5355\u5143":43,"\u662fpaddlepaddle\u8d1f\u8d23\u63d0\u4f9b\u6570\u636e\u7684\u6a21\u5757":65,"\u662fpod\u5185\u7684\u5bb9\u5668\u90fd\u53ef\u4ee5\u8bbf\u95ee\u7684\u5171\u4eab\u76ee\u5f55":55,"\u662fpython\u5c01\u88c5\u7684\u7c7b\u540d":46,"\u662frnn\u72b6\u6001":44,"\u663e":65,"\u663e\u5f0f\u6307\u5b9a\u8fd4\u56de\u7684\u662f\u4e00\u4e2a28":3,"\u665a":41,"\u666e\u901a\u7528\u6237\u8bf7\u8d70\u5b89\u88c5\u6d41\u7a0b":34,"\u6682\u4e0d\u8003\u8651\u5728\u5185":30,"\u6682\u65f6\u4e0d\u8003\u8651\u591a\u4e2aparamet":19,"\u66b4\u9732\u8fd9\u4e2a\u6982\u5ff5\u5fc5\u8981\u51fd\u6570":27,"\u66f4\u591a\u5173\u4e8edocker\u7684\u5b89\u88c5\u4e0e\u4f7f\u7528":30,"\u66f4\u591a\u5185\u5bb9\u53ef\u67e5\u770b\u53c2\u8003\u6587\u732e":69,"\u66f4\u591a\u7684\u7ec6\u8282\u53ef\u4ee5\u5728\u6587\u732e\u4e2d\u627e\u5230":69,"\u66f4\u591a\u7684\u7f16\u8bd1\u914d\u7f6e\u89c1":39,"\u66f4\u597d\u5730\u5b8c\u6210\u4e00\u4e9b\u590d\u6742\u7684\u8bed\u8a00\u7406\u89e3\u4efb\u52a1":43,"\u66f4\u5feb":44,"\u66f4\u65b0":30,"\u66f4\u65b0\u6a21\u5f0f":30,"\u66f4\u65b9\u4fbf\u7684\u8bbe\u7f6e\u65b9\u5f0f":30,"\u66f4\u8be6\u7ec6\u6570\u636e\u683c\u5f0f\u548c\u7528\u4f8b\u8bf7\u53c2\u8003":65,"\u66f4\u8be6\u7ec6\u7684\u7f51\u7edc\u914d\u7f6e\u8fde\u63a5\u8bf7\u53c2\u8003":65,"\u66f4\u8be6\u7ec6\u7684\u8bf4\u660e":65,"\u66f4\u8fdb\u4e00\u6b65":43,"\u66f4\u9ad8":44,"\u66ff\u6211\u4eec\u5b8c\u6210\u4e86\u539f\u59cb\u8f93\u5165\u6570\u636e\u7684\u62c6\u5206":43,"\u6700":41,"\u6700\u4e0d\u540c\u7684\u7279\u8272\u662f\u5b83\u5e76\u6ca1\u6709\u5c06\u8f93\u5165\u8bed\u53e5\u7f16\u7801\u4e3a\u4e00\u4e2a\u5355\u72ec\u7684\u5b9a\u957f\u5411\u91cf":70,"\u6700\u4e3b\u8981\u7684\u5de5\u4f5c\u5c31\u662f\u89e3\u6790\u51fa":57,"\u6700\u4f73\u63a8\u8350":3,"\u6700\u540e":[3,45,46,50,65,69],"\u6700\u540e\u4e00\u4e2a":40,"\u6700\u540e\u4e00\u5c42cost\u4e2d\u8bb0\u5f55\u4e86\u795e\u7ecf\u7f51\u7edc\u7684\u6240\u6709\u62d3\u6251\u7ed3\u6784":36,"\u6700\u540e\u4e00\u90e8\u5206\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u914d\u7f6e":31,"\u6700\u540e\u5220\u9664":29,"\u6700\u540e\u6211\u4eec\u4f7f\u7528\u94fe\u5f0f\u6cd5\u5219\u8ba1\u7b97":46,"\u6700\u597d\u7684\u6a21\u578b\u662f":69,"\u6700\u5c11\u663e\u793a\u591a\u5c11\u4e2a\u8282\u70b9":52,"\u6700\u5e38\u89c1\u7684\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u662fexcept":26,"\u6700\u65b0\u7684\u4ee3\u7801":45,"\u6700\u65b0log":69,"\u6700\u7ec8":46,"\u6700\u7ec8\u5b9e\u73b0\u4e00\u4e2a\u5c42\u6b21\u5316\u7684\u590d\u6742rnn":43,"\u6700\u7ec8\u6211\u4eec\u53ef\u4ee5\u8c03\u7528trainer\u7684train\u65b9\u6cd5\u542f\u52a8\u8bad\u7ec3":36,"\u6700\u7ec8\u751f\u6210\u540d\u4e3apaddl":33,"\u6700\u7ec8\u7684\u8f93\u51fa\u7ed3\u679c":43,"\u6700\u7ec8\u8d8b\u4e8e\u63a5\u8fd1":31,"\u6708\u6e56":41,"\u6709":41,"\u6709\u4e00\u4e2a\u57fa\u672c\u7684\u8ba4\u8bc6":55,"\u6709\u4e00\u4e9b\u5fc5\u987b\u914d\u7f6e\u7684\u53c2\u6570":[38,39],"\u6709\u4e24\u4e2a\u7279\u6b8a\u6807\u8bc6":70,"\u6709\u4e86meta\u914d\u7f6e\u6587\u4ef6\u4e4b\u540e":67,"\u6709\u4e9b\u5c42\u53ef\u80fd\u9700\u8981\u9ad8\u7cbe\u5ea6\u6765\u4fdd\u8bc1\u68af\u5ea6\u68c0\u67e5\u5355\u6d4b\u6b63\u786e\u6267\u884c":46,"\u6709\u4e9b\u5c42\u6216\u8005\u6fc0\u6d3b\u9700\u8981\u505a\u5f52\u4e00\u5316\u4ee5\u4fdd\u8bc1\u5b83\u4eec\u7684\u8f93\u51fa\u7684\u548c\u662f\u4e00\u4e2a\u5e38\u6570":46,"\u6709\u4e9b\u7279\u5f81\u7684\u53d6\u503c\u8fbe\u5230\u6570\u767e\u4e07":30,"\u6709\u4e9b\u7535\u5f71id\u53ef\u80fd\u4e0e\u5b9e\u9645\u7535\u5f71\u4e0d\u76f8\u7b26\u5408":66,"\u6709\u5173":41,"\u6709\u5173\u53c2\u6570\u914d\u7f6e\u7684\u8be6\u7ec6\u8bf4\u660e\u89c1":38,"\u6709\u5173\u5982\u4f55\u7f16\u5199\u6570\u636e\u63d0\u4f9b\u7a0b\u5e8f\u7684\u66f4\u591a\u7ec6\u8282\u63cf\u8ff0":44,"\u6709\u5173\u7ebf\u6027\u56de\u5f52\u7684\u5b9e\u9645\u5e94\u7528":36,"\u6709\u5173kubernetes\u76f8\u5173\u6982\u5ff5\u4ee5\u53ca\u5982\u4f55\u642d\u5efa\u548c\u914d\u7f6ekubernetes\u96c6\u7fa4":57,"\u6709\u52a9\u4e8e\u7406\u89e3\u7528\u6237\u5bf9\u4e0d\u540c\u516c\u53f8":69,"\u6709\u52a9\u4e8e\u8bca\u65ad\u5206\u5e03\u5f0f\u9519\u8bef":50,"\u6709\u65f6\u79f0\u4e3a":69,"\u6709\u6807\u51c6\u7684":26,"\u6709\u7684\u65f6\u5019":26,"\u6709\u7684\u65f6\u5019\u7b80\u7b80\u5355\u5355\u7684\u6539\u53d8\u5c31\u80fd\u5728\u6027\u80fd\u4e0a\u4ea7\u751f\u660e\u663e\u7684\u4f18\u5316\u6548\u679c":49,"\u670d\u52a1":41,"\u670d\u52a1\u5458":41,"\u672a\u5305\u542b\u5728\u5b57\u5178\u4e2d\u7684\u5355\u8bcd":70,"\u672a\u6807\u8bb0\u7684\u8bc4\u4ef7\u6837\u672c":69,"\u672a\u77e5\u8bcd":61,"\u672c\u4f8b\u4e2d\u4e3a0":61,"\u672c\u4f8b\u4e2d\u4e3a32":61,"\u672c\u4f8b\u4e2d\u4e3a4":61,"\u672c\u4f8b\u4e2d\u4f7f\u7528for\u5faa\u73af\u8fdb\u884c\u591a\u6b21\u8c03\u7528":3,"\u672c\u4f8b\u4e2d\u7684\u539f\u59cb\u6570\u636e\u4e00\u5171\u670910\u4e2a\u6837\u672c":41,"\u672c\u4f8b\u4e2d\u7684\u8f93\u5165\u7279\u5f81\u662f\u8bcdid\u7684\u5e8f\u5217":3,"\u672c\u4f8b\u6839\u636e\u7f51\u7edc\u914d\u7f6e\u4e2d":3,"\u672c\u4f8b\u6bcf\u884c\u4fdd\u5b58\u4e00\u6761\u6837\u672c":65,"\u672c\u4f8b\u7531\u6613\u5230\u96be\u5c55\u793a4\u79cd\u4e0d\u540c\u7684\u6587\u672c\u5206\u7c7b\u7f51\u7edc\u914d\u7f6e":65,"\u672c\u4f8b\u7684":3,"\u672c\u4f8b\u7684\u6240\u6709\u5b57\u7b26\u90fd\u5c06\u8f6c\u6362\u4e3a\u8fde\u7eed\u6574\u6570\u8868\u793a\u7684id\u4f20\u7ed9\u6a21\u578b":65,"\u672c\u4f8b\u91c7\u7528\u82f1\u6587\u60c5\u611f\u5206\u7c7b\u7684\u6570\u636e":3,"\u672c\u4f8b\u91c7\u7528adam\u4f18\u5316\u65b9\u6cd5":65,"\u672c\u5217\u8868\u8bf4\u660epaddle\u53d1\u7248\u4e4b\u524d\u9700\u8981\u6d4b\u8bd5\u7684\u529f\u80fd\u70b9":29,"\u672c\u5730\u6d4b\u8bd5":51,"\u672c\u5730\u8bad\u7ec3":51,"\u672c\u5730\u8bad\u7ec3\u7684\u5b9e\u9a8c":54,"\u672c\u5b9e\u4f8b\u4e2d":61,"\u672c\u5c0f\u8282\u6211\u4eec\u5c06\u4ecb\u7ecd\u6a21\u578b\u7f51\u7edc\u7ed3\u6784":65,"\u672c\u5c42\u5c3a\u5bf8":63,"\u672c\u5c42\u6709\u56db\u4e2a\u53c2\u6570":63,"\u672c\u6559\u7a0b\u4e2d\u6211\u4eec\u7ed9\u51fa\u4e86\u4e09\u4e2aresnet\u6a21\u578b":63,"\u672c\u6559\u7a0b\u5c06\u4ecb\u7ecd\u4f7f\u7528\u6df1\u5ea6\u53cc\u5411\u957f\u77ed\u671f\u8bb0\u5fc6":68,"\u672c\u6559\u7a0b\u5c06\u6307\u5bfc\u4f60\u5982\u4f55\u5728":44,"\u672c\u6559\u7a0b\u5c06\u6307\u5bfc\u60a8\u5b8c\u6210\u957f\u671f\u77ed\u671f\u8bb0\u5fc6":69,"\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7528\u4e8eimagenet\u4e0a\u7684\u5377\u79ef\u5206\u7c7b\u7f51\u7edc\u6a21\u578b":63,"\u672c\u6587\u4e2d\u6240\u6709\u7684\u4f8b\u5b50":41,"\u672c\u6587\u4e2d\u7531\u4e8e\u8f93\u5165\u6570\u636e\u662f\u968f\u673a\u751f\u6210\u7684\u4e0d\u9700\u8981\u8bfb\u8f93\u5165\u6587\u4ef6":31,"\u672c\u6587\u4e2d\u793a\u4f8b\u6240\u4f7f\u7528\u7684\u5355\u5143\u6d4b\u8bd5\u6587\u4ef6\u662f":41,"\u672c\u6587\u4ee5paddlepaddle\u7684\u53cc\u5c42rnn\u5355\u5143\u6d4b\u8bd5\u4e3a\u793a\u4f8b":41,"\u672c\u6587\u53ea\u4f7f\u7528\u4e86\u9ed8\u8ba4\u547d\u540d\u7a7a\u95f4":55,"\u672c\u6587\u5c06\u4ecb\u7ecd\u5728kubernetes\u5bb9\u5668\u7ba1\u7406\u5e73\u53f0\u4e0a\u5feb\u901f\u6784\u5efapaddlepaddle\u5bb9\u5668\u96c6\u7fa4":57,"\u672c\u6587\u6863\u4ecb\u7ecd\u5982\u4f55\u5728paddlepaddle\u5e73\u53f0\u4e0a":61,"\u672c\u6587\u6863\u5185\u4e0d\u91cd\u590d\u4ecb\u7ecd":55,"\u672c\u6587\u6863\u5c06\u4ee5linux":38,"\u672c\u6587\u6863\u63cf\u8ff0paddl":27,"\u672c\u6765":41,"\u672c\u6b21\u8bad\u7ec3\u6587\u4ef6\u6240\u5728\u76ee\u5f55":57,"\u672c\u6b21\u8bad\u7ec3\u7684yaml\u6587\u4ef6\u53ef\u4ee5\u5199\u6210":57,"\u672c\u6b21\u8bad\u7ec3\u8981\u6c42\u67093\u4e2apaddlepaddle\u8282\u70b9":57,"\u672c\u6b21\u8bd5\u9a8c":65,"\u672c\u793a\u4f8b\u4e2d\u4f7f\u7528\u7684\u539f\u59cb\u6570\u636e\u5982\u4e0b":41,"\u672c\u793a\u4f8b\u610f\u56fe\u4f7f\u7528\u5355\u5c42rnn\u548c\u53cc\u5c42rnn\u5b9e\u73b0\u4e24\u4e2a\u5b8c\u5168\u7b49\u4ef7\u7684\u5168\u8fde\u63a5rnn":41,"\u672c\u793a\u4f8b\u7684\u9884\u6d4b\u7ed3\u679c":69,"\u672c\u7bc7\u6559\u7a0b\u5728paddlepaddle\u4e2d\u91cd\u73b0\u4e86\u8fd9\u4e00\u826f\u597d\u7684\u8bad\u7ec3\u7ed3\u679c":70,"\u672c\u7bc7\u6559\u7a0b\u5c06\u4f1a\u6307\u5bfc\u4f60\u901a\u8fc7\u8bad\u7ec3\u4e00\u4e2a":70,"\u672c\u8d28\u4e0a\u4e0e\u8bad\u7ec3\u6a21\u578b\u4e00\u6837":70,"\u673a\u5668\u7684\u8bbe\u5907":54,"\u673a\u5668\u7ffb\u8bd1":[29,64],"\u6743\u91cd\u66f4\u65b0\u7684\u68af\u5ea6":52,"\u6761\u4ef6\u4e0b":55,"\u6765":41,"\u6765\u4ee3\u66ff":45,"\u6765\u4fdd\u8bc1\u8bad\u7ec3\u8fc7\u7a0b\u53ef\u4ee5\u4ece\u4e2d\u95f4\u72b6\u6001\u91cd\u65b0\u542f\u52a8":19,"\u6765\u505a\u68af\u5ea6\u68c0\u67e5":46,"\u6765\u505ableu\u8bc4\u4f30":70,"\u6765\u505c\u6b62\u8bad\u7ec3":67,"\u6765\u5206\u6790\u6267\u884c\u6587\u4ef6":49,"\u6765\u5206\u79bb\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6587\u4ef6":67,"\u6765\u5206\u9694\u6bcf\u4e00\u884c":67,"\u6765\u521d\u59cb\u5316\u53c2\u6570":30,"\u6765\u5b89\u88c5":50,"\u6765\u5b8c\u6210\u7f51\u7edc\u7684\u8bad\u7ec3":36,"\u6765\u5b9a\u4e49\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":44,"\u6765\u5bf9\u6bd4\u5206\u6790\u4e24\u8005\u8bed\u4e49\u76f8\u540c\u7684\u539f\u56e0":41,"\u6765\u5e2e\u52a9\u4f60\u7406\u89e3paddlepaddle\u7684\u5185\u90e8\u8fd0\u884c\u673a\u5236":65,"\u6765\u5f00\u542f\u672c\u5730\u7684\u8bad\u7ec3":69,"\u6765\u5f71\u54cdpaddlepaddle\u7684\u7f16\u8bd1\u8fc7\u7a0b":[38,39],"\u6765\u5f97\u5230\u67d0\u4e2a\u7279\u5b9a\u53c2\u6570\u7684\u68af\u5ea6\u77e9\u9635":46,"\u6765\u6307\u5b9a\u7f51\u7edc\u5c42\u7684\u6570\u76ee":63,"\u6765\u63a5\u53d7\u4e0d\u4f7f\u7528\u7684\u51fd\u6570\u4ee5\u4fdd\u8bc1\u517c\u5bb9\u6027":3,"\u6765\u642d\u5efa\u795e\u7ecf\u7f51\u7edc":36,"\u6765\u6ce8\u518c\u8be5\u5c42":46,"\u6765\u6df7\u5408\u4f7f\u7528gpu\u548ccpu\u8ba1\u7b97\u7f51\u7edc\u5c42\u7684\u53c2\u6570":54,"\u6765\u751f\u6210\u5e8f\u5217":70,"\u6765\u7684\u79d2\u6570":66,"\u6765\u786e\u4fdd\u628a":26,"\u6765\u786e\u5b9a\u5bf9\u5e94\u5173\u7cfb":3,"\u6765\u81ea\u5b9a\u4e49\u4f20\u6570\u636e\u7684\u8fc7\u7a0b":2,"\u6765\u83b7\u5f97\u8f93\u51fa\u7684\u68af\u5ea6":46,"\u6765\u8868\u793a":44,"\u6765\u8868\u793a\u53c2\u6570\u4f4d\u7f6e":68,"\u6765\u8868\u793a\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":44,"\u6765\u8868\u793apaddle\u5185\u90e8\u7c7b":26,"\u6765\u8ba1\u7b97\u68af\u5ea6":46,"\u6765\u8bb2\u89e3\u5982\u4f55\u4f7f\u7528\u53cc\u5c42rnn":41,"\u6765\u8bbe\u7f6e":30,"\u6765\u8bbf\u95ee\u7528\u6237\u81ea\u5df1\u7684\u6570\u636e":20,"\u6765\u8bf4\u660epydataprovider2\u7684\u7b80\u5355\u4f7f\u7528\u573a\u666f":3,"\u6765\u8c03\u6574":45,"\u6765\u8fd0\u884c":50,"\u6765\u8fd0\u884c\u6027\u80fd\u5206\u6790\u548c\u8c03\u4f18":49,"\u6765\u8fd0\u884c\u955c\u50cf":33,"\u6765\u8fdb\u884c\u8ba8\u8bba":27,"\u6765\u9884\u6d4b\u8fd9\u4e2a\u4e2d\u95f4\u7684\u8bcd":30,"\u676f\u5b50":41,"\u6784\u5efa":38,"\u6784\u6210\u4e86\u8f93\u51fa\u53cc\u5c42\u5e8f\u5217\u7684\u7b2ci\u4e2a":40,"\u6784\u9020":57,"\u6784\u9020paddl":5,"\u67b6\u6784\u6765\u8bad\u7ec3\u60c5\u611f\u5206\u6790\u6a21\u578b":69,"\u67d0\u4e00\u4e2a\u795e\u7ecf\u5143\u7684\u4e00\u4e2a\u8f93\u5165\u4e3a\u4e0a\u4e00\u4e2a\u65f6\u95f4\u6b65\u7f51\u7edc\u4e2d\u67d0\u4e00\u4e2a\u795e\u7ecf\u5143\u7684\u8f93\u51fa":41,"\u67d0\u4e9b\u53c2\u6570\u53ea\u53ef\u7528\u4e8e\u7279\u5b9a\u7684\u5c42\u4e2d":51,"\u67e5\u770b":[45,65],"\u67e5\u770b\u5b89\u88c5\u540e\u7684paddl":35,"\u67e5\u770b\u5f53\u524d\u72b6\u6001":45,"\u67e5\u770b\u5f53\u524d\u8fdc\u7a0b\u4ed3\u5e93\u7684\u540d\u5b57":45,"\u67e5\u770b\u6587\u4ef6\u5177\u4f53\u88ab\u4fee\u6539\u7684\u5185\u5bb9":45,"\u67e5\u770bjob\u7684\u8be6\u7ec6\u60c5\u51b5":56,"\u6807\u51c6\u5dee\u4e3a":30,"\u6807\u51c6\u8868\u793apaddle\u7248\u672c\u53f7":29,"\u6807\u51c6lstm\u4ee5\u6b63\u5411\u5904\u7406\u8be5\u5e8f\u5217":68,"\u6807\u793a\u56fe\u7247\u662f\u5f69\u8272\u56fe\u6216\u7070\u5ea6\u56fe":62,"\u6807\u793a\u662f\u5426\u4e3a\u5f69\u8272\u56fe\u7247":62,"\u6807\u7b7e0\u8868\u793a\u8d1f\u9762\u7684\u8bc4\u8bba":69,"\u6807\u7b7e1\u8868\u793a\u6b63\u9762\u7684\u8bc4\u8bba":69,"\u6807\u7b7e\u4e0b\u627e\u5230\u6700\u65b0\u7684paddle\u955c\u50cf\u7248\u672c":33,"\u6807\u7b7e\u6587\u4ef6":68,"\u6807\u7b7e\u65b9\u6848\u6765\u81ea":68,"\u6807\u8bc6\u662f\u5426\u4e3a\u8fde\u7eed\u7684batch\u8ba1\u7b97":52,"\u6839\u636e\u4f60\u7684\u4efb\u52a1":54,"\u6839\u636e\u524d\u6587\u7684\u63cf\u8ff0":57,"\u6839\u636e\u5728\u6a21\u578b\u914d\u7f6e\u6587\u4ef6\u4e2d\u4f7f\u7528\u7684\u540d\u4e3a":50,"\u6839\u636e\u6570\u636e\u91cf\u89c4\u6a21":66,"\u6839\u636e\u7528\u6237\u6307\u5b9a\u7684\u5b57\u5178":61,"\u6839\u636e\u7d22\u5f15\u77e9\u9635\u548c\u5b57\u5178\u6253\u5370\u6587\u672c":44,"\u6839\u636e\u7f51\u7edc\u914d\u7f6e\u4e2d\u7684":52,"\u6839\u636e\u8fd9\u4e9b\u53c2\u6570\u7684\u4f7f\u7528\u573a\u5408":51,"\u6839\u636e\u9ed8\u8ba4\u503c\u9012\u589e":52,"\u6839\u636e\u9ed8\u8ba4\u7aef\u53e3\u53f7\u9012\u589e":52,"\u6839\u636ecpu":33,"\u6839\u636ejob\u5bf9\u5e94\u7684pod\u4fe1\u606f":56,"\u683c\u5f0f":52,"\u683c\u5f0f\u5982\u4e0b":65,"\u683c\u5f0f\u8bf4\u660e":61,"\u68af\u5ea6\u4f1a\u5c31\u5730":46,"\u68af\u5ea6\u53c2\u6570\u7684\u5206\u5757\u6570\u76ee":52,"\u68af\u5ea6\u5c31\u53ef\u4ee5\u901a\u8fc7\u8fd9\u4e2a\u65b9\u7a0b\u8ba1\u7b97\u5f97\u5230":46,"\u68af\u5ea6\u670d\u52a1\u5668\u7684\u6570\u91cf":52,"\u68af\u5ea6\u68c0\u67e5\u5355\u5143\u6d4b\u8bd5\u901a\u8fc7\u6709\u9650\u5dee\u5206\u6cd5\u6765\u9a8c\u8bc1\u4e00\u4e2a\u5c42\u7684\u68af\u5ea6":46,"\u68af\u5ea6\u68c0\u67e5\u7684\u8f93\u5165\u6570\u636e\u7684\u6279\u6b21\u5927\u5c0f":46,"\u68c0\u67e5\u70b9\u4fdd\u5b58\u7a0b\u5e8f\u6d41\u7a0b":19,"\u68d2":65,"\u697c\u5c42":41,"\u6a21\u5757":62,"\u6a21\u5757\u4e2d\u7684":3,"\u6a21\u578b":68,"\u6a21\u578b\u4e00\u76f4\u4e0d\u6536\u655b":30,"\u6a21\u578b\u4fdd\u5b58\u5728\u76ee\u5f55":69,"\u6a21\u578b\u5171\u5305\u542b1":61,"\u6a21\u578b\u5217\u8868\u6587\u4ef6":68,"\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9\u901a\u8fc7\u5b9a\u671f\u5411\u78c1\u76d8\u4e0a\u4fdd\u5b58\u4e00\u4efd\u5b58\u50a8\u5728paramet":19,"\u6a21\u578b\u53ca\u53c2\u6570\u4f1a\u88ab\u4fdd\u5b58\u5728\u8def\u5f84":62,"\u6a21\u578b\u5b58\u50a8\u8def\u5f84":65,"\u6a21\u578b\u5c06\u4fdd\u5b58\u5728\u76ee\u5f55":68,"\u6a21\u578b\u5c31\u8bad\u7ec3\u6210\u529f\u4e86":70,"\u6a21\u578b\u6570\u636e\u68c0\u67e5\u70b9\u7684\u5b9e\u73b0":19,"\u6a21\u578b\u6587\u4ef6\u5c06\u88ab\u5199\u5165\u8282\u70b9":50,"\u6a21\u578b\u6765\u5c06\u6cd5\u8bed\u7ffb\u8bd1\u6210\u82f1\u8bed":70,"\u6a21\u578b\u6765\u6307\u5bfc\u4f60\u5b8c\u6210\u8fd9\u4e9b\u6b65\u9aa4":44,"\u6a21\u578b\u6f14\u793a\u5982\u4f55\u914d\u7f6e\u590d\u6742\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u6a21\u578b":44,"\u6a21\u578b\u7684\u4ee3\u7801\u53ef\u4ee5\u5728":44,"\u6a21\u578b\u7684\u7ed3\u6784\u548c\u8bad\u7ec3\u8fc7\u7a0b":61,"\u6a21\u578b\u7684\u7f16\u7801\u5668\u90e8\u5206\u5982\u4e0b\u6240\u793a":44,"\u6a21\u578b\u88ab\u4fdd\u5b58\u5728":67,"\u6a21\u578b\u8bad\u7ec3\u4f1a\u770b\u5230\u7c7b\u4f3c\u4e0a\u9762\u8fd9\u6837\u7684\u65e5\u5fd7\u4fe1\u606f":65,"\u6a21\u578b\u8bad\u7ec3\u548c\u6700\u540e\u7684\u7ed3\u679c\u9a8c\u8bc1":31,"\u6a21\u578b\u8bad\u7ec3\u7b49\u4efb\u52a1":36,"\u6a21\u578b\u8def\u5f84":[63,68],"\u6a21\u578b\u8f93\u51fa\u8def\u5f84":68,"\u6a21\u578b\u914d\u7f6e":1,"\u6a21\u578b\u914d\u7f6e\u89e3\u6790":26,"\u6a21\u578b\u91c7\u7528":61,"\u6a21\u578b\u9884\u6d4b":5,"\u6b21":41,"\u6b22\u8fce\u901a\u8fc7":45,"\u6b63\u6837\u672c":65,"\u6b63\u786e\u7684\u89e3\u51b3\u65b9\u6cd5\u662f":30,"\u6b63\u8d1f\u5bf9\u9a8c\u8bc1":51,"\u6b63\u9762\u7684\u8bc4\u8bba\u7684\u5f97\u5927\u4e8e\u7b49\u4e8e7":69,"\u6b63\u9762\u8bc4\u4ef7\u6837\u672c":69,"\u6b64\u547d\u4ee4\u5c06\u5728your":38,"\u6b64\u5904":61,"\u6b64\u5904\u90fd\u4e3a2":41,"\u6b64\u5916":[38,45],"\u6b64\u6559\u7a0b\u5c06\u5411\u60a8\u5206\u6b65\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u5185\u7f6e\u7684\u5b9a\u65f6\u5de5\u5177":49,"\u6b64\u6570\u636e\u96c6\u5305\u542b\u7535\u5f71\u8bc4\u8bba\u53ca\u5176\u76f8\u5173\u8054\u7684\u7c7b\u522b\u6807\u7b7e":69,"\u6b64\u65f6\u60a8\u53ef\u4ee5\u8fd0\u884c\u8fd9\u4e2a\u547d\u4ee4\u5728\u5f00\u53d1\u673a\u4e0a\u8fdb\u884c\u6d4b\u8bd5\u8fd0\u884c":33,"\u6b64\u65f6master\u5c06\u8d1f\u8d23\u542f\u52a8\u4e00\u4e2a\u65b0\u7684train":19,"\u6bb5\u843d\u53ef\u4ee5\u770b\u4f5c\u662f\u4e00\u4e2a\u5d4c\u5957\u7684\u53cc\u5c42\u7684\u5e8f\u5217":43,"\u6bcf100\u4e2abatch\u6253\u5370\u4e00\u6b21\u7edf\u8ba1\u4fe1\u606f":69,"\u6bcf100\u4e2abatch\u663e\u793a\u53c2\u6570\u7edf\u8ba1":68,"\u6bcf20\u4e2abatch\u6253\u5370\u4e00\u6b21\u65e5\u5fd7":69,"\u6bcf20\u4e2abatch\u8f93\u51fa\u65e5\u5fd7":68,"\u6bcf\u4e00\u4e2a":29,"\u6bcf\u4e00\u4e2a\u4efb\u52a1\u6d41\u7a0b\u90fd\u53ef\u4ee5\u88ab\u5212\u5206\u4e3a\u5982\u4e0b\u4e94\u4e2a\u6b65\u9aa4":65,"\u6bcf\u4e00\u4e2a\u6587\u4ef6\u662f\u6570\u636e\u96c6\u7684\u4e00\u4e2ashard":20,"\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65":41,"\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u4e4b\u95f4\u7684\u795e\u7ecf\u7f51\u7edc\u5177\u6709\u4e00\u5b9a\u7684\u76f8\u5173\u6027":41,"\u6bcf\u4e00\u4e2a\u6d4b\u8bd5\u5468\u671f\u6d4b\u8bd5\u4e00\u6b21\u6240\u6709\u6570\u636e":67,"\u6bcf\u4e00\u4e2a\u8282\u70b9\u90fd\u6709\u76f8\u540c\u7684\u65e5\u5fd7\u7ed3\u6784":50,"\u6bcf\u4e00\u4e2akey\u7531":67,"\u6bcf\u4e00\u7ec4\u5185\u7684\u6240\u6709\u53e5\u5b50\u548clabel":41,"\u6bcf\u4e00\u884c\u8868\u793a\u4e00\u4e2a\u5b9e\u4f8b":69,"\u6bcf\u4e2a":[44,50,68],"\u6bcf\u4e2a\u503c\u7684\u7c7b\u578b\u53ef\u4ee5\u662f\u6574\u5f62":20,"\u6bcf\u4e2a\u5143\u7d20\u662f\u4e00\u4e2a0\u5c42\u5e8f\u5217":40,"\u6bcf\u4e2a\u5143\u7d20\u662f\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":40,"\u6bcf\u4e2a\u5355\u5c42rnn":43,"\u6bcf\u4e2a\u5355\u8bcd\u7684\u9884\u6d4b\u9519\u8bef\u7387":70,"\u6bcf\u4e2a\u53e5\u5b50\u53c8\u662f\u5355\u8bcd\u7684\u6570\u7ec4":41,"\u6bcf\u4e2a\u53e5\u5b50\u90fd\u4ee5\u5f00\u59cb\u6807\u8bb0\u5f00\u5934":44,"\u6bcf\u4e2a\u53e5\u5b50\u90fd\u4ee5\u7ed3\u675f\u6807\u8bb0\u7ed3\u5c3e":44,"\u6bcf\u4e2a\u5b50\u5e8f\u5217\u957f\u5ea6\u53ef\u4ee5\u4e0d\u4e00\u81f4":41,"\u6bcf\u4e2a\u5b50\u6587\u4ef6\u5939\u4e0b\u5b58\u50a8\u76f8\u5e94\u5206\u7c7b\u7684\u56fe\u7247":62,"\u6bcf\u4e2a\u5b57\u5178\u5305\u542b\u603b\u517130000\u4e2a\u5355\u8bcd":70,"\u6bcf\u4e2a\u5b57\u5178\u90fd\u6709dictsize\u4e2a\u5355\u8bcd":70,"\u6bcf\u4e2a\u5c42\u5728\u5176":46,"\u6bcf\u4e2a\u5c42\u90fd\u6709\u4e00\u4e2a\u6216\u591a\u4e2ainput":65,"\u6bcf\u4e2a\u6279\u6b21\u6570\u636e":52,"\u6bcf\u4e2a\u6574\u6570\u5217\u8868\u88ab\u89c6\u4e3a\u4e00\u4e2a\u6574\u6570\u5e8f\u5217":44,"\u6bcf\u4e2a\u6587\u4ef6\u5939\u90fd\u5305\u542b\u6cd5\u8bed\u5230\u82f1\u8bed\u7684\u5e73\u884c\u8bed\u6599\u5e93":70,"\u6bcf\u4e2a\u6587\u4ef6\u662f\u4e00\u4e2a\u7535\u5f71\u8bc4\u8bba":69,"\u6bcf\u4e2a\u6587\u672c\u6587\u4ef6\u5305\u542b\u4e00\u4e2a\u6216\u8005\u591a\u4e2a\u5b9e\u4f8b":69,"\u6bcf\u4e2a\u65f6\u95f4\u6b65\u4e4b\u5185\u7684\u8fd0\u7b97\u662f\u72ec\u7acb\u7684":43,"\u6bcf\u4e2a\u65f6\u95f4\u6b65\u90fd\u7528\u4e86\u4e0a\u4e00\u4e2a\u65f6\u95f4\u6b65\u7684\u8f93\u51fa\u7ed3\u679c":41,"\u6bcf\u4e2a\u6743\u91cd\u5bf9\u5e94\u4e00\u4e2a\u8f93\u5165":46,"\u6bcf\u4e2a\u6837\u672c\u7531\u4e24\u90e8\u5206\u7ec4\u6210":41,"\u6bcf\u4e2a\u6837\u672c\u95f4\u7528\u7a7a\u884c\u5206\u5f00":41,"\u6bcf\u4e2a\u6d4b\u8bd5\u5468\u671f\u6d4b\u8bd5":67,"\u6bcf\u4e2a\u7279\u5f81\u7684meta\u914d\u7f6e":67,"\u6bcf\u4e2a\u72b6\u6001":43,"\u6bcf\u4e2a\u7c7b\u522b\u4e2d\u968f\u673a\u62bd\u53d6\u4e8610\u5f20\u56fe\u7247":62,"\u6bcf\u4e2a\u7c7b\u5305\u542b6000\u5f20":62,"\u6bcf\u4e2a\u7ebf\u7a0b":52,"\u6bcf\u4e2a\u7ebf\u7a0b\u5206\u914d\u5230128\u4e2a\u6837\u672c\u7528\u4e8e\u8bad\u7ec3":52,"\u6bcf\u4e2a\u8282\u70b9\u6709\u4e24\u4e2a6\u6838cpu":70,"\u6bcf\u4e2a\u8bad\u7ec3\u8282\u70b9\u5fc5\u987b\u6307\u5b9a\u4e00\u4e2a\u552f\u4e00\u7684id\u53f7":52,"\u6bcf\u4e2a\u8bb0\u5fc6\u5355\u5143\u5305\u542b\u56db\u4e2a\u4e3b\u8981\u7684\u5143\u7d20":69,"\u6bcf\u4e2a\u8bc4\u8bba\u7684\u7f51\u5740":69,"\u6bcf\u4e2a\u8f93\u5165\u90fd\u662f\u4e00\u4e2a":46,"\u6bcf\u4e2a\u8f93\u51fa\u8282\u70b9\u90fd\u8fde\u63a5\u5230\u6240\u6709\u7684\u8f93\u5165\u8282\u70b9\u4e0a":46,"\u6bcf\u4e2a\u91cc\u9762\u90fd\u5305\u542b202mb\u7684\u5168\u90e8\u7684\u6a21\u578b\u53c2\u6570":70,"\u6bcf\u4e2acommit\u53ea\u505a\u4e86\u5c11\u91cf\u7684\u4fee\u6539":45,"\u6bcf\u4e2adata":20,"\u6bcf\u4e2aparamet":19,"\u6bcf\u4e2apass\u7684\u7b2c0\u4e2abatch\u5230\u5f53\u524dbatch\u6240\u6709\u6837\u672c\u7684\u5e73\u5747\u5206\u7c7b\u9519\u8bef\u7387":65,"\u6bcf\u4e2apass\u7684\u7b2c0\u4e2abatch\u5230\u5f53\u524dbatch\u6240\u6709\u6837\u672c\u7684\u5e73\u5747cost":65,"\u6bcf\u4e2apod\u5305\u542b\u4e00\u4e2apaddlepaddle\u5bb9\u5668":57,"\u6bcf\u4e2ashard\u5206\u522b\u5b58\u50a8\u5728\u5176\u4e2d\u4e00\u53f0paramet":19,"\u6bcf\u4f4d\u7528\u6237\u81f3\u5c11\u670920\u6761\u8bc4\u5206":66,"\u6bcf\u5c42\u4e0a\u53ea\u80fd\u4fdd\u5b58\u56fa\u5b9a\u6570\u76ee\u4e2a\u6700\u597d\u7684\u72b6\u6001":52,"\u6bcf\u5c42\u4f7f\u7528\u7684gpu\u53f7\u4f9d\u8d56\u4e8e\u53c2\u6570train":54,"\u6bcf\u5f53\u6a21\u578b\u5728\u7ffb\u8bd1\u8fc7\u7a0b\u4e2d\u751f\u6210\u4e86\u4e00\u4e2a\u5355\u8bcd":70,"\u6bcf\u6279\u6b21":52,"\u6bcf\u6b21":33,"\u6bcf\u6b21\u63d0\u4ea4\u4ee3\u7801":45,"\u6bcf\u6b21\u63d0\u4ea4\u65f6":45,"\u6bcf\u6b21\u6d4b\u8bd5\u90fd\u6d4b\u8bd5\u6240\u6709\u6570\u636e":69,"\u6bcf\u6b21\u751f\u62101\u4e2a\u5e8f\u5217":70,"\u6bcf\u6b21\u8bfb\u53d6\u4e00\u6761\u6570\u636e\u540e":65,"\u6bcf\u6b21\u8f93\u51fa\u4e00\u4e2adata":20,"\u6bcf\u6b21\u90fd\u4f1a\u4ecepython\u7aef\u8bfb\u53d6\u6570\u636e":3,"\u6bcf\u884c\u5b58\u50a8\u4e00\u4e2a\u8bcd":61,"\u6bcf\u884c\u5b58\u50a8\u7684\u662f\u4e00\u4e2a\u6837\u672c\u7684\u7279\u5f81":63,"\u6bcf\u884c\u6253\u537032\u4e2a\u53c2\u6570\u4ee5":61,"\u6bcf\u884c\u8868\u793a\u4e00\u4e2a\u6279\u6b21\u4e2d\u7684\u5355\u4e2a\u8f93\u5165":46,"\u6bcf\u884c\u90fd\u662f\u4e00\u4e2a\u6cd5\u8bed\u6216\u8005\u82f1\u8bed\u7684\u53e5\u5b50":70,"\u6bcf\u8f6e\u4f1a\u5c06\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u8bad\u7ec3\u6837\u672c\u4f7f\u7528\u4e00\u6b21":52,"\u6bcf\u8f6e\u7ed3\u675f\u65f6\u5bf9\u6240\u6709\u6d4b\u8bd5\u6570\u636e\u8fdb\u884c\u6d4b\u8bd5":52,"\u6bcf\u8f6e\u90fd\u4f1a\u4fdd\u5b58\u9884\u6d4b\u7ed3\u679c":52,"\u6bcf\u8fd0\u884c\u591a\u5c11\u4e2a\u6279\u6b21\u6267\u884c\u4e00\u6b21\u7a00\u758f\u53c2\u6570\u5206\u5e03\u7684\u68c0\u67e5":52,"\u6bcf\u969410\u5206\u949f":19,"\u6bcf\u9694\u591a\u5c11batch\u6253\u5370\u4e00\u6b21\u65e5\u5fd7":65,"\u6bcfdot":52,"\u6bcflog":52,"\u6bcfsave":52,"\u6bcftest":52,"\u6bd4\u5982":[20,30,33,38,45,65],"\u6bd4\u5982\u4e00\u53e5\u8bdd\u4e2d\u7684\u6bcf\u4e00\u4e2a\u5355\u8bcd":41,"\u6bd4\u5982\u5982\u679c\u8981build\u4e00\u4e2a\u4e0d\u4f9d\u8d56gpu":45,"\u6bd4\u5982\u5e0c\u671b\u6700\u5c0f\u5316\u751f\u6210\u7684\u5e93\u7684\u5927\u5c0f":[38,39],"\u6bd4\u5982\u6bcf\u969410\u5206\u949f\u6700\u65b0\u7684\u5feb\u7167":19,"\u6bd4\u5982\u6d41\u5f0f\u6570\u636e\u5904\u7406":20,"\u6bd4\u5982\u8bbe\u7f6e\u4e00\u4e2a\u5168\u8fde\u63a5\u5c42\u7684\u53c2\u6570\u521d\u59cb\u5316\u65b9\u5f0f\u548cbias\u521d\u59cb\u5316\u65b9\u5f0f":30,"\u6bd4\u5982\u901a\u8fc78080\u7aef\u53e3":55,"\u6bd4\u5982ide\u914d\u7f6e\u91cc":45,"\u6bd4\u5982imagenet\u8fd9\u4e2a\u6570\u636e\u96c6\u53ef\u80fd\u88ab\u5206\u62101000\u4e2ashard":20,"\u6c34\u6e29":41,"\u6c49\u5ead":41,"\u6c60\u5316\u5c42":62,"\u6ca1":41,"\u6ca1\u6709\u4f5c\u7528":3,"\u6ca1\u6709\u4f7f\u7528avx\u6307\u4ee4\u96c6":35,"\u6ca1\u6709\u5b9e\u9645\u610f\u4e49":61,"\u6ca1\u6709\u6d4b\u8bd5\u6570\u636e":3,"\u6ca1\u6709\u8bbe\u7f6e":[38,39],"\u6ca1\u6709\u8fdb\u884c\u6b63\u786e\u6027\u7684\u68c0\u67e5":66,"\u6ca1\u6709\u8fdb\u884c\u7ed3\u6784\u7684\u5fae\u8c03":67,"\u6cd5\u8bed":70,"\u6ce8":19,"\u6ce8\u610f":[3,32,33,36,38,39,44,46,47,57,62],"\u6ce8\u610f\u4e0a\u8ff0\u547d\u4ee4\u4e2d":57,"\u6ce8\u610f\u5230\u6211\u4eec\u5df2\u7ecf\u5047\u8bbe\u673a\u5668\u4e0a\u67094\u4e2agpu":54,"\u6ce8\u610f\u5e94\u8be5\u786e\u4fdd\u9ed8\u8ba4\u6a21\u578b\u8def\u5f84":69,"\u6ce8\u610f\u9884\u6d4b\u6570\u636e\u901a\u5e38\u4e0d\u5305\u542blabel":5,"\u6ce8\u610fnode":57,"\u6ce8\u91ca\u6389":69,"\u6cf3\u6c60":41,"\u6d41":41,"\u6d41\u7a0b\u6765\u63d0\u4ea4\u4ee3\u7801":45,"\u6d44":41,"\u6d4b\u8bd5":45,"\u6d4b\u8bd5\u6570\u636e":50,"\u6d4b\u8bd5\u6570\u636e\u4e5f\u5305\u542b":50,"\u6d4b\u8bd5\u6570\u636e\u548c\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":50,"\u6d4b\u8bd5\u6570\u636e\u548c\u751f\u6210\u6570\u636e":70,"\u6d4b\u8bd5\u6570\u636e\u653e\u7f6e\u5728\u5de5\u4f5c\u7a7a\u95f4\u4e2d\u4e0d\u540c\u76ee\u5f55\u7684\u8981\u6c42":50,"\u6d4b\u8bd5\u6570\u636e\u7684\u6240\u6709\u76f8\u5bf9\u6216\u7edd\u5bf9\u6587\u4ef6\u8def\u5f84":50,"\u6d4b\u8bd5\u6570\u6910\u96c6":69,"\u6d4b\u8bd5\u65f6\u6307\u5b9a\u7684\u5b58\u50a8\u6a21\u578b\u5217\u8868\u7684\u6587\u4ef6":52,"\u6d4b\u8bd5\u65f6\u9ed8\u8ba4\u4e0dshuffl":3,"\u6d4b\u8bd5\u662f":45,"\u6d4b\u8bd5\u6837\u672c":50,"\u6d4b\u8bd5\u6a21\u578b\u662f\u6307\u4f7f\u7528\u8bad\u7ec3\u51fa\u7684\u6a21\u578b\u8bc4\u4f30\u5df2\u6807\u8bb0\u7684\u9a8c\u8bc1\u96c6":69,"\u6d4b\u8bd5\u7684\u6a21\u578b\u5305\u62ec\u4ece\u7b2cm\u8f6e\u5230\u7b2cn":54,"\u6d4b\u8bd5\u811a\u672c\u662f":68,"\u6d4b\u8bd5\u96c6\u548c\u8bad\u7ec3\u96c6\u76ee\u5f55\u5305\u542b\u4e0b\u9762\u7684\u6587\u4ef6":69,"\u6d4b\u8bd5docker\u955c\u50cf":29,"\u6d4b\u8bd5model_list":51,"\u6d4b\u8bd5save_dir":51,"\u6d4f\u89c8\u4ee3\u7801":33,"\u6d6a\u6f2b\u7247":66,"\u6d6e\u70b9\u578b\u6570\u636e":20,"\u6d6e\u70b9\u5f02\u5e38\u901a\u5e38\u7684\u539f\u56e0\u662f\u6d6e\u70b9\u6570\u6ea2\u51fa":30,"\u6d6e\u70b9\u6570\u5360\u7528\u7684\u5b57\u8282\u6570":61,"\u6d6e\u70b9\u7a00\u758f\u6570\u636e":46,"\u6dd8\u5b9d\u7b49":69,"\u6df1\u5ea6\u53cc\u5411lstm\u5c42\u63d0\u53d6softmax\u5c42\u7684\u7279\u5f81":68,"\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u8bfe\u7a0b":37,"\u6df7\u5408":68,"\u6df7\u5408\u5f53\u524d\u8bcd\u5411\u91cf\u548cattention\u52a0\u6743\u7f16\u7801\u5411\u91cf":44,"\u6dfb\u52a0\u542f\u52a8\u811a\u672c":57,"\u6e05\u7406\u6389\u8001\u65e7\u7684paddlepaddle\u5b89\u88c5\u5305":30,"\u6e29\u99a8":41,"\u6e90":70,"\u6e90\u4ee3\u7801":[33,65],"\u6e90\u4ee3\u7801\u683c\u5f0f":45,"\u6e90\u5b57\u5178":70,"\u6e90\u5e8f\u5217":44,"\u6e90\u7801":33,"\u6e90\u7801\u4e0edemo":56,"\u6e90\u7801\u7f16\u8bd1\u6587\u6863":47,"\u6e90\u7801\u7f16\u8bd1paddlepaddl":39,"\u6e90\u8bed\u8a00\u5230\u76ee\u6807\u8bed\u8a00\u7684\u5e73\u884c\u8bed\u6599\u5e93\u6587\u4ef6":70,"\u6e90\u8bed\u8a00\u548c\u76ee\u6807\u8bed\u8a00\u5171\u4eab\u76f8\u540c\u7684\u7f16\u7801\u5b57\u5178":61,"\u6e90\u8bed\u8a00\u548c\u76ee\u6807\u8bed\u8a00\u90fd\u662f\u76f8\u540c\u7684\u8bed\u8a00":61,"\u6e90\u8bed\u8a00\u77ed\u8bed\u548c\u76ee\u6807\u8bed\u8a00\u77ed\u8bed\u7684\u5b57\u5178\u5c06\u88ab\u5408\u5e76":61,"\u6ee4\u6ce2\u5668\u6838\u5728\u5782\u76f4\u65b9\u5411\u4e0a\u7684\u5c3a\u5bf8":63,"\u6ee4\u6ce2\u5668\u6838\u5728\u6c34\u5e73\u65b9\u5411\u4e0a\u7684\u5c3a\u5bf8":63,"\u6f14\u793a\u4e2d\u4f7f\u7528\u7684":68,"\u6f14\u793a\u91c7\u7528":68,"\u6fc0\u6d3b":46,"\u6fc0\u6d3b\u51fd\u6570\u7c7b\u578b":65,"\u6fc0\u6d3b\u65b9\u7a0b":46,"\u6fc0\u6d3b\u7684\u7c7b\u578b":46,"\u7075\u6d3b\u6027\u548c\u53ef\u6269\u5c55\u6027":0,"\u70ed\u60c5":41,"\u7136\u540e":[49,50,61,67],"\u7136\u540e\u4ea4\u7ed9\u7528\u6237\u81ea\u5b9a\u4e49\u7684\u51fd\u6570":31,"\u7136\u540e\u4ea4\u7ed9step\u51fd\u6570":43,"\u7136\u540e\u4f60\u53ea\u9700\u8981\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4":70,"\u7136\u540e\u4f7f\u7528\u4e0b\u9762\u7684\u811a\u672c":69,"\u7136\u540e\u518d\u505a\u4e00\u6b21\u6587\u672c\u5377\u79ef\u7f51\u7edc\u64cd\u4f5c":67,"\u7136\u540e\u5229\u7528\u89c2\u6d4b\u6570\u636e\u8c03\u6574":31,"\u7136\u540e\u5355\u51fb":45,"\u7136\u540e\u53ef\u4ee5\u4f7f\u7528\u547d\u4ee4\u884c\u5de5\u5177\u521b\u5efajob":57,"\u7136\u540e\u53ef\u4ee5\u8f6c\u6362\u4e3a\u56fe\u7247":63,"\u7136\u540e\u5728":70,"\u7136\u540e\u5728\u4e0b\u4e00\u4e2a\u65f6\u95f4\u6b65\u8f93\u5165\u7ed9\u53e6\u4e00\u4e2a\u795e\u7ecf\u5143":41,"\u7136\u540e\u5728\u6d4f\u89c8\u5668\u4e2d\u8f93\u5165\u4ee5\u4e0b\u7f51\u5740":33,"\u7136\u540e\u5728\u89e3\u7801\u88ab\u7ffb\u8bd1\u7684\u8bed\u53e5\u65f6":70,"\u7136\u540e\u5728dataprovider\u91cc\u9762\u6839\u636e\u8be5\u5730\u5740\u52a0\u8f7d\u5b57\u5178":30,"\u7136\u540e\u5728etcd\u7684":19,"\u7136\u540e\u5b9a\u4e49":44,"\u7136\u540e\u5c06\u6784\u5efa\u6210\u529f\u7684\u955c\u50cf\u4e0a\u4f20\u5230\u955c\u50cf\u4ed3\u5e93":57,"\u7136\u540e\u5c31\u53ef\u4ee5\u5e76\u53d1\u5199\u5165\u591a\u4e2achunk":24,"\u7136\u540e\u5f97\u5230\u5e73\u5747\u91c7\u6837\u7684\u7ed3\u679c":67,"\u7136\u540e\u6211\u4eec\u5229\u7528\u591a\u8f93\u5165\u7684":67,"\u7136\u540e\u6211\u4eec\u53d1\u73b0pass":70,"\u7136\u540e\u6211\u4eec\u5806\u53e0\u4e00\u5bf9\u5bf9\u7684":68,"\u7136\u540e\u6211\u4eec\u6c42\u8fd9\u4e24\u4e2a\u7279\u5f81\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6":67,"\u7136\u540e\u6240\u6709\u7528":45,"\u7136\u540e\u624d\u80fd\u4f7f\u7528pfsclient":24,"\u7136\u540e\u6267\u884c\u4e0b\u9762\u7684\u547d\u4ee4":63,"\u7136\u540e\u628a\u8fd9\u4e2a\u5305\u542b\u4e86\u8bad\u7ec3\u6570\u636e\u7684container\u4fdd\u5b58\u4e3a\u4e00\u4e2a\u65b0\u7684\u955c\u50cf":56,"\u7136\u540e\u63d0\u4ea4\u65b0\u6dfb\u52a0\u7684":45,"\u7136\u540e\u63d0\u53d6\u9690\u85cflstm\u5c42\u7684\u6240\u6709\u65f6\u95f4\u6b65\u957f\u7684\u6700\u5927\u8bcd\u5411\u91cf\u4f5c\u4e3a\u6574\u4e2a\u5e8f\u5217\u7684\u8868\u793a":69,"\u7136\u540e\u662f\u5bf9\u5e94\u7684\u82f1\u8bed\u5e8f\u5217":70,"\u7136\u540e\u6dfb\u52a0\u4e00\u4e2a\u5168\u8fde\u63a5\u5c42":67,"\u7136\u540e\u70b9\u51fb":45,"\u7136\u540e\u7528\u5bc6\u7801":33,"\u7136\u540e\u7528pickle\u547d\u4ee4\u5c06\u7279\u5f81":67,"\u7136\u540e\u7533\u660e\u4e00\u4e2a\u5b58\u50a8\u5377":57,"\u7136\u540e\u89c2\u5bdf\u5230\u8f93\u51fa\u7684\u53d8\u5316\u4e3a":46,"\u7136\u540e\u89e3\u538b":70,"\u7136\u540e\u89e3\u7801\u5668\u901a\u8fc7\u8fd9\u4e2a\u5411\u91cf\u751f\u6210\u4e00\u4e2a\u76ee\u6807\u8bed\u53e5":70,"\u7136\u540e\u8f93\u51fa\u9884\u6d4b\u5206\u6570":67,"\u7136\u540e\u8fd4\u56de\u7ed9paddlepaddle\u8fdb\u7a0b":3,"\u7136\u540e\u8fdb\u884c\u968f\u673a\u6253\u4e71":67,"\u7136\u540e\u901a\u8fc7\u51fd\u6570":57,"\u7136\u540e\u901a\u8fc7\u81ea\u8eab\u7684ip\u5730\u5740\u5728":57,"\u7136\u540e\u91cd\u65b0cmake\u5373\u53ef":30,"\u7136\u800c":[44,52],"\u7136\u800c\u6709\u4e9b\u8bc4\u8bba\u4e0a\u4e0b\u6587\u975e\u5e38\u957f":69,"\u7248\u672c":35,"\u7248\u672c\u5206\u652f":29,"\u7248\u672c\u53f7":29,"\u7248\u672c\u53f7rc":29,"\u7248\u672c\u5728":45,"\u7248\u672cfork\u51fa\u81ea\u5df1\u7684\u529f\u80fd\u5206\u652f":29,"\u7279\u522b\u611f\u8c22paddlepaddle\u7684":0,"\u7279\u522b\u662f\u5728lstm\u7b49rnn\u4e2d":30,"\u7279\u522b\u662f\u5f53\u76f8\u540c\u7684\u8bcd\u5728\u53e5\u5b50\u4e2d\u51fa\u73b0\u591a\u4e8e\u4e00\u6b21\u65f6":68,"\u7279\u5f81":67,"\u7279\u5f81\u56fe\u5747\u503c":63,"\u7279\u5f81\u56fe\u65b9\u5dee":63,"\u7279\u5f81\u5c06\u4f1a\u5b58\u5230":63,"\u7279\u5f81\u6587\u4ef6":68,"\u7279\u5f81\u7684\u7c7b\u578b\u548c\u7ef4\u5ea6":67,"\u72af\u7f6a\u7247":66,"\u72ec\u7acb\u5de5\u5177\u94fe":38,"\u72ec\u7acb\u5de5\u5177\u94fe\u6240\u5728\u7684\u7edd\u5bf9\u8def\u5f84":38,"\u73af\u5883\u53d8\u91cf":57,"\u73af\u5883\u53d8\u91cf\u6765\u6307\u5b9a\u7279\u5b9a\u7684gpu":30,"\u73b0\u5728\u6211\u4eec\u53ef\u4ee5\u5f00\u59cbpaddle\u8bad\u7ec3\u4e86":67,"\u73b0\u9636\u6bb5paddle\u6709\u4e00\u4e2a\u95ee\u9898\u662f":26,"\u751a\u81f3\u4e0d\u540c\u7ade\u4e89\u5bf9\u624b\u4ea7\u54c1\u7684\u504f\u597d":69,"\u751a\u81f3\u80fd\u89e3\u91ca\u4e3a\u4ec0\u4e48\u67d0\u4e2a\u64cd\u4f5c\u82b1\u4e86\u5f88\u957f\u65f6\u95f4":49,"\u751f\u4ea7\u73af\u5883\u4e2d\u7684\u8bad\u7ec3\u6570\u636e\u96c6\u901a\u5e38\u4f53\u79ef\u5f88\u5927":20,"\u751f\u4ea7\u73af\u5883\u7684\u65e5\u5fd7\u6570\u636e\u4f1a\u901a\u8fc7\u5b9e\u65f6\u6d41\u7684\u65b9\u5f0f":20,"\u751f\u4ea7\u955c\u50cf":[33,45],"\u751f\u4ea7\u955c\u50cf\u7684\u751f\u6210\u5206\u4e3a\u4e24\u6b65":33,"\u751f\u6210":57,"\u751f\u6210\u5404\u79cd\u8bed\u8a00\u7684\u7ed1\u5b9a\u4ee3\u7801":26,"\u751f\u6210\u540e\u7684\u6587\u6863\u5206\u522b\u5b58\u50a8\u5728\u7f16\u8bd1\u76ee\u5f55\u7684":47,"\u751f\u6210\u5e8f\u5217\u7684\u6700\u5927\u957f\u5ea6":44,"\u751f\u6210\u5f53\u524d\u5c42\u7684\u6240\u6709\u540e\u7ee7\u72b6\u6001":70,"\u751f\u6210\u6570\u636e\u7684\u76ee\u5f55":70,"\u751f\u6210\u6587\u6863":26,"\u751f\u6210\u751f\u4ea7\u955c\u50cf\u53ea\u9700\u8981\u8fd0\u884c":33,"\u751f\u6210\u7684":20,"\u751f\u6210\u7684\u6570\u636e\u5c06\u4f1a\u5b58\u50a8\u5728\u8fd9\u4e2avolume\u4e0b":57,"\u751f\u6210\u7684\u6570\u636e\u7f13\u5b58\u5728\u5185\u5b58\u91cc":30,"\u751f\u6210\u7684\u7ed3\u679c\u6587\u4ef6":70,"\u751f\u6210\u7684html\u7248\u672c\u7684c":33,"\u751f\u6210\u7684meta\u914d\u7f6e\u6587\u4ef6\u5982\u4e0b\u6240\u793a":67,"\u751f\u6210\u7ed3\u679c\u6587\u4ef6\u7684\u8def\u5f84":44,"\u751f\u6210\u7ed9\u5b9a":20,"\u751f\u6210\u7f51\u7edc\u5c42\u914d\u7f6e":46,"\u751f\u6210\u81ea\u5df1\u76ee\u5f55\u4e0b\u7684\u4ed3\u5e93":45,"\u751f\u6210\u8bad\u7ec3\u9700\u8981\u7684\u6837\u672c":67,"\u751f\u6210\u8fd0\u884c\u7a0b\u5e8f":33,"\u751f\u6210api\u6587\u6863":26,"\u751f\u6210docker\u955c\u50cf\u7684\u65b9\u5f0f\u6709\u4e24\u4e2a":33,"\u751f\u6210pfsclient\u548cpfsserver\u7684\u6846\u67b6\u90e8\u5206":24,"\u7528":[24,66,67,68],"\u75280\u548c1\u8868\u793a":3,"\u7528\u4e86\u4e24\u4e2a\u6708\u4e4b\u540e\u8fd9\u4e2a\u663e\u793a\u5668\u5c4f\u5e55\u788e\u4e86":65,"\u7528\u4e8e":50,"\u7528\u4e8e\u5207\u5206\u5355\u5355\u8bcd\u548c\u6807\u70b9\u7b26\u53f7":69,"\u7528\u4e8e\u521d\u59cb\u5316\u53c2\u6570\u548c\u8bbe\u7f6e":46,"\u7528\u4e8e\u5c06\u4e0b\u4e00\u884c\u7684\u6570\u636e\u8f93\u5165\u51fd\u6570\u6807\u8bb0\u6210\u4e00\u4e2apydataprovider2":3,"\u7528\u4e8e\u5c06\u53c2\u6570\u4f20\u9012\u7ed9\u7f51\u7edc\u914d\u7f6e":54,"\u7528\u4e8e\u5c06\u8bcdid\u8f6c\u6362\u4e3a\u8bcd\u7684\u5b57\u5178\u6587\u4ef6":44,"\u7528\u4e8e\u6307\u5b9a\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6":52,"\u7528\u4e8e\u653e\u7f6e":50,"\u7528\u4e8e\u6784\u6210\u65b0\u7684\u8bcd\u8868":61,"\u7528\u4e8e\u6807\u8bc6\u751f\u6210\u7684\u6587\u4ef6\u4e2d\u7684\u76f8\u5e94\u8f93\u51fa":44,"\u7528\u4e8e\u7a00\u758f\u8bad\u7ec3\u4e2d":52,"\u7528\u4e8e\u7edf\u8ba1\u8bcd\u9891\u7684bow\u6a21\u578b\u7279\u5f81":69,"\u7528\u4e8e\u81ea\u5b9a\u4e49\u6bcf\u6761\u6570\u636e\u7684batch":3,"\u7528\u4e8e\u8ba1\u7b97\u7f16\u7801\u5411\u91cf\u7684\u52a0\u6743\u548c":44,"\u7528\u4e8e\u8bbe\u7f6e\u8bad\u7ec3\u7b97\u6cd5":62,"\u7528\u4e8e\u8bfb\u53d6\u8bad\u7ec3":50,"\u7528\u4e8e\u96c6\u7fa4\u901a\u4fe1\u901a\u9053\u7684\u7aef\u53e3\u6570":50,"\u7528\u53cc\u5411\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7f16\u7801":44,"\u7528\u591a\u5bf9\u6548\u679c\u5b8c\u5168\u76f8\u540c\u7684":41,"\u7528\u6237":50,"\u7528\u62371\u7684\u7279\u5f81":67,"\u7528\u6237\u4e0a\u4f20\u6570\u636e\u540e":20,"\u7528\u6237\u4e5f\u53ef\u4ee5\u4e0a\u4f20label":20,"\u7528\u6237\u4e5f\u53ef\u4ee5\u5728\u81ea\u5df1\u719f\u6089\u7684\u5f00\u53d1\u5e73\u53f0\u4e0a":39,"\u7528\u6237\u4e5f\u53ef\u4ee5\u5728c":2,"\u7528\u6237\u53ea\u9700\u5b9a\u4e49rnn\u5728\u4e00\u4e2a\u65f6\u95f4\u6b65\u5185\u5b8c\u6210\u7684\u8ba1\u7b97":43,"\u7528\u6237\u53ea\u9700\u6267\u884c":68,"\u7528\u6237\u53ea\u9700\u6267\u884c\u4ee5\u4e0b\u547d\u4ee4\u5c31\u53ef\u4ee5\u4e0b\u8f7d\u5e76\u5904\u7406\u539f\u59cb\u6570\u636e":68,"\u7528\u6237\u53ef\u4ee5\u4f7f\u7528\u5f00\u53d1\u955c\u50cf\u4ee3\u66ff\u914d\u7f6e\u672c\u5730\u73af\u5883":33,"\u7528\u6237\u53ef\u4ee5\u4f7f\u7528ssh\u767b\u5f55\u5230\u8fd9\u53f0\u670d\u52a1\u5668\u4e0a\u5e76\u6267\u884c":33,"\u7528\u6237\u53ef\u4ee5\u5206\u522b\u67e5\u770b\u6700\u65b0\u7684":47,"\u7528\u6237\u53ef\u4ee5\u53c2\u8003sphinx\u6559\u7a0b\u8fdb\u884c\u4e66\u5199":47,"\u7528\u6237\u53ef\u4ee5\u5728\u8f93\u51fa\u7684\u6587\u672c\u6a21\u578b\u4e2d\u770b\u5230":61,"\u7528\u6237\u53ef\u4ee5\u5b89\u5168\u7684\u91ca\u653e\u67d0\u4e2ac":27,"\u7528\u6237\u53ef\u4ee5\u628a\u81ea\u5df1\u7684\u6570\u636e\u5206\u4eab\u7ed9\u522b\u4eba":20,"\u7528\u6237\u53ef\u4ee5\u6839\u636e\u8bad\u7ec3\u65e5\u5fd7":65,"\u7528\u6237\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u8fd9\u4e2a\u52a8\u6001\u5e93\u6765\u5f15\u5165paddl":27,"\u7528\u6237\u53ef\u4ee5\u81ea\u5b9a\u4e49beam":52,"\u7528\u6237\u53ef\u4ee5\u8bbe\u7f6e":54,"\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u4f7f\u7528python\u63a5\u53e3":2,"\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u7f51\u9875\u6d4f\u89c8\u6587\u6863":33,"\u7528\u6237\u53ef\u5728\u8c03\u7528cmake\u7684\u65f6\u5019\u8bbe\u7f6e\u5b83\u4eec":32,"\u7528\u6237\u53ef\u5728cmake\u7684\u547d\u4ee4\u884c\u4e2d":32,"\u7528\u6237\u53ef\u5c06":38,"\u7528\u6237\u53ef\u6839\u636e\u81ea\u5df1\u7684\u7f16\u8bd1\u76ee\u6807\u67b6\u6784":38,"\u7528\u6237\u53ef\u81ea\u884c\u524d\u5f80":39,"\u7528\u6237\u53ef\u81ea\u884c\u524d\u5f80\u4e0b\u8f7d\u9884\u7f16\u8bd1\u597d\u7684\u7248\u672c":38,"\u7528\u6237\u53ef\u901a\u8fc7\u4ea4\u53c9\u7f16\u8bd1\u7684\u65b9\u5f0f":38,"\u7528\u6237\u53ef\u901a\u8fc7ssh\u7b49\u65b9\u5f0f\u767b\u5f55\u5230raspberri":39,"\u7528\u6237\u5728\u4f7f\u7528paddlepaddl":30,"\u7528\u6237\u5728\u672c\u5730\u8f6c\u6362\u597d\u518d\u4e0a\u4f20":20,"\u7528\u6237\u5728\u9996\u6b21\u7f16\u8bd1\u5b89\u88c5paddlepaddle\u65f6":47,"\u7528\u6237\u5b9a\u4e49\u7684\u53c2\u6570":3,"\u7528\u6237\u5c06\u914d\u7f6e\u4e0e\u8bad\u7ec3\u6570\u636e\u5207\u5206\u597d\u653e\u5728\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf\u9884\u5148\u5206\u914d\u597d\u7684\u76ee\u5f55\u4e2d":57,"\u7528\u6237\u5e94\u8be5\u63d0\u4f9b\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6":68,"\u7528\u6237\u5f3a\u5236\u6307\u5b9a\u7279\u5b9a\u7684python\u7248\u672c":30,"\u7528\u6237\u6307\u5b9a\u65b0\u7684\u5b57\u5178\u7684\u8def\u5f84":61,"\u7528\u6237\u6587\u4ef6\u4e2d\u6709\u56db\u79cd\u7c7b\u578b\u7684\u5b57\u6bb5":67,"\u7528\u6237\u6587\u4ef6\u53ef\u80fd\u662f\u6bd4\u8f83\u5927\u7684":24,"\u7528\u6237\u7279\u5f81":67,"\u7528\u6237\u8fd8\u53ef\u4ee5\u6839\u636e\u6982\u7387\u5206\u5e03\u77e9\u9635\u5b9e\u73b0\u67f1\u641c\u7d22\u6216\u7ef4\u7279\u6bd4\u89e3\u7801":68,"\u7528\u6237\u8fd8\u53ef\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u8bbe\u7f6e\u5176\u4ed6\u7f16\u8bd1\u53c2\u6570":[38,39],"\u7528\u6237\u901a\u8fc7c":27,"\u7528\u6237\u9700\u8981\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u6307\u5b9a":54,"\u7528\u6237\u9700\u8981\u5728cmake\u65f6\u624b\u52a8\u8bbe\u7f6e\u8fd9\u4e9b\u503c":[38,39],"\u7528\u6237\u9700\u8981\u6307\u5b9a\u672c\u673a\u4e0apython\u7684\u8def\u5f84":30,"\u7528\u6237\u9700\u8981\u63d0\u524d\u51c6\u5907\u597d\u4ea4\u53c9\u7f16\u8bd1\u73af\u5883":[38,39],"\u7528\u6237\u9700\u8981\u9996\u5148\u786e\u8ba4py_paddle\u5305\u5df2\u7ecf\u5b89\u88c5":47,"\u7528\u6237\u9884\u6d4b\u7684\u547d\u4ee4\u884c\u754c\u9762\u5982\u4e0b":67,"\u7528\u6237id":66,"\u7528\u6237id\u8303\u56f4\u4ece1\u52306040":66,"\u7528\u6765\u4ece\u53c2\u6570\u670d\u52a1\u5668\u9884\u53d6\u53c2\u6570\u77e9\u9635\u76f8\u5e94\u7684\u884c":46,"\u7528\u6765\u4f30\u8ba1\u7ebf\u6027\u51fd\u6570\u7684\u53c2\u6570w":31,"\u7528\u6765\u5177\u4f53\u63cf\u8ff0":67,"\u7528\u6765\u5177\u4f53\u8bf4\u660e\u6570\u636e\u96c6\u7684\u5b57\u6bb5\u548c\u6587\u4ef6\u683c\u5f0f":67,"\u7528\u6765\u8ba1\u7b97\u6a21\u578b\u7684\u8bef\u5dee":31,"\u7528\u8fd9\u4e2a\u955c\u50cf\u521b\u5efa\u7684\u5bb9\u5668\u9700\u8981\u6709\u4ee5\u4e0b\u4e24\u4e2a\u529f\u80fd":57,"\u7531":43,"\u7531\u4e8e\u4e0a\u4e00\u6b65cmake\u914d\u7f6e\u4e2d":39,"\u7531\u4e8e\u4e0d\u540c\u7684paddle\u7684\u7248\u672c\u53ef\u80fd\u9700\u8981\u4e0d\u540c\u7684\u4f9d\u8d56\u548c\u5de5\u5177":33,"\u7531\u4e8e\u5b83\u5185\u90e8\u5305\u542b\u4e86\u6bcf\u7ec4\u6570\u636e\u4e2d\u7684\u6240\u6709\u53e5\u5b50":41,"\u7531\u4e8e\u5bb9\u5668\u4e4b\u95f4\u5171\u4eabnet":55,"\u7531\u4e8e\u5bf9parameters\u7684\u66f4\u65b0\u9700\u8981\u83b7\u53d6parameters\u5185\u5b58\u7684":19,"\u7531\u4e8e\u5df2\u7ecf\u77e5\u9053\u4e86\u771f\u5b9e\u7b54\u6848":31,"\u7531\u4e8e\u610f\u5916\u7684\u526f\u672c\u8bb0\u5f55\u548c\u6d4b\u8bd5\u8bb0\u5f55":66,"\u7531\u4e8e\u6211\u4eec\u60f3\u8981\u7684\u53d8\u6362\u662f\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217":41,"\u7531\u4e8e\u6211\u4eec\u652f\u6301\u8bad\u7ec3\u6570\u636e\u6709\u4e0d\u540c\u7684\u6279\u6b21\u5927\u5c0f":46,"\u7531\u4e8e\u6570\u636e\u8bb8\u53ef\u7684\u539f\u56e0":68,"\u7531\u4e8e\u6807\u51c6\u7684\u7ffb\u8bd1\u7ed3\u679c\u5df2\u7ecf\u4e0b\u8f7d\u5230\u8fd9\u91cc":70,"\u7531\u4e8e\u6bcf\u4e2a\u5377\u79ef\u5c42\u540e\u9762\u8fde\u63a5\u7684\u662fbatch":63,"\u7531\u4e8e\u8fd9\u4e2a\u5730\u5740\u4f1a\u88abdataprovider\u4f7f\u7528":2,"\u7531\u4e8e\u8fd9\u6837\u505a\u53ef\u4ee5\u907f\u514d\u5f88\u591a\u6b7b\u9501\u95ee\u9898":3,"\u7531\u4e8e\u96c6\u7fa4\u4e2d\u540c\u65f6\u5b58\u5728\u4e24\u53f0\u673a\u5668\u6545\u969c\u7684\u6982\u7387\u6781\u4f4e":19,"\u7531\u4e8e\u987a\u5e8f\u8c03\u7528\u8fd9\u4e9bgenerator\u4e0d\u4f1a\u51fa\u73b0\u4e0a\u8ff0\u95ee\u9898":3,"\u7531\u4e8ec":26,"\u7531\u4e8echunk\u6bd4\u8f83\u5c0f":24,"\u7531\u4e8epaddlepaddle\u5df2\u7ecf\u5b9e\u73b0\u4e86\u4e30\u5bcc\u7684\u7f51\u7edc\u5c42":31,"\u7531\u4e8estep":43,"\u7531\u4e8etest_data\u5305\u542b\u4e24\u6761\u9884\u6d4b\u6570\u636e":5,"\u7531\u8bcd\u8bed\u6784\u6210\u7684\u53e5\u5b50":40,"\u7531grouplen":66,"\u7533\u8bf7\u7528\u6237\u7a7a\u95f4":24,"\u7535\u5f711\u7684\u7279\u5f81":67,"\u7535\u5f71\u4fe1\u606f\u4ee5\u53ca\u7535\u5f71\u8bc4\u5206":66,"\u7535\u5f71\u540d\u5b57\u6bb5":67,"\u7535\u5f71\u540d\u79f0":66,"\u7535\u5f71\u548c\u7528\u6237":67,"\u7535\u5f71\u548c\u7528\u6237\u6709\u8bb8\u591a\u7684\u7279\u5f81":67,"\u7535\u5f71\u5927\u90e8\u5206\u662f\u624b\u5de5\u8f93\u5165\u6570\u636e":66,"\u7535\u5f71\u7279\u5f81":67,"\u7535\u5f71\u7c7b\u578b":66,"\u7535\u5f71\u7c7b\u578b\u5982\u7b26\u5408\u591a\u79cd\u7528\u7ba1\u9053\u7b26\u53f7":66,"\u7535\u5f71id":66,"\u7535\u5f71id\u8303\u56f4\u4ece1\u52303952":66,"\u7535\u8111":41,"\u767e\u4e07\u6570\u636e\u96c6":66,"\u7684":[41,45,50,56,57,65,69],"\u768410\u7ef4\u6574\u6570\u6807\u7b7e":3,"\u7684\u4e00\u4e2a\u7b80\u5355\u8c03\u7528\u5982\u4e0b":43,"\u7684\u4e00\u4e2a\u7ebf\u6027\u51fd\u6570":31,"\u7684\u4e00\u79cd":70,"\u7684\u4e3a0":52,"\u7684\u4e3b\u8981\u90e8\u5206":68,"\u7684\u4efb\u4e00\u4e00\u79cd":30,"\u7684\u4efb\u52a1":70,"\u7684\u4f7f\u7528\u793a\u4f8b\u5982\u4e0b":40,"\u7684\u503c":[38,39],"\u7684\u503c\u81ea\u52a8\u63a8\u5bfc\u5f97\u5230":38,"\u7684\u504f\u7f6e\u5411\u91cf":46,"\u7684\u5185\u5b58":30,"\u7684\u5185\u5bb9\u5982\u4e0b\u6240\u793a":70,"\u7684\u5185\u5bb9\u6765\u5b9a\u5236imag":57,"\u7684\u5185\u6838block\u4f7f\u7528\u60c5\u51b5":49,"\u7684\u5206\u7c7b\u4efb\u52a1\u4e2d\u8d62\u5f97\u4e86\u7b2c\u4e00\u540d":63,"\u7684\u522b\u540d":[6,7],"\u7684\u5355\u8bcd\u7ea7\u522b\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc":67,"\u7684\u53cd\u5411\u4f20\u64ad\u5c06\u4f1a\u6253\u5370\u65e5\u5fd7\u4fe1\u606f":52,"\u7684\u53d8\u6362\u77e9\u9635":46,"\u7684\u53e5\u5b50\u7684\u60c5\u611f":69,"\u7684\u540d\u5b57":3,"\u7684\u540d\u79f0\u76f8\u540c":44,"\u7684\u540e\u7f00":66,"\u7684\u5411\u91cf":46,"\u7684\u542f\u52a8\u53c2\u6570":57,"\u7684\u542f\u52a8\u53c2\u6570\u5e76\u6267\u884c\u8fdb\u7a0b":57,"\u7684\u547d\u540d\u98ce\u683c\u5e76\u4e0d\u80fd\u9002\u5e94\u5176\u4ed6\u7b2c\u4e09\u65b9\u8bed\u8a00":26,"\u7684\u5730\u5740":55,"\u7684\u5730\u65b9":45,"\u7684\u5747\u5300\u5206\u5e03":30,"\u7684\u5934\u6587\u4ef6":26,"\u7684\u5dee\u8ddd\u4e0d\u65ad\u51cf\u5c0f":31,"\u7684\u5e73\u5747\u503c":40,"\u7684\u5e8f\u5217\u5f62\u72b6\u4e00\u81f4":41,"\u7684\u5f00\u53d1\u5de5\u4f5c\u90fd\u5e94\u8be5\u5728\u4e00\u4e2a\u65b0\u7684\u5206\u652f\u4e0a\u5b8c\u6210":45,"\u7684\u603b":50,"\u7684\u63a5\u53e3\u6837\u5f0f":26,"\u7684\u63cf\u8ff0\u8bf4\u660e\u4e2d":45,"\u7684\u6570\u636e\u6d41\u56fe":20,"\u7684\u6570\u636e\u8bfb\u53d6\u811a\u672c\u548c\u7c7b\u4f3c\u4e8e":65,"\u7684\u6570\u76ee\u4e00\u81f4":40,"\u7684\u6587\u4ef6\u4e5f\u5e26\u5230\u65b0\u5206\u652f\u4e0a":45,"\u7684\u65b9\u6cd5\u5df2\u88ab\u8bc1\u660e\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u6a21\u578b":70,"\u7684\u65b9\u7a0b":46,"\u7684\u65f6\u5019\u5982\u679c\u62a5\u4e00\u4e9b\u4f9d\u8d56\u672a\u627e\u5230\u7684\u9519\u8bef\u662f\u6b63\u5e38\u7684":35,"\u7684\u65f6\u95f4\u6b65\u4fe1\u606f\u6210\u6b63\u6bd4":30,"\u7684\u66f4\u8be6\u7ec6\u51c6\u786e\u7684\u5b9a\u4e49":41,"\u7684\u6700\u5c0f\u503c":52,"\u7684\u6700\u65b0\u4ee3\u7801\u5e76\u66f4\u65b0\u5f53\u524d\u5206\u652f":45,"\u7684\u67b6\u6784\u7684\u793a\u4f8b":44,"\u7684\u6837\u5f0f":45,"\u7684\u6838\u5fc3\u662f\u8bbe\u8ba1step\u51fd\u6570\u7684\u8ba1\u7b97\u903b\u8f91":43,"\u7684\u6bb5\u843d\u5b9a\u4e49\u4e3a\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":43,"\u7684\u6d4b\u8bd5\u6570\u636e\u96c6":68,"\u7684\u6e90\u7801\u4ee5\u53ca\u751f\u6210\u6587\u6863\u9700\u8981\u591a\u79cd\u5f00\u53d1\u5de5\u5177":45,"\u7684\u6e90\u7801\u91cc\u4f7f\u7528\u4e86":26,"\u7684\u7248\u672c\u53f7":61,"\u7684\u7279\u5f81":63,"\u7684\u72b6\u6001":43,"\u7684\u7528\u6237\u53c2\u8003":50,"\u7684\u76ee\u5f55\u7ed3\u6784\u4e3a":67,"\u7684\u771f\u5b9e\u5173\u7cfb\u4e3a":31,"\u7684\u77e9\u9635":46,"\u7684\u795e\u7ecf\u7f51\u7edc\u673a\u5668\u7ffb\u8bd1":70,"\u7684\u7a20\u5bc6\u5411\u91cf\u4f5c\u4e3a\u8f93\u5165":46,"\u7684\u7aef\u5230\u7aef\u7cfb\u7edf\u6765\u89e3\u51b3srl\u4efb\u52a1":68,"\u7684\u7b2ci\u4e2a\u503c":46,"\u7684\u7b2cj\u4e2a\u503c":46,"\u7684\u7d22\u5f15\u6587\u4ef6\u5f15\u7528\u8bad\u7ec3":50,"\u7684\u7ed3\u6784\u5982\u4e0b":67,"\u7684\u7ef4\u5ea6":61,"\u7684\u7f29\u5199":24,"\u7684\u884c\u6570\u5e94\u8be5\u4e00\u81f4":70,"\u7684\u89c4\u8303":26,"\u7684\u89d2\u5ea6":20,"\u7684\u8bad\u7ec3\u6a21\u578b\u811a\u672c":65,"\u7684\u8bdd":30,"\u7684\u8def\u5f84\u4e2d":69,"\u7684\u8f93\u5165":43,"\u7684\u8f93\u51fa":49,"\u7684\u8f93\u51fa\u4fe1\u606f\u5165\u624b\u662f\u4e2a\u4e0d\u9519\u7684\u9009\u62e9":49,"\u7684\u8f93\u51fa\u51fd\u6570\u8fd4\u56de\u7684\u662f\u4e0b\u4e00\u4e2a\u65f6\u523b\u8f93\u51fa\u8bcd\u7684":44,"\u7684\u8f93\u51fa\u683c\u5f0f":41,"\u7684\u8f93\u51fa\u88ab\u7528\u4f5c":44,"\u7684\u8fd4\u56de\u503c\u4e00\u81f4":66,"\u7684\u8fdc\u7a0b\u4ed3\u5e93\u7684\u540d\u5b57":45,"\u7684\u90e8\u5206":50,"\u7684\u914d\u7f6e":61,"\u7684\u914d\u7f6e\u5199\u5230\u914d\u7f6e\u6587\u4ef6\u4e2d":20,"\u7684\u9875\u9762\u5220\u9664\u8fdc\u7a0b\u4ed3\u5e93\u7684\u5206\u652f":45,"\u7684docker\u955c\u50cf":33,"\u76ee\u524d":[43,68],"\u76ee\u524d\u4f7f\u7528":45,"\u76ee\u524d\u53ea\u652f\u6301":38,"\u76ee\u524d\u53ea\u8003\u8651\u52a8\u6001\u6269\u5bb9trainer\u6570\u91cf":19,"\u76ee\u524d\u5d4c\u5165python\u89e3\u91ca\u5668":26,"\u76ee\u524d\u5df2\u88ab\u767e\u5ea6\u5185\u90e8\u591a\u4e2a\u4ea7\u54c1\u7ebf\u5e7f\u6cdb\u4f7f\u7528":0,"\u76ee\u524d\u5fc5\u987b\u8bbe\u7f6e\u6210":[38,39],"\u76ee\u524d\u6211\u4eec\u7528cephfs\u6765\u642d\u5efa":24,"\u76ee\u524d\u652f\u6301\u4e24\u79cd":40,"\u76ee\u524d\u652f\u6301fail":52,"\u76ee\u524d\u8be5\u53c2\u6570\u4ec5\u7528\u4e8eaucvalidationlayer\u548cpnpairvalidationlayer\u5c42":52,"\u76ee\u524d\u8fd8\u672a\u652f\u6301":43,"\u76ee\u524dpaddle\u7684\u8fdb\u7a0b\u6a21\u578b\u662fc":26,"\u76ee\u524dpaddlepaddle\u7684develop\u5206\u652f\u7684\u6587\u6863\u662f\u81ea\u52a8\u89e6\u53d1\u66f4\u65b0\u7684":47,"\u76ee\u5f55":[38,39,50,56,57,69],"\u76ee\u5f55\u4e0b":[27,46,65,70],"\u76ee\u5f55\u4e0b\u627e\u5230":65,"\u76ee\u5f55\u4e0b\u7684\u751f\u6210\u6587\u4ef6\u7528\u4e8e\u6df1\u5ea6\u5b66\u4e60\u76f8\u5173android":38,"\u76ee\u5f55\u4e0b\u7684demo\u8bad\u7ec3\u51fa\u6765":5,"\u76ee\u5f55\u4e0b\u7684python\u5305":30,"\u76ee\u5f55\u4e2d":[50,67],"\u76ee\u5f55\u4e2d\u4f1a\u5305\u542b":[38,39],"\u76ee\u5f55\u4e2d\u7684":[49,50],"\u76ee\u5f55\u4e2dpaddl":57,"\u76ee\u5f55\u4f1a\u51fa\u73b0\u5982\u4e0b\u51e0\u4e2a\u65b0\u7684\u6587\u4ef6":68,"\u76ee\u5f55\u548c":[38,39],"\u76ee\u5f55\u5c31\u6210\u4e3a\u4e86\u5171\u4eab\u5b58\u50a8":57,"\u76ee\u5f55\u7ed3\u6784\u5982\u4e0b":70,"\u76ee\u5f55\u91cc\u63d0\u4f9b\u4e86\u8be5\u6570\u636e\u7684\u4e0b\u8f7d\u811a\u672c\u548c\u9884\u5904\u7406\u811a\u672c":65,"\u76ee\u6807":70,"\u76ee\u6807\u51fd\u6570\u662f\u6807\u7b7e\u7684\u4ea4\u53c9\u71b5":68,"\u76ee\u6807\u5411\u91cf":44,"\u76ee\u6807\u5b57\u5178":70,"\u76ee\u6807\u673a\u7248protobuf\u5e93":[38,39],"\u76ee\u6807\u67b6\u6784abi":38,"\u76f4\u5230\u8bad\u7ec3\u6536\u655b\u4e3a\u6b62":30,"\u76f4\u5230\u903c\u8fd1\u771f\u5b9e\u89e3":31,"\u76f4\u63a5\u4f7f\u7528c\u8bed\u8a00\u7684":26,"\u76f4\u63a5\u5220\u9664\u8fd9\u4e2a\u53c2\u6570\u5373\u53ef":27,"\u76f4\u63a5\u5bfc\u51fa\u5230c\u7684\u63a5\u53e3\u6bd4\u8f83\u56f0\u96be":26,"\u76f4\u63a5\u7f16\u8bd1raspberri":39,"\u76f4\u63a5\u8fd0\u884c":33,"\u76f4\u63a5\u8fd4\u56de\u8ba1\u7b97\u7ed3\u679c":5,"\u76f4\u63a5\u8fdb\u5165\u8bad\u7ec3\u6a21\u578b\u7ae0\u8282":65,"\u76f8\u5173\u6587\u6863":39,"\u76f8\u5173\u6587\u6863\u6240\u8ff0":39,"\u76f8\u5173\u6982\u5ff5\u662f":3,"\u76f8\u5173\u7684\u9e1f\u7c7b\u6570\u636e\u96c6\u53ef\u4ee5\u4ece\u5982\u4e0b\u5730\u5740\u4e0b\u8f7d":62,"\u76f8\u5173\u8bba\u6587":68,"\u76f8\u53cd":70,"\u76f8\u540c\u540d\u5b57\u7684\u53c2\u6570":30,"\u76f8\u5bf9":41,"\u76f8\u5bf9\u4e8epaddlepaddle\u7a0b\u5e8f\u8fd0\u884c\u65f6\u7684\u8def\u5f84":2,"\u76f8\u5bf9mnist\u800c\u8a00":3,"\u76f8\u5e94\u7684\u6570\u636e\u8bfb\u53d6\u811a\u672c\u548c\u8bad\u7ec3\u6a21\u578b\u811a\u672c":65,"\u76f8\u5e94\u7684\u6570\u636e\u8fed\u4ee3\u5668\u5982\u4e0b":68,"\u76f8\u5e94\u7684\u6807\u8bb0\u53e5\u5b50\u662f":68,"\u76f8\u5f53":41,"\u77e9\u9635":51,"\u7814\u7a76\u4eba\u5458\u5206\u6790\u4e86\u51e0\u4e2a\u5173\u4e8e\u6d88\u8d39\u8005\u4fe1\u5fc3\u548c\u653f\u6cbb\u89c2\u70b9\u7684\u8c03\u67e5":69,"\u7814\u7a76\u751f":66,"\u786e\u4fdd\u7f16\u8bd1\u5668\u9009\u9879":45,"\u793a":65,"\u793a\u4f8b":[30,63],"\u793a\u4f8b3\u5bf9\u4e8e\u5355\u5c42rnn\u548c\u53cc\u5c42rnn\u6570\u636e\u5b8c\u5168\u76f8\u540c":41,"\u793a\u4f8b3\u7684\u914d\u7f6e\u4f7f\u7528\u4e86\u5355\u5c42rnn\u548c\u53cc\u5c42rnn":41,"\u793a\u4f8b3\u7684\u914d\u7f6e\u5206\u522b\u4e3a":41,"\u793e\u533a\u53c2\u4e0e\u56f0\u96be":26,"\u793e\u533a\u8d21\u732e\u4ee3\u7801\u5b66\u4e60\u6210\u672c\u9ad8":26,"\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u53c2\u6570":19,"\u795e\u7ecf\u7f51\u7edc\u4e5f\u9700\u8981\u4e00\u4e9b\u7279\u5b9a\u7684layer\u4f5c\u4e3a\u8f93\u5165\u63a5\u53e3":36,"\u795e\u7ecf\u7f51\u7edc\u53c2\u6570\u4ee5\u53ca\u8fed\u4ee3\u65b9\u7a0b":36,"\u795e\u7ecf\u7f51\u7edc\u5728\u8bad\u7ec3\u7684\u65f6\u5019":30,"\u795e\u7ecf\u7f51\u7edc\u673a\u5668\u7ffb\u8bd1":70,"\u795e\u7ecf\u7f51\u7edc\u7684\u7f51\u7edc\u7ed3\u6784\u4e2d\u5177\u6709\u6709\u5411\u73af\u7ed3\u6784":41,"\u795e\u7ecf\u7f51\u7edc\u7684\u8bad\u7ec3\u672c\u8eab\u662f\u4e00\u4e2a\u975e\u5e38\u6d88\u8017\u5185\u5b58\u548c\u663e\u5b58\u7684\u5de5\u4f5c":30,"\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e":31,"\u79bb":41,"\u79bb\u7ebf\u6279\u5904\u7406":20,"\u79d1\u5b66\u5bb6":66,"\u79d1\u5e7b\u7247":66,"\u79f0\u4e3a":[44,45],"\u79f0\u4e3a\u5f00\u53d1\u955c\u50cf":33,"\u79f0\u4e3a\u751f\u4ea7\u955c\u50cf":33,"\u79f0\u4e4b\u4e3a\u53cc\u5c42\u5e8f\u5217\u7684\u4e00\u4e2a\u5b50\u5e8f\u5217":40,"\u79f0\u4e4b\u4e3a\u96c6\u675f\u5927\u5c0f":52,"\u7a00\u758f\u6570\u636e\u7684\u683c\u5f0f":46,"\u7a00\u758f\u66f4\u65b0\u7684\u7aef\u53e3\u6570\u91cf":57,"\u7a00\u758f\u768401\u5411\u91cf":[3,36],"\u7a00\u758f\u7684\u5411\u91cf":[3,36],"\u7a00\u758f\u77e9\u9635\u7684\u4e58\u79ef\u5e94\u7528\u4e8e\u524d\u5411\u4f20\u64ad\u8fc7\u7a0b":54,"\u7a0b\u5e8f\u4ece\u6b64\u76ee\u5f55\u62f7\u8d1d\u6587\u4ef6\u5230\u5bb9\u5668\u5185\u8fdb\u884c\u8bad\u7ec3":57,"\u7a0b\u5e8f\u505c\u6b62":52,"\u7a0b\u5e8f\u5458":66,"\u7a0b\u5e8f\u76f4\u63a5\u9000\u51fa":52,"\u7a20\u5bc6\u5411\u91cf":46,"\u7a20\u5bc6\u66f4\u65b0\u7684\u7aef\u53e3\u6570\u91cf":57,"\u7a20\u5bc6\u7684\u6d6e\u70b9\u6570\u5411\u91cf":[3,36],"\u7a97\u6237":41,"\u7acb\u523b\u9000\u51fa":30,"\u7aef\u53e3":50,"\u7aef\u53e3\u6570":50,"\u7aef\u53e3\u9644\u52a0\u5230\u4e3b\u673a\u540d\u4e0a":50,"\u7aef\u81ea\u5b9a\u4e49\u4e00\u4e2a":2,"\u7aef\u8bfb\u53d6\u6570\u636e":30,"\u7b2c":41,"\u7b2c\u4e00\u4e2a":45,"\u7b2c\u4e00\u4e2a\u53c2\u6570\u662fsettings\u5bf9\u8c61":3,"\u7b2c\u4e00\u4e2a\u65b9\u6cd5\u7684\u597d\u5904\u662f\u7b80\u5355\u5feb\u6377":33,"\u7b2c\u4e00\u4e2a\u6837\u672c\u540c\u65f6encode\u4e24\u6761\u6570\u636e\u6210\u4e24\u4e2a\u5411\u91cf":41,"\u7b2c\u4e00\u4e2apass\u4f1a\u4ecepython\u7aef\u8bfb\u53d6\u6570\u636e":3,"\u7b2c\u4e00\u4e2atag\u4e3a":29,"\u7b2c\u4e00\u5929":41,"\u7b2c\u4e00\u6b65\u662f\u8fd0\u884c":33,"\u7b2c\u4e00\u7ae0\u8282":36,"\u7b2c\u4e00\u884c\u4ece":69,"\u7b2c\u4e00\u884c\u5b58\u7684\u662f\u56fe\u50cf":63,"\u7b2c\u4e00\u884c\u662f":61,"\u7b2c\u4e00\u884c\u7684":70,"\u7b2c\u4e00\u90e8\u5206\u5b9a\u4e49\u4e86\u6570\u636e\u8f93\u5165":31,"\u7b2c\u4e00\u90e8\u5206\u662f\u56fe\u7247\u7684\u6807\u7b7e":3,"\u7b2c\u4e09":69,"\u7b2c\u4e09\u5217\u662f\u751f\u6210\u7684\u82f1\u8bed\u5e8f\u5217":70,"\u7b2c\u4e09\u6b65":63,"\u7b2c\u4e09\u6b65\u5b8c\u6210\u540e":29,"\u7b2c\u4e8c":69,"\u7b2c\u4e8c\u4e2a\u4e3a":29,"\u7b2c\u4e8c\u4e2a\u65b9\u6cd5\u7684\u597d\u5904\u662fdockerfile\u53ef\u4ee5\u628a\u6574\u4e2a\u751f\u6210\u6d41\u7a0b\u63cf\u8ff0\u5f88\u6e05\u695a":33,"\u7b2c\u4e8c\u5217\u662f\u96c6\u675f\u641c\u7d22\u7684\u5f97\u5206":70,"\u7b2c\u4e8c\u6b65":[61,63],"\u7b2c\u4e8c\u6b65\u662f\u8fd0\u884c":33,"\u7b2c\u4e8c\u884c\u5b58\u7684\u662f\u56fe\u50cf":63,"\u7b2c\u4e8c\u90e8\u5206\u4e3b\u8981\u662f\u9009\u62e9\u5b66\u4e60\u7b97\u6cd5":31,"\u7b2c\u4e8c\u90e8\u5206\u662f28":3,"\u7b2ci\u884c\u7b2cj\u5217\u7684\u6570\u503c":46,"\u7b49":27,"\u7b49\u5168\u90e8\u9759\u6001\u5e93\u4e2d\u7684\u76ee\u6807\u6587\u4ef6\u5168\u90e8\u6253\u5305\u540e\u4ea7\u751f\u7684\u6587\u4ef6":27,"\u7b49\u5176\u4ed6":70,"\u7b49\u53c2\u6570":57,"\u7b49\u591a\u79cd\u516c\u6709\u4e91\u73af\u5883":55,"\u7b49\u5f85\u8fd9\u4e2a\u7a0b\u5e8f\u6267\u884c\u6210\u529f\u5e76\u8fd4\u56de0\u5219\u6210\u529f\u9000\u51fa":55,"\u7b49\u6587\u4ef6":27,"\u7b49\u7b49":[65,70],"\u7b49\u90fd\u5c5e\u4e8e\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4":55,"\u7b80\u4ecb":48,"\u7b80\u5355\u6765\u8bf4":49,"\u7b80\u5355\u7684\u5168\u8fde\u63a5\u7f51\u7edc":30,"\u7b80\u5355\u7684\u57fa\u4e8e\u5b57\u6bcd\u7684\u8bcd\u5d4c\u5165":67,"\u7b80\u5355\u7684\u6027\u80fd\u5206\u6790":49,"\u7b80\u5355\u7684\u6574\u4e2a\u8bcd\u5d4c\u5165":67,"\u7b80\u5355\u7684pydataprovider2\u6837\u4f8b\u5c31\u8bf4\u660e\u5b8c\u6bd5\u4e86":3,"\u7b80\u5355\u7684yaml\u6587\u4ef6\u5982\u4e0b":56,"\u7b80\u76f4":41,"\u7b97\u6cd5":[30,31,44,69],"\u7b97\u6cd5\u4e2d\u7684beam\u5927\u5c0f":44,"\u7b97\u6cd5\u914d\u7f6e":69,"\u7ba1\u7406\u4eba\u5458":66,"\u7ba1\u7406\u5458":66,"\u7c7b\u4f3c":[27,40],"\u7c7b\u4f3c\u5730":68,"\u7c7b\u4f5c\u4e3a\u53c2\u6570\u7684\u62bd\u8c61":46,"\u7c7b\u522b\u4e2a\u6570":62,"\u7c7b\u522b\u4e2d\u7684\u53c2\u6570\u53ef\u7528\u4e8e\u6240\u6709\u573a\u5408":51,"\u7c7b\u522bid":65,"\u7c7b\u522bid\u548c\u6587\u672c\u4fe1\u606f\u7528":65,"\u7c7b\u540d\u548cc":26,"\u7c7b\u578b":[26,52,67],"\u7c7b\u578b\u53ef\u4ee5\u662fpaddlepaddle\u652f\u6301\u7684\u4efb\u610f\u8f93\u5165\u6570\u636e\u7c7b\u578b":40,"\u7c7b\u578b\u662fsparse_binary_vector":[3,36],"\u7c7b\u578b\u662fsparse_float_vector":[3,36],"\u7c7b\u578b\u6765\u8bbe\u7f6e":3,"\u7c7b\u578b\u7684":41,"\u7c7b\u7684\u6784\u9020\u51fd\u6570\u548c\u6790\u6784\u51fd\u6570":46,"\u7c7b\u9700\u8981\u5b9e\u73b0\u521d\u59cb\u5316":46,"\u7cfb\u7edf\u7f16\u8bd1wheel\u5305\u7684\u65f6\u5019":30,"\u7ea2\u697c\u68a6":61,"\u7eaa\u5f55\u7247":66,"\u7eafcpu\u955c\u50cf\u4ee5\u53cagpu\u955c\u50cf\u90fd\u4f1a\u7528\u5230avx\u6307\u4ee4\u96c6":33,"\u7ebf\u6027\u56de\u5f52\u7684\u8f93\u5165\u662f\u4e00\u6279\u70b9":31,"\u7ebf\u6027\u56de\u5f52\u7684\u8f93\u51fa\u662f\u4ece\u8fd9\u6279\u70b9\u4f30\u8ba1\u51fa\u6765\u7684\u53c2\u6570":31,"\u7ebf\u6027\u8ba1\u7b97\u7f51\u7edc\u5c42":31,"\u7ebf\u7a0bid\u53f7":54,"\u7ec6\u8282\u63cf\u8ff0":53,"\u7ecf\u5e38\u4f1a\u6d88\u8017\u657010gb\u7684\u5185\u5b58\u548c\u6570gb\u7684\u663e\u5b58":30,"\u7ed3\u5408":55,"\u7ed3\u675f\u6807\u8bb0":44,"\u7ed3\u6784\u5982\u4e0b":69,"\u7ed3\u6784\u5982\u4e0b\u56fe":61,"\u7ed3\u679c\u4fdd\u5b58\u5728":68,"\u7ed3\u679c\u53d1\u73b0\u5b83\u4eec\u4e0e\u540c\u65f6\u671f\u7684twitter\u6d88\u606f\u4e2d\u7684\u60c5\u7eea\u8bcd\u9891\u7387\u76f8\u5173":69,"\u7ed3\u8bba":26,"\u7ed9":41,"\u7ed9\u4e2a\u7b80\u5355\u7684":45,"\u7ed9\u51fa\u56fe\u7247\u5c3a\u5bf8":62,"\u7ed9\u51fa\u8f93\u5165\u6570\u636e\u6240\u5728\u8def\u5f84":62,"\u7ed9\u5b9a\u52a8\u8bcd":68,"\u7ed9\u5b9a\u7684\u6587\u672c\u53ef\u4ee5\u662f\u4e00\u4e2a\u6587\u6863":69,"\u7ed9\u5b9aencoder\u8f93\u51fa\u548c\u5f53\u524d\u8bcd":43,"\u7edf\u4e00\u7528":20,"\u7ee7\u7eed\u8bad\u7ec3\u6216\u9884\u6d4b":3,"\u7ef4\u57fa\u767e\u79d1\u4e2d\u6587\u9875\u9762":41,"\u7ef4\u57fa\u767e\u79d1\u9875\u9762":41,"\u7ef4\u5ea6\u4e3aword_dim":65,"\u7ef4\u5ea6\u662f\u7c7b\u522b\u4e2a\u6570":65,"\u7ef4\u5ea6\u662f\u8bcd\u5178\u5927\u5c0f":65,"\u7ef4\u62a4":55,"\u7ef4\u7a7a\u95f4":44,"\u7ef4\u7a7a\u95f4\u5b8c\u6210":44,"\u7f13\u5b58\u6c60\u7684\u51cf\u5c0f":30,"\u7f13\u5b58\u8bad\u7ec3\u6570\u636e\u5230\u5185\u5b58":3,"\u7f16\u5199\u597d\u6570\u636e\u63d0\u4f9b\u811a\u672c\u540e":67,"\u7f16\u5199\u5b8cyaml\u6587\u4ef6\u540e":57,"\u7f16\u5199\u672c\u6b21\u8bad\u7ec3\u7684yaml\u6587\u4ef6":57,"\u7f16\u53f7":67,"\u7f16\u53f7\u4ece0\u5f00\u59cb":30,"\u7f16\u53f7\u5b57\u6bb5":67,"\u7f16\u7801\u5411\u91cf":44,"\u7f16\u7801\u5668\u8f93\u51fa":44,"\u7f16\u7801\u6e90\u5e8f\u5217":44,"\u7f16\u89e3\u7801\u6a21\u578b\u5c06\u4e00\u4e2a\u6e90\u8bed\u53e5\u7f16\u7801\u4e3a\u4e00\u4e2a\u5b9a\u957f\u7684\u5411\u91cf":70,"\u7f16\u8bd1":[33,45],"\u7f16\u8bd1\u548c\u5b89\u88c5paddlepaddl":39,"\u7f16\u8bd1\u548c\u5b89\u88c5paddlepaddle\u9884\u6d4b\u5e93":38,"\u7f16\u8bd1\u5668":[38,39],"\u7f16\u8bd1\u5668\u6ca1\u6709":26,"\u7f16\u8bd1\u578b\u8bed\u8a00":26,"\u7f16\u8bd1\u5b8c\u6210\u4e4b\u540e":47,"\u7f16\u8bd1\u5b8c\u6210\u540e":47,"\u7f16\u8bd1\u5de5\u5177\u94fe\u6240\u5728\u7684\u7edd\u5bf9\u8def\u5f84":39,"\u7f16\u8bd1\u6210\u52a8\u6001\u5e93":52,"\u7f16\u8bd1\u6d41\u7a0b\u4e3b\u8981\u63a8\u8350\u9ad8\u7ea7\u7528\u6237\u67e5\u770b":34,"\u7f16\u8bd1\u751f\u6210":47,"\u7f16\u8bd1\u8fd9\u4e2a\u7248\u672c\u7684docker\u53d1\u884c\u955c\u50cf":29,"\u7f16\u8bd1\u8fd9\u4e2a\u7248\u672c\u7684ubuntu":29,"\u7f16\u8bd1\u9009\u9879":32,"\u7f16\u8bd1c":27,"\u7f16\u8bd1master\u5206\u652f\u7684docker\u53d1\u884c\u955c\u50cf":29,"\u7f16\u8bd1ubuntu\u7684deb\u5305":29,"\u7f16\u8f91":55,"\u7f29\u653e\u53c2\u6570":63,"\u7f51\u7edc":[68,69],"\u7f51\u7edc\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf":55,"\u7f51\u7edc\u540d\u79f0":65,"\u7f51\u7edc\u5c42\u53ef\u4ee5\u6709\u591a\u4e2a\u8f93\u5165":46,"\u7f51\u7edc\u5c42\u7684\u6807\u8bc6\u7b26\u4e3a":46,"\u7f51\u7edc\u5c42\u7684\u7c7b\u578b":46,"\u7f51\u7edc\u5c42\u7684\u7ec6\u8282\u53ef\u4ee5\u901a\u8fc7\u4e0b\u9762\u8fd9\u4e9b\u4ee3\u7801\u7247\u6bb5\u6765\u6307\u5b9a":46,"\u7f51\u7edc\u5c42\u7684\u8f93\u51fa\u662f\u7ecf\u8fc7\u6fc0\u6d3b\u51fd\u6570\u4e4b\u540e\u7684\u503c":52,"\u7f51\u7edc\u5c42\u914d\u7f6e\u5305\u542b\u4ee5\u4e0b\u51e0\u9879":46,"\u7f51\u7edc\u63a5\u53e3\u5361":50,"\u7f51\u7edc\u6a21\u5757":63,"\u7f51\u7edc\u6a21\u578b\u5c06\u8f93\u51fa\u6807\u7b7e\u7684\u6982\u7387\u5206\u5e03":68,"\u7f51\u7edc\u7684\u8bad\u7ec3\u8fc7\u7a0b":69,"\u7f51\u7edc\u7ed3\u6784\u5982\u4e0b\u56fe\u6240\u793a":67,"\u7f51\u7edc\u901a\u4fe1":46,"\u7f51\u7edc\u914d\u7f6e":[50,65,69],"\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6":[63,68],"\u800c":[31,44,56,67],"\u800c\u4e0d\u4f7f\u7528imdb\u6570\u6910\u96c6\u4e2d\u7684imdb":69,"\u800c\u4e0d\u5fc5\u5728\u610fpaddl":27,"\u800c\u4e0d\u652f\u6301pypy\u89e3\u91ca\u5668":26,"\u800c\u4e0d\u662f\u4f7f\u7528\u540c\u6b65":50,"\u800c\u4e0d\u662f\u6e90\u7801\u76ee\u5f55\u91cc":30,"\u800c\u4e0d\u662f\u7279\u5f81\u7684\u96c6\u5408":41,"\u800c\u4e0d\u662f\u7ec4\u5408\u4e0a\u4e0b\u6587\u7ea7\u522b\u4fe1\u606f":69,"\u800c\u4e0d\u66b4\u9732\u6982\u5ff5\u7684\u5b9e\u73b0":27,"\u800c\u4e0d\u7528\u5173\u5fc3\u6570\u636e\u5982\u4f55\u4f20\u8f93":3,"\u800c\u4e14":70,"\u800c\u4e14\u5728\u4f20\u8f93\u7684\u8fc7\u7a0b\u4e2d\u4e5f\u53ef\u80fd\u51fa\u73b0\u7f51\u7edc\u4e0d\u7a33\u5b9a\u7684\u60c5\u51b5":24,"\u800c\u4e4b\u524d\u7684\u53c2\u6570\u5c06\u4f1a\u88ab\u5220\u9664":52,"\u800c\u4ece\u5e94\u7528\u7684\u89d2\u5ea6":49,"\u800c\u4f18\u5316\u6027\u80fd\u7684\u9996\u8981\u4efb\u52a1":49,"\u800c\u5176\u4ed6\u5c42\u4f7f\u7528cpu\u8ba1\u7b97":54,"\u800c\u51fa\u73b0\u9636\u6bb5\u6027\u7684\u8fd0\u884c\u505c\u6ede":19,"\u800c\u53cc\u5c42rnn\u662f\u53ef\u4ee5\u5904\u7406\u8fd9\u79cd\u8f93\u5165\u6570\u636e\u7684\u7f51\u7edc\u7ed3\u6784":41,"\u800c\u53f3\u56fe\u7684\u74f6\u9888\u8fde\u63a5\u6a21\u5757\u7528\u4e8e50\u5c42":63,"\u800c\u5728cpp\u91cc\u9762\u5b9e\u73b0\u8fd9\u4e2ac\u7684\u63a5\u53e3":26,"\u800c\u591a\u8bed\u8a00\u63a5\u53e3\u9700\u8981\u76f4\u63a5\u8bfb\u53d6\u751f\u6210\u7684\u4e8c\u8fdb\u5236":26,"\u800c\u5927\u591a\u6570\u65b9\u6cd5\u53ea\u662f\u5229\u7528n":69,"\u800c\u5bf9\u4e8e\u53cc\u5c42\u5e8f\u5217":41,"\u800c\u5bf9\u4e8e\u6bcf\u4e00\u4e2a\u5185\u5c42\u7279\u5f81\u6570\u636e\u800c\u8a00":41,"\u800c\u5bf9\u4e8egolang":26,"\u800c\u5bf9\u4e8egolang\u9519\u8bef\u5904\u7406\u5e94\u8be5\u4f7f\u7528\u8fd4\u56de\u503c":26,"\u800c\u5c06\u8fd9\u4e2a\u6bb5\u843d\u7684\u6bcf\u4e00\u53e5\u8bdd\u7528lstm\u7f16\u7801\u6210\u4e00\u4e2a\u5411\u91cf":41,"\u800c\u5f53\u524d\u5df2\u7ecf\u67095":49,"\u800c\u662f\u76f4\u63a5\u4ece\u5185\u5b58\u7684\u7f13\u5b58\u91cc\u8bfb\u53d6\u6570\u636e":30,"\u800c\u662f\u76f4\u63a5\u4fee\u6539paddl":27,"\u800c\u662f\u76f4\u63a5\u7528api\u7684\u63a5\u53e3\u8fdc\u7a0b\u8bbf\u95ee":20,"\u800c\u66f4\u6df1\u5165\u7684\u5206\u6790":49,"\u800c\u6709\u4e9b\u53c2\u6570\u9700\u8981\u5728\u96c6\u7fa4\u591a\u673a\u8bad\u7ec3\u4e2d\u4f7f\u7528\u7b49":51,"\u800c\u6ca1\u6709\u77ed\u65f6\u8bb0\u5fc6\u7684\u635f\u5931":69,"\u800c\u6e90\u5e8f\u5217\u7684\u7f16\u7801\u5411\u91cf\u53ef\u4ee5\u88ab\u65e0\u8fb9\u754c\u7684memory\u8bbf\u95ee":44,"\u800c\u795e\u7ecf\u7f51\u7edc\u662f\u6211\u4eec\u8981\u642d\u5efa\u7684\u5b9d\u5854":36,"\u800c\u7a00\u758f\u66f4\u65b0\u5728\u53cd\u5411\u4f20\u64ad\u4e4b\u540e\u7684\u6743\u91cd\u66f4\u65b0\u65f6\u8fdb\u884c":54,"\u800c\u7cfb\u7edf\u4e2d\u7684":30,"\u800c\u8fd9\u4e00\u53e5\u8bdd\u5c31\u53ef\u4ee5\u8868\u793a\u6210\u8fd9\u4e9b\u4f4d\u7f6e\u7684\u6570\u7ec4":41,"\u800c\u8fd9\u4e2acontext\u53ef\u80fd\u4f1a\u975e\u5e38\u5927":3,"\u800c\u8fd9\u6bcf\u4e00\u4e2a\u6570\u7ec4\u5143\u7d20":41,"\u800c\u975e\u76f4\u63a5\u56de\u590d\u7684\u65b9\u5f0f":45,"\u800c\u975e\u9759\u6001\u52a0\u8f7dcuda\u52a8\u6001\u5e93":32,"\u800cpaddlepaddle\u5219\u4f1a\u5e2e\u7528\u6237\u505a\u4ee5\u4e0b\u5de5\u4f5c":3,"\u800crnn\u662f\u6700\u6d41\u884c\u7684\u9009\u62e9":43,"\u800cswig\u53ea\u80fd\u7b80\u5355\u7684\u66b4\u9732c":26,"\u800ctrainer\u9700\u8981\u8bfb\u53d6\u8bad\u7ec3\u6570\u636e\u8fdb\u884c\u8bad\u7ec3":36,"\u800cweight":62,"\u800cy_predict\u662f\u63a5\u6536x\u4f5c\u4e3a\u8f93\u5165":36,"\u804c\u4e1a":66,"\u804c\u4e1a\u4ece\u4e0b\u9762\u6240\u5217\u4e2d\u9009\u62e9":66,"\u80fd\u591f\u5904\u7406\u53cc\u5c42\u5e8f\u5217":43,"\u80fd\u591f\u5bf9\u53cc\u5411\u5e8f\u5217\u8fdb\u884c\u5904\u7406\u7684\u6709":43,"\u80fd\u591f\u627e\u5230\u8fd9\u91cc\u4f7f\u7528\u7684\u6240\u6709\u6570\u636e":65,"\u80fd\u591f\u8bb0\u5f55\u4e0a\u4e00\u4e2asubseq":43,"\u80fd\u83b7\u53d6":50,"\u811a\u672c":[50,62,67],"\u811a\u672c\u4fdd\u5b58\u5728":62,"\u811a\u672c\u5f00\u59cb\u65f6":57,"\u811a\u672c\u63d0\u4f9b\u4e86\u4e00\u4e2a\u9884\u6d4b\u63a5\u53e3":69,"\u811a\u672c\u65f6\u9700\u8981\u52a0\u4e0a":69,"\u811a\u672c\u8fd0\u884c\u5b8c\u6210\u540e":62,"\u81ea\u52a8\u5173\u95ed\u5bf9\u5e94\u7684":45,"\u81ea\u52a8\u5730\u5c06\u8fd9\u4e9b\u9009\u9879\u5e94\u7528\u5230":50,"\u81ea\u52a8\u5b8c\u6210\u8fd9\u4e00\u8fc7\u7a0b":43,"\u81ea\u52a8\u6302\u8f7d\u5206\u5e03\u5f0f\u5b58\u50a8\u76ee\u5f55":19,"\u81ea\u52a8\u751f\u6210":47,"\u81ea\u52a8\u83b7\u53d6\u4e0a\u4e00\u4e2a\u751f\u6210\u7684\u8bcd":44,"\u81ea\u5e95\u5411\u4e0a\u6cd5":65,"\u81ea\u6b64":38,"\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7b49":54,"\u81ea\u7531\u804c\u4e1a\u8005":66,"\u81f3\u4e8e\u4e3a\u4ec0\u4e48\u9700\u8981c":27,"\u81f3\u6b64":[3,41,45],"\u8212\u9002":41,"\u826f\u597d\u7684\u6587\u6863":26,"\u827a\u672f\u5bb6":66,"\u8282\u70b9\u4e2d\u7684":50,"\u82e5":46,"\u82e5\u5e0c\u671b\u6700\u5feb\u7684\u6267\u884c\u901f\u5ea6":[38,39],"\u82e5\u5e72\u4e2a\u53e5\u5b50\u6784\u6210\u4e00\u4e2a\u6bb5\u843d":40,"\u82e5\u6709\u4e0d\u4e00\u81f4\u4e4b\u5904":49,"\u82e5\u6709\u5fc5\u8981":46,"\u82e5\u6ca1\u6709\u663e\u5f0f\u8bbe\u7f6e":38,"\u82e5\u73af\u5883\u53d8\u91cf":[38,39],"\u82e5\u8f93\u51fa\u662f\u5355\u5c42\u5e8f\u5217":40,"\u82e5\u8f93\u51fa\u662f\u53cc\u5c42\u5e8f\u5217":40,"\u82f1\u6587\u6587\u6863":47,"\u82f1\u6587\u6587\u6863\u76ee\u5f55":47,"\u82f1\u8bed":70,"\u8303\u56f4":54,"\u83b7\u53d6":45,"\u83b7\u53d6\u5229\u7528":65,"\u83b7\u53d6\u5b57\u5178\u7ef4\u5ea6":69,"\u83b7\u53d6\u6700\u65b0\u7684\u68c0\u67e5\u70b9\u7684\u6587\u4ef6uuid":19,"\u83b7\u53d6\u8be5\u6761\u6837\u672c\u7c7b\u522bid":65,"\u83b7\u53d6\u901a\u8fc7":69,"\u83b7\u53d6trainer":57,"\u83b7\u5f97\u53c2\u6570\u5c3a\u5bf8":46,"\u867d\u7136":31,"\u867d\u7136\u4e0d\u9f13\u52b1\u8fd9\u6837":27,"\u867d\u7136\u6bcf\u4e2agenerator\u5728\u6ca1\u6709\u8c03\u7528\u7684\u65f6\u5019":3,"\u867d\u7136\u8fd9\u4e9b\u6587\u4ef6\u5e76\u975e\u90fd\u9700\u8981\u96c6\u7fa4\u8bad\u7ec3":50,"\u867d\u7136paddle\u770b\u8d77\u6765\u5305\u542b\u4e86\u4f17\u591a\u53c2\u6570":51,"\u884c":61,"\u884c\u4f18\u5148\u6b21\u5e8f\u5b58\u50a8":63,"\u884c\u5185\u4f7f\u7528":3,"\u884c\u653f\u5de5\u4f5c":66,"\u884c\u955c\u50cf\u5305\u62ec\u7eafcpu\u7248\u672c\u548cgpu\u7248\u672c\u4ee5\u53ca\u5176\u5bf9\u5e94\u7684\u975eavx\u7248\u672c":33,"\u8865\u5145\u4e0a\u6b21\u7684commit":45,"\u8868\u660e\u4e86\u8fd9\u4e9b\u884c\u7684\u6807\u53f7":46,"\u8868\u660e\u8fd9\u4e2a\u5c42\u7684\u4e00\u4e2a\u5b9e\u4f8b\u662f\u5426\u9700\u8981\u504f\u7f6e":46,"\u8868\u793a\u4e00\u4e2akubernetes\u96c6\u7fa4\u4e2d\u7684\u4e00\u4e2a\u5de5\u4f5c\u8282\u70b9":55,"\u8868\u793a\u4e3adeviceid":54,"\u8868\u793a\u5728\u96c6\u7fa4\u4f5c\u4e1a":50,"\u8868\u793a\u5973\u6027":66,"\u8868\u793a\u5c06\u5916\u5c42\u7684outer_mem\u4f5c\u4e3a\u5185\u5c42memory\u7684\u521d\u59cb\u72b6\u6001":41,"\u8868\u793a\u5f53\u524d\u96c6\u7fa4\u4f5c\u4e1a\u7684\u8282\u70b9":50,"\u8868\u793a\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":50,"\u8868\u793a\u751f\u6210\u6570\u636e\u7684\u5e8f\u5217id":70,"\u8868\u793a\u7528\u4e8e\u8bad\u7ec3\u6216\u9884\u6d4b":3,"\u8868\u793a\u7537\u6027":66,"\u8868\u793a\u7684\u6bcf\u4e2a\u5355\u8bcd":65,"\u8868\u793a\u7b2c0\u4e2abatch\u5230\u5f53\u524dbatch\u7684\u5206\u7c7b\u9519\u8bef":69,"\u8868\u793a\u8bad\u7ec3\u4e86xx\u4e2a\u6837\u672c":69,"\u8868\u793a\u8bad\u7ec3\u4e86xx\u4e2abatch":69,"\u8868\u793a\u8bfb\u8005\u6240\u4f7f\u7528\u7684docker\u955c\u50cf\u4ed3\u5e93\u5730\u5740":57,"\u8868\u793a\u8fc7\u4e8620\u4e2abatch":65,"\u8868\u793a\u8fc7\u4e862560\u4e2a\u6837\u672c":65,"\u8868\u793a\u8fd9\u4e2ajob\u7684\u540d\u5b57":57,"\u88ab":45,"\u88ab\u6269\u5c55\u4e3a\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":40,"\u88ab\u653e\u5728":46,"\u88ab\u79f0\u4e3a":44,"\u897f\u90e8\u7247":66,"\u8981\u4e0b\u8f7d\u548c\u89e3\u538b\u6570\u636e\u96c6":67,"\u8981\u4e0b\u8f7d\u89e3\u538b\u8fd9\u4e2a\u6a21\u578b":70,"\u8981\u4f7f\u7528\u547d\u4ee4\u884c\u5206\u6790\u5de5\u5177":49,"\u8981\u5728\u5df2\u6709\u7684kubernetes\u96c6\u7fa4\u4e0a\u8fdb\u884cpaddlepaddle\u7684\u5206\u5e03\u5f0f\u8bad\u7ec3":57,"\u8981\u5728\u6240\u6709\u8282\u70b9\u4e0a\u5b58\u5728":50,"\u8981\u5bf9\u4e00\u4e2a\u56fe\u7247\u7684\u8fdb\u884c\u5206\u7c7b\u9884\u6d4b":62,"\u8981\u5c06\u5b57\u6bb5\u914d\u7f6e\u6587\u4ef6\u8f6c\u5316\u4e3ameta\u914d\u7f6e\u6587\u4ef6":67,"\u8981\u6c42\u5355\u5c42\u5e8f\u5217\u542b\u6709\u5143\u7d20\u7684\u6570\u76ee":40,"\u8981\u751f\u6210\u7684\u76ee\u6807\u5e8f\u5217":43,"\u8981\u8c03\u7528":46,"\u89e3\u51b3\u529e\u6cd5\u662f":30,"\u89e3\u51b3\u65b9\u6848\u662f":30,"\u89e3\u538b":70,"\u89e3\u6790\u5668\u80fd\u901a\u8fc7\u6587\u4ef6\u7684\u6269\u5c55\u540d\u81ea\u52a8\u8bc6\u522b\u6587\u4ef6\u7684\u683c\u5f0f":67,"\u89e3\u6790\u6570\u636e\u96c6\u4e2d\u7684\u6bcf\u4e00\u4e2a\u5b57\u6bb5":67,"\u89e3\u6790\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":5,"\u89e3\u6790\u73af\u5883\u53d8\u91cf\u5f97\u5230":57,"\u89e3\u6790\u8bad\u7ec3\u6a21\u578b\u65f6\u7528\u7684\u914d\u7f6e\u6587\u4ef6":5,"\u89e3\u7801\u5668\u4f7f\u7528":44,"\u89e3\u7801\u5668\u57fa\u4e8e\u7f16\u7801\u6e90\u5e8f\u5217\u548c\u6700\u540e\u751f\u6210\u7684\u76ee\u6807\u8bcd\u9884\u6d4b\u4e0b\u4e00\u76ee\u6807\u8bcd":44,"\u89e3\u7801\u5668\u662f\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":44,"\u89e3\u7801\u5668\u6839\u636e\u4e0a\u4e0b\u6587\u5411\u91cf\u9884\u6d4b\u51fa\u4e00\u4e2a\u76ee\u6807\u5355\u8bcd":70,"\u89e3\u91ca":65,"\u89e3\u91ca\u578b\u8bed\u8a00\u53ea\u80fd\u8c03\u7528\u52a8\u6001\u5e93":26,"\u89e3\u91ca\u6027\u8bed\u8a00\u5b9e\u9645\u8fd0\u884c\u7684\u4e8c\u8fdb\u5236\u662f\u89e3\u91ca\u5668\u672c\u8eab":26,"\u8ba1\u7b97":44,"\u8ba1\u7b97\u504f\u7f6e\u7684\u68af\u5ea6":46,"\u8ba1\u7b97\u5355\u5143\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u6838\u5fc3":31,"\u8ba1\u7b97\u53cd\u5411rnn\u7684\u7b2c\u4e00\u4e2a\u5b9e\u4f8b":44,"\u8ba1\u7b97\u53d8\u6362\u77e9\u9635\u7684\u5927\u5c0f\u548c\u683c\u5f0f":46,"\u8ba1\u7b97\u5f53\u524d\u5c42\u6743\u91cd\u7684\u68af\u5ea6":46,"\u8ba1\u7b97\u6bcf\u4e2a\u8bcd\u7684\u8bcd\u5411\u91cf":44,"\u8ba1\u7b97\u6fc0\u6d3b\u51fd\u6570\u7684\u68af\u5ea6":46,"\u8ba1\u7b97\u7684\u7ec6\u8282\u5c06\u5728\u4e0b\u9762\u7684\u5c0f\u8282\u7ed9\u51fa":46,"\u8ba1\u7b97\u8bef\u5dee\u51fd\u6570":31,"\u8ba1\u7b97\u8f6c\u6362\u77e9\u9635\u548c\u8f93\u5165\u7684\u68af\u5ea6":46,"\u8ba1\u7b97\u8f93\u5165\u548c\u53c2\u6570\u7684\u68af\u5ea6":46,"\u8ba1\u7b97\u8f93\u5165\u5c42\u7684\u504f\u5dee":46,"\u8ba1\u7b97\u8f93\u51fa":46,"\u8ba1\u7b97\u8fd9\u4e2a\u6587\u4ef6\u7684md5":19,"\u8ba9\u6a21\u578b\u80fd\u591f\u5f97\u5230\u8bad\u7ec3\u66f4\u65b0":65,"\u8ba9\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u8fdb\u884c\u8bad\u7ec3\u6216\u9884\u6d4b":2,"\u8ba9\u8fd9\u4e2a\u793a\u4f8b\u53d8\u5f97\u66f4\u597d":67,"\u8ba9paddle\u6838\u5fc3\u4e2d":27,"\u8bad\u7ec3":[51,69],"\u8bad\u7ec3\u4e0e\u5e94\u7528":1,"\u8bad\u7ec3\u4efb\u52a1\u7684\u8fd0\u884c\u53ef\u80fd\u4f1a\u5360\u6ee1trainer\u548cparamet":19,"\u8bad\u7ec3\u4f5c\u4e1a":50,"\u8bad\u7ec3\u53ca\u6d4b\u8bd5\u8bef\u5dee\u66f2\u7ebf\u56fe\u4f1a\u88ab":62,"\u8bad\u7ec3\u53ef\u4ee5\u8bbe\u7f6e\u4e3atrue":68,"\u8bad\u7ec3\u540e":68,"\u8bad\u7ec3\u548c\u7eaf\u4f7f\u7528":29,"\u8bad\u7ec3\u5931\u8d25\u65f6\u53ef\u4ee5\u68c0\u67e5\u9519\u8bef\u65e5\u5fd7":50,"\u8bad\u7ec3\u597d\u4e00\u4e2a\u6df1\u5c42\u795e\u7ecf\u7f51\u7edc\u901a\u5e38\u8981\u8017\u8d39\u975e\u5e38\u957f\u7684\u65f6\u95f4":49,"\u8bad\u7ec3\u5b8c\u6210\u540e":31,"\u8bad\u7ec3\u6570\u636e\u548c\u6d4b\u8bd5\u6570\u636e\u7684\u76ee\u5f55":70,"\u8bad\u7ec3\u6570\u636e\u662f":3,"\u8bad\u7ec3\u6570\u636e\u6709\u95ee\u9898":30,"\u8bad\u7ec3\u6570\u6910\u96c6":69,"\u8bad\u7ec3\u65f6":57,"\u8bad\u7ec3\u65f6\u6240\u9700\u8bbe\u7f6e\u7684\u4e3b\u8981\u53c2\u6570\u5982\u4e0b":65,"\u8bad\u7ec3\u65f6\u9ed8\u8ba4shuffl":3,"\u8bad\u7ec3\u6a21\u578b\u4e4b\u524d":69,"\u8bad\u7ec3\u6a21\u578b\u540e":44,"\u8bad\u7ec3\u6a21\u578b\u6b63\u786e\u6027":29,"\u8bad\u7ec3\u7684\u635f\u5931\u51fd\u6570\u9ed8\u8ba4\u6bcf\u969410\u4e2abatch\u6253\u5370\u4e00\u6b21":70,"\u8bad\u7ec3\u7684\u811a\u672c\u662f":68,"\u8bad\u7ec3\u7ed3\u675f\u540e\u67e5\u770b\u8f93\u51fa\u7ed3\u679c":57,"\u8bad\u7ec3\u811a\u672c":65,"\u8bad\u7ec3\u811a\u672c\u7b49\u7b49":65,"\u8bad\u7ec3\u81f3\u591a":67,"\u8bad\u7ec3\u8282\u70b9\u6570\u91cf":57,"\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u8ddd\u79bb":30,"\u8bad\u7ec3\u8f6e\u6b21":65,"\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u53c2\u6570\u6216\u8005\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u68af\u5ea6\u5c3a\u5ea6\u8fc7\u5927":30,"\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u6d4b\u8bd5test_period":51,"\u8bad\u7ec3\u8fc7\u7a0b\u662f\u5426\u4e3a\u672c\u5730\u6a21\u5f0f":52,"\u8bad\u7ec3\u8fc7\u7a0b\u662f\u5426\u4f7f\u7528gpu":52,"\u8bad\u7ec3\u914d\u7f6e\u4e2d\u7684\u8bbe\u5907\u5c5e\u6027\u5c06\u4f1a\u65e0\u6548":52,"\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u6587\u4ef6\u5217\u8868":69,"\u8bad\u7ec3\u96c6\u5df2\u7ecf\u505a\u4e86\u968f\u673a\u6253\u4e71\u6392\u5e8f\u800c\u6d4b\u8bd5\u96c6\u6ca1\u6709":69,"\u8bad\u7ec3\u96c6\u5df2\u7ecf\u968f\u673a\u6253\u4e71":69,"\u8bad\u7ec3\u96c6\u5e73\u5747\u503c":62,"\u8bad\u7ec3dot_period":51,"\u8bb0\u5f55\u4e0b\u6240\u6709\u5931\u8d25\u7684\u4f8b\u5b50":29,"\u8bb0\u5fc6\u6a21\u5757":44,"\u8bba\u6587":63,"\u8bbe\u4e3a\u5df2\u90e8\u7f72\u7684\u5de5\u4f5c\u7a7a\u95f4\u76ee\u5f55":50,"\u8bbe\u4e3a\u672c\u5730":50,"\u8bbe\u7f6e":27,"\u8bbe\u7f6e\u4e3a":[39,46],"\u8bbe\u7f6e\u4e3atrue\u4f7f\u7528\u672c\u5730\u8bad\u7ec3\u6216\u8005\u4f7f\u7528\u96c6\u7fa4\u4e0a\u7684\u4e00\u4e2a\u8282\u70b9":52,"\u8bbe\u7f6e\u4e3atrue\u4f7f\u7528gpu\u6a21\u5f0f":52,"\u8bbe\u7f6e\u4efb\u52a1\u7684\u6a21\u5f0f\u4e3a\u6d4b\u8bd5":70,"\u8bbe\u7f6e\u4fdd\u5b58\u6a21\u578b\u7684\u8f93\u51fa\u8def\u5f84":70,"\u8bbe\u7f6e\u5168\u5c40\u5b66\u4e60\u7387":69,"\u8bbe\u7f6e\u5185\u5b58\u4e2d\u6682\u5b58\u7684\u6570\u636e\u6761\u6570":3,"\u8bbe\u7f6e\u5185\u5b58\u4e2d\u6700\u5c0f\u6682\u5b58\u7684\u6570\u636e\u6761\u6570":3,"\u8bbe\u7f6e\u53c2\u6570\u7684\u540d\u5b57":30,"\u8bbe\u7f6e\u547d\u4ee4\u884c\u53c2\u6570":[30,48,67],"\u8bbe\u7f6e\u5b57\u5178\u6587\u4ef6":69,"\u8bbe\u7f6e\u5de5\u4f5c\u6a21\u5f0f\u4e3a\u8bad\u7ec3":69,"\u8bbe\u7f6e\u5e73\u5747sgd\u7a97\u53e3":69,"\u8bbe\u7f6e\u6210":30,"\u8bbe\u7f6e\u6210\u4e00\u4e2a\u5c0f\u4e00\u4e9b\u7684\u503c":30,"\u8bbe\u7f6e\u6570\u636e\u904d\u5386\u6b21\u6570":68,"\u8bbe\u7f6e\u6807\u7b7e\u7c7b\u522b\u5b57\u5178":69,"\u8bbe\u7f6e\u6a21\u578b\u8def\u5f84":69,"\u8bbe\u7f6e\u7684\u547d\u4ee4\u884c\u53c2\u6570":69,"\u8bbe\u7f6e\u795e\u7ecf\u7f51\u7edc\u7684\u914d\u7f6e\u6587\u4ef6":70,"\u8bbe\u7f6e\u7c7b\u522b\u6570":69,"\u8bbe\u7f6e\u7ebf\u7a0b\u6570":[68,69],"\u8bbe\u7f6e\u7f51\u7edc\u914d\u7f6e":69,"\u8bbe\u7f6e\u8f93\u51fa\u7684\u5c3a\u5bf8":46,"\u8bbe\u7f6e\u8f93\u51fa\u8def\u5f84\u4ee5\u4fdd\u5b58\u8bad\u7ec3\u5b8c\u6210\u7684\u6a21\u578b":69,"\u8bbe\u7f6e\u8fd9\u4e2apydataprovider2\u8fd4\u56de\u4ec0\u4e48\u6837\u7684\u6570\u636e":3,"\u8bbe\u7f6e\u9ed8\u8ba4\u8bbe\u5907\u53f7\u4e3a0":54,"\u8bbe\u7f6ebatch":69,"\u8bbe\u7f6ecpu\u7ebf\u7a0b\u6570\u6216\u8005gpu\u8bbe\u5907\u6570":70,"\u8bbe\u7f6egpu":52,"\u8bbe\u7f6epass":69,"\u8bbe\u7f6epasses\u7684\u6570\u91cf":70,"\u8bbf\u95eekubernetes\u7684\u63a5\u53e3\u6765\u67e5\u8be2\u6b64job\u5bf9\u5e94\u7684\u6240\u6709pod\u4fe1\u606f":57,"\u8bc4\u4ef7\u9884\u6d4b\u7684\u6548\u679c":31,"\u8bc4\u4f30\u8be5\u4ea7\u54c1\u7684\u8d28\u91cf":65,"\u8bc4\u5206":[66,67],"\u8bc4\u5206\u6587\u4ef6\u7684\u6bcf\u4e00\u884c\u4ec5\u4ec5\u63d0\u4f9b\u7535\u5f71\u6216\u7528\u6237\u7684\u7f16\u53f7\u6765\u4ee3\u8868\u76f8\u5e94\u7684\u7535\u5f71\u6216\u7528\u6237":67,"\u8bc4\u5206\u88ab\u8c03\u6574\u4e3a5\u661f\u7684\u89c4\u6a21":66,"\u8bc4\u5ba1\u4eba\u4e00\u822c\u4e0d\u505a\u8bc4\u5ba1":45,"\u8bc4\u5ba1\u4eba\u7684\u6bcf\u4e2a\u610f\u89c1\u90fd\u5fc5\u987b\u56de\u590d":45,"\u8bc4\u5ba1\u4eba\u9700\u8981\u9010\u4e00\u67e5\u770b\u6bcf\u4e2acommit\u624d\u80fd\u77e5\u9053\u505a\u4e86\u54ea\u4e9b\u4fee\u6539":45,"\u8bc4\u8bba\u6846\u4e2d\u52a0\u4e0a":45,"\u8bc6\u522b\u6570\u5b57":29,"\u8bcd\u5411\u91cf":[29,61],"\u8bcd\u5411\u91cf\u6a21\u578b":64,"\u8bcd\u5411\u91cf\u6a21\u578b\u540d\u79f0":61,"\u8bcd\u672c\u8eab\u548c\u8bcd\u9891":61,"\u8bcd\u9891\u6700\u9ad8\u7684":70,"\u8bd5\u7740\u8ba9\u8f93\u51fa\u7684\u5206\u6790\u6570\u636e\u548c\u7406\u8bba\u503c\u5bf9\u5e94":49,"\u8be5":[50,68],"\u8be5\u51fd\u6570\u5177\u6709\u4e24\u4e2a\u53c2\u6570":3,"\u8be5\u51fd\u6570\u5728\u521d\u59cb\u5316\u7684\u65f6\u5019\u4f1a\u88ab\u8c03\u7528":3,"\u8be5\u51fd\u6570\u7684\u529f\u80fd\u662f":3,"\u8be5\u53c2\u6570\u5728\u7f51\u7edc\u914d\u7f6e\u7684output":52,"\u8be5\u53c2\u6570\u5728\u96c6\u7fa4\u63d0\u4ea4\u73af\u5883\u4e2d\u81ea\u52a8\u8bbe\u7f6e":52,"\u8be5\u53c2\u6570\u5df2\u7ecf\u5728\u96c6\u7fa4\u63d0\u4ea4\u73af\u5883\u4e2d\u5b8c\u6210\u8bbe\u7f6e":52,"\u8be5\u53c2\u6570\u5fc5\u987b\u80fd\u88abflag":52,"\u8be5\u53c2\u6570\u6307\u793a\u662f\u5426\u6253\u5370\u65e5\u5fd7\u622a\u65ad\u4fe1\u606f":52,"\u8be5\u53c2\u6570\u6307\u793a\u662f\u5426\u6253\u5370\u9519\u8bef\u622a\u65ad\u65e5\u5fd7":52,"\u8be5\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u52a8\u6001\u5e93\u8def\u5f84":52,"\u8be5\u53c2\u6570\u7684\u610f\u601d\u662f\u8bad\u7ec3num":52,"\u8be5\u53c2\u6570\u9ed8\u8ba4\u4e3anull":52,"\u8be5\u5bf9\u8c61\u5177\u6709\u4ee5\u4e0b\u4e24\u4e2a\u5c5e\u6027":3,"\u8be5\u5c42\u4ec5\u9700\u8981\u8fd9\u4e9b\u975e\u96f6\u6837\u672c\u4f4d\u7f6e\u6240\u5bf9\u5e94\u7684\u53d8\u6362\u77e9\u9635\u7684\u90a3\u4e9b\u884c":46,"\u8be5\u5c42\u795e\u7ecf\u5143\u4e2a\u6570":65,"\u8be5\u622a\u65ad\u4f1a\u5f71\u54cd":52,"\u8be5\u6279\u6b21\u7684\u8f93\u5165\u4e2d\u4ec5\u6709\u4e00\u4e2a\u5b50\u96c6\u662f\u975e\u96f6\u7684":46,"\u8be5\u63a5\u53e3\u4f7f\u7528\u591a\u7ebf\u7a0b\u8bfb\u53d6\u6570\u636e":3,"\u8be5\u63a5\u53e3\u53ef\u7528\u4e8e\u9884\u6d4b\u548c\u5b9a\u5236\u5316\u8bad\u7ec3":32,"\u8be5\u6570\u636e\u53ca\u6709\u5f88\u591a\u4e0d\u540c\u7684\u7248\u672c":66,"\u8be5\u6570\u636e\u96c6":61,"\u8be5\u6570\u636e\u96c6\u4e8e2003\u5e742\u6708\u53d1\u5e03":66,"\u8be5\u6570\u636e\u96c6\u5305\u542b\u4e00\u4e9b\u7528\u6237\u4fe1\u606f":66,"\u8be5\u6570\u76ee\u662f\u63d0\u524d\u5b9a\u4e49\u597d\u7684":52,"\u8be5\u6587\u4ef6\u53ef\u4ee5\u4ece\u5b57\u6bb5\u914d\u7f6e\u6587\u4ef6\u751f\u6210":67,"\u8be5\u6587\u4ef6\u662f\u7531cpickle\u4ea7\u751f\u7684":63,"\u8be5\u6587\u4ef6\u662fpython\u7684pickle\u5bf9\u8c61":67,"\u8be5\u6587\u4ef6\u8d1f\u8d23\u4ea7\u751f\u56fe\u7247\u6570\u636e\u5e76\u4f20\u9012\u7ed9paddle\u7cfb\u7edf":62,"\u8be5\u6a21\u578b\u4f9d\u7136\u4f7f\u7528\u903b\u8f91\u56de\u5f52\u5206\u7c7b\u7f51\u7edc\u7684\u6846\u67b6":65,"\u8be5\u6a21\u578b\u5728\u957f\u8bed\u53e5\u7ffb\u8bd1\u7684\u573a\u666f\u4e0b\u6548\u679c\u63d0\u5347\u66f4\u52a0\u660e\u663e":70,"\u8be5\u6a21\u578b\u7684\u7f51\u7edc\u914d\u7f6e\u5982\u4e0b":31,"\u8be5\u6a21\u578b\u7684\u8bf4\u660e\u5982\u4e0b\u56fe\u6240\u793a":44,"\u8be5\u6a21\u578b\u7f51\u7edc\u53ea\u662f\u7528\u4e8e\u8fdb\u884cdemo\u5c55\u793apaddle\u5982\u4f55\u5de5\u4f5c":67,"\u8be5\u793a\u4f8b\u5c06\u5c55\u793apaddle\u5982\u4f55\u8fdb\u884c\u8bcd\u5411\u91cf\u5d4c\u5165":67,"\u8be5\u793a\u4f8b\u7684\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6":67,"\u8be5\u7b97\u6cd5\u6bcf\u6279\u91cf":31,"\u8be5\u7c7b\u7684\u5b9e\u73b0\u7ec6\u8282\u5728":46,"\u8be5\u7f16\u8bd1\u5de5\u5177\u94fe\u9700\u8981\u7cfb\u7edfglibc\u652f\u63012":39,"\u8be5\u811a\u672c\u4ec5\u4ec5\u662f\u5f00\u59cb\u4e00\u4e2apaddle\u8bad\u7ec3\u8fc7\u7a0b":67,"\u8be5\u811a\u672c\u4f1a\u751f\u6210\u4e00\u4e2adot\u6587\u4ef6":63,"\u8be5\u811a\u672c\u5c06\u8f93\u51fa\u9884\u6d4b\u5206\u7c7b\u7684\u6807\u7b7e":62,"\u8be5\u8bed\u53e5\u4f1a\u4e3a\u6bcf\u4e2a\u5c42\u521d\u59cb\u5316\u5176\u6240\u9700\u8981\u7684\u53d8\u91cf\u548c\u8fde\u63a5":46,"\u8be5github\u4ed3\u5e93\u4e2d\u5305\u542b\u82e5\u5e72\u4e2a\u9884\u7f16\u8bd1\u597d\u7684":39,"\u8be6\u7ec6\u4ecb\u7ecd\u53ef\u4ee5\u53c2\u8003":41,"\u8be6\u7ec6\u4fe1\u606f\u8bf7\u68c0\u67e5":50,"\u8be6\u7ec6\u5185\u5bb9\u8bf7\u53c2\u89c1":65,"\u8be6\u7ec6\u53ef\u53c2\u8003":45,"\u8be6\u7ec6\u5730\u5c55\u793a\u4e86\u6574\u4e2a\u7279\u5f81\u63d0\u53d6\u7684\u8fc7\u7a0b":63,"\u8be6\u7ec6\u6587\u6863\u53c2\u8003":30,"\u8be6\u7ec6\u7684\u53c2\u6570\u89e3\u91ca":65,"\u8be6\u7ec6\u7684cmake\u4f7f\u7528\u65b9\u6cd5\u53ef\u4ee5\u53c2\u8003":32,"\u8be6\u7ec6\u89c1":40,"\u8be6\u7ec6\u8bbe\u8ba1":24,"\u8bed\u4e49\u89d2\u8272\u6807\u6ce8":[64,68],"\u8bed\u610f\u89d2\u8272\u6807\u6ce8":29,"\u8bed\u8a00\u6a21\u578b":61,"\u8bf4\u660e":[19,32,57],"\u8bf4\u660e\u63d0\u4ea4\u7684\u4ee3\u7801\u5b58\u5728\u95ee\u9898":45,"\u8bf4\u660e\u6bcf\u4e2a\u7279\u5f81\u6587\u4ef6\u5177\u4f53\u5b57\u6bb5\u662f":67,"\u8bf4\u660e\u8fd9\u4e2a\u5c42\u7684\u8f93\u5165":46,"\u8bf7\u4fdd\u8bc1travi":45,"\u8bf7\u5148\u4f7f\u7528":[38,39],"\u8bf7\u518d\u6b21\u786e\u8ba4py_paddle\u5305\u5df2\u7ecf\u5b89\u88c5":47,"\u8bf7\u53c2\u7167\u7f51\u7edc\u914d\u7f6e\u7684\u6587\u6863\u4e86\u89e3\u66f4\u8be6\u7ec6\u7684\u4fe1\u606f":54,"\u8bf7\u53c2\u8003":[3,27,30,36,41,44,46,47,65],"\u8bf7\u53c2\u8003\u5982\u4e0b\u8868\u683c":65,"\u8bf7\u53c2\u8003\u9875\u9762":67,"\u8bf7\u53c2\u8003layer\u6587\u6863":62,"\u8bf7\u53c2\u89c1":45,"\u8bf7\u53c2\u9605":44,"\u8bf7\u53c2\u9605\u60c5\u611f\u5206\u6790\u7684\u6f14\u793a\u4ee5\u4e86\u89e3\u6709\u5173\u957f\u671f\u77ed\u671f\u8bb0\u5fc6\u5355\u5143\u7684\u66f4\u591a\u4fe1\u606f":68,"\u8bf7\u5728\u8be5pull":45,"\u8bf7\u5b89\u88c5cuda":35,"\u8bf7\u5c06with_doc\u9009\u9879\u5173\u95ed":47,"\u8bf7\u60a8\u6bcf\u6b21\u63d0\u4ea4\u4ee3\u7801\u65f6":45,"\u8bf7\u60a8\u9075\u5b88\u4ee5\u4e0b\u7ea6\u5b9a":45,"\u8bf7\u628a\u6587\u6863\u91cc\u547d\u4ee4\u4e2d\u7684paddlepaddl":33,"\u8bf7\u6307\u5b9a\u8be5\u76ee\u5f55":52,"\u8bf7\u67e5\u770b":61,"\u8bf7\u6ce8\u610f":[33,44,56,61],"\u8bf7\u6ce8\u610f\u6bcf\u4e2acommit\u7684\u540d\u79f0":45,"\u8bf7\u6ce8\u610fcommit\u7684\u6570\u91cf":45,"\u8bf7\u770b\u4e0b\u9762\u7684\u4f8b\u5b50":54,"\u8bf7\u786e\u4fdd":45,"\u8bf7\u7ed9\u51fa\u603b\u4f53\u7684\u4fee\u6539\u60c5\u51b5":45,"\u8bf7\u7ed9\u51fa\u60a8\u81ea\u5df1\u7684\u53cd\u9a73\u7406\u7531":45,"\u8bf7\u8bb0\u4f4f":50,"\u8bf7\u8fd0\u884c":33,"\u8bf7\u9009\u62e9\u5408\u9002\u7684\u8bcd\u6c47":45,"\u8bf7\u9009\u62e9\u6b63\u786e\u7684\u7248\u672c":30,"\u8bf7\u9075\u5b88":45,"\u8bf7\u91c7\u7528":45,"\u8bf8\u5982\u56fe\u50cf\u5206\u7c7b":54,"\u8bfb\u53d612\u4e2a\u91c7\u6837\u6570\u636e\u8fdb\u884c\u968f\u673a\u68af\u5ea6\u8ba1\u7b97\u6765\u66f4\u65b0\u66f4\u65b0":31,"\u8bfb\u53d6\u6570\u636e":3,"\u8bfb\u53d6\u6bcf\u4e00\u884c":3,"\u8bfb\u53d6volume\u4e2d\u7684\u6570\u636e\u8fdb\u884c\u8fd9\u6b21\u5206\u5e03\u5f0f\u8bad\u7ec3":57,"\u8bfb\u8005\u53ef\u4ee5\u67e5\u770b":57,"\u8bfb\u8005\u9700\u8981\u66ff\u6362\u6210\u81ea\u5df1\u4f7f\u7528\u7684\u4ed3\u5e93\u5730\u5740":57,"\u8c03\u7528":[46,62],"\u8c03\u7528\u4e00\u6b21":3,"\u8c03\u7528\u4e0a\u9762\u7684process\u51fd\u6570\u83b7\u5f97\u89c2\u6d4b\u6570\u636e":31,"\u8c03\u7528\u65b9\u6cd5\u89c1c":38,"\u8c03\u7528\u7684pydataprovider2\u662f":3,"\u8c03\u7528\u7b2c\u4e8c\u6b21\u7684\u65f6\u5019":3,"\u8c03\u7528\u8be5\u51fd\u6570\u540e":46,"\u8c03\u7528\u8fd9\u4e2apydataprovider2\u7684\u65b9\u6cd5":3,"\u8c13\u8bcd\u4e0a\u4e0b\u6587":68,"\u8d1f\u6837\u672c":65,"\u8d1f\u9762\u7684\u8bc4\u8bba\u7684\u5f97\u5206\u5c0f\u4e8e\u7b49\u4e8e4":69,"\u8d1f\u9762\u8bc4\u4ef7\u6837\u672c":69,"\u8d21\u732e\u6587\u6863":47,"\u8d44\u6e90\u5bf9\u8c61\u7684\u540d\u5b57\u662f\u552f\u4e00\u7684":55,"\u8d77":41,"\u8def\u5f84\u4e0b":[31,63],"\u8df3\u8f6c\u5230":45,"\u8f6c\u4e3ajpeg\u6587\u4ef6\u5e76\u5b58\u5165\u7279\u5b9a\u7684\u76ee\u5f55":62,"\u8f6c\u6362\u751f\u6210\u7684\u6587\u4ef6\u540d\u4f1a\u662f\u4ee5\u4e0b\u683c\u5f0f":20,"\u8f6c\u6362\u8fc7\u6765\u7684":63,"\u8f6e":67,"\u8f83":41,"\u8f93\u5165":[40,44],"\u8f93\u5165\u548c\u8f93\u51fa\u90fd\u662f\u5355\u5c42\u5e8f\u5217":43,"\u8f93\u5165\u548c\u8f93\u51fa\u90fd\u662f\u53cc\u5c42\u5e8f\u5217":43,"\u8f93\u5165\u56fe\u7247\u7684\u9ad8\u5ea6\u53ca\u5bbd\u5ea6":62,"\u8f93\u5165\u5c42\u5c3a\u5bf8":63,"\u8f93\u5165\u6570\u636e\u4e3a\u4e00\u4e2a\u5b8c\u6574\u7684\u65f6\u95f4\u5e8f\u5217":41,"\u8f93\u5165\u6570\u636e\u4e3a\u5728\u5355\u5c42rnn\u6570\u636e\u91cc\u9762":41,"\u8f93\u5165\u6570\u636e\u6574\u4f53\u4e0a\u662f\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217":41,"\u8f93\u5165\u6570\u636e\u7684\u5b57\u5178\u7ef4\u6570\u662f1\u767e\u4e07":54,"\u8f93\u5165\u6570\u6910\u6240\u5728\u76ee\u5f55":69,"\u8f93\u5165\u6587\u672c":61,"\u8f93\u5165\u6587\u672c\u4e2d\u6ca1\u6709\u5934\u90e8":61,"\u8f93\u5165\u662f\u5426\u662f\u8f6c\u7f6e\u7684":46,"\u8f93\u5165\u662f\u7531\u4e00\u4e2alist\u4e2d\u7684\u7f51\u7edc\u5c42\u5b9e\u4f8b\u7684\u540d\u5b57\u7ec4\u6210\u7684":46,"\u8f93\u5165\u7279\u5f81\u56fe\u7684\u901a\u9053\u6570\u76ee":63,"\u8f93\u5165\u7684":61,"\u8f93\u5165\u7684\u539f\u59cb\u6570\u636e\u96c6\u8def\u5f84":70,"\u8f93\u5165\u7684\u540d\u5b57":46,"\u8f93\u5165\u7684\u5927\u5c0f":46,"\u8f93\u5165\u7684\u6587\u672c\u683c\u5f0f\u5982\u4e0b":61,"\u8f93\u5165\u7684\u6587\u672c\u8bcd\u5411\u91cf\u6a21\u578b\u540d\u79f0":61,"\u8f93\u5165\u7684\u7c7b\u578b":46,"\u8f93\u5165\u95e8":69,"\u8f93\u5165\u9884\u6d4b\u6837\u672c":69,"\u8f93\u5165n\u4e2a\u5355\u8bcd":65,"\u8f93\u51fa":[40,44],"\u8f93\u51fa\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":43,"\u8f93\u51fa\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":43,"\u8f93\u51fa\u4e3an\u4e2aword_dim\u7ef4\u5ea6\u5411\u91cf":65,"\u8f93\u51fa\u51fd\u6570":44,"\u8f93\u51fa\u5e8f\u5217\u7684\u7c7b\u578b":40,"\u8f93\u51fa\u5e8f\u5217\u7684\u8bcd\u8bed\u6570\u548c\u8f93\u5165\u5e8f\u5217\u4e00\u81f4":43,"\u8f93\u51fa\u5e94\u8be5\u7c7b\u4f3c\u5982\u4e0b":67,"\u8f93\u51fa\u6587\u4ef6\u7684\u683c\u5f0f\u8bf4\u660e":61,"\u8f93\u51fa\u65e5\u5fd7\u4fdd\u5b58\u5728\u8def\u5f84":69,"\u8f93\u51fa\u65e5\u5fd7\u8bf4\u660e\u5982\u4e0b":69,"\u8f93\u51fa\u67092\u5217":61,"\u8f93\u51fa\u7279\u5f81\u56fe\u7684\u901a\u9053\u6570\u76ee":63,"\u8f93\u51fa\u7684\u4e8c\u8fdb\u5236\u8bcd\u5411\u91cf\u6a21\u578b\u540d\u79f0":61,"\u8f93\u51fa\u7684\u6587\u672c\u6a21\u578b\u540d\u79f0":61,"\u8f93\u51fa\u7684\u68af\u5ea6":52,"\u8f93\u51fa\u76ee\u5f55":63,"\u8f93\u51fa\u7ed3\u679c\u53ef\u80fd\u4f1a\u968f\u7740\u5bb9\u5668\u7684\u6d88\u8017\u800c\u88ab\u5220\u9664":56,"\u8fbe\u5230\u5bb9\u707e\u7684\u76ee\u7684":19,"\u8fc7\u4e86\u4e00\u4e2a\u5f88\u7b80\u5355\u7684recurrent_group":41,"\u8fc7\u5b8c\u6240\u6709\u8bad\u7ec3\u6570\u636e\u5373\u4e3a\u4e00\u4e2apass":30,"\u8fd0":33,"\u8fd0\u884c":35,"\u8fd0\u884c\u4e0b\u9762\u547d\u4ee4\u5373\u53ef":67,"\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\u6765\u4e0b\u8f7d\u8fd9\u4e2a\u811a\u672c":70,"\u8fd0\u884c\u4ee5\u4e0b\u6307\u4ee4":33,"\u8fd0\u884c\u4ee5\u4e0b\u7684\u547d\u4ee4\u4e0b\u8f7d\u548c\u83b7\u53d6\u6211\u4eec\u7684\u5b57\u5178\u548c\u9884\u8bad\u7ec3\u6a21\u578b":61,"\u8fd0\u884c\u4ee5\u4e0b\u7684\u547d\u4ee4\u4e0b\u8f7d\u6570\u636e\u96c6":61,"\u8fd0\u884c\u4ee5\u4e0b\u8bad\u7ec3\u547d\u4ee4":31,"\u8fd0\u884c\u4ee5\u53ca\u53d1\u5e03\u60a8\u7684ai\u7a0b\u5e8f":33,"\u8fd0\u884c\u5206\u5e03\u5f0f\u4f5c\u4e1a":50,"\u8fd0\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3":[30,48,65],"\u8fd0\u884c\u5355\u5143\u6d4b\u8bd5":33,"\u8fd0\u884c\u5931\u8d25":54,"\u8fd0\u884c\u5b8c\u4ee5\u4e0a\u547d\u4ee4":61,"\u8fd0\u884c\u5b8c\u6210\u540e":50,"\u8fd0\u884c\u5b8c\u811a\u672c":69,"\u8fd0\u884c\u6210\u529f\u4ee5\u540e":61,"\u8fd0\u884c\u6210\u529f\u540e\u76ee\u5f55":69,"\u8fd0\u884c\u65e5\u5fd7":50,"\u8fd0\u884c\u7684\u4e00\u4e9b\u53c2\u6570\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\u4f20\u9012\u5230\u5bb9\u5668\u5185":57,"\u8fd1":41,"\u8fd1\u671f\u63d0\u51fa\u7684nmt\u6a21\u578b\u901a\u5e38\u90fd\u5c5e\u4e8e\u7f16\u89e3\u7801\u6a21\u578b":70,"\u8fd4\u56de":[8,9,10,13,15],"\u8fd4\u56de0":3,"\u8fd4\u56de8\u4e2a\u7279\u5f81list\u548c1\u4e2a\u6807\u7b7elist":68,"\u8fd4\u56de\u4e00\u6761\u5b8c\u6574\u7684\u6837\u672c":3,"\u8fd4\u56de\u6570\u636e\u7684\u6bcf\u4e00\u6761\u6837\u672c\u7ed9":67,"\u8fd4\u56de\u65f6":3,"\u8fd4\u56de\u7684\u662f":[3,36],"\u8fd4\u56de\u7684\u987a\u5e8f\u9700\u8981\u548cinput_types\u4e2d\u5b9a\u4e49\u7684\u987a\u5e8f\u4e00\u81f4":3,"\u8fd4\u56de\u7b2c\u4e8c\u6b65":29,"\u8fd4\u56de\u7b2ci\u4e2a\u8f93\u5165\u77e9\u9635":46,"\u8fd4\u56de\u7c7b\u578b":[8,9,10,13,15],"\u8fd8\u4f1a":41,"\u8fd8\u4f1a\u5f3a\u5236\u8bbe\u7f6e\u4e00\u4e9bpaddlepaddle\u53c2\u6570\u7684\u503c":38,"\u8fd8\u4f1a\u8f93\u51fa\u4e00\u4e2a":45,"\u8fd8\u662f":41,"\u8fd8\u662f\u4ece":20,"\u8fd8\u6709":41,"\u8fd8\u80fd\u5904\u7406\u5176\u4ed6\u7528\u6237\u81ea\u5b9a\u4e49\u7684\u6570\u636e":69,"\u8fd8\u91c7\u7528\u4e86\u4e24\u4e2a\u5176\u4ed6\u7279\u5f81":68,"\u8fd8\u9700\u8981\u8fdb\u884c\u9884\u5904\u7406":62,"\u8fd9":[30,41,65],"\u8fd9\u4e00\u5757\u7684\u8017\u65f6\u6bd4\u4f8b\u771f\u7684\u592a\u9ad8":49,"\u8fd9\u4e00\u5c42\u8fdb\u884c\u5c01\u88c5":27,"\u8fd9\u4e00\u6982\u5ff5\u4e0d\u518d\u7410\u788e":27,"\u8fd9\u4e00\u8fc7\u7a0b\u5bf9\u7528\u6237\u662f\u5b8c\u5168\u900f\u660e\u7684":43,"\u8fd9\u4e09\u4e2a\u5206\u652f":29,"\u8fd9\u4e09\u4e2a\u6b65\u9aa4\u53ef\u914d\u7f6e\u4e3a":65,"\u8fd9\u4e0e\u672c\u5730\u8bad\u7ec3\u76f8\u540c":50,"\u8fd9\u4e24\u4e2a\u6587\u4ef6\u5939\u4e0b\u5404\u81ea\u670910\u4e2a\u5b50\u6587\u4ef6\u5939":62,"\u8fd9\u4e24\u4e2a\u6807\u51c6":68,"\u8fd9\u4e2a":[41,55],"\u8fd9\u4e2a\u4efb\u52a1\u7684\u914d\u7f6e\u4e3a":30,"\u8fd9\u4e2a\u4efb\u52a1\u7684dataprovider\u4e3a":30,"\u8fd9\u4e2a\u51fd\u6570\u7684":44,"\u8fd9\u4e2a\u51fd\u6570\u8fdb\u884c\u53d8\u6362":41,"\u8fd9\u4e2a\u51fd\u6570\u9700\u8981\u8bbe\u7f6e":44,"\u8fd9\u4e2a\u52a8\u6001\u5e93\u7684\u8fde\u63a5\u53c2\u6570\u4e0epaddle\u7684\u5176\u4ed6\u4e8c\u8fdb\u5236":27,"\u8fd9\u4e2a\u53c2\u6570\u4e5f\u4e0d\u4f1a\u4e00\u5e76\u5220\u9664":27,"\u8fd9\u4e2a\u5411\u91cf\u4e0e\u6e90\u4e2d\u641c\u7d22\u51fa\u7684\u4f4d\u7f6e\u548c\u6240\u6709\u4e4b\u524d\u751f\u6210\u7684\u76ee\u6807\u5355\u8bcd\u6709\u5173":70,"\u8fd9\u4e2a\u5730\u5740\u5219\u4e3a\u5b83\u7684\u7edd\u5bf9\u8def\u5f84\u6216\u76f8\u5bf9\u8def\u5f84":2,"\u8fd9\u4e2a\u5730\u5740\u6765\u8868\u793a\u6b64\u6b65\u9aa4\u6240\u6784\u5efa\u51fa\u7684\u955c\u50cf":57,"\u8fd9\u4e2a\u57fa\u7c7b":46,"\u8fd9\u4e2a\u5934\u6587\u4ef6\u4e0d\u5047\u8bbe\u5176\u4ed6\u6587\u4ef6\u7684\u5f15\u7528\u987a\u5e8f":27,"\u8fd9\u4e2a\u5b57\u5178\u662f\u6574\u6570\u6807\u7b7e\u548c\u5b57\u7b26\u4e32\u6807\u7b7e\u7684\u4e00\u4e2a\u5bf9\u5e94":69,"\u8fd9\u4e2a\u5e8f\u5217\u7684\u6bcf\u4e2a\u5143\u7d20\u53c8\u662f\u4e00\u4e2a\u5e8f\u5217":43,"\u8fd9\u4e2a\u63a5\u53e3\u9700\u8981\u505a\u5230":26,"\u8fd9\u4e2a\u6570\u636e\u4e5f\u88ab\u5355\u5c42rnn\u7f51\u7edc\u76f4\u63a5\u4f7f\u7528":41,"\u8fd9\u4e2a\u6570\u91cf\u79f0\u4e3abeam":70,"\u8fd9\u4e2a\u6587\u4ef6\u5177\u6709\u72ec\u7279\u7684\u8bed\u6cd5":26,"\u8fd9\u4e2a\u663e\u793a\u5668\u5f88\u68d2":65,"\u8fd9\u4e2a\u6a21\u578b\u5bf9\u4e8e\u7f16\u89e3\u7801\u6a21\u578b\u6765\u8bf4":70,"\u8fd9\u4e2a\u76ee\u5f55\u4e2d\u9664\u4e86":27,"\u8fd9\u4e2a\u795e\u7ecf\u7f51\u7edc\u5355\u5143\u5c31\u53ebmemori":41,"\u8fd9\u4e2a\u7a0b\u5e8f\u662f\u60a8\u5728\u5f00\u53d1\u673a\u4e0a\u4f7f\u7528\u5f00\u53d1\u955c\u50cf\u5b8c\u6210\u5f00\u53d1":33,"\u8fd9\u4e2a\u7c7b\u7684\u53c2\u6570\u5305\u62ec":46,"\u8fd9\u4e2a\u7c7b\u9700\u8981\u7ee7\u627f":46,"\u8fd9\u4e2a\u7cfb\u7edf\u5c06srl\u4efb\u52a1\u89c6\u4e3a\u5e8f\u5217\u6807\u6ce8\u95ee\u9898":68,"\u8fd9\u4e2a\u7ed3\u6784\u4f53\u4e2d\u7684\u53e6\u4e00\u4e2a\u9879\u76ee\u662f":27,"\u8fd9\u4e2a\u7ed3\u6784\u4f53\u5305\u542b\u4e24\u4e2a\u9879\u76ee":27,"\u8fd9\u4e2a\u8282\u70b9\u53ef\u4ee5\u662f\u7269\u7406\u673a\u6216\u8005\u865a\u62df\u673a":55,"\u8fd9\u4e2a\u8868\u683c":55,"\u8fd9\u4e2a\u8fc7\u7a0b\u5bf9\u7528\u6237\u4e5f\u662f\u900f\u660e\u7684":43,"\u8fd9\u4e2a\u8fc7\u7a0b\u5c31\u662f\u6a21\u578b\u7684\u8bad\u7ec3\u8fc7\u7a0b":31,"\u8fd9\u4e2a\u8fc7\u7a0b\u9664\u4e86\u7f16\u8bd1paddlepaddle\u4e3a":45,"\u8fd9\u4e2a\u914d\u7f6e\u4e0e":61,"\u8fd9\u4e2a\u914d\u7f6e\u6587\u4ef6":55,"\u8fd9\u4e2a\u914d\u7f6e\u662f\u5426\u7528\u6765\u751f\u6210":70,"\u8fd9\u4e2a\u955c\u50cf\u5305\u542b\u4e86paddle\u76f8\u5173\u7684\u5f00\u53d1\u5de5\u5177\u4ee5\u53ca\u7f16\u8bd1\u548c\u8fd0\u884c\u73af\u5883":33,"\u8fd9\u4e2a\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u751f\u6210\u4e00\u7cfb\u5217\u6743\u91cd":44,"\u8fd9\u4e2a\u95ee\u9898\u662fpydataprovider\u8bfb\u6570\u636e\u65f6\u5019\u7684\u903b\u8f91\u95ee\u9898":3,"\u8fd9\u4e2a\u9759\u6001\u5e93\u5305\u542b\u4e86paddle\u7684\u5168\u90e8\u7b26\u53f7":27,"\u8fd9\u4e2adataprovider\u8f83\u590d\u6742":3,"\u8fd9\u4e2ainstance\u53ef\u4ee5\u662f\u5355\u4e2a\u503c":20,"\u8fd9\u4e2ajob\u624d\u7b97\u6210\u529f\u7ed3\u675f":57,"\u8fd9\u4e2alayer\u7684\u8f93\u51fa\u4f1a\u4f5c\u4e3a\u6574\u4e2a":43,"\u8fd9\u4e5f\u4f1a\u6781\u5927\u51cf\u5c11\u6570\u636e\u8bfb\u5165\u7684\u8017\u65f6":30,"\u8fd9\u4e9b":50,"\u8fd9\u4e9b\u5206\u5e03\u5f0f\u5b58\u50a8\u670d\u52a1\u901a\u5e38\u4f1a\u628a\u6570\u636e\u5207\u5272\u6210\u591a\u4e2a\u5206\u7247\u5206\u5e03\u5f0f\u7684\u5b58\u50a8\u5728\u591a\u4e2a\u8282\u70b9\u4e4b\u4e0a":20,"\u8fd9\u4e9b\u53c2\u6570\u7684\u5177\u4f53\u63cf\u8ff0":57,"\u8fd9\u4e9b\u53c2\u6570\u7684\u7b80\u77ed\u4ecb\u7ecd\u5982\u4e0b":67,"\u8fd9\u4e9b\u540d\u5b57\u5fc5\u987b\u8981\u5199\u5bf9":46,"\u8fd9\u4e9b\u6570\u636e\u4f1a\u88ab\u7528\u6765\u66f4\u65b0\u53c2\u6570":30,"\u8fd9\u4e9b\u6570\u636e\u4f7f\u7528\u7684\u5185\u5b58\u4e3b\u8981\u548c\u4e24\u4e2a\u53c2\u6570\u6709\u5173\u7cfb":30,"\u8fd9\u4e9b\u6587\u4ef6\u5c06\u4f1a\u88ab\u4fdd\u5b58\u5728":63,"\u8fd9\u4e9b\u6a21\u578b\u90fd\u662f\u7531\u539f\u4f5c\u8005\u63d0\u4f9b\u7684\u6a21\u578b":63,"\u8fd9\u4e9b\u7279\u5f81\u503c\u4e0e\u4e0a\u8ff0\u4f7f\u7528c":63,"\u8fd9\u4e9b\u7279\u5f81\u548c\u6807\u7b7e\u5b58\u50a8\u5728":68,"\u8fd9\u4e9b\u7279\u5f81\u6570\u636e\u4e4b\u95f4\u7684\u987a\u5e8f\u662f\u6709\u610f\u4e49\u7684":41,"\u8fd9\u4efd\u6559\u7a0b\u5c55\u793a\u4e86\u5982\u4f55\u5728paddlepaddle\u4e2d\u5b9e\u73b0\u4e00\u4e2a\u81ea\u5b9a\u4e49\u7684\u7f51\u7edc\u5c42":46,"\u8fd9\u4efd\u7b80\u77ed\u7684\u4ecb\u7ecd\u5c06\u5411\u4f60\u5c55\u793a\u5982\u4f55\u5229\u7528paddlepaddle\u6765\u89e3\u51b3\u4e00\u4e2a\u7ecf\u5178\u7684\u7ebf\u6027\u56de\u5f52\u95ee\u9898":31,"\u8fd9\u4f1a\u63d0\u793a\u5f53\u524d\u76ee\u5f55\u7684\u4e00\u4e9b\u53d8\u5316":45,"\u8fd9\u4f1a\u7ed9\u8bc4\u5ba1\u4eba\u5e26\u6765\u5f88\u5927\u56f0\u6270":45,"\u8fd9\u4f1a\u81ea\u52a8\u8fdb\u884c\u7f51\u7edc\u914d\u7f6e\u4e2d\u58f0\u660e\u7684\u6fc0\u6d3b\u64cd\u4f5c":46,"\u8fd9\u4f7f\u5f97nmt\u6a21\u578b\u5f97\u4ee5\u89e3\u653e\u51fa\u6765":70,"\u8fd9\u4fbf\u662f\u4e00\u79cd\u53cc\u5c42rnn\u7684\u8f93\u5165\u6570\u636e":41,"\u8fd9\u51e0\u4e2a\u7f16\u8bd1\u9009\u9879\u7684\u8bbe\u7f6e":32,"\u8fd9\u53ef\u4ee5\u8ba9\u5176\u4ed6\u4eba\u77e5\u9053\u8fd9\u6b21\u63d0\u4ea4\u505a\u4e86\u54ea\u4e9b\u6539\u53d8":45,"\u8fd9\u53ef\u4ee5\u901a\u8fc7":45,"\u8fd9\u548c\u5355\u5c42rnn\u7684\u914d\u7f6e\u662f\u7b49\u4ef7\u7684":41,"\u8fd9\u56db\u4e2a\u7b80\u5355\u7684\u7279\u5f81\u662f\u6211\u4eec\u7684srl\u7cfb\u7edf\u6240\u9700\u8981\u7684":68,"\u8fd9\u56db\u6761\u6570\u636e\u540c\u65f6\u5904\u7406\u7684\u53e5\u5b50\u6570\u91cf\u4e3a":41,"\u8fd9\u5728\u5f88\u5927\u7a0b\u5ea6\u4e0a\u4f18\u4e8e\u5148\u524d\u7684\u6700\u5148\u8fdb\u7684\u7cfb\u7edf":68,"\u8fd9\u5728\u6784\u9020\u975e\u5e38\u590d\u6742\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u65f6\u662f\u6709\u7528\u7684":44,"\u8fd9\u5bf9\u4e8e\u901a\u5e38\u7684java\u7684\u5f00\u53d1\u8005\u6765\u8bf4":26,"\u8fd9\u5c06\u82b1\u8d39\u6570\u5206\u949f\u7684\u65f6\u95f4":70,"\u8fd9\u5df2\u7ecf\u5728":69,"\u8fd9\u610f\u5473\u7740":44,"\u8fd9\u610f\u5473\u7740\u6a21\u578b\u5728\u8bad\u7ec3\u6570\u636e\u4e0a\u4e0d\u65ad\u7684\u6539\u8fdb":31,"\u8fd9\u610f\u5473\u7740\u9664\u4e86\u6307\u5b9adevic":54,"\u8fd9\u65f6\u8fdb\u884c\u77e9\u9635\u4e58\u6cd5\u8fd0\u7b97\u5c31\u53ef\u80fd\u5bfc\u81f4\u6d6e\u70b9\u6570\u6ea2\u51fa":30,"\u8fd9\u662f\u4e00\u4e2a\u57fa\u4e8e\u7edf\u8ba1\u7684\u673a\u5668\u7ffb\u8bd1\u7cfb\u7edf":69,"\u8fd9\u662f\u4e00\u79cd\u975e\u5e38\u7075\u6d3b\u7684\u6570\u636e\u7ec4\u7ec7\u65b9\u5f0f":40,"\u8fd9\u662f\u56e0\u4e3a":26,"\u8fd9\u662f\u56e0\u4e3a\u5b83\u53d1\u6398\u51fa\u4e86\u56fe\u7247\u7684\u4e24\u7c7b\u91cd\u8981\u4fe1\u606f":62,"\u8fd9\u662f\u5f00\u6e90\u793e\u533a\u7684\u57fa\u672c\u793c\u8c8c":45,"\u8fd9\u662f\u666e\u901a\u7684\u5355\u5c42\u65f6\u95f4\u5e8f\u5217\u7684dataprovider\u4ee3\u7801":41,"\u8fd9\u662f\u76ee\u524dcmake\u5bfb\u627epython\u7684\u903b\u8f91\u5b58\u5728\u7f3a\u9677":30,"\u8fd9\u662f\u96c6\u675f\u641c\u7d22\u7684\u7ed3\u679c":70,"\u8fd9\u6765\u81ea\u4e8epaddlepaddle\u7684\u5185\u5b58\u4e2d":70,"\u8fd9\u6837":[27,31,50],"\u8fd9\u6837\u4fdd\u8bc1":29,"\u8fd9\u6837\u505a\u53ef\u4ee5\u6781\u5927\u7684\u51cf\u5c11\u5185\u5b58\u5360\u7528":30,"\u8fd9\u6837\u53ef\u4ee5\u51cf\u5c0fgpu\u5185\u5b58":54,"\u8fd9\u6837\u5bb9\u5668\u7684":57,"\u8fd9\u6837\u5c31\u4f1a\u751f\u6210\u4e24\u4e2a\u6587\u4ef6":67,"\u8fd9\u6837\u5c31\u53ef\u4ee5\u5728\u4e91\u7aef\u6267\u884c\u591a\u79cd\u6570\u636e\u7c7b\u8ba1\u7b97\u4efb\u52a1":20,"\u8fd9\u6837\u5df2\u7ecf\u4f20\u8f93\u6210\u529f\u7684\u90e8\u5206\u5c31\u4e0d\u7528\u91cd\u65b0\u4f20\u8f93\u4e86":24,"\u8fd9\u6837\u5f00\u53d1\u4eba\u5458\u53ef\u4ee5\u4ee5\u4e00\u81f4\u7684\u65b9\u5f0f\u5728\u4e0d\u540c\u7684\u5e73\u53f0\u4e0a\u5de5\u4f5c":33,"\u8fd9\u6837\u5f53\u8be5pull":45,"\u8fd9\u6837\u7684\u88c5\u9970\u5668":46,"\u8fd9\u6837\u7684\u8bdd":56,"\u8fd9\u6837\u7684\u8bdd\u6bcf\u4f4d\u7528\u6237\u5728\u6d4b\u8bd5\u6587\u4ef6\u4e2d\u5c06\u4e0e\u8bad\u7ec3\u6587\u4ef6\u542b\u6709\u540c\u6837\u7684\u4fe1\u606f":67,"\u8fd9\u6b63\u662f\u5b83\u4eec\u901f\u5ea6\u5feb\u7684\u539f\u56e0":49,"\u8fd9\u6bb5\u7b80\u77ed\u7684\u914d\u7f6e\u5c55\u793a\u4e86paddlepaddle\u7684\u57fa\u672c\u7528\u6cd5":31,"\u8fd9\u7528\u4e8e\u5728\u591a\u7ebf\u7a0b\u548c\u591a\u673a\u4e0a\u66f4\u65b0\u53c2\u6570":46,"\u8fd9\u79cd\u521d\u59cb\u5316\u65b9\u5f0f\u5728\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u4f1a\u4ea7\u751f\u5f88\u5dee\u7684\u7ed3\u679c":30,"\u8fd9\u79cd\u60c5\u51b5\u4e0b\u4e0d\u9700\u8981\u91cd\u5199\u8be5\u51fd\u6570":46,"\u8fd9\u79cd\u65b9\u5f0f\u5fc5\u987b\u4f7f\u7528paddle\u5b58\u50a8\u7684\u6a21\u578b\u8def\u5f84\u683c\u5f0f":54,"\u8fd9\u79cd\u751f\u6210\u6280\u672f\u53ea\u7528\u4e8e\u7c7b\u4f3c\u89e3\u7801\u5668\u7684\u751f\u6210\u8fc7\u7a0b":44,"\u8fd9\u79cd\u7c7b\u578b\u7684\u8f93\u5165\u5fc5\u987b\u901a\u8fc7":43,"\u8fd9\u79cd\u96c6\u7fa4\u8282\u70b9\u7ba1\u7406\u65b9\u5f0f\u4f1a\u5728\u5c06\u6765\u4f7f\u7528":57,"\u8fd9\u7bc7\u6587\u6863":45,"\u8fd9\u7bc7\u6587\u6863\u5c06\u4ee5linux":39,"\u8fd9\u7bc7\u6587\u7ae0":70,"\u8fd9\u7ec4\u8bed\u4e49\u76f8\u540c\u7684\u793a\u4f8b\u914d\u7f6e\u5982\u4e0b":41,"\u8fd9\u901a\u8fc7\u83b7\u5f97\u53cd\u5411\u5faa\u73af\u7f51\u7edc\u7684\u7b2c\u4e00\u4e2a\u5b9e\u4f8b":44,"\u8fd9\u90fd\u9700\u8981\u8fd9\u4e2a\u63a5\u53e3\u6309\u7167\u7ea6\u5b9a\u4fd7\u6210\u7684\u89c4\u5219\u6765\u6ce8\u91ca\u5b8c\u5907":26,"\u8fd9\u91cc":[30,33,44,45,55,57,63,68],"\u8fd9\u91cc\u4ecb\u7ecd\u5982\u751f\u6210\u9020\u8fd9\u4e2a\u5f00\u53d1\u955c\u50cf":33,"\u8fd9\u91cc\u4ee5":65,"\u8fd9\u91cc\u4f7f\u7528\u4e00\u4e2a\u57fa\u4e8emomentum\u7684\u968f\u673a\u68af\u5ea6\u4e0b\u964d":31,"\u8fd9\u91cc\u4f7f\u7528\u4e86\u4e09\u79cd\u7f51\u7edc\u5355\u5143":31,"\u8fd9\u91cc\u4f7f\u7528\u4e86paddlepaddle\u7684python\u63a5\u53e3\u6765\u52a0\u8f7d\u6570\u6910":69,"\u8fd9\u91cc\u4f7f\u7528\u4e86paddlepaddle\u9884\u5b9a\u4e49\u597d\u7684rnn\u5904\u7406\u51fd\u6570":41,"\u8fd9\u91cc\u4f7f\u7528\u7b80\u5355\u7684":30,"\u8fd9\u91cc\u5229\u7528\u5b83\u5efa\u6a21\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb":31,"\u8fd9\u91cc\u53ea\u52a0\u8f7d":70,"\u8fd9\u91cc\u53ea\u7b80\u5355\u4ecb\u7ecd\u4e86\u5355\u673a\u8bad\u7ec3":65,"\u8fd9\u91cc\u5c06\u4ecb\u7ecdpaddlepaddle\u7684\u57fa\u672c\u4f7f\u7528\u6982\u5ff5":36,"\u8fd9\u91cc\u5c55\u793a\u5982\u4f55\u4f7f\u7528\u89c2\u6d4b\u6570\u636e\u6765\u62df\u5408\u8fd9\u4e00\u7ebf\u6027\u5173\u7cfb":31,"\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528":67,"\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528\u7684\u662f\u4e00\u4e2a\u5c0f\u7684vgg\u7f51\u7edc":62,"\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528\u7684\u662fgpu\u6a21\u5f0f\u8fdb\u884c\u8bad\u7ec3":62,"\u8fd9\u91cc\u6211\u4eec\u5728movielens\u6570\u636e\u96c6\u63cf\u8ff0\u4e00\u79cd":67,"\u8fd9\u91cc\u6211\u4eec\u5c55\u793a\u4e00\u4efd\u7b80\u5316\u8fc7\u7684\u4ee3\u7801":46,"\u8fd9\u91cc\u6211\u4eec\u7528paddl":33,"\u8fd9\u91cc\u6211\u4eec\u901a\u8fc7\u5728kubernetes\u96c6\u7fa4\u4e0a\u542f\u52a8\u4e00\u4e2ajob\u6765\u4e0b\u8f7d\u5e76\u5207\u5272\u6570\u636e":57,"\u8fd9\u91cc\u6307\u5b9a\u8bcd\u5178":65,"\u8fd9\u91cc\u6570\u636e\u5c42\u6709\u4e24\u4e2a":31,"\u8fd9\u91cc\u662f\u4e00\u4e2a\u4f8b\u5b50":70,"\u8fd9\u91cc\u6709\u4e00\u4e9b\u4e0d\u540c\u7684\u53c2\u6570\u9700\u8981\u6307\u5b9a":70,"\u8fd9\u91cc\u68c0\u9a8c\u8fd0\u884c\u65f6\u95f4\u6a21\u578b\u7684\u6536\u655b":50,"\u8fd9\u91cc\u6bcf\u4e2a5\u4e2abatch\u6253\u5370\u4e00\u4e2a\u70b9":70,"\u8fd9\u91cc\u6bcf\u9694100\u4e2abatch\u663e\u793a\u4e00\u6b21\u53c2\u6570\u7edf\u8ba1\u4fe1\u606f":70,"\u8fd9\u91cc\u6bcf\u969410\u4e2abatch\u6253\u5370\u4e00\u6b21\u65e5\u5fd7":70,"\u8fd9\u91cc\u7684\u5217\u51fa\u7684\u548c":62,"\u8fd9\u91cc\u76f4\u63a5\u901a\u8fc7\u9884\u6d4b\u811a\u672c":65,"\u8fd9\u91cc\u7ed9\u51fa\u96c6\u4e2d\u5e38\u89c1\u7684\u90e8\u7f72\u65b9\u6cd5":55,"\u8fd9\u91cc\u8bbe\u7f6e\u4e3a\u4f7f\u7528cpu":70,"\u8fd9\u91cc\u8bbe\u7f6e\u4e3afals":70,"\u8fd9\u91cc\u8bbe\u7f6e\u4e3atrue":70,"\u8fd9\u91cc\u91c7\u7528adam\u4f18\u5316\u65b9\u6cd5":65,"\u8fd9\u91cc\u9700\u8981\u7528\u6237\u989d\u5916\u6ce8\u610f":19,"\u8fdb\u4e3b\u4ed3\u5e93\u540e":45,"\u8fdb\u5165":69,"\u8fdb\u5165\u5bb9\u5668":56,"\u8fdb\u5165\u5f00\u53d1\u955c\u50cf\u5e76\u5f00\u59cb\u5de5\u4f5c":33,"\u8fdb\u7a0b\u542f\u52a8\u7684\u5fc5\u8981\u53c2\u6570":57,"\u8fdb\u7a0b\u7684":50,"\u8fdb\u7a0b\u7684\u542f\u52a8\u53c2\u6570":57,"\u8fdb\u7a0b\u7684\u8fd0\u884c\u73af\u5883":57,"\u8fdb\u7a0b\u9700\u8981\u7684":57,"\u8fdb\u800c\u8fdb\u884c\u4ee3\u7801\u8bc4\u5ba1":29,"\u8fdb\u884c\u4e86":41,"\u8fdb\u884c\u4f7f\u7528":62,"\u8fdb\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3\u7684\u65b9\u6848":57,"\u8fdb\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3\u7684\u65b9\u6cd5":57,"\u8fdb\u884c\u56de\u590d":45,"\u8fdb\u884c\u5f00\u53d1":45,"\u8fdb\u884c\u62c6\u89e3":41,"\u8fdb\u884c\u6fc0\u6d3b\u64cd\u4f5c":46,"\u8fdb\u884c\u8bfb\u5165\u548c\u9884\u5904\u7406\u4ece\u800c\u5f97\u5230\u771f\u5b9e\u8f93\u5165":31,"\u8fdb\u884c\u9884\u6d4b":65,"\u8fdb\u9636\u6307\u5357":[36,60],"\u8fde\u63a5":43,"\u8fde\u63a5\u4e09\u4e2alstm\u9690\u85cf\u5c42":69,"\u9000\u4f11\u4eba\u5458":66,"\u9000\u51fa\u5bb9\u5668":56,"\u9002\u4e2d":41,"\u9002\u5408\u81ea\u5df1\u5b9e\u9a8c":33,"\u9009":41,"\u9009\u62e9":41,"\u9009\u62e9\u6d4b\u8bd5\u7ed3\u679c\u6700\u597d\u7684\u6a21\u578b\u6765\u9884\u6d4b":65,"\u9009\u62e9\u76ee\u6807\u5206\u652f":45,"\u9009\u62e9\u8def\u5f84\u6765\u52a8\u6001\u52a0\u8f7dnvidia":52,"\u9009\u62e9\u8fc7\u540e\u7684":70,"\u9009\u62e9\u9002\u5408\u60a8\u7684\u573a\u666f\u7684\u5408\u9002\u65b9\u6848":55,"\u9009\u81ea\u4e0b\u5217\u7c7b\u578b":66,"\u9009\u9879":[32,61],"\u900f\u4f20\u7528\u6237\u8eab\u4efd\u7684\u529e\u6cd5":24,"\u9012\u5f52\u795e\u7ecf\u7f51\u7edc":51,"\u901a\u5e38":[27,50,69],"\u901a\u5e38\u4f1a\u4f7f\u7528\u73af\u5883\u53d8\u91cf\u914d\u7f6ejob\u7684\u914d\u7f6e\u4fe1\u606f":57,"\u901a\u5e38\u4f7f\u7528\u7a00\u758f\u8bad\u7ec3\u6765\u52a0\u901f\u8ba1\u7b97\u8fc7\u7a0b":54,"\u901a\u5e38\u505a\u6cd5\u662f\u4ece\u4e00\u4e2a\u6bd4\u8f83\u5927\u7684learning_rate\u5f00\u59cb\u8bd5":30,"\u901a\u5e38\u540d\u5b57\u662f":45,"\u901a\u5e38\u60c5\u51b5\u4e0b":49,"\u901a\u5e38\u6211\u4eec\u4f1a\u5b89\u88c5ceph\u7b49\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf\u6765\u5b58\u50a8\u8bad\u7ec3\u6570\u636e":56,"\u901a\u5e38\u6307\u5c06\u4e00\u4e2a\u6574\u4f53\u62c6\u5206\u6210\u591a\u4efd\u7684\u5176\u4e2d\u7684\u4e00\u4efd":19,"\u901a\u5e38\u6bcf\u4e2ajob\u5305\u62ec\u4e00\u4e2a\u6216\u8005\u591a\u4e2apod":55,"\u901a\u5e38\u7684\u505a\u6cd5\u662f\u4f7f\u7528":44,"\u901a\u5e38\u7684\u505a\u6cd5\u662f\u5c06\u914d\u7f6e\u5b58\u4e8e":46,"\u901a\u5e38\u8981\u6c42\u65f6\u95f4\u6b65\u4e4b\u95f4\u5177\u6709\u4e00\u4e9b\u4f9d\u8d56\u6027":41,"\u901a\u5e38\u90fd\u4f1a\u4f7f\u7528\u4e0b\u9762\u8fd9\u4e9b\u547d\u4ee4\u884c\u53c2\u6570":54,"\u901a\u7528":51,"\u901a\u77e5":41,"\u901a\u8fc7":[30,41,45,46,50,65],"\u901a\u8fc7\u4e24\u4e2a\u5d4c\u5957\u7684":43,"\u901a\u8fc7\u4ea4\u53c9\u7f16\u8bd1\u7684\u65b9\u5f0f\u6765\u7f16\u8bd1":39,"\u901a\u8fc7\u4ea4\u66ff\u4f7f\u7528\u5377\u79ef\u548c\u6c60\u5316\u5904\u7406":62,"\u901a\u8fc7\u4f7f\u7528":32,"\u901a\u8fc7\u51fd\u6570":57,"\u901a\u8fc7\u5377\u79ef\u64cd\u4f5c\u4ece\u56fe\u7247\u6216\u7279\u5f81\u56fe\u4e2d\u63d0\u53d6\u7279\u5f81":62,"\u901a\u8fc7\u547d\u4ee4\u884c\u53c2\u6570":30,"\u901a\u8fc7\u5f15\u7528memory\u5f97\u5230\u8fd9\u4e2alayer\u4e0a\u4e00\u4e2a\u65f6\u523b\u7684\u8f93\u51fa":43,"\u901a\u8fc7\u5f15\u7528memory\u5f97\u5230\u8fd9\u4e2alayer\u4e0a\u4e00\u4e2a\u65f6\u523b\u8f93\u51fa":43,"\u901a\u8fc7\u6240\u6709\u5355\u5143\u6d4b\u8bd5":45,"\u901a\u8fc7\u6240\u6709\u8bad\u7ec3\u96c6\u4e00\u6b21\u79f0\u4e3a\u4e00\u904d":69,"\u901a\u8fc7\u67e5\u770b\u4e70\u5bb6\u5bf9\u67d0\u4e2a\u4ea7\u54c1\u7684\u8bc4\u4ef7\u53cd\u9988":65,"\u901a\u8fc7\u6a21\u578b\u63a8\u65adapi\u7684\u5b9e\u73b0\u4f5c\u4e3a\u4e00\u4e2a\u6837\u4f8b":27,"\u901a\u8fc7\u7ec4\u5408\u4e0d\u540c\u7684layer":36,"\u901a\u8fc7\u7f16\u8bd1\u4f1a\u751f\u6210py_paddle\u8f6f\u4ef6\u5305":5,"\u901a\u8fc7\u7f51\u7edc\u5c42\u7684\u6807\u8bc6\u7b26\u6765\u6307\u5b9a":46,"\u901a\u8fc7\u8c03\u7528":5,"\u901a\u8fc7\u914d\u7f6e\u7c7b\u4f3c\u4e8e":65,"\u901a\u8fc7data":43,"\u901a\u8fc7volum":55,"\u903b\u8f91\u5212\u4e0a\u6587\u4ef6\u5206\u5757\u7684\u5355\u4f4d":24,"\u903b\u8f91\u56de\u5f52":65,"\u9053\u6b49":41,"\u9069":41,"\u9075\u5b88\u4ee5\u4e0b\u7ea6\u5b9a":45,"\u9075\u5faa\u4ee5\u4e0b\u6d41\u7a0b":29,"\u9075\u5faa\u5982\u4e0b\u7684\u683c\u5f0f":66,"\u9075\u5faa\u6587\u7ae0":61,"\u90a3\u4e48":[27,43,46],"\u90a3\u4e480\u5c42\u5e8f\u5217\u5373\u4e3a\u4e00\u4e2a\u8bcd\u8bed":43,"\u90a3\u4e48\u53ef\u4ee5\u8ba4\u4e3a\u8bad\u7ec3\u4e0d\u6536\u655b":30,"\u90a3\u4e48\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4e0d\u4f1a\u6267\u884c\u6d4b\u8bd5\u64cd\u4f5c":2,"\u90a3\u4e48\u5982\u4f55\u5224\u65ad\u8bad\u7ec3\u4e0d\u6536\u655b\u5462":30,"\u90a3\u4e48\u5e38\u6570\u8f93\u51fa\u6240\u80fd\u8fbe\u5230\u7684\u6700\u5c0fcost\u662f":30,"\u90a3\u4e48\u5f53check\u51fa\u6570\u636e\u4e0d\u5408\u6cd5\u65f6":3,"\u90a3\u4e48\u6211\u4eec\u53ef\u4ee5\u5224\u65ad\u4e3a\u8bad\u7ec3\u4e0d\u6536\u655b":30,"\u90a3\u4e48\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u6536\u96c6\u5e02\u573a\u4e0a\u623f\u5b50\u7684\u5927\u5c0f\u548c\u4ef7\u683c":31,"\u90a3\u4e48\u63a8\u8350\u4f7f\u7528":44,"\u90a3\u4e48\u63a8\u8350\u4f7f\u7528\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u65b9\u6cd5":44,"\u90a3\u4e48\u6536\u655b\u53ef\u80fd\u5f88\u6162":30,"\u90a3\u4e48\u6700\u597d\u5c06\u6570\u636e\u6587\u4ef6\u5728\u6bcf\u6b21\u8bfb\u53d6\u4e4b\u524d\u505a\u4e00\u6b21shuffl":30,"\u90a3\u4e48\u7528\u6237\u9700\u8981\u5728\u672c\u5730\u7f16\u8bd1\u5b89\u88c5paddlepaddl":47,"\u90a3\u4e48\u7528\u6237\u9700\u8981\u62c9\u53d6\u6240\u6709\u7684\u8fdc\u7a0b\u5206\u652f\u5230\u672c\u673a":30,"\u90a3\u4e48\u8bad\u7ec3\u6709\u53ef\u80fd\u4e0d\u6536\u655b":30,"\u90a3\u4e48\u8be5\u4f18\u5316\u7b97\u6cd5\u81f3\u5c11\u9700\u8981":30,"\u90a3\u4e48fc1\u548cfc2\u5c42\u5c06\u4f1a\u4f7f\u7528\u7b2c1\u4e2agpu\u6765\u8ba1\u7b97":54,"\u90a3\u4e48paddlepaddle\u4f1a\u6839\u636elayer\u7684\u58f0\u660e\u987a\u5e8f":3,"\u90a3\u4e5f\u5c31\u4e0d\u9700\u8981\u6025\u7740\u4f18\u5316\u6027\u80fd\u5566":49,"\u90a3\u4f30\u8ba1\u8fd9\u91cc\u7684\u6f5c\u529b\u5c31\u6ca1\u5565\u597d\u6316\u7684\u4e86":49,"\u90a3\u51cf\u5c11\u5b66\u4e60\u738710\u500d\u7ee7\u7eed\u8bd5\u9a8c":30,"\u90a3\u6211\u4f1a\u671f\u671b\u5206\u6790\u5de5\u5177\u7edf\u8ba1\u5230\u901f\u5ea6\u662f100gb":49,"\u90a3\u7a0b\u5e8f\u5206\u6790\u5de5\u5177\u662f\u5fc5\u4e0d\u53ef\u5c11\u7684\u5229\u5668":49,"\u90ae\u7f16":66,"\u90e8\u7f72\u548c\u914d\u7f6e\u6bd4\u8f83\u7b80\u5355":55,"\u90fd":41,"\u90fd\u4f1a\u4ea7\u751f\u5f53\u524d\u5c42\u72b6\u6001\u7684\u6240\u6709\u7ee7\u627f\u7ed3\u679c":52,"\u90fd\u4f7f\u7528\u968f\u673a\u503c\u521d\u59cb\u5316":31,"\u90fd\u53ea\u662f\u4ecb\u7ecd\u53cc\u5c42rnn\u7684api\u63a5\u53e3":41,"\u90fd\u662f\u4e94\u4f4d\u7684\u6570\u5b57":20,"\u90fd\u662f\u5bf9layer1\u5143\u7d20\u7684\u62f7\u8d1d":40,"\u90fd\u662f\u5c06\u6bcf\u4e00\u53e5\u5206\u597d\u8bcd\u540e\u7684\u53e5\u5b50":41,"\u90fd\u662fabi\u8c03\u7528\u6807\u51c6\u7684":26,"\u90fd\u7528":45,"\u90fd\u9700\u8981\u5199\u63d0\u4ea4\u8bf4\u660e":45,"\u90fd\u9700\u8981\u8c03\u7528\u4e00\u6b21":46,"\u914d\u7f6e":69,"\u914d\u7f6e\u4e86\u7f51\u7edc":67,"\u914d\u7f6e\u521b\u5efa\u5b8c\u6bd5\u540e":62,"\u914d\u7f6e\u5982\u4e0b":61,"\u914d\u7f6e\u6253\u5f00":49,"\u914d\u7f6e\u6587\u4ef6":65,"\u914d\u7f6e\u6587\u4ef6\u63a5\u53e3\u662ffc_layer":46,"\u914d\u7f6e\u6a21\u578b\u6587\u4ef6":61,"\u914d\u7f6e\u7684\u65b9\u6cd5\u53c2\u8003":24,"\u914d\u7f6e\u7b49\u6587\u4ef6\u7684\u76ee\u5f55\u89c6\u4e3a":50,"\u914d\u7f6e\u7b80\u5355\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u4f8b\u5b50":44,"\u914d\u7f6e\u7f51\u7edc\u5c42\u7684\u8f93\u5165":46,"\u914d\u7f6eapi":40,"\u9152\u5e97":41,"\u91c7\u6837\u5c42":67,"\u91c7\u7528":68,"\u91c7\u7528\u53e6\u4e00\u79cd\u65b9\u6cd5\u6765\u5806\u53e0lstm\u5c42":68,"\u91c7\u7528\u5747\u5300\u5206\u5e03\u6216\u8005\u9ad8\u65af\u5206\u5e03\u521d\u59cb\u5316":52,"\u91c7\u7528multi":30,"\u91ca\u653e\u5bf9paramters\u5185\u5b58\u7684\u9501\u5b9a":19,"\u91cc\u4ecb\u7ecd\u4e86\u7528paddle\u6e90\u7801\u4e2d\u7684\u811a\u672c\u4e0b\u8f7d\u8bad\u7ec3\u6570\u636e\u7684\u8fc7\u7a0b":56,"\u91cc\u4f1a\u7ee7\u7eed\u5b89\u88c5":35,"\u91cc\u6240\u6709\u7684\u7b26\u53f7\u90fd\u5199\u5165\u81ea\u5df1\u7684\u7a0b\u5e8f\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u91cc":26,"\u91cc\u6307\u5b9a\u56fe\u50cf\u6570\u636e\u5217\u8868":63,"\u91cc\u7684\u65e5\u5fd7":50,"\u91cc\u9762\u6db5\u76d6\u4e86":33,"\u91cc\u9762\u6db5\u76d6\u4e86paddlepaddle\u8fd0\u884c\u6240\u9700\u7684\u6240\u6709\u73af\u5883":33,"\u91cd\u547d\u540d\u6210":26,"\u91cd\u65b0\u7f16\u8bd1paddlepaddl":49,"\u9488\u5bf9\u4e0d\u540c\u5e73\u53f0\u7684\u7f16\u8bd1\u5de5\u5177":39,"\u9488\u5bf9\u4efb\u52a1\u8fd0\u884c\u5b8c\u6210\u540e\u5bb9\u5668\u81ea\u52a8\u9000\u51fa\u7684\u573a\u666f":56,"\u9488\u5bf9\u5185\u5b58\u548c\u663e\u5b58":30,"\u9488\u5bf9\u6587\u672c":67,"\u94fe\u63a5\u4f55\u79cdblas\u5e93\u7b49":32,"\u94fe\u63a5\u5230\u81ea\u5df1\u7684\u7a0b\u5e8f\u91cc":26,"\u94fe\u63a5\u5f85\u8865\u5145":65,"\u9500\u552e":66,"\u9519\u8bef\u5904\u7406":26,"\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u662f\u8fd4\u56de\u503c":26,"\u9519\u8bef\u5904\u7406\u7684\u65b9\u5f0f\u4e5f\u4e0d\u5c3d\u76f8\u540c":26,"\u9519\u8bef\u7387":65,"\u9519\u8bef\u7684define_py_data_sources2\u7c7b\u4f3c":30,"\u955c\u50cf\u91cc\u6709":56,"\u957f\u5ea6":30,"\u95e8\u63a7\u5faa\u73af\u5355\u5143\u5355\u6b65\u51fd\u6570\u548c\u8f93\u51fa\u51fd\u6570":44,"\u95e8\u63a7\u5faa\u73af\u5355\u5143\u7684\u8f93\u51fa\u88ab\u7528\u4f5c\u8f93\u51famemori":44,"\u95ee\u9898":31,"\u95f4\u9694":65,"\u9650\u5236\u5957\u63a5\u5b57\u53d1\u9001\u7f13\u51b2\u533a\u7684\u5927\u5c0f":52,"\u9650\u5236\u5957\u63a5\u5b57\u63a5\u6536\u7f13\u51b2\u533a\u7684\u5927\u5c0f":52,"\u9664\u4e86":3,"\u9664\u4e86boot_lay":41,"\u9664\u53bbdata\u5c42":65,"\u9664\u6784\u9020\u67d0\u79cd\u7c7b\u578b\u7684\u51fd\u6570":27,"\u9664\u8bcd\u5411\u91cf\u6a21\u578b\u5916\u7684\u53c2\u6570\u5c06\u4f7f\u7528\u6b63\u6001\u5206\u5e03\u968f\u673a\u521d\u59cb\u5316":61,"\u9664\u96f6\u7b49\u95ee\u9898":30,"\u968f\u540e\u53ef\u4ee5\u7528\u8fd9\u4e2a\u5f00\u53d1\u955c\u50cf\u5f00build":45,"\u968f\u673a\u521d\u59cb\u4e0d\u5b58\u5728\u7684\u53c2\u6570":68,"\u968f\u673a\u6570\u7684\u79cd\u5b50":52,"\u968f\u673a\u6570seed":51,"\u968f\u7740\u8f6e\u6570\u589e\u52a0\u8bef\u5dee\u4ee3\u4ef7\u51fd\u6570\u7684\u8f93\u51fa\u5728\u4e0d\u65ad\u7684\u51cf\u5c0f":31,"\u9694\u5f00":63,"\u96c6":66,"\u96c6\u675f\u641c\u7d22\u4e2d\u7684\u6269\u5c55\u5e7f\u5ea6":70,"\u96c6\u675f\u641c\u7d22\u4f7f\u7528\u5e7f\u5ea6\u4f18\u5148\u641c\u7d22\u6765\u6784\u5efa\u641c\u7d22\u6811":70,"\u96c6\u675f\u641c\u7d22\u4f7f\u7528\u5e7f\u5ea6\u4f18\u5148\u641c\u7d22\u7684\u65b9\u5f0f\u6784\u5efa\u67e5\u627e\u6811":52,"\u96c6\u7fa4\u4e0a\u542f\u52a8\u4e00\u4e2a\u5355\u673a\u4f7f\u7528cpu\u7684paddle\u8bad\u7ec3\u4f5c\u4e1a":56,"\u96c6\u7fa4\u4f5c\u4e1a\u4e2d\u6240\u6709\u8fdb\u7a0b\u7684\u73af\u5883\u8bbe\u7f6e":50,"\u96c6\u7fa4\u4f5c\u4e1a\u5c06\u4f1a\u5728\u51e0\u79d2\u540e\u542f\u52a8":50,"\u96c6\u7fa4\u5de5\u4f5c":50,"\u96c6\u7fa4\u6d4b\u8bd5":51,"\u96c6\u7fa4\u8bad\u7ec3":51,"\u96c6\u7fa4\u8fdb\u7a0b":50,"\u96c6\u7fa4\u901a\u4fe1\u4fe1\u9053\u7684\u7aef\u53e3\u6570":50,"\u96c6\u7fa4\u901a\u4fe1\u901a\u9053\u7684":50,"\u96c6\u7fa4\u901a\u4fe1\u901a\u9053\u7684\u7aef\u53e3\u53f7":50,"\u9700\u5728nvvp\u754c\u9762\u4e2d\u9009\u4e0a\u624d\u80fd\u5f00\u542f":49,"\u9700\u8981":20,"\u9700\u8981\u4f7f\u7528\u5176\u5236\u5b9a\u7684\u65b9\u5f0f\u6302\u8f7d\u540e\u5e76\u5bfc\u5165\u6570\u636e":57,"\u9700\u8981\u4fdd\u6301\u5f53\u524d\u5206\u652f\u76ee\u5f55":45,"\u9700\u8981\u5148\u6302\u8f7d\u5230\u670d\u52a1\u5668node\u4e0a\u518d\u901a\u8fc7kubernet":55,"\u9700\u8981\u53ef\u4ee5\u8de8\u5e73\u53f0\u6267\u884c":24,"\u9700\u8981\u540c\u6b65\u539f\u4ed3\u5e93":45,"\u9700\u8981\u542f\u52a8\u7684\u8282\u70b9\u4e2a\u6570\u4ee5\u53ca":57,"\u9700\u8981\u5728":50,"\u9700\u8981\u5728\u521b\u5efa\u5bb9\u5668\u524d\u6302\u8f7d\u5377\u4ee5\u4fbf\u6211\u4eec\u4fdd\u5b58\u8bad\u7ec3\u7ed3\u679c":56,"\u9700\u8981\u5728\u7cfb\u7edf\u91cc\u5148\u5b89\u88c5\u597ddocker\u5de5\u5177\u5305":47,"\u9700\u8981\u5728cmake\u7684\u65f6\u5019":27,"\u9700\u8981\u5b89\u88c5graphviz\u6765\u8f6c\u6362dot\u6587\u4ef6\u4e3a\u56fe\u7247":63,"\u9700\u8981\u5bf9":55,"\u9700\u8981\u5c06\u5176parameter\u8bbe\u7f6e\u6210":30,"\u9700\u8981\u5c06\u6807\u8bb0\u7684\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6837\u672c\u79fb\u52a8\u5230\u53e6\u4e00\u4e2a\u8def\u5f84":69,"\u9700\u8981\u5c06bugfix\u7684\u5206\u652f\u540c\u65f6merge\u5230":29,"\u9700\u8981\u5f15\u7528":27,"\u9700\u8981\u6307\u5b9a\u4e0e\u67d0\u4e00\u4e2a\u8f93\u5165\u7684\u5e8f\u5217\u4fe1\u606f\u662f\u4e00\u81f4\u7684":41,"\u9700\u8981\u6307\u5b9alayer\u7684\u8f93\u5165\u6765\u6e90":36,"\u9700\u8981\u660e\u786e\u6307\u5b9a":52,"\u9700\u8981\u6709\u4e00\u4e2a\u5916\u90e8\u7684\u5b58\u50a8\u670d\u52a1\u6765\u4fdd\u5b58\u8bad\u7ec3\u6240\u9700\u6570\u636e\u548c\u8bad\u7ec3\u8f93\u51fa":55,"\u9700\u8981\u6709\u7a33\u5b9a\u7684\u5bfc\u51fa\u7b26\u53f7":26,"\u9700\u8981\u6839\u636e\u4e0d\u540c\u7684\u5206\u5e03\u5f0f\u5b58\u50a8\u6765\u7ed1\u5b9a\u4e00\u4e2a":57,"\u9700\u8981\u6ce8\u610f\u7684\u662f":[29,52,67],"\u9700\u8981\u6ce8\u610f\u7684\u662f\u68af\u5ea6\u68c0\u67e5\u4ec5\u4ec5\u9a8c\u8bc1\u4e86\u68af\u5ea6\u7684\u8ba1\u7b97":46,"\u9700\u8981\u6ce8\u610f\u7684\u662fpaddlepaddle\u76ee\u524d\u53ea\u652f\u6301\u5b50\u5e8f\u5217\u6570\u76ee\u4e00\u6837\u7684\u591a\u8f93\u5165\u53cc\u5c42rnn":41,"\u9700\u8981\u7528\u5230\u7684\u7f16\u8bd1\u5de5\u5177\u548c\u7cfb\u7edf\u5e93":38,"\u9700\u8981\u88ab\u66b4\u9732\u5230\u5176\u4ed6\u8bed\u8a00":27,"\u9700\u8981\u9075\u5faa\u4ee5\u4e0b\u7ea6\u5b9a":43,"\u975e\u5e38\u6570":46,"\u975e\u96f6\u6570\u5b57\u7684\u4e2a\u6570":46,"\u9762\u5411\u67b6\u6784\u4e3a32\u4f4darm\u67b6\u6784":38,"\u97f3\u4e50\u5267":66,"\u987a\u5e8f":41,"\u9884\u5904\u7406\u6570\u636e\u4e00\u822c\u7684\u547d\u4ee4\u4e3a":67,"\u9884\u5904\u7406\u811a\u672c":69,"\u9884\u5b9a\u4e49\u7f51\u7edc":69,"\u9884\u5b9a\u4e49\u7f51\u7edc\u5982\u56fe3\u6240\u793a":69,"\u9884\u63d0\u4ea4\u94a9\u5b50":45,"\u9884\u6d4b\u540e":68,"\u9884\u6d4b\u63a5\u53e3\u811a\u672c":69,"\u9884\u6d4b\u6982\u7387\u53d6\u5e73\u5747":63,"\u9884\u6d4b\u7a0b\u5e8f\u5c06\u8bfb\u53d6\u7528\u6237\u7684\u8f93\u5165":67,"\u9884\u6d4b\u7ed3\u679c\u4ee5\u6587\u672c\u7684\u5f62\u5f0f\u4fdd\u5b58\u5728":65,"\u9884\u6d4b\u811a\u672c\u662f":68,"\u9884\u6d4bid":65,"\u9884\u6d4bimdb\u7684\u672a\u6807\u8bb0\u8bc4\u8bba\u7684\u4e00\u4e2a\u5b9e\u4f8b\u5982\u4e0b":69,"\u9884\u8bad\u7ec3\u6a21\u578b\u4f7f\u7528\u7684\u5b57\u5178\u7684\u8def\u5f84":61,"\u9884\u8bad\u7ec3\u8bcd\u5411\u91cf\u5b57\u5178\u6a21\u578b\u7684\u8def\u5f84":61,"\u989c\u8272\u901a\u9053\u987a\u5e8f\u4e3a":63,"\u989d\u5916\u7684\u53c2\u6570":65,"\u9996\u5148":[3,31,41,44,46,61,63,65,68,69],"\u9996\u5148\u4e0b\u8f7dcifar":62,"\u9996\u5148\u5728\u7cfb\u7edf\u8def\u5f84":32,"\u9996\u5148\u5b89\u88c5\u5e76\u5728\u5f53\u524d\u76ee\u5f55\u8fd0\u884c\u5b83":45,"\u9996\u5148\u5b89\u88c5paddlepaddl":69,"\u9996\u5148\u5bf9\u8f93\u5165\u505a\u4e00\u4e2a\u5c0f\u7684\u6270\u52a8":46,"\u9996\u5148\u6211\u4eec\u9700\u8981\u63a8\u5bfc\u8be5\u7f51\u7edc\u5c42\u7684":46,"\u9996\u5148\u662f\u6cd5\u8bed\u5e8f\u5217":70,"\u9996\u5148\u901a\u8fc7":45,"\u9996\u5148\u9700\u8981\u52a0\u8f7d\u76f8\u5e94\u7684python\u5e93":36,"\u9a71\u52a8":47,"\u9ad8\u4e2d\u6bd5\u4e1a\u751f":66,"\u9ad8\u4eae\u90e8\u5206":41,"\u9ad8\u53ef\u7528":55,"\u9ad8\u5ea6\u652f\u6301\u7075\u6d3b\u548c\u9ad8\u6548\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e":44,"\u9ad8\u6548\u6027":0,"\u9ad8\u65af\u5206\u5e03":30,"\u9ed1\u8272\u7535\u5f71":66,"\u9ed8\u8ba4":[3,52,70],"\u9ed8\u8ba4256k":24,"\u9ed8\u8ba4\u4e00\u4e2apass\u4fdd\u5b58\u4e00\u6b21\u6a21\u578b":65,"\u9ed8\u8ba4\u4e0d\u663e\u793a":52,"\u9ed8\u8ba4\u4e0d\u8bbe\u7f6e":43,"\u9ed8\u8ba4\u4e3a0":[52,54],"\u9ed8\u8ba4\u4e3a1":[3,54],"\u9ed8\u8ba4\u4e3a100":54,"\u9ed8\u8ba4\u4e3a4096mb":52,"\u9ed8\u8ba4\u4e3a\u4e0d\u4f7f\u7528":67,"\u9ed8\u8ba4\u4e3a\u7b2c\u4e00\u4e2a\u8f93\u5165":43,"\u9ed8\u8ba4\u4e3anull":52,"\u9ed8\u8ba4\u4f7f\u7528concurrentremoteparameterupdat":52,"\u9ed8\u8ba4\u503c":[32,40,54],"\u9ed8\u8ba4\u503c\u4e3a":[38,39],"\u9ed8\u8ba4\u521d\u59cb\u72b6\u4e3a0":43,"\u9ed8\u8ba4\u60c5\u51b5\u4e0b":[30,50,69],"\u9ed8\u8ba4\u60c5\u51b5\u4e0b\u4f7f\u7528\u6b64\u7f51\u7edc":69,"\u9ed8\u8ba4\u6307\u5b9a\u7b2c\u4e00\u4e2a\u8f93\u5165":41,"\u9ed8\u8ba4\u7528\u6765\u5207\u5206\u5355\u8bb0\u548c\u6807\u70b9\u7b26\u53f7":69,"\u9ed8\u8ba4\u7684":56,"\u9ed8\u8ba4\u8bbe\u7f6e\u4e3a\u771f":54,"\u9ed8\u8ba4\u8bbe\u7f6e\u6210\u73af\u5883\u53d8\u91cf":[38,39],"\u9ed8\u8ba4\u914d\u7f6e\u5982\u4e0b":50,"adamax\u7b49":65,"amazon\u7535\u5b50\u4ea7\u54c1\u8bc4\u8bba\u6570\u636e":65,"android\u5e73\u53f0\u4e0a\u4f7f\u7528\u7684c":38,"android\u5e73\u53f0\u53ef\u9009\u914d\u7f6e\u53c2\u6570":38,"api\u4e2d\u4f7f\u7528":26,"api\u5bf9\u6bd4\u4ecb\u7ecd":42,"api\u5bfc\u51fa\u7684\u52a8\u6001\u5e93":27,"api\u5bfc\u51fa\u7684\u9759\u6001\u5e93":27,"api\u63a5\u53d7\u7684\u7c7b\u578b\u5168\u662f":27,"api\u63a5\u53e3":[24,55],"api\u63a5\u53e3\u7684\u53c2\u6570\u8f6c\u53d1\u7ed9":27,"api\u6587\u6863":38,"api\u6587\u6863\u751f\u6210\u8fc7\u7a0b\u4f9d\u8d56\u4e8epy_paddl":47,"api\u65f6":27,"api\u65f6\u6240\u552f\u4e00\u9700\u8981\u5f15\u5165\u7684\u5934\u6587\u4ef6":27,"api\u662f\u591a\u8bed\u8a00api\u7684\u57fa\u7840\u90e8\u5206":27,"api\u66b4\u9732\u7684\u7c7b\u578b":27,"api\u6765\u9884\u6d4b":38,"api\u751f\u6210\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u4f1a\u88ab\u5b89\u88c5\u5230":27,"api\u7684\u5934\u6587\u4ef6":[38,39],"api\u7684\u5b9e\u4f8b":27,"api\u7684\u5b9e\u73b0\u7ec6\u8282":27,"api\u7684\u63a5\u53e3":27,"api\u7684\u65f6\u5019\u63a8\u8350paddle\u4e0d\u5d4c\u5165python\u89e3\u91ca\u5668":27,"api\u7684\u7f16\u8bd1\u9009\u9879\u9ed8\u8ba4\u5173\u95ed":27,"api\u76ee\u5f55\u7ed3\u6784\u5982\u4e0a\u56fe\u8868\u6240\u793a":27,"api\u7ea7\u522b":38,"api\u7ea7\u522b\u4e0d\u5c0f\u4e8e21":38,"api\u7ea7\u522b\u4e3a21":38,"api\u83b7\u5f97\u4e86\u795e\u7ecf\u7f51\u7edc\u7684\u53c2\u6570\u5b9e\u4f8b":27,"app\u4e2d":38,"async_sgd\u8fdb\u884c\u8bad\u7ec3\u65f6":30,"atlas\u7684\u8def\u5f84":32,"avx\u7684\u955c\u50cf":33,"awselasticblockstore\u7b49":55,"batch\u4e2d\u5305\u542b":30,"batches\u4e2a\u6279\u6b21\u4fdd\u5b58\u4e00\u6b21\u53c2\u6570":52,"batches\u6b21":52,"bin\u548c\u8bc4\u5206\u6587\u4ef6":67,"blas\u7684\u8def\u5f84":32,"block\u6784\u6210\u4e00\u4e2amodel":19,"book\u4e00\u5b9a\u662f\u60a8\u6700\u597d\u7684\u9009\u62e9":33,"book\u4e2d\u6240\u6709\u7ae0\u8282\u529f\u80fd\u7684\u6b63\u786e\u6027":29,"book\u662f\u4e3a\u7528\u6237\u548c\u5f00\u53d1\u8005\u5236\u4f5c\u7684\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7684jupyt":33,"book\u7684":36,"book\u7684docker\u955c\u50cf":33,"bool\u578b\u53c2\u6570":3,"boolean":[9,25,26],"bugfix\u5206\u652f\u4e5f\u662f\u5728\u5f00\u53d1\u8005\u81ea\u5df1\u7684fork\u7248\u672c\u5e93\u7ef4\u62a4":29,"bugfix\u5206\u652f\u9700\u8981\u5206\u522b\u7ed9\u4e3b\u7248\u672c\u5e93\u7684":29,"build\u6307\u4ee4\u6309\u7167dockerfile\u751f\u6210\u955c\u50cf":33,"build\u8fd9\u4e2a\u547d\u4ee4\u7684":33,"byte":[24,30],"c99\u662f\u76ee\u524dc\u6700\u5e7f\u6cdb\u7684\u4f7f\u7528\u6807\u51c6":26,"c\u6709\u6807\u51c6\u7684abi":26,"c\u8bed\u8a00\u662f\u6709\u5bfc\u51fa\u7b26\u53f7\u7684\u6807\u51c6\u7684":26,"caoying\u7684pul":70,"case":[9,27,28,49],"char":22,"class":[6,7,8,9,10,11,12,13,15,16,26,30,46,69],"cmake\u4e2d\u5c06":49,"cmake\u53c2\u6570\u5982\u4e0b":39,"cmake\u5b98\u65b9\u5bf9android\u5e73\u53f0\u7684\u4ea4\u53c9\u7f16\u8bd1\u63d0\u4f9b\u4e86\u901a\u7528\u7684\u652f\u6301":38,"cmake\u627e\u5230\u7684python\u5e93\u548cpython\u89e3\u91ca\u5668\u7248\u672c\u53ef\u80fd\u6709\u4e0d\u4e00\u81f4\u73b0\u8c61":30,"cmake\u7cfb\u7edf\u5bf9\u4ea4\u53c9\u7f16\u8bd1\u63d0\u4f9b\u4e86\u652f\u6301":[38,39],"cmake\u7f16\u8bd1\u65f6":32,"cmake\u7f16\u8bd1\u7684\u76ee\u6807\u5e73\u53f0":[38,39],"cmake\u914d\u7f6e\u4e2d\u5c06":49,"cmake\u914d\u7f6e\u5b8c\u6210\u540e":[38,39],"conf\u4f5c\u4e3a\u914d\u7f6e":70,"const":[22,46],"container\u4e2d":56,"core\u4e2d\u7684\u6a21\u578b\u8fd8\u5728\u4f7f\u7528\u8fd9\u4e2a\u53c2\u6570":27,"core\u4e2d\u8fd9\u4e00\u7c7b\u578b\u63a5\u53e3\u7684\u667a\u80fd\u6307\u9488":27,"core\u662f\u5426\u8fd8\u5728\u4f7f\u7528\u8fd9\u4e2a\u5b9e\u4f8b":27,"core\u6982\u5ff5":27,"cost\u63a5\u6536y_predict\u4e0ey\u4f5c\u4e3a\u8f93\u5165":36,"cost\u8fd8\u5927\u4e8e\u8fd9\u4e2a\u6570":30,"count\u4e2agpu\u4e0a\u4f7f\u7528\u6570\u636e\u5e76\u884c\u6765\u8ba1\u7b97\u67d0\u4e00\u5c42":54,"count\u548cgpu":54,"cpu\u7248\u672c":35,"cuda\u5e73\u53f0":35,"cuda\u5e93":52,"cudnn\u5e93":[32,52],"dat\u4e2d":67,"data\u5230\u5206\u5e03\u5f0f\u5b58\u50a8\u8865\u5145\u8bad\u7ec3\u6570\u636e":20,"data\u76ee\u5f55\u4e2d\u5b58\u653e\u5207\u5206\u597d\u7684\u6570\u636e":57,"dataprovider\u5171\u8fd4\u56de\u4e24\u4e2a\u6570\u636e":41,"dataprovider\u5171\u8fd4\u56de\u4e24\u7ec4\u6570\u636e":41,"dataprovider\u662fpaddlepaddle\u8d1f\u8d23\u63d0\u4f9b\u6570\u636e\u7684\u6a21\u5757":2,"dataprovider\u7684\u4ecb\u7ecd":[4,65],"dataprovider\u7f13\u51b2\u6c60\u5185\u5b58":30,"deb\u5305":29,"deb\u5305\u7f16\u8bd1\u95ee\u9898":29,"deb\u5b89\u88c5\u5305":35,"decay\u5219\u4e3a0":62,"decoder\u5faa\u73af\u5c55\u5f00\u7684\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u4f1a\u5f15\u7528\u5168\u90e8\u7ed3\u679c":43,"decoder\u63a5\u53d7\u4e24\u4e2a\u8f93\u5165":43,"decoder\u6bcf\u6b21\u9884\u6d4b\u4ea7\u751f\u4e0b\u4e00\u4e2a\u6700\u53ef\u80fd\u7684\u8bcd\u8bed":43,"decoer\u67b6\u6784":43,"default":[7,8,9,10,12,13,15,16,17,54,56,57,69],"demo\u9884\u6d4b\u8f93\u51fa\u5982\u4e0b":5,"dictionary\u662f\u4ece\u7f51\u7edc\u914d\u7f6e\u4e2d\u4f20\u5165\u7684dict\u5bf9\u8c61":3,"dictionary\u7531\u89e3\u6790\u81ea\u52a8\u751f\u6210":67,"dir\u4e2d\u670916\u4e2a\u5b50\u76ee\u5f55":70,"docker\u5b58\u5728\u95ee\u9898":33,"docker\u5b89\u88c5\u8bf7\u53c2\u8003":47,"docker\u7684\u5b98\u7f51":47,"docker\u955c\u50cf\u6765\u670d\u52a1html\u4ee3\u7801":33,"dockerfile\u4f4d\u4e8epaddlepaddl":33,"dockers\u8bbe\u7f6e":33,"dropout\u7684\u6bd4\u4f8b":46,"elec\u6d4b\u8bd5\u96c6":65,"embedding\u6a21\u578b\u9700\u8981\u7a0d\u5fae\u6539\u53d8\u63d0\u4f9b\u6570\u636e\u7684python\u811a\u672c":65,"encode\u6210\u7684\u6700\u540e\u4e00\u4e2a\u5411\u91cf":41,"encoder\u548cdecoder\u53ef\u4ee5\u662f\u80fd\u591f\u5904\u7406\u5e8f\u5217\u7684\u4efb\u610f\u795e\u7ecf\u7f51\u7edc\u5355\u5143":43,"encoder\u8f93\u51fa":43,"entropy\u4f5c\u4e3acost":30,"enum":22,"evaluator\u7684\u503c\u4f4e\u4e8e0":70,"export":[30,33,35,62],"f\u4ee3\u8868\u4e00\u4e2a\u6d6e\u70b9\u6570":[3,36],"false\u7684\u60c5\u51b5":3,"fc1\u548cfc2\u5c42\u5728gpu\u4e0a\u8ba1\u7b97":54,"fc3\u5c42\u4f7f\u7528cpu\u8ba1\u7b97":54,"final":[10,67],"float":[3,7,8,9,11,13,31,49,63,67],"float\u7b49":54,"function":[9,10,11,13,16,18,21,22,23,28,44,69],"gen\u6587\u4ef6\u5939\u4e2d\u7684\u6587\u4ef6\u5217\u8868":70,"generator\u4fbf\u4f1a\u5b58\u4e0b\u5f53\u524d\u7684\u4e0a\u4e0b\u6587":3,"generator\u81f3\u5c11\u9700\u8981\u8c03\u7528\u4e24\u6b21\u624d\u4f1a\u77e5\u9053\u662f\u5426\u505c\u6b62":3,"git\u6d41\u5206\u652f\u6a21\u578b":45,"github\u9996\u9875":45,"golang\u53ef\u4ee5\u4f7f\u7528":26,"golang\u7684":26,"gpu\u4e8c\u8fdb\u5236\u6587\u4ef6":32,"gpu\u5219\u8fd8\u9700\u8981\u9ad8\u5e76\u884c\u6027":49,"gpu\u53cc\u7f13\u5b58":3,"gpu\u548c\u975eavx\u533a\u5206\u4e86\u5982\u4e0b4\u4e2a\u955c\u50cf":33,"gpu\u6027\u80fd\u5206\u6790\u4e0e\u8c03\u4f18":48,"gpu\u6838\u5728\u8bad\u7ec3\u914d\u7f6e\u4e2d\u6307\u5b9a":52,"gpu\u7248\u672c":35,"gpu\u7248\u672c\u5e76\u60f3\u4f7f\u7528":68,"gpu\u7684docker\u955c\u50cf\u7684\u65f6\u5019":30,"gram\u7ea7\u522b\u7684\u77e5\u8bc6":69,"group\u6559\u7a0b":42,"gru\u6216lstm":44,"gru\u6a21\u578b":65,"gru\u6a21\u578b\u914d\u7f6e":65,"h\u5e76\u4e0d\u56f0\u96be":26,"hot\u7a20\u5bc6\u5411\u91cf":67,"html\u5373\u53ef\u8bbf\u95ee\u672c\u5730\u6587\u6863":47,"i\u4ee3\u8868\u4e00\u4e2a\u6574\u6570":[3,36],"id\u4e3a0\u7684\u6982\u7387":65,"id\u4e3a1\u7684\u6982\u7387":65,"id\u6307\u5b9a\u4f7f\u7528\u54ea\u4e2agpu\u6838":52,"id\u6307\u5b9a\u7684gpu":54,"id\u65e0\u6548":52,"image\u91cc":56,"images\u6570\u636e\u96c6\u4e0a\u4f20\u5230\u4e91\u7aef\u7684":20,"imdb\u6570\u636e\u96c6\u5305\u542b25":69,"imdb\u6709\u4e24\u4e2a\u6807\u7b7e":69,"imdb\u7684\u6570\u6910\u96c6":69,"import":[3,5,8,9,16,30,31,36,47,61,62,63,67,69,70],"include\u4e0b\u9700\u8981\u5305\u542bcbla":32,"include\u4e0b\u9700\u8981\u5305\u542bmkl":32,"ingress\u9700\u8981\u628apfsclient\u7684\u8eab\u4efd\u4fe1\u606f\u4f20\u7ed9pfsserv":24,"init_hook\u53ef\u4ee5\u4f20\u5165\u4e00\u4e2a\u51fd\u6570":3,"instance\u4e0e\u751f\u6210\u6570\u636e\u96c6\u65f6":20,"instance\u5305\u6db5\u4e24\u4e2a\u503c":20,"instance\u662f\u4e00\u6a21\u4e00\u6837\u7684":20,"int":[3,7,8,9,10,13,21,22,23,26,27,28,41,46,54,65,67,68],"interface\u6587\u4ef6\u7684\u5199\u6cd5\u975e\u5e38":26,"issue\u7f16\u53f7":45,"job\u542f\u52a8\u540e\u4f1a\u521b\u5efa\u8fd9\u4e9bpod\u5e76\u5f00\u59cb\u6267\u884c\u4e00\u4e2a\u7a0b\u5e8f":55,"job\u6216\u8005\u5e94\u7528\u7a0b\u5e8f\u5728\u5bb9\u5668\u4e2d\u8fd0\u884c\u65f6\u751f\u6210\u7684\u6570\u636e\u4f1a\u5728\u5bb9\u5668\u9500\u6bc1\u65f6\u6d88\u5931":55,"job\u662f\u672c\u6b21\u8bad\u7ec3\u5bf9\u5e94\u7684job":57,"job\u7684\u540d\u5b57":57,"kubernetes\u4e3a\u8fd9\u6b21\u8bad\u7ec3\u521b\u5efa\u4e863\u4e2apod\u5e76\u4e14\u8c03\u5ea6\u5230\u4e863\u4e2anode\u4e0a\u8fd0\u884c":57,"kubernetes\u5206\u5e03\u5f0f\u8bad\u7ec3":48,"kubernetes\u5355\u673a\u8bad\u7ec3":48,"kubernetes\u53ef\u4ee5\u5728\u7269\u7406\u673a\u6216\u865a\u62df\u673a\u4e0a\u8fd0\u884c":55,"kubernetes\u53ef\u4ee5\u901a\u8fc7yaml\u6587\u4ef6\u6765\u521b\u5efa\u76f8\u5173\u5bf9\u8c61":57,"kubernetes\u5c31\u4f1a\u521b\u5efa3\u4e2apod\u4f5c\u4e3apaddlepaddle\u8282\u70b9\u7136\u540e\u62c9\u53d6\u955c\u50cf":57,"kubernetes\u63d0\u4f9b\u4e86\u591a\u79cd\u96c6\u7fa4\u90e8\u7f72\u7684\u65b9\u6848":55,"kubernetes\u652f\u6301\u591a\u79cdvolum":55,"kubernetes\u6709job\u7c7b\u578b\u7684\u8d44\u6e90\u6765\u652f\u6301":56,"kubernetes\u96c6\u7fa4\u5c31\u662f\u7531node\u8282\u70b9\u4e0emaster\u8282\u70b9\u7ec4\u6210\u7684":55,"label\u662f\u539f\u59cb\u6570\u636e\u4e2d\u5bf9\u4e8e\u6bcf\u4e00\u53e5\u8bdd\u7684\u5206\u7c7b\u6807\u7b7e":41,"labels\u662f\u6bcf\u7ec4\u5185\u6bcf\u4e2a\u53e5\u5b50\u7684\u6807\u7b7e":41,"layer1\u5fc5\u987b\u662f\u4e00\u4e2a0\u5c42\u5e8f\u5217":40,"layer1\u5fc5\u987b\u662f\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":40,"layer\u62ff\u5230\u7684\u7528\u6237\u8f93\u5165":43,"layer\u662f\u6211\u4eec\u7684\u79ef\u6728":36,"layer\u7c7b\u53ef\u4ee5\u81ea\u52a8\u8ba1\u7b97\u4e0a\u9762\u7684\u5bfc\u6570":46,"lib\u4e0b\u9700\u8981\u5305\u542bcblas\u548catlas\u4e24\u4e2a\u5e93":32,"lib\u4e0b\u9700\u8981\u5305\u542bcblas\u5e93":32,"lib\u4e0b\u9700\u8981\u5305\u542bopenblas\u5e93":32,"lib\u76ee\u5f55\u4e0b\u9700\u8981\u5305\u542bmkl_cor":32,"list\u4e2d\u7684\u6bcf\u4e00\u884c\u90fd\u4f20\u9012\u7ed9process\u51fd\u6570":3,"list\u4f5c\u4e3a\u68c0\u67e5\u5217\u8868":29,"list\u5199\u5165\u90a3\u4e2a\u6587\u672c\u6587\u4ef6\u7684\u5730\u5740":3,"list\u548ctest":2,"list\u5982\u4e0b\u6240\u793a":54,"list\u5b58\u653e\u5728\u672c\u5730":2,"list\u6307\u5b9a\u6d4b\u8bd5\u7684\u6a21\u578b\u5217\u8868":54,"long":[9,10,13],"lstm\u67b6\u6784\u7684\u6700\u5927\u4f18\u70b9\u662f\u5b83\u53ef\u4ee5\u5728\u957f\u65f6\u95f4\u95f4\u9694\u5185\u8bb0\u5fc6\u4fe1\u606f":69,"lstm\u6a21\u578b":65,"lstm\u6a21\u578b\u914d\u7f6e":65,"lstm\u7f51\u7edc\u7c7b\u4f3c\u4e8e\u5177\u6709\u9690\u85cf\u5c42\u7684\u6807\u51c6\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":69,"memory\u4e0d\u80fd\u72ec\u7acb\u5b58\u5728":43,"memory\u4e5f\u53ef\u4ee5\u5177\u6709":44,"memory\u4e5f\u53ef\u4ee5\u662f\u5e8f\u5217":44,"memory\u53ea\u80fd\u5728":43,"memory\u53ef\u4ee5\u7f13\u5b58\u4e0a\u4e00\u4e2a\u65f6\u523b\u67d0\u4e00\u4e2a\u795e\u7ecf\u5143\u7684\u8f93\u51fa":41,"memory\u6307\u5411\u4e00\u4e2alay":43,"memory\u662f\u5728\u5355\u6b65\u51fd\u6570\u4e2d\u5faa\u73af\u4f7f\u7528\u7684\u72b6\u6001":44,"memory\u662fpaddlepaddle\u5b9e\u73b0rnn\u65f6\u5019\u4f7f\u7528\u7684\u4e00\u4e2a\u6982\u5ff5":41,"memory\u7684":44,"memory\u7684\u521d\u59cb\u72b6\u6001":43,"memory\u7684\u65f6\u95f4\u5e8f\u5217\u957f\u5ea6\u4e00\u81f4\u7684\u60c5\u51b5":41,"memory\u7684\u66f4\u591a\u8ba8\u8bba\u8bf7\u53c2\u8003\u8bba\u6587":43,"memory\u7684\u8f93\u51fa\u5b9a\u4e49\u5728":44,"memory\u7684i":43,"memory\u9ed8\u8ba4\u521d\u59cb\u5316\u4e3a0":43,"mkl\u7684\u8def\u5f84":32,"mkl_sequential\u548cmkl_intel_lp64\u4e09\u4e2a\u5e93":32,"mnist\u662f\u4e00\u4e2a\u5305\u542b\u670970":3,"mode\u548cattent":70,"mode\u7684python\u51fd\u6570":70,"model\u505a\u5206\u652f\u7ba1\u7406":29,"model\u53ef\u4ee5\u901a\u8fc7":5,"model\u6765\u5b9e\u73b0\u624b\u5199\u8bc6\u522b\u7684\u9884\u6d4b\u4ee3\u7801":5,"movielens\u6570\u636e\u96c6":67,"name\u662f\u4f53\u88c1":67,"name\u662f\u5e74\u9f84":67,"name\u662f\u6027\u522b":67,"name\u662f\u7535\u5f71\u540d":67,"name\u662f\u804c\u4e1a":67,"name\u7ec4\u5408\u53ef\u4ee5\u627e\u5230\u672c\u6b21\u8bad\u7ec3\u9700\u8981\u7684\u6587\u4ef6\u8def\u5f84":57,"ndarray\u7c7b\u578b\u7684\u503c\u548c\u6574\u578b\u7684\u503c":20,"ndk\u4e2d\u5305\u542b\u4e86\u6240\u6709android":38,"new":[9,13,17,18,21,22,28,45,46],"nfs\u7684\u90e8\u7f72\u65b9\u6cd5\u53ef\u4ee5\u53c2\u8003":55,"nmt\u6a21\u578b\u53d7\u5236\u4e8e\u6e90\u8bed\u53e5\u7684\u7f16\u7801":70,"noavx\u7248\u672c":35,"normalization\u5c42":63,"normalization\u5c42\u7684\u53c2\u6570":63,"note\u7684\u4e66\u5199":29,"notebook\u662f\u4e00\u4e2a\u5f00\u6e90\u7684web\u7a0b\u5e8f":33,"null":[9,46,52,67],"num_gradient_servers\u53c2\u6570":57,"openblas\u7684\u8def\u5f84":32,"out\u4e0b\u5305\u542b":62,"out\u7684\u6587\u4ef6\u5939":62,"outer_mem\u662f\u4e00\u4e2a\u5b50\u53e5\u7684\u6700\u540e\u4e00\u4e2a\u5411\u91cf":41,"output\u6587\u4ef6\u5939\u5b58\u653e\u8bad\u7ec3\u7ed3\u679c\u4e0e\u65e5\u5fd7":57,"packages\u91cc\u9762":30,"packages\u91cc\u9762\u7684python\u5305":30,"paddepaddle\u901a\u8fc7\u7f16\u8bd1\u65f6\u6307\u5b9a\u8def\u5f84\u6765\u5b9e\u73b0\u5f15\u7528\u5404\u79cdbla":32,"paddle\u4e00\u4e2a\u52a8\u6001\u5e93\u53ef\u4ee5\u5728\u4efb\u4f55linux\u7cfb\u7edf\u4e0a\u8fd0\u884c":26,"paddle\u4e2d\u7684\u4e00\u6761pass\u8868\u793a\u8bad\u7ec3\u6570\u636e\u96c6\u4e2d\u6240\u6709\u7684\u6837\u672c\u4e00\u6b21":70,"paddle\u4e2d\u7ecf\u5e38\u4f1a\u5c06\u65f6\u95f4\u5e8f\u5217\u6210\u4e3a":41,"paddle\u4e8c\u8fdb\u5236\u5728\u8fd0\u884c\u65f6\u6355\u83b7\u4e86\u6d6e\u70b9\u6570\u5f02\u5e38":30,"paddle\u4f7f\u7528git":29,"paddle\u5185\u5d4c\u7684python\u89e3\u91ca\u5668\u548c\u5916\u90e8\u4f7f\u7528\u7684python\u5982\u679c\u7248\u672c\u4e0d\u540c":26,"paddle\u5185\u90e8\u7684\u7c7b\u4e3ac":26,"paddle\u5f00\u53d1\u8fc7\u7a0b\u4f7f\u7528":29,"paddle\u66ff\u6362\u6210":33,"paddle\u6bcf\u6b21\u53d1\u65b0\u7684\u7248\u672c":29,"paddle\u6bcf\u6b21\u53d1\u7248\u672c\u9996\u5148\u8981\u4fdd\u8bc1paddl":29,"paddle\u7684\u4e3b\u7248\u672c\u5e93\u9075\u5faa":29,"paddle\u7684\u5404\u7248\u672c\u955c\u50cf\u53ef\u4ee5\u53c2\u8003":56,"paddle\u7684\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0\u5305\u62ec\u4e00\u4e0b\u51e0\u4e2a\u65b9\u9762":26,"paddle\u7684\u7c7b\u578b\u5168\u90e8\u9000\u5316\u6210":27,"paddle\u7684\u94fe\u63a5\u65b9\u5f0f\u6bd4\u8f83\u590d\u6742":26,"paddle\u7684c":27,"paddle\u7684dock":56,"paddle\u7684docker\u5f00\u53d1\u955c\u50cf\u5e26\u6709\u4e00\u4e2a\u901a\u8fc7":33,"paddle\u8bad\u7ec3\u4efb\u52a1":20,"paddle\u8def\u5f84\u4e0b":27,"paddle\u955c\u50cf":56,"paddle\u9700\u8981\u4e00\u4e2a\u591a\u8bed\u8a00\u63a5\u53e3":26,"paddle\u9700\u8981\u66b4\u9732\u7684api\u5f88\u591a":27,"paddle\u9759\u6001\u5e93\u94fe\u63a5\u590d\u6742":26,"paddle_\u7c7b\u578b\u540d":27,"paddle_\u7c7b\u578b\u540d_\u51fd\u6570\u540d":27,"paddlepaddle\u4e2d":[40,43],"paddlepaddle\u4e2d\u7684\u4e00\u4e2apass\u610f\u5473\u7740\u5bf9\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u6837\u672c\u8fdb\u884c\u4e00\u6b21\u8bad\u7ec3":69,"paddlepaddle\u4e2d\u7684\u8bb8\u591alayer\u5e76\u4e0d\u5728\u610f\u8f93\u5165\u662f\u5426\u662f\u65f6\u95f4\u5e8f\u5217":41,"paddlepaddle\u4e2d\u901a\u8fc7reader\u6765\u52a0\u8f7d\u6570\u636e":36,"paddlepaddle\u4e3a\u4ea4\u53c9\u7f16\u8bd1\u63d0\u4f9b\u4e86\u5de5\u5177\u94fe\u914d\u7f6e\u6587\u6863":[38,39],"paddlepaddle\u4e3a\u6df1\u5ea6\u5b66\u4e60\u7814\u7a76\u4eba\u5458\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684api":36,"paddlepaddle\u4f1a\u5728\u8c03\u7528\u8bfb\u53d6\u6570\u636e\u7684python\u811a\u672c\u4e4b\u524d":65,"paddlepaddle\u4f7f\u7528\u5747\u503c0":30,"paddlepaddle\u4f7f\u7528avx":30,"paddlepaddle\u4f7f\u7528swig\u5bf9\u5e38\u7528\u7684\u9884\u6d4b\u63a5\u53e3\u8fdb\u884c\u4e86\u5c01\u88c5":5,"paddlepaddle\u4fdd\u7559\u6dfb\u52a0\u53c2\u6570\u7684\u6743\u529b":3,"paddlepaddle\u5148\u4ece\u4e00\u4e2a\u6587\u4ef6\u5217\u8868\u91cc\u83b7\u5f97\u6570\u636e\u6587\u4ef6\u5730\u5740":31,"paddlepaddle\u53d1\u5e03\u65b0\u7248\u672c\u7684\u65f6\u5019\u90fd\u4f1a\u53d1\u5e03\u5bf9\u5e94\u7248\u672c\u7684\u751f\u4ea7\u955c\u50cf\u4ee5\u53ca\u5f00\u53d1\u955c\u50cf":33,"paddlepaddle\u53ef\u4ee5\u4f7f\u7528cudnn":32,"paddlepaddle\u53ef\u4ee5\u6267\u884c\u7528\u6237\u7684python\u811a\u672c\u7a0b\u5e8f\u6765\u8bfb\u53d6\u5404\u79cd\u683c\u5f0f\u7684\u6570\u636e\u6587\u4ef6":65,"paddlepaddle\u53ef\u4ee5\u6bd4\u8f83\u7b80\u5355\u7684\u5224\u65ad\u54ea\u4e9b\u8f93\u51fa\u662f\u5e94\u8be5\u8de8\u8d8a\u65f6\u95f4\u6b65\u7684":41,"paddlepaddle\u53ef\u4ee5\u901a\u8fc7\u8be5\u673a\u5236\u5224\u65ad\u662f\u5426\u5df2\u7ecf\u6536\u96c6\u9f50\u6240\u6709\u7684\u68af\u5ea6":46,"paddlepaddle\u5728\u5b9e\u73b0rnn\u7684\u65f6\u5019":41,"paddlepaddle\u5b58\u7684\u662f\u6709\u503c\u4f4d\u7f6e\u7684\u7d22\u5f15":[3,36],"paddlepaddle\u5b9a\u4e49\u7684\u53c2\u6570":3,"paddlepaddle\u5c06\u4ee5\u8bbe\u7f6e\u53c2\u6570\u7684\u65b9\u5f0f\u6765\u8bbe\u7f6e":65,"paddlepaddle\u5c06\u5728\u89c2\u6d4b\u6570\u636e\u96c6\u4e0a\u8fed\u4ee3\u8bad\u7ec330\u8f6e":31,"paddlepaddle\u5c06\u6839\u636e":38,"paddlepaddle\u5c06\u6bcf\u4e2a\u6a21\u578b\u53c2\u6570\u4f5c\u4e3a\u4e00\u4e2anumpy\u6570\u7ec4\u5355\u72ec\u5b58\u4e3a\u4e00\u4e2a\u6587\u4ef6":31,"paddlepaddle\u5c06\u81ea\u52a8\u4e0b\u8f7d\u548c\u7f16\u8bd1\u6240\u6709\u7b2c\u4e09\u65b9\u4f9d\u8d56\u5e93":[38,39],"paddlepaddle\u5c06train":3,"paddlepaddle\u5f00\u53d1\u955c\u50cf\u5c31\u88ab\u6784\u5efa\u5b8c\u6bd5\u4e86":33,"paddlepaddle\u63d0\u4f9b\u4e13\u7528\u7684":20,"paddlepaddle\u63d0\u4f9b\u4e86\u5f88\u591a\u4f18\u79c0\u7684\u5b66\u4e60\u7b97\u6cd5":31,"paddlepaddle\u63d0\u4f9b\u4e86ubuntu":35,"paddlepaddle\u63d0\u4f9b\u6570\u4e2a\u9884\u7f16\u8bd1\u7684\u4e8c\u8fdb\u5236\u6765\u8fdb\u884c\u5b89\u88c5":34,"paddlepaddle\u652f\u6301\u4e0d\u540c\u7c7b\u578b\u7684\u8f93\u5165\u6570\u636e":36,"paddlepaddle\u652f\u6301\u4ee5\u4e0b\u4efb\u610f\u4e00\u79cdblas\u5e93":32,"paddlepaddle\u652f\u6301\u5927\u91cf\u7684\u8ba1\u7b97\u5355\u5143\u548c\u4efb\u610f\u6df1\u5ea6\u7684\u7f51\u7edc\u8fde\u63a5":31,"paddlepaddle\u652f\u6301\u975e\u5e38\u591a\u7684\u4f18\u5316\u7b97\u6cd5":30,"paddlepaddle\u652f\u6301sparse\u7684\u8bad\u7ec3":30,"paddlepaddle\u6587\u6863\u4f7f\u7528":47,"paddlepaddle\u6587\u6863\u9700\u8981\u51c6\u5907\u7684\u73af\u5883\u76f8\u5bf9\u8f83\u590d\u6742":47,"paddlepaddle\u662f\u4e00\u4e2a\u6700\u65e9\u7531\u767e\u5ea6\u79d1\u5b66\u5bb6\u548c\u5de5\u7a0b\u5e08\u5171\u540c\u7814\u53d1\u7684\u5e76\u884c\u5206\u5e03\u5f0f\u6df1\u5ea6\u5b66\u4e60\u5e73\u53f0":0,"paddlepaddle\u662f\u6e90\u4e8e\u767e\u5ea6\u7684\u4e00\u4e2a\u6df1\u5ea6\u5b66\u4e60\u5e73\u53f0":[31,36],"paddlepaddle\u6bcf\u6b21\u53d1\u5e03\u65b0\u7248\u672c\u90fd\u4f1a\u53d1\u5e03\u5bf9\u5e94\u7684\u5f00\u53d1\u955c\u50cf\u4f9b\u5f00\u53d1\u8005\u76f4\u63a5\u4f7f\u7528":33,"paddlepaddle\u7684\u5185\u5b58\u5360\u7528\u4e3b\u8981\u5206\u4e3a\u5982\u4e0b\u51e0\u4e2a\u65b9\u9762":30,"paddlepaddle\u7684\u53c2\u6570\u4f7f\u7528\u540d\u5b57":30,"paddlepaddle\u7684\u5df2\u7ecf\u5b89\u88c5\u5b8c\u6210":38,"paddlepaddle\u7684\u6570\u636e\u5305\u62ec\u56db\u79cd\u4e3b\u8981\u7c7b\u578b":3,"paddlepaddle\u7684\u6587\u6863\u5305\u62ec\u82f1\u6587\u6587\u6863":47,"paddlepaddle\u7684\u6587\u6863\u6784\u5efa\u6709\u76f4\u63a5\u6784\u5efa\u548c\u57fa\u4e8edocker\u6784\u5efa\u4e24\u79cd\u65b9\u5f0f":47,"paddlepaddle\u7684\u6e90\u7801":45,"paddlepaddle\u7684\u7f16\u8bd1\u9009\u9879":34,"paddlepaddle\u7684bas":46,"paddlepaddle\u7684cmake\u7cfb\u7edf\u5c06\u6839\u636e\u8be5\u503c\u81ea\u52a8\u63a8\u5bfc\u548c\u8bbe\u7f6e\u9700\u8981\u4f7f\u7528\u7684\u4ea4\u53c9\u7f16\u8bd1\u5668":38,"paddlepaddle\u7684cmake\u7cfb\u7edf\u5c06\u6839\u636e\u8be5\u503c\u81ea\u52a8\u8bbe\u7f6e\u9700\u8981\u4f7f\u7528\u7684\u4ea4\u53c9\u7f16\u8bd1\u5668":39,"paddlepaddle\u7684cmake\u7cfb\u7edf\u624d\u8ba4\u4e3a\u5728\u662f\u5728\u4ea4\u53c9\u7f16\u8bd1raspberri":39,"paddlepaddle\u7684cmake\u7cfb\u7edf\u624d\u8ba4\u4e3a\u662f\u5728\u4ea4\u53c9\u7f16\u8bd1android\u7cfb\u7edf\u7684\u7248\u672c":38,"paddlepaddle\u7684docker\u5bb9\u5668\u4f7f\u7528\u65b9\u5f0f":34,"paddlepaddle\u76ee\u524d\u53ea\u652f\u6301\u5728\u6bcf\u4e2a\u65f6\u95f4\u6b65\u4e2d":41,"paddlepaddle\u76ee\u524d\u552f\u4e00\u5b98\u65b9\u652f\u6301\u7684\u8fd0\u884c\u7684\u65b9\u5f0f\u662fdocker\u5bb9\u5668":33,"paddlepaddle\u76ee\u524d\u5df2\u7ecf\u5f00\u653e\u6e90\u7801":0,"paddlepaddle\u76ee\u524d\u63d0\u4f9b\u4e24\u79cd\u53c2\u6570\u521d\u59cb\u5316\u7684\u65b9\u5f0f":30,"paddlepaddle\u82e5\u68c0\u6d4b\u5230\u7528\u6237\u4f7f\u7528\u7684cmake\u7248\u672c\u4e0d\u4f4e\u4e8e3":38,"paddlepaddle\u8981\u6c42\u4f7f\u7528\u7684\u7f16\u8bd1\u5de5\u5177\u94fe\u6240\u652f\u6301\u7684andoid":38,"paddlepaddle\u8c03\u7528process\u51fd\u6570\u6765\u8bfb\u53d6\u6570\u636e":65,"paddlepaddle\u8d1f\u8d23\u5b8c\u6210\u4fe1\u606f\u548c\u68af\u5ea6\u5728\u65f6\u95f4\u5e8f\u5217\u4e0a\u7684\u4f20\u64ad":43,"paddlepaddle\u8d1f\u8d23\u5b8c\u6210\u4fe1\u606f\u548c\u8bef\u5dee\u5728\u65f6\u95f4\u5e8f\u5217\u4e0a\u7684\u4f20\u64ad":43,"paddlepaddle\u955c\u50cf\u9700\u8981\u63d0\u4f9b":57,"paddlepaddle\u9700\u8981\u7528\u6237\u5728\u7f51\u7edc\u914d\u7f6e":2,"paddlepaddle\u9700\u8981\u7684\u6240\u6709\u7f16\u8bd1\u5de5\u5177":33,"pass\u4e2a\u6a21\u578b\u5230\u7b2c":52,"pass\u5230":70,"pass\u5c06\u4e0d\u8d77\u4f5c\u7528":52,"pass\u8f6e\u5f00\u59cb\u8bad\u7ec3":52,"pass\u8f6e\u7684\u6a21\u578b\u7528\u4e8e\u6d4b\u8bd5":52,"passes\u8f6e":52,"patch\u53f7":29,"patch\u53f7\u52a0\u4e00":29,"path\u6307\u5b9a\u6d4b\u8bd5\u7684\u6a21\u578b":54,"period\u4e2a\u6279\u6b21\u5bf9\u6240\u6709\u6d4b\u8bd5\u6570\u636e\u8fdb\u884c\u6d4b\u8bd5":52,"period\u4e2a\u6279\u6b21\u6253\u5370\u65e5\u5fd7\u8fdb\u5ea6":52,"period\u4e2a\u6279\u6b21\u8f93\u51fa\u53c2\u6570\u7edf\u8ba1":52,"period\u4e2a\u6279\u6b21\u8f93\u51fa\u7b26\u53f7":52,"period\u4e2abatch\u5904\u7406\u7684\u5f53\u524d\u635f\u5931":69,"period\u4e2abatch\u7684\u5206\u7c7b\u9519\u8bef":69,"period\u6574\u9664":52,"period\u8f6e\u4fdd\u5b58\u8bad\u7ec3\u53c2\u6570":52,"pfsclient\u9700\u8981\u548cingress\u4e4b\u95f4\u505a\u53cc\u5411\u9a8c\u8bc1":24,"pfsclient\u9700\u8981\u5728\u4f20\u8f93\u5b8c\u6bd5\u6700\u540e\u4e00\u4e2achunk\u7684\u65f6\u5019\u68c0\u67e5destination\u6587\u4ef6\u7684md5\u503c\u662f\u5426\u548csource\u6587\u4ef6\u4e00\u81f4":24,"pfsserver\u63d0\u4f9brest":24,"pi\u5e73\u53f0\u4e0a\u9002\u7528\u7684paddlepaddle\u5e93":39,"pi\u5e73\u53f0\u4e0a\u9002\u7528\u7684paddlepaddle\u7684\u65b9\u6cd5\u548c\u6b65\u9aa4":39,"pi\u5e73\u53f0\u4f7f\u7528\u7684c":39,"pi\u5e73\u53f0\u53ef\u9009\u914d\u7f6e\u53c2\u6570":39,"pi\u7248\u672c\u7684\u5e93":39,"pi\u7248\u672cpaddlepaddle\u5e93\u65f6":39,"pi\u7cfb\u7edf":39,"pi\u7cfb\u7edf\u4e0a":39,"pi\u7cfb\u7edf\u7684\u7248\u672c":39,"pod\u4e2d\u7684\u5bb9\u5668\u5171\u4eabnet":55,"pod\u662fkubernetes\u7684\u6700\u5c0f\u8c03\u5ea6\u5355\u5143":55,"pooling\u5bf9\u7279\u5f81\u56fe\u4e0b\u91c7\u6837":62,"process\u51fd\u6570\u4f1a\u7528yield\u8bed\u53e5\u8f93\u51fa\u8fd9\u6761\u6570\u636e":65,"prod\u7684\u751f\u4ea7\u955c\u50cf":33,"public":[13,46,56,69],"py_paddle\u91cc\u9762\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5de5\u5177\u7c7b":5,"pydataprovider2\u4f1a\u5c3d\u53ef\u80fd\u591a\u7684\u4f7f\u7528\u5185\u5b58":3,"pydataprovider2\u63d0\u4f9b\u4e86\u4e24\u79cd\u7b80\u5355\u7684cache\u7b56\u7565":3,"pydataprovider2\u662fpaddlepaddle\u4f7f\u7528python\u63d0\u4f9b\u6570\u636e\u7684\u63a8\u8350\u63a5\u53e3":3,"pydataprovider2\u7684\u4f7f\u7528":[2,4,30,44,65,67],"pydataprovider\u4f7f\u7528\u7684\u662f\u5f02\u6b65\u52a0\u8f7d":30,"python\u4ee3\u7801\u5c06\u968f\u673a\u4ea7\u751f2000\u4e2a\u89c2\u6d4b\u70b9":31,"python\u5305":47,"python\u53ef\u4ee5\u89e3\u9664\u6389\u5185\u90e8\u53d8\u91cf\u7684\u5f15\u7528":3,"python\u5c01\u88c5\u7684\u5b9e\u73b0\u4f7f\u5f97\u6211\u4eec\u53ef\u4ee5\u5728\u914d\u7f6e\u6587\u4ef6\u4e2d\u4f7f\u7528\u65b0\u5b9e\u73b0\u7684\u7f51\u7edc\u5c42":46,"query\u6539\u5199":70,"rate\u4e3a0":70,"rate\u4e3a5":70,"rate\u88ab\u8bbe\u7f6e\u4e3a0":62,"reader\u7684\u4f7f\u7528\u65b9\u5f0f\u90fd\u662f\u4e00\u81f4\u7684":20,"reader\u8f93\u51fa\u7684data":20,"recommendation\u6587\u4ef6\u5939\u5185\u5b58\u653e\u8bad\u7ec3\u6587\u4ef6":57,"release\u9875\u9762":29,"repo\u7684\u6839\u76ee\u5f55":33,"request\u524d":45,"request\u7684":45,"request\u88ab\u5408\u5e76\u540e":45,"research\u5b9e\u9a8c\u5ba4\u641c\u96c6\u6574\u7406":66,"resnet\u6a21\u578b":64,"return":[3,9,10,12,13,15,16,20,22,23,31,36,41,44,46,57,63,65,67],"rnn\u5373\u65f6\u95f4\u9012\u5f52\u795e\u7ecf\u7f51\u7edc":41,"rnn\u5bf9\u4e8e\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u901a\u8fc7\u4e86\u4e00\u4e2alstm\u7f51\u7edc":41,"rnn\u603b\u662f\u5f15\u7528\u4e0a\u4e00\u65f6\u523b\u9884\u6d4b\u51fa\u7684\u8bcd\u7684\u8bcd\u5411\u91cf":43,"rnn\u6a21\u578b":65,"rnn\u76f8\u5173\u6a21\u578b":48,"s3\u4e4b\u7c7b\u7684\u5206\u5e03\u5f0f\u5b58\u50a8\u4e4b\u4e0a":20,"search\u7684\u65b9\u6cd5":52,"sentences\u662f\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217\u7684\u6570\u636e":41,"seq\u53c2\u6570\u5fc5\u987b\u4e3afals":43,"server\u4e2a\u6279\u6b21\u6253\u5370\u65e5\u5fd7\u8fdb\u5ea6":52,"server\u4e4b\u4e0a":19,"server\u4e4b\u95f4\u7684\u7f51\u7edc\u5e26\u5bbd":19,"server\u4f1a\u6682\u505c\u53c2\u6570\u66f4\u65b0\u5e76\u7b49\u5f85":19,"server\u4f1a\u83b7\u53d6parameters\u5185\u5b58\u7684":19,"server\u5185\u5b58\u4e2d\u7684\u6a21\u578b\u6570\u636e\u7684\u5b8c\u6574\u955c\u50cf":19,"server\u540c\u6b65\u7684\u4fdd\u5b58\u4e00\u4e2a\u7279\u5b9a\u65f6\u95f4\u70b9\u7684\u5168\u5c40\u68c0\u67e5\u70b9":19,"server\u5728\u96c6\u7fa4\u4e2d\u542f\u52a8\u540e":19,"server\u6545\u969c\u540e\u88abkubernetes\u91cd\u65b0\u542f\u52a8":19,"server\u6b64\u65f6\u8fd8\u9700\u8981\u901a\u8fc7\u7f51\u7edc\u8bbf\u95ee\u5206\u5e03\u5f0f\u5b58\u50a8\u4ee5\u4fdd\u5b58\u5feb\u7167":19,"server\u751f\u6210\u4e00\u4e2auuid":19,"server\u7684\u5355\u70b9\u6216\u591a\u70b9\u540c\u65f6\u6545\u969c":19,"server\u7684\u6570\u636e\u5feb\u7167":19,"server\u7684\u68c0\u67e5\u70b9\u5404\u81ea\u72ec\u7acb\u4fdd\u5b58":19,"server\u7b2c\u4e00\u6b21\u542f\u52a8\u6216\u4efb\u610f\u65f6\u95f4paramet":19,"sh\u6765\u8bad\u7ec3\u6a21\u578b":62,"sh\u6765\u8fdb\u884c\u6784\u5efa":47,"sh\u8c03\u7528\u4e86":63,"short":[9,10],"simd\u6307\u4ee4\u63d0\u9ad8cpu\u6267\u884c\u6548\u7387":30,"size\u4e3a1":70,"size\u4e3a50":70,"size\u4e3a512":52,"size\u53ef\u80fd\u4f1a\u5bf9\u8bad\u7ec3\u7ed3\u679c\u4ea7\u751f\u5f71\u54cd":30,"size\u5927\u5c0f\u4e3a128":69,"size\u662f3":70,"size\u672c\u8eab\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u8d85\u53c2\u6570":30,"size\u7684\u503c":3,"softmax\u5c42":61,"softmax\u6fc0\u6d3b\u7684\u8f93\u51fa\u7684\u548c\u603b\u662f1":46,"sparse\u8bad\u7ec3\u9700\u8981\u8bad\u7ec3\u7279\u5f81\u662f":30,"srl\u4f5c\u4e3a\u5f88\u591a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u4e2d\u7684\u4e2d\u95f4\u6b65\u9aa4\u662f\u5f88\u6709\u7528\u7684":68,"ssh\u65b9\u5f0f\u7684\u4e00\u4e2a\u4f18\u70b9\u662f\u6211\u4eec\u53ef\u4ee5\u4ece\u591a\u4e2a\u7ec8\u7aef\u8fdb\u5165\u5bb9\u5668":33,"ssh\u8fdb\u5165\u5bb9\u5668":33,"static":[22,27],"step\u51fd\u6570\u4e2d\u7684memori":43,"step\u51fd\u6570\u5185\u90e8\u53ef\u4ee5\u81ea\u7531\u7ec4\u5408paddlepaddle\u652f\u6301\u7684\u5404\u79cdlay":43,"subseq\u7684\u6bcf\u4e2a\u5143\u7d20\u662f\u4e00\u4e2a0\u5c42\u5e8f\u5217":40,"super":46,"swig\u652f\u6301\u7684\u8bed\u8a00\u6216\u8005\u89e3\u91ca\u5668\u6709\u5c40\u9650":26,"swig\u66b4\u9732\u7684\u63a5\u53e3\u4fdd\u7559\u4e86c":26,"swig\u751f\u6210\u7684\u4ee3\u7801\u4e0d\u80fd\u4fdd\u8bc1\u591a\u8bed\u8a00\u4ee3\u7801\u98ce\u683c\u7684\u4e00\u81f4\u6027":26,"swig\u76f4\u63a5\u8bfb\u53d6c":26,"swig\u9700\u8981\u5199\u4e00\u4e2ainterface\u6587\u4ef6":26,"swig_paddle\u4e2d\u7684\u9884\u6d4b\u63a5\u53e3\u7684\u53c2\u6570\u662f\u81ea\u5b9a\u4e49\u7684c":5,"switch":27,"t\u6307\u5b9a\u4e86\u751f\u6210\u7684\u955c\u50cf\u7684\u540d\u5b57":33,"tag\u4e3a":29,"test\u548cgen\u8fd9\u4e09\u4e2a\u6587\u4ef6\u5939\u662f\u56fa\u5b9a\u7684":70,"tests\u7684paddlepaddl":45,"tflops\u4e86":49,"toolchain\u76ee\u5f55\u751f\u6210\u4e00\u5957\u7f16\u8bd1\u5de5\u5177\u94fe":38,"trainer\u63a5\u6536\u4e09\u4e2a\u53c2\u6570":36,"true":[7,8,9,10,12,13,15,16,28,30,41,44,46,54,57,63,67,68,69,70],"true\u8868\u793a\u53cd\u5411\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":44,"try":[11,17,18,21,22,28,30,67],"type\u5b57\u6bb5\u5747\u4e0d\u5c3d\u76f8\u540c":27,"type\u662fon":67,"ubuntu\u5b89\u88c5\u5305\u7684\u529f\u80fd\u6b63\u786e\u6027":29,"ubuntu\u7684deb\u5b89\u88c5\u5305\u7b49":34,"ubuntu\u90e8\u7f72paddlepaddl":34,"update\u53c2\u6570\u65f6\u624d\u6709\u6548":52,"utf8\u7f16\u7801":61,"uts\u7b49linux":55,"v2\u4e4b\u540e\u7684\u4efb\u4f55\u4e00\u4e2a\u7248\u672c\u6765\u7f16\u8bd1\u8fd0\u884c":32,"vocab\u4e2d\u6bcf\u4e2a\u5207\u5206\u5355\u8bcd\u7684\u9884\u671f\u8bc4\u7ea7":69,"vocab\u505a\u4e3a\u5b57\u5178":69,"void":[22,26,27,46],"volume\u6302\u8f7d\u5230\u5bb9\u5668\u4e2d":55,"w0\u548c":63,"wbias\u662f\u9700\u8981\u5b66\u4e60\u7684\u53c2\u6570":63,"while":[7,13,28,57,70],"words\u5373\u4e3a\u8fd9\u4e2a\u6570\u636e\u4e2d\u7684\u5355\u5c42\u65f6\u95f4\u5e8f\u5217":41,"words\u662f\u539f\u59cb\u6570\u636e\u4e2d\u7684\u6bcf\u4e00\u53e5\u8bdd":41,"x\u4e0ey\u4e3a\u4e4b\u524d\u63cf\u8ff0\u7684\u8f93\u5165\u5c42":36,"x\u548cwindow":[33,38],"x\u548cwindows\u4e0a\u7684\u786c\u4ef6\u8d44\u6e90":33,"y\u8868\u793a\u8f93\u5165\u6570\u636e\u662f\u4e00\u4e2a\u7ef4\u5ea6\u4e3a1\u7684\u7a20\u5bc6\u5411\u91cf":36,"yaml\u6587\u4ef6\u4e2d\u5404\u4e2a\u5b57\u6bb5\u7684\u5177\u4f53\u542b\u4e49":57,"yaml\u6587\u4ef6\u63cf\u8ff0\u4e86\u8fd9\u6b21\u8bad\u7ec3\u4f7f\u7528\u7684docker\u955c\u50cf":57,"zero\u4e09\u79cd\u64cd\u4f5c":52,AGE:56,AWS:[20,55,58,59],And:[8,9,11,13,23,28],But:[9,10,30],EOS:9,For:[5,8,9,11,13,16,21,22,23,28,35,49],IDs:13,NFS:55,Not:[16,18],One:[8,10,15],PFS:24,QoS:56,TLS:[16,24],That:[9,13,28],The:[3,6,7,8,9,10,11,13,15,16,17,18,21,23,25,27,28,46,57,65,67,68,70],Their:[9,18],Then:[9,67],There:[8,9,13,15,16,18,22,23,25],Use:[16,25,28],Used:10,Using:[18,69],Will:[13,15],With:[9,10],YES:23,Yes:33,___embedding_0__:57,___embedding_1__:57,__file__:23,__init__:46,__list_to_map__:67,__main__:[5,63],__meta__:67,__mse_cost_0__:57,__name__:[5,63],__rnn_step__:44,_binari:17,_librari:17,_link:10,_proj:9,_recurrent_group:44,_res2_1_branch1_bn:63,_source_language_embed:[44,61],_target_language_embed:[44,61],_test:17,aaaaa:20,abc:9,abl:[9,16],about:[9,10,23,25,68,70],abov:[3,9,16,17,18,21,49],abs:10,acceler:19,accept:[13,16,28,68],access:[9,10,16,21,23],accord:[8,9,22],accordingli:9,accrod:10,accumul:[18,22],accuraci:8,acl:69,aclimdb:69,across:9,act:[9,10,30,31,36,41,44],act_typ:65,activ:[9,10,14,36,65],activi:10,actual:9,adadelta:[30,65],adagrad:65,adam:[16,22,65,69],adamoptim:[61,65,69,70],adapt:[8,11],add:[9,10,13,17,30,45,67],add_depend:17,add_execut:17,add_input:46,add_test:[17,46],add_to:9,add_unittest_without_exec:46,addbia:46,added:[8,45],addit:[9,10],address:18,addrow:46,addtolay:9,adversari:28,affect:9,afford:21,afi:3,aforement:17,after:[9,13,21,22,25,45],again:[16,18],age:[13,57,67],agg_level:[9,40,41],aggregatelevel:[40,41],ago:17,aircraft:70,airplan:62,aistat:9,alex:[9,69],alexnet_pass1:54,alexnet_pass2:54,algo_hrnn_demo:41,algorithm:[9,11,21,61,69,70],align:[9,10,13,70],all:[3,7,8,9,11,16,18,22,23,25,27,43,57,67,68,69],alloc:[7,23],allow:[16,22,65],allow_only_one_model_on_one_gpu:[51,52,54],almost:10,alreadi:[18,30,35],also:[9,10,13,16,17,28,49,65],alwai:[9,10,15,28,57],amazon:56,ambigu:28,amd64:55,amend:45,analysi:[68,69],android:38,android_abi:38,android_arm_neon:38,android_native_api_level:38,android_standalone_toolchain:38,androideabi:38,ani:[9,10,13,16,17,18,22,23,28],annot:68,annual:68,anoth:[9,16,23],anroid_arm_mod:38,anyth:[13,28,68],api:[13,15,16,17,22,23,24,29,49,57,60,69],api_shar:17,api_test:17,api_trainer_config:67,api_trainer_config_helpers_data_sourc:67,api_trainer_config_helpers_lay:[44,65],api_trainer_config_helpers_layers_context_project:67,api_trainer_config_helpers_layers_cos_sim:67,api_trainer_config_helpers_layers_data_lay:67,api_trainer_config_helpers_layers_embedding_lay:67,api_trainer_config_helpers_layers_fc_lay:67,api_trainer_config_helpers_layers_pooling_lay:67,apiserv:55,apivers:[55,56,57],apo:70,append:[3,15,28,41,44,57,67],append_gradient_machin:15,appleyard:49,appli:[9,10],applic:[49,56],approach:[9,65],apt:[35,62],arbitrari:9,arch:38,architectur:70,archiv:[13,26,27],arg:[3,8,9,10,13,30,31,57,62,63,65,67,68,69],arg_nam:9,argpars:57,args_ext:57,argument:[3,9,13,21,57,65,67,68],argumentpars:57,argv:63,arm64:38,arm:[38,39],arm_soft_fp_abi:38,armeabi:38,around:[3,9],arrai:[5,9,13,15,22,28,36,63],articl:45,arxiv:[9,10,69],ask:18,assert:5,assign:[9,21],associ:68,assum:9,astyp:28,asyc:18,async:[18,51],async_count:[51,52],async_lagged_grad_discard_ratio:52,async_lagged_ratio_default:[51,52],async_lagged_ratio_min:[51,52],asynchron:18,atla:32,atlas_root:32,attenion:10,attent:[9,10,70],attr1:9,attr2:9,attr:[7,9,10],attribut:[9,10,14],auc:51,auc_evalu:8,author:[24,55],authorized_kei:50,auto:[26,46,49],automat:[9,16,22,70],automaticli:9,automobil:62,avail:[18,22],averag:[8,9,12,21,68],average_test_period:[51,52,68],averagepool:9,avg:[40,49,65],avgcost:[65,67,69,70],avgpool:[9,65],avoid:[18,49],avx:33,await:56,awar:[16,18],aws:24,azur:55,b2t:61,b363:56,b8561f5c79193550d64fa47418a9e67ebdd71546186e840f88de5026b8097465:56,back:18,backward:[6,9,10,22,46],backward_first:44,backwardactiv:46,bag:65,baidu:[9,56],balasubramanyan:69,bank:68,bardward:10,bare:[55,56],barrierstatset:49,base:[9,10,12,13,16,21],baseactiv:9,baseev:15,basematrix:46,basenam:8,basepoolingtyp:[9,10],basestr:[7,8,9,10,12,15,67],bash:[33,45,47,56,57],basic:[9,15],batch:[9,10,11,13,15,16,18,20,31,36,50,56,57,62,63,65,67,69,70],batch_0:63,batch_id:[15,36],batch_norm:10,batch_norm_typ:9,batch_read:[20,28],batch_siz:[13,30,31,36,50,61,62,65,67,69,70],batchsiz:[9,46],bazel:17,bbbbb:20,bcd:9,bcebo:13,bcm2708:39,beam:[9,44,70],beam_gen:[9,44],beam_search:[15,43,44],beam_siz:[9,44,51,52,54],beamsiz:70,becaus:[9,13,16,17,18,22,28,41],been:[21,68],befor:[9,10,18,25,28,30,67],begin:[8,9,22,25],beginiter:[15,16],beginn:44,beginpass:[15,16],begintrain:16,being:[18,28],belong:9,below:[9,13,18,22,28],benefit:[10,23],bengio:9,bertolami:69,besid:[9,13],best:[9,67],best_model_path:68,besteffort:56,beta1:11,beta2:11,beta:63,better:[9,10,17],between:[9,11,17,18,22,27,70],bgr:63,bi_gru:10,bi_lstm:10,bia:[9,10,11,46,63],bias:9,bias_attr:[9,10,30,31,41,44],bias_param:30,bias_param_attr:10,biases_:46,biasparameter_:46,biassiz:46,bidi:56,bidirect:[10,68,69],bidirectional_lstm_net:69,bigger:18,bilinear:9,bilinear_interpol:9,bilinearfwdbwd:49,bin:[33,35,50,55,56,57,67],binari:[8,9,13,17,23,65],bird:62,bitext:70,blank:9,bleu:70,block:[9,19,21,22],block_i:9,block_x:9,bn_attr:10,bn_bias_attr:10,bn_param_attr:10,bollen:69,book:13,bool:[7,8,9,10,12,13,46,52,54,65,67,69],boot:[9,43,44],boot_bia:9,boot_bias_active_typ:9,boot_lay:[9,41,44],boot_with_const_id:9,bos_id:[9,44],both:[6,7,9,10,16,17,18],bottom:65,bow:65,branch:[9,16,29,45],brendan:69,brief:22,broadcast:18,broken:45,browser:33,bryan:69,buf_siz:13,buffer:[13,22,28],buffered_read:28,bug:45,build:[13,17,23,33,38,39,45,57,58,59,70],build_dict:13,build_doc:47,built:[17,49],bunk:69,c11:26,c703c041:45,c99:27,cach:[30,65,67,68],cache_pass_in_mem:[3,30,65,67,68],cachetyp:[3,30,65,67,68],calc_batch_s:[3,68],calcul:[8,9,10,11,18,22],call:[9,10,16,21,22,23,49,57,65],callabl:[9,13],callback:46,calrnn:41,caltech:62,can:[7,8,9,10,13,16,18,21,23,28,49,65],can_over_batch_s:[3,68],candid:9,cannot:30,capi:26,capi_prvi:27,caption:70,card:50,care:[10,23,28],cat:[33,57,62,63,69],categori:[9,13,18,65],categorig:13,categoryfil:56,caus:[18,25],cc_:17,cc_binari:17,cc_test:17,ccb2_pc30:70,cde:9,cdn:13,ceil:9,ceil_mod:9,cell:[9,10],ceph:[20,55],cephf:[20,23,24],certif:[16,24,55],cffi:26,cfg:56,cgo:26,chain:13,challeng:18,chanc:16,chang:[9,13,17,21,23,28,45,69],channel:[9,49],char_bas:67,check:[3,13,30,35,45,46,52],check_align:13,check_eq:46,check_fail_continu:3,check_l:46,check_sparse_distribution_batch:[51,52],check_sparse_distribution_in_pserv:[51,52],check_sparse_distribution_ratio:[51,52],check_sparse_distribution_unbalance_degre:[51,52],checkgrad:52,checkgrad_ep:52,checkout:45,checksum:24,chunk:[21,24],chunk_evalu:8,chunk_schem:8,chunktyp:8,cifar:62,cifar_vgg_model:62,claimnam:57,clang:[26,33,45],class1:69,class2:69,class_dim:69,classic:9,classif:[9,65,69,70],classifi:63,classification_cost:[30,41,62,65],classification_error_evalu:[8,65,69,70],classification_error_printer_evalu:8,clean:[17,30,45],clear:17,client:55,clip:[7,52,65],clock:9,clone:[33,39],close:[3,28,45],cloud:[18,23,24,25],cluster:[16,18,22,50,55,57],cluster_train:50,cmake:[27,30,32,38,39,45,47,49],cmake_build_typ:[38,39],cmake_c:[38,39],cmake_system_nam:[38,39],cmakelist:[17,46],cmatrix:[26,27],cna:9,cnn:[9,56,65],code:[3,5,13,16,17,28,33,46,56,67],coded_stream:30,codedinputstream:30,coeff:9,coeffici:9,collabor:18,collect:[9,13,15],collectbia:46,color:[62,63],colour:13,column:[8,9,28],column_sum_evalu:8,com:[9,10,13,33,35,38,39,45,55,56,63],combin:[9,10,13,15,67],command:[17,23,25,46,54,56,57,58,59],commandlin:[49,57],comment:[10,17,41,57,65],commit:56,common:20,common_util:[50,67],commonli:25,commun:[18,22],compil:[17,35,38,39],complet:[9,10,13,15,18,21,22,24,56,57],complex:[10,28],complic:9,compos:[13,16],composenotalign:13,compress:21,comput:[9,10,16,18,68,69],conat:9,concat:70,concat_lay:44,concaten:10,concept:16,concern:16,concurr:18,condit:[9,21,56],conf:[5,9,30,41,50,57,61,63,70],conf_paddle_gradient_num:57,conf_paddle_n:57,conf_paddle_port:57,conf_paddle_ports_num:57,conf_paddle_ports_num_spars:57,config:[7,9,10,20,25,31,36,46,50,51,52,54,55,56,57,62,65,67,68,69,70],config_:52,config_arg:[51,52,54,63,65,68,69],config_bas:[8,9,10,15],config_fil:68,config_gener:[50,67],config_lay:46,config_pars:[5,46],config_proto:22,configprotostr:30,configur:[9,22,23,46,61,63,70],confirm:25,conflict:45,confront:70,conll05st:68,conll:[13,68],connect:[10,23,56,65],connectionist:[9,69],connor:69,consequ:[9,10],consid:[8,9,11],consider:10,consist:[9,13,21,28],construct:[16,67],construct_featur:67,consum:18,contact:18,contain:[3,8,9,10,12,13,15,16,21,56,57,65],content:[22,25],content_len:22,context:[3,9,10,13,44,55],context_attr:10,context_len:[9,10,65,67],context_proj_nam:10,context_proj_param_attr:10,context_project:[10,67],context_start:[9,10,65],continu:18,contrast:9,control:[7,56,70],conv:10,conv_act:10,conv_attr:10,conv_batchnorm_drop_r:10,conv_bias_attr:10,conv_filter_s:10,conv_num_filt:10,conv_op:9,conv_pad:10,conv_param_attr:10,conv_strid:10,conv_with_batchnorm:10,conveni:16,convent:22,convert:[3,5,13,20,28,65,67],convlay:9,convolut:[9,10],convoper:9,convtran:9,convtranslay:9,cool:45,coordin:18,copi:[15,16,21,25,67],core:[7,27],corpora:70,corpu:[13,68],correct:[8,9],correctli:[8,13],correspoind:16,correspond:16,corss_entropi:16,cos:9,cos_sim:67,cosin:9,cost:[11,15,16,31,36,67,69,70],cost_id:9,could:[9,13,15,16,21,28],couldn:35,count:[18,23,28,49,52,54,56,67,68,69,70],counter:[18,21],cours:23,cpickl:67,cpp:[26,27,30,41,46,49,57,65,67,70],cpu:[3,7,9,23,29,33,35,49,54,56,57,68],cpuinfo:33,cpusparsematrix:27,crash:[18,49],creat:[7,9,13,15,16,18,24,25,36,45,46,56,57],create_bias_paramet:46,create_input_paramet:46,createfromconfigproto:5,creator:[13,20],credenti:25,crf:68,critic:69,crlf:45,crop:63,crop_siz:63,cross:[9,30,38,39,65],cross_entropi:[9,16],cross_entropy_with_selfnorm:9,crt:[24,55],csc:46,cslm:70,csr:46,ctc:8,ctc_error_evalu:8,ctest:[33,45],ctrl:[50,67],ctx:68,ctx_0:68,ctx_0_slot:68,ctx_n1:68,ctx_n1_slot:68,ctx_n2:68,ctx_n2_slot:68,ctx_p1:68,ctx_p1_slot:68,ctx_p2:68,ctx_p2_slot:68,cub:62,cuda:[17,35,49,50,52],cuda_dir:[51,52],cuda_so:[30,33],cuda_visible_devic:30,cudaconfigurecal:49,cudadevicegetattribut:49,cudaeventcr:49,cudaeventcreatewithflag:49,cudafre:49,cudagetdevic:49,cudagetdevicecount:49,cudagetdeviceproperti:49,cudagetlasterror:49,cudahostalloc:49,cudalaunch:49,cudamalloc:49,cudamemcpi:49,cudaprofilerstart:49,cudaprofilerstop:49,cudaprofilestop:49,cudaruntimegetvers:49,cudasetdevic:49,cudasetupargu:49,cudastreamcr:49,cudastreamcreatewithflag:49,cudastreamsynchron:49,cudeviceget:49,cudevicegetattribut:49,cudevicegetcount:49,cudevicegetnam:49,cudevicetotalmem:49,cudnn:[9,12,17],cudnn_batch_norm:9,cudnn_conv:9,cudnn_conv_workspace_limit_in_mb:[51,52],cudnn_convt:9,cudnn_dir:[51,52],cudnnv5:32,cudrivergetvers:49,cuinit:49,cumul:9,curl:55,current:[3,9,18,55,65],current_word:44,currentcost:[65,67,69,70],currentev:[65,67,69,70],curv:16,custom:[16,23],custom_batch_read:28,cutoff:13,cxx_compil:[38,39],cxx_flags_minsizerel:[38,39],cycl:18,cyclic:9,cython:26,dalla:3,dan:68,dandroid_abi:38,dandroid_arm_mod:38,dandroid_arm_neon:38,dandroid_standalone_toolchain:38,darwin:55,dat:[13,20,50,67],data:[3,8,10,11,15,16,20,21,24,30,35,36,41,50,51,56,57,58,61,62,63,65,67,68,69,70],data_config:5,data_dir:[61,62,69,70],data_feed:13,data_fil:31,data_initialz:65,data_lay:[3,30,31,41,44,62,65,67,68],data_nam:13,data_read:[13,28],data_reader_creator_random_imag:28,data_typ:[9,13,36],databas:13,datacent:[20,25],datacenter1:20,datacenter2:20,datacenter_1:20,datacenter_2:20,datacenter_nam:20,datadim:9,datalay:9,dataprovid:[2,30,31,44,50,57,65,67,68],dataprovider_:65,dataprovider_bow:65,dataprovider_emb:65,dataproviderconvert:5,datasci:9,dataset:[20,23,28,36,63,65,66,69,70],date:68,db_lstm:68,dcmake_install_prefix:[38,39],dcmake_system_nam:[38,39],dcudnn_root:32,dead:18,deb:[35,45],debug:25,decai:11,decent:21,decid:[16,28],declar:9,decod:[9,10,43,44,70],decoder_boot:44,decoder_group_nam:44,decoder_input:44,decoder_mem:44,decoder_prev:10,decoder_s:44,decoder_st:[10,44],deconv:9,deconvolut:9,decor:[3,13],deep:[9,49,62,63],deer:62,def:[3,5,9,13,16,20,23,28,30,31,36,41,44,46,57,63,65,67,68],defalut:9,default_decor:57,default_devic:54,default_valu:54,defin:[3,9,10,13,16,17,18,28,30,36,65,67],define_py_data_sources2:[3,30,31,62,63,65,67],defini:70,definit:[13,18,21,61],degre:9,del:67,delar:65,delet:[18,23,24,45],delimit:[8,67],demand:18,demo:[5,9,13,44,50,56,58,61,62,63,65,67,69,70],dens:[9,13,22,67],dense_vector:[3,5,9,13,31,36,67],dense_vector_sequ:13,dep:17,depend:[17,18,23],deriv:[6,16],descent:[9,18],describ:[16,17,21,56,65],descript:[17,57],deseri:15,design:[9,13,26],desir:[18,56],destin:[22,25],detail:[7,9,10,11,21,23,25],detect:[8,45],determin:[9,13],dev:[30,33,45,62,67,70],develop:[17,29,45,70],deviat:7,devic:[7,30,33,54],deviceid:54,devid:9,dez:69,dict:[3,13,15,30,41,57,65,67,69,70],dict_dim:[30,41,69],dict_fil:[8,41,44,65,68],dict_siz:13,dictionai:65,dictionari:[3,8,9,13,15,16,30,65,70],dictrionari:65,dictsiz:70,diff:45,differ:[8,9,17,18,22],digit:9,dim:[13,46,61,69],dimens:[6,9,12,13,30,65],dimes:9,din:67,dir:[38,50,57,63,67,68,69,70],direct:[9,10],directli:[10,23],directori:[17,20,24,25,45,49,56],disabl:30,discard:[13,18,21,45,52],discount:9,discov:18,discuss:[16,21,22],displai:[23,25],disput:70,dist_train:[16,23],distanc:8,distinguish:17,distribut:[9,21,22,52,58,59],distribute_test:[51,52],disucss:16,divid:11,diy_beam_search_prob_so:[51,52],dmkl_root:32,do_forward_backward:28,doc:[5,10,13,33,47,57],doc_cn:47,docker:[29,30,33,45,56,57,58,59],docker_build:16,docker_push:16,dockerfil:[33,45,57],dockerhub:33,document:[10,24],documentari:3,doe:[10,18,21,22,23,28],doesn:[7,9,13,16,28],dog:[62,63],don:[10,16,17,28],done:[9,10,17,18,21,22,45,49,57],dot:70,dot_period:[52,54,57,62,67,69,70],dotmuloper:9,dotmulproject:9,doubl:52,down:49,download:[13,18,24,56],download_cifar:62,doxygen:45,dpkg:35,dpython_execut:30,dpython_include_dir:30,dpython_librari:30,drop_rat:7,dropout:[7,9],dropout_r:10,drpi_arm_neon:39,drpi_toolchain:39,drwxr:56,dso_handl:35,dst:22,dtoh:49,dtype:[5,31,63],dubai:70,due:[21,67],dummi:21,durat:21,dure:[3,9,18,21,23,65,70],dwith_c_api:[27,38,39],dwith_gpu:[32,39],dwith_profil:49,dwith_python:[27,39],dwith_swig_pi:[27,38,39],dwith_tim:49,dynam:[3,22,27,28],dynamic_cast:46,each:[3,8,9,12,13,15,17,18,21,22,23,28,65,67],each_feature_vector:6,each_meta:67,each_pixel_str:3,each_sequ:[9,40,41],each_time_step_output:6,each_timestep:[9,40],each_word:3,eaqual:9,eas:[13,28],easi:28,easier:[16,28],easili:[16,28],ec2:55,echo:[30,33,67,69],edit:8,edu:[13,56,62],efg:9,either:[9,13,15,16,65],electron:56,elem_dim:9,element:[8,9,10,13,15,21,28],element_typ:22,elif:[16,67],els:[9,16,23,33,41,46,63,65,67],emailweixu:17,emb1:41,emb2:41,emb:[30,41,56,65],emb_group:41,emb_sum:30,embed:[16,61,67,69],embedding_lay:[30,41,44,65,67],embedding_nam:[9,44],embedding_s:[9,44],empir:9,emplace_back:46,empti:[8,13,18,31],enabl:[7,17,21,49],enable_grad_shar:[51,52],enable_parallel_vector:52,enc_proj:[10,44],enc_seq:10,enc_vec:44,encapsul:22,encod:[10,21,41,70],encoded_proj:[10,44],encoded_sequ:[10,44],encoded_vector:44,encoder1:41,encoder1_expand:41,encoder1_rep:41,encoder2:41,encoder2_rep:41,encoder_last:9,encoder_proj:44,encoder_s:44,end:[8,9,28,44,45,68,69],end_pass:16,enditer:[15,16,36],endpass:[15,16],endpoint:20,endtrain:16,engin:23,english:[9,70],ensembl:10,ensur:18,entir:[9,10,22],entri:[13,21,23],entropi:[9,65],entry_point:23,enumer:[6,30,65,67],enumerate_data_types_of_data_lay:13,env:[30,57],environ:[16,30,49,56],eol:45,eos_id:[9,44],epsilon:11,equal:[9,10,18,41],equat:[9,10,11],equival:[9,16],error:[7,8,9,11,16,21,25,30,52,65,67,69,70],error_clipping_threshold:7,especi:10,essenc:16,essenti:[9,16],estim:[9,16],eta:56,etc:[13,28,70],etcd:[18,21],eth0:[50,57],eval:[8,65,67,69,70],eval_bleu:70,evalu:[9,14,15,49,50,67,69,70],evaluate_pass:69,even:[16,28],evenli:22,event:[36,56],event_handl:[15,16,36],everi:[8,9,10,13,16,18,21,22],exactli:[8,9,10],exampl:[9,10,11,13,15,23,25,28,63,65],exc_path:30,exceed:9,except:[13,67],excluded_chunk_typ:8,exconv:9,exconvt:9,exdb:13,exe:55,exec:33,execut:[17,18,21,23],exist:[16,18,25,28,69],exit:[22,25,56],expand:41,expand_a:[9,40,41],expand_lay:41,expand_level:[9,40],expandconvlay:9,expandlevel:40,expect:9,expert:17,expir:18,explain:[8,18],explan:[9,23],explicit:46,explicitli:16,explor:9,exponenti:6,express:16,extend:67,extent:27,extern:[17,26,27],external_librari:17,extra:[7,9,10],extra_input:9,extra_lay:15,extraattr:[7,54],extraattribut:[9,10],extraattributenon:9,extract:[9,61,63,68],extract_fea_c:63,extract_fea_pi:63,extralayerattribut:[7,9],extrapaddl:10,extrem:9,f1205:30,f120da72:56,fa0wx:56,fabric:50,face:17,facotr:9,factor:[7,9,11],factori:26,fail:[18,21,30,35,52,56],failur:[18,22],fake_imag:28,falloc:24,fals:[7,8,9,10,11,13,28,30,31,36,41,44,46,54,56,61,65,67,68,69,70],false_label:28,false_read:28,faq:60,fast:[9,21,49],faster:[9,10,18],fault:21,fbd1f2bb71f4:56,fc1:[46,54],fc2:54,fc3:54,fc4:54,fc_act:10,fc_attr:10,fc_bias_attr:10,fc_layer:[9,30,31,41,54,65,67],fc_mat:15,fc_name:10,fc_param:30,fc_param_attr:10,fclayer:46,fdata:[41,68],fea:63,fea_output:63,feat:69,featur:[3,6,9,13,45,63,65,67,68],feature_a:30,feature_b:30,feature_map:67,feed:[10,13,15,16,36],feeder:13,fernan:69,festiv:3,fetch:[13,18,30,45],few:[17,18,28],fewer:9,fg0:9,field1:15,field2:15,field:[9,15,67],figur:[16,17,61,70],file1:70,file2:70,file:[3,8,9,13,15,16,17,18,20,21,22,23,24,25,27,28,45,63,65,66,67,69,70],file_list:3,file_nam:[31,41,63,65,68],filenam:[3,20,30,67],fileoffset:24,filer:9,filesystem:[23,24],fill:[9,18,21,65],filter:9,filter_s:[9,10],filter_size_i:9,find:[9,11,18,35],fine:[7,21],finish:[18,21,23,56],first:[9,13,16,18,21,23,25,65,67],first_seq:44,firstn:13,firstseen:56,fit:13,fix:[7,26,45],flag:13,flexiabl:28,flexibl:[9,10,16,22],flight:70,float32:[5,13,28,31,63],floor:9,flow:29,fly:65,folder:[20,23,25,70],follow:[8,9,10,11,13,16,17,18,21,23,28,58,59,67],forbid:16,force_load:26,forget:[11,16],form:[10,11],format:[8,21,22,45,46],former:[16,17],formula:[9,10],formular:9,forward:[6,10,22,46],forwardactiv:46,forwardtest:5,found:9,frame:8,framework:[16,65],free:13,french:70,frequenc:13,frequent:[21,28],frog:62,from:[3,5,9,10,13,15,17,18,20,21,22,25,28,30,31,33,43,49,56,61,62,65,67,68,69,70],from_sequ:[9,40],from_timestep:[9,40],fromfil:[28,31,63],fulfil:49,full:[9,18],full_matrix_project:[10,41,44],fulli:65,fullmatrixproject:9,fully_matrix_project:10,fullyconnectedlay:46,func:[3,13,21],further:9,fusion:67,gain:9,gamma:63,gan:16,gangliao:17,gate:[9,10],gate_act:[9,10,41],gate_recurr:9,gather:[9,67],gauss:7,gcc:[26,33,38,39],gce:55,gcepersistentdisk:55,gdebi:35,gen:[9,70],gen_conf:70,gen_data:70,gen_result:70,gen_trans_fil:44,gender:[13,57,67],gener:[3,8,9,10,13,15,16,17,18,20,21,22,28,49,54,57,61,65,70],generated_word_embed:9,generatedinput:[9,43,44],genr:[57,67],gereat:8,get:[3,9,10,13,15,17,21,22,23,24,35,46,56,62,65,67,68,69],get_batch_s:68,get_best_pass:69,get_config_arg:[54,65,67,69],get_data:[56,65,68],get_dict:13,get_embed:13,get_imdb:69,get_input_lay:46,get_model:63,get_movie_title_dict:13,get_output_attr:10,get_sample_from_lin:30,get_shap:15,get_word_dict:13,getbatchs:46,getenv:[16,23,57],gethostbynam:57,gethostnam:57,getidmap:57,getinput:46,getinputgrad:46,getinputvalu:46,getoutputgrad:46,getoutputvalu:46,getparameterptr:46,getpodlist:57,getsiz:46,gettask:21,gettranspos:46,getw:46,getweight:46,getwgrad:46,gildea:68,gist:10,git:[29,30,33,39,45],github:[9,10,33,35,39,45,63],give:[3,18],given:[13,15,22,28,65],global:[7,16,17,18,49,67],global_learning_r:7,globalstat:49,globalstatinfo:49,globe:3,glusterf:55,gnueabihf:39,go_librari:17,go_test:17,gob:21,godoc:26,goe:[9,10,18],good:[9,28],goodfellow13:9,googl:[16,30,38],googleapi:55,gpu:[7,9,12,23,29,30,33,35,49,54,63,68,69,70],gpu_id:[30,52,54],gpugpu_id:51,grab:18,grad:[22,52],grad_share_block_num:[51,52],gradient:[7,8,9,11,15,18,21,52,57,65],gradient_clipping_threshold:[7,65,69],gradient_machin:[15,27],gradient_printer_evalu:8,gradientmachin:[5,15,27,57,67,70],gram:61,graph:[9,15,17,18],grave:69,greater:9,grep:[33,69],groudtruth:44,ground:[8,9,70],group:[10,21],group_id:67,group_input:[41,44],grouplen:[13,66],gru:[9,65],gru_attr:10,gru_bias_attr:10,gru_decod:44,gru_decoder_with_attent:44,gru_encoder_decod:[61,70],gru_memori:10,gru_siz:65,gru_step:[10,44],gru_step_lay:44,grumemori:[10,44],gserver:[9,46],gsizex:49,guid:[24,56],gur_group:10,gzip:[21,56],hadoop:16,handi:17,handl:[16,23,28],handler:15,handwrit:69,happen:21,harvest:65,has:[9,10,11,13,16,18,21,22,49,65,68],has_kei:15,hassubseq:41,have:[8,9,10,13,16,17,18,21,22,23,28],hdf:20,head:69,header:[22,27,31,63,67],height:[9,13,26,28,46],held:18,hello:16,help:[5,25,45],helper:[9,10],here:[7,9,10,13,16,17,18,25,28],heurist:[9,70],hidden:[9,10,30,67],hidden_a:30,hidden_b:30,hidden_dim:41,hidden_s:[10,67],hierach:43,hierarch:[9,41],high:7,highest:13,highli:13,him:16,hint:5,hl_dso_load:35,hl_get_sync_flag:46,hold:[16,18,21],home:[20,25,50,56,57],honor:21,hook2:41,hook:[41,67,68],horizont:9,hors:62,horst:69,host:[23,50,56],host_c:[38,39],hostnetwork:57,hostpath:[55,56,57],hot:65,hous:[3,13],how:[7,9,16,18,21,25],howardjohnson:41,howev:[10,28],howto:57,hpp:26,html:[13,62],htod:49,http:[9,10,13,23,33,35,38,39,45,55,56,62,63,66,70],huber:9,huge:9,huina:69,hyper:9,hyperplan:13,i0601:67,i0706:70,i0719:70,i1116:57,i1117:49,ib0:50,ics:13,icwsm:69,id_input:[8,44],idea:[9,17,28],identityoffsetproject:9,identityproject:9,idmap:57,ids:[8,9,30,65,67],idx:[21,46],ieee:69,ies:25,ignor:[3,8,9],ijcnlp:69,illustr:[18,22],ilsvrc:63,imag:[12,13,16,28,45,56,57,58,59,62,63,70],image_a:28,image_b:28,image_classif:62,image_fil:28,image_lay:28,image_list_provid:63,image_nam:16,image_path:28,image_provid:62,image_reader_cr:28,image_s:63,imagenet:20,imagepullpolici:57,imageri:9,images_reader_cr:28,imdber:69,img:[3,9,62],img_conv:10,img_pool:10,img_siz:62,imgsiz:49,imgsizei:49,imgsizex:49,immutable_paramet:16,implement:[9,10,11,13,21,22,23,26,27],impli:17,importerror:67,inarg:5,inc_path:30,includ:[9,10,13,16,17,22,23,26,27,38,39,45,49],incorrect:9,increas:[18,21,30],incupd:46,inde:[13,28],independ:[9,22],index:[8,9,12,13,15,18,21,41,67],indexslot:9,indic:[8,9,22],individu:18,industri:18,infer:[16,18,26],infiniband:50,info:[8,9,13,36,41,46,50,57],inform:[13,23,25],ininst:16,init:[7,36,46,57,67,68],init_hook:[41,65,67,68],init_model_path:[51,52,54,61,65,68],initi:[3,7,9,13,17,21,52,65],initial_max:[7,30],initial_mean:[7,9,30],initial_min:[7,30],initial_std:[7,9,30],initpaddl:5,inlcud:10,inner:[30,41],inner_:41,inner_mem:41,inner_param_attr:10,inner_rnn_output:41,inner_rnn_st:41,inner_rnn_state_:41,inner_step:41,inner_step_impl:41,input1:[9,10],input2:9,input:[3,6,8,9,10,12,13,15,28,30,31,36,40,41,43,44,46,54,57,61,62,65,67,68,70],input_data:46,input_data_target:46,input_featur:6,input_fil:[31,68],input_hassub_sequence_data:46,input_id:9,input_imag:[10,62],input_index:46,input_label:46,input_lay:46,input_nam:16,input_sequence_data:46,input_sequence_label:46,input_sparse_float_value_data:46,input_sparse_non_value_data:46,input_t:46,input_typ:[30,31,41,44,65,67],inputdef:46,inputlayers_:46,inputtyp:13,insert:45,insid:[8,9,18,28],instal:[23,30,35,38,39,45,50,56,62,67],instanc:[9,18,20],instanti:18,instead:[9,10,12,17,23,28],int32:52,int64:24,integ:[3,8,9,13,21,23,26,36,65],integer_sequ:30,integer_valu:[3,13,30,36,41,65],integer_value_sequ:[3,13,41,44,65,68],integer_value_sub_sequ:41,integr:68,inter:9,intercept:9,interfac:[7,9,10,21,23,25,50],intergr:9,intermedi:25,intern:[9,10,13,15],internet:18,interpret:8,introduc:18,invalid:28,invoc:17,invok:[3,9,15,49,67],iob:8,ioe:8,ip_str:57,ipc:55,ips:57,ipt:[9,30,41,44],ipython:16,is_gener:[9,61,70],is_kei:67,is_loc:15,is_predict:[65,67,69],is_seq:[9,44,67],is_sequ:67,is_stat:7,is_test:[63,68,69],is_train:3,isinst:[5,36],ispodallrun:57,isspars:46,issue_numb:45,item:[9,13,15,28,57],iter:[3,9,10,11,13,15,16,18,28],its:[3,8,9,10,16,18,21,35,49],itself:[10,18],java:26,jeremi:49,jie:[68,69],jmlr:9,job:[13,50,51,52,54,55,57,63,65,68,69,70],job_dispatch_packag:50,job_id:13,job_mod:61,job_nam:[23,57],job_namespac:57,job_path:57,job_path_output:57,job_workspac:50,jobnam:57,jobpath:57,jobselector:57,jobserv:23,johan:69,join:[18,41],joint:70,jointli:[10,70],journal:[68,69],jpg:63,json:[50,56,67],jth:10,jupyt:[23,33],just:[6,8,9,10,13,21,22],jypyt:16,k8s:57,k8s_data:57,k8s_job:16,k8s_token:16,k8s_train:57,k8s_user:16,kafka:20,kaim:9,kaimingh:63,kebilinearinterpbw:49,kebilinearinterpfw:49,keep:[9,18],kei:[3,13,15,18,20,21,24,45,49,55,57,67],kernel:9,key1:52,key2:52,keyword:57,kill:18,kind:[16,18,55,56,57],kingsburi:68,know:[10,16,21],kriz:[13,62],ksimonyan:10,kube:55,kube_cluster_tl:16,kube_ctrl_start_job:16,kube_list_containers_in_job_and_return_current_containers_rank:16,kubeadm:55,kubectl:[55,56,57],kuberent:18,kubernet:[16,18,48,50,57,58,59],kubernetes_service_host:16,kwarg:[3,8,9,10,11,13,41,65,67,68],l1_rate:7,l2_rate:7,l2regular:[62,65,69],label:[3,8,9,11,13,15,28,30,31,36,41,56,62,63,65,67,68,69],label_dict:68,label_dim:[9,41,65],label_fil:[28,68],label_lay:28,label_list:68,label_path:28,label_slot:68,labeledbow:69,labelselector:57,lag:52,lake:3,lambdacost:9,lambdarank:9,languag:[9,13,61],larg:[12,13,45,70],larger:[7,8,9],last:[8,9,10,40,41],last_seq:41,last_time_step_output:9,lastseen:56,later:[17,65],latest:[9,18,30,56,57],launcher:16,layer1:[9,10,40],layer2:[9,40],layer3:9,layer:[5,7,8,10,12,13,14,15,28,36,40,43,44,46,63,65,67,68],layer_0:46,layer_attr:[9,44,54],layer_num:[54,63],layer_s:9,layer_typ:9,layerbas:46,layerconfig:46,layergradutil:46,layermap:46,layeroutout:9,layeroutput:[9,67],layers_test:30,lbl:[8,62],ld_library_path:[35,50],learn:[7,8,9,10,11,13,16,22,28,33,49,62,63,68,69,70],learnabl:9,learning_method:[31,61,62,65,67,69,70],learning_r:[7,22,30,31,61,62,65,67,69,70],leas:18,least:[8,9,18],lecun:13,left:9,leman:70,len:[3,9,22,24,41,44,46,57,65,67,68],length:[9,10,13,22,56],less:[9,16],less_than:16,let02:56,let:[9,16],level:[7,9,43],lgtest:17,lgtest_main:17,lib64:[30,33,35,50,52],lib:[27,32,35,38,39],lib_path:30,libapi:17,libari:27,libcuda:[30,33],libjpeg:62,libnvidia:[30,33],libpaddl:[26,27,45],libpaddle_capi:27,libpaddle_gserv:27,libpaddle_math:27,libprotobuf:30,librari:[9,17,27,35,50,52],life:18,like:[8,9,13,17,18,23,28,63],limit:[9,13,30,49],linaro:39,line:[3,8,13,17,23,25,30,41,45,54,65,67,68],line_count:30,linear:[9,36],linearactiv:[9,31],linguist:68,link:[9,10,17,24,25,43,69],linux:[24,33,38,39,55],lipeng:61,lipton:69,list:[2,3,8,9,13,15,16,21,23,25,31,50,54,62,63,65,67,68,69,70],littl:22,lium:70,live:18,liwicki:69,load:[9,16,18,31,57,63,67,68,69,70],load_data_arg:5,load_featur:63,load_feature_c:63,load_feature_pi:63,load_missing_parameter_strategi:[51,52,54,61,68],loadparamet:5,loadsave_parameters_in_pserv:[51,52],local:[7,18,32,35,47,50,51,52,57],localhost:[33,55],localip:57,localpath:25,lock:[18,21,22],log:[21,25,30,46,50,52,56,57,62,67,68,69,70],log_barrier_abstract:[51,52],log_barrier_lowest_nod:[51,52],log_barrier_show_log:[51,52],log_clip:[51,52],log_error_clip:[51,52],log_period:[52,54,56,57,62,65,67,68,69,70],log_period_serv:[51,52],logarithm:6,logger:[3,41],logist:65,look:[3,23,65],loop:28,loss:[9,65],low:9,lpaddle_capi_shar:27,lpaddle_capi_whol:27,lst:67,lstm:[9,41,44,56,65],lstm_attr:10,lstm_bias_attr:10,lstm_cell_attr:10,lstm_group:[10,41],lstm_group_input:41,lstm_input:41,lstm_last:41,lstm_nest_group:41,lstm_output:41,lstm_size:65,lstm_step:10,lstmemori:[10,41,44],lstmemory_group:[9,41],ltr:9,mac:[27,33,38],machan:10,machin:[9,10,13,15,43,69,70],made:[18,22],mai:[9,28],main:5,maintain:[9,21],majel:17,major:70,make:[3,16,17,18,21,22,28,35,38,39,45,46,49,69],man:24,manag:[18,22,25],mandarin:9,mani:[9,10,21],manufactur:70,mao:69,map:[9,13,15,16,21,36,67],map_read:13,mapreduc:16,marcu:69,mark:68,mark_slot:68,market:69,martha:68,mask:[7,9],master:[16,29,55,69],mat:[26,27],mat_param_attr:10,math:[10,26,46,49],matirx:9,matplotlib:62,matrix:[8,9,10,13,15,26,27,46],matrixptr:46,matrixtyp:27,max:[7,9,13,40,49,54,67],max_id:[9,15],max_job_id:13,max_length:[9,44],max_movie_id:13,max_sort_s:9,max_user_id:13,maxframe_printer_evalu:8,maxid:8,maxid_printer_evalu:8,maxim:9,maximum:[8,13,22],maxinum:12,maxpool:9,mayb:[9,10],md5:[13,19],mean:[7,8,9,10,11,12,13,15,17,28,30,52,63,65,67],mean_img_s:62,mean_meta:63,mean_meta_224:63,mean_valu:63,mechan:[9,10],meet:68,mem:[9,23,41],member:[13,16],memcpi:49,memori:[10,21,23,44,49,56,65],memory_nam:9,memory_threshold_on_load_data:[51,52],mention:21,mere:10,merg:[22,45],mergedict:[61,70],messag:[45,56],meta:[50,62,63,67],meta_config:[50,67],meta_fil:67,meta_gener:[50,67],meta_path:62,meta_to_head:67,metadata:[24,56,57],metal:55,metaplotlib:16,method:[3,9,11,15,67,70],mfs:57,might:[9,17],million:13,min:[7,49,54,67],min_pool_s:[3,30],min_word_freq:13,mini:[9,13,15,18],mini_batch:28,minibatch:9,minibatch_data:13,minikub:55,minim:11,minimum:9,minsizerel:[38,39],minut:18,mip:38,mislead:22,miss:68,mix:10,mixed_attr:10,mixed_bias_attr:10,mixed_lay:[9,41,44,68],mixedlayertyp:9,mkdir:25,mkl:32,mkl_root:32,ml_data:[50,67],mnist:[3,5,20,28],mnist_model:5,mnist_provid:3,mnist_random_image_batch_read:28,mnist_train:[3,28],mnist_train_batch_read:28,mnt:57,mod:68,mode:[9,45,57,69],model:[9,10,13,18,19,54,61,62,65,67,68,69,70],model_config:5,model_list:[52,54,68,69],model_output:69,model_path:54,model_zoo:[61,63],modifi:45,modul:[3,10,13,15,30,31,62,63,65,67],modulo:9,momentum:[7,30,36,65],momentumoptim:[31,62],mon:56,mono:9,month:[17,70],mood:69,moosef:55,more:[8,9,10,13,16,18,21,23,25,28,30,49],morin:9,mose:[69,70],moses_bleu:70,mosesdecod:69,most:[9,13,16,28],mount:23,mountpath:[56,57],move:[9,18,21,25],movi:[3,13,67],movie_categori:13,movie_featur:67,movie_head:67,movie_id:[57,67],movie_info:13,movie_meta:67,movie_nam:67,movie_review:13,movieinfo:13,movielen:66,moving_average_fract:9,mpi:50,mse_cost:[31,36,67],much:[9,18,28],mul:46,multi:[9,63,70],multi_binary_label_cross_entropi:9,multi_crop:63,multinomi:9,multipl:[8,9,10,13,15,16,21,22],multipli:[8,9],must:[6,8,9,10,28,35,46],my_standalone_toolchain:38,mypaddl:[56,57],name:[3,7,8,9,10,12,13,15,16,18,20,22,23,27,30,31,33,36,41,44,46,49,54,55,56,57,58,59,62,65,67,70],name_prefix:20,namespac:[26,46,55,56,57],nativ:9,natur:21,nchw:9,ndarrai:[15,20],ndarri:15,ndcg:9,ndcg_num:9,ndk:38,necessari:[9,22,65],need:[9,10,13,16,17,21,22,23,25,30,49,57,65],neg:[3,8,9,65,68,69],neg_distribut:9,nest:[9,13],net:[9,10,69],net_conf:69,net_diagram:63,network:[5,7,8,9,11,13,14,15,16,18,28,36,41,50,54,57,61,62,63,67,68,69,70],network_config:54,neural:[9,10,11,13,15,16,18,41,43,61,67,68,69,70],neuralnetwork:9,never:[13,28,56,57],newest:22,next:[3,9,13,18],nfs:57,nfsdir:57,nginx:33,ngram:13,nic:[50,51,52,57],nil:21,nine:13,nlp:9,nltk:13,nmt:70,nnz:46,no_cach:3,no_sequ:[3,9,36,67],noah:69,noavx:33,node0:57,node:[9,17,55,56,57],node_0:57,node_1:57,node_2:57,nodebook:33,nodefil:50,nois:[9,18],non:[9,18],none:[3,5,7,8,9,10,11,12,13,15,16,31,44,63,65],norm_by_tim:9,normal:[9,10,13,56,57,63],notat:9,note:[7,9,10,12,15,16,21,23,28,35,69],notebook:23,noth:[6,15,45],novel:69,now:[9,17,18,43],np_arrai:13,ntst1213:70,ntst14:70,nullptr:[35,46],num:[9,50,52,57,68,69,70],num_channel:[9,10,62],num_chunk_typ:8,num_class:[9,10,62],num_filt:[9,10],num_gradient_serv:[51,52],num_group:9,num_neg_sampl:9,num_parameter_serv:16,num_pass:[15,31,36,51,52,54,56,57,65,67,68,69,70],num_repeat:9,num_result:8,num_results_per_sampl:9,num_shard:20,number:[8,9,13,18,20,28,70],numchunktyp:8,numdevices_:54,numlogicaldevices_:54,numofallsampl:8,numofwrongpredict:8,numpi:[13,15,20,28,31,36,63],numsampl:49,numtagtyp:8,numtimeout:21,nv_:17,nv_librari:17,nv_test:17,nvcc:[17,33],nvidia:[30,33],obj:[3,30,31,62,63,65,67],object:[3,7,9,10,11,13,15,16,26,49,65,67],observ:11,occup:[57,67],occur:[13,15],oct:56,odd:9,off:[27,32,33,35,38,39,45],offlin:[18,20],offset:[9,67],ograd:46,old:22,omit:[30,65],on_init:3,onc:[9,18,21],one:[3,6,8,9,10,11,12,13,16,18,21,22,23,28,65,68,69],one_host_dens:67,one_hot_dens:67,onli:[8,9,10,12,13,15,16,17,21,22,23,25,41,43],onlin:[11,18,20,28],open:[3,9,16,20,28,30,31,41,63,65,67,68],openbla:32,openblas_root:32,oper:[9,10,11],opinion:69,ops:17,ops_test:17,opt:[16,32,57],optim:[7,14,15,30,36],option:[8,9,16,17],order:[9,10,13,15,28,57],org:[9,10,13,20,24,33,66],organ:9,origin:[9,13,45],other:[8,9,10,13,18,22,25,65,67],otherchunktyp:8,otherwis:[9,13,16,18,22,28],our:[16,17],out:[9,15,16,17,21,41,43,44,62],out_dir:57,out_left:9,out_mem:44,out_right:9,out_size_i:9,out_size_x:9,outer:41,outer_mem:41,outer_rnn_st:41,outer_rnn_state_:41,outer_step:41,outout_lay:15,outout_layer1:15,outout_layer2:15,output:[6,7,8,9,10,12,13,15,16,20,25,28,30,31,41,44,50,54,56,57,61,62,63,65,67,68,69],output_:9,output_dir:63,output_fil:68,output_id:9,output_lay:[15,63],output_max_index:12,output_mem:[9,44],output_path:20,outputh:9,outputw:9,outsid:[3,9,10],outv:46,over:[9,10,16],overrid:[18,25],overview:[21,22],overwrit:25,own:22,packag:[13,21,23,30],pad_c:9,pad_h:9,pad_w:9,padding_attr:9,padding_i:9,padding_x:9,paddl:[3,5,6,7,8,9,10,11,12,13,15,16,18,20,23,25,26,27,30,31,33,35,36,45,46,47,49,50,54,56,57,61,62,65,67,68,69,70],paddle_begin_init_param:22,paddle_element_typ:22,paddle_element_type_float32:22,paddle_element_type_float64:22,paddle_element_type_int32:22,paddle_element_type_int64:22,paddle_element_type_uint32:22,paddle_element_type_uint64:22,paddle_error:[26,27],paddle_exampl:23,paddle_finish_init_param:22,paddle_get_param:22,paddle_gradi:22,paddle_init_param:22,paddle_job:23,paddle_matrix:[26,27],paddle_matrix_cr:27,paddle_matrix_get_shap:26,paddle_matrix_shap:26,paddle_n:[50,57],paddle_new_pserver_cli:22,paddle_on_cloud:23,paddle_output:56,paddle_paramet:22,paddle_port:[50,57],paddle_ports_num:[50,57],paddle_ports_num_for_spars:50,paddle_ports_num_spars:57,paddle_process_by_paddl:57,paddle_pserver2:50,paddle_pserver_cli:22,paddle_pserver_client_releas:22,paddle_root:61,paddle_save_model:22,paddle_send_grad:22,paddle_server_num:57,paddle_source_root:61,paddle_train:[27,29,50,57],paddledev:[30,33,56,57],paddlepaddl:[9,10,13,18,20,22,23,24,25,28,30,33,35,44,45,49,50,58,59,61,68],pair:8,palceholder_just_ignore_the_embed:61,palmer:68,paper:[9,70],para:61,paraconvert:61,parallel:[49,54,56,57,70],parallel_nn:[7,51,52],param:[7,9,22,67],param_attr:[9,10,30,31,44],param_config_proto:22,paramattr:[7,9,30,31,44],paramet:[8,9,10,11,12,13,14,17,19,25,28,36,52,57,67,68,69,70],parameter_attribut:9,parameter_block_s:[51,52],parameter_block_size_for_spars:[51,52],parameter_learning_r:7,parameter_nam:[15,16],parameter_serv:16,parameterattribut:[7,9,10],parameterclient2:57,parametermap:46,parameters_:46,parameterset:16,parametris:11,paramutil:67,paraphras:[61,70],paraphrase_data:61,paraphrase_model:61,paraphrase_modeldata:61,paraspars:46,pars:[13,17,67],parse_config:5,parse_known_arg:57,parsefromstr:30,parser:57,part:[67,69],partial:9,partit:[18,20],paserv:57,pass:[3,9,13,15,18,28,30,31,36,45,49,52,54,56,57,62,65,67,68,69,70],pass_id:[15,36],pass_idx:28,passtyp:46,past:16,patch:24,path:[8,13,15,18,21,22,23,28,35,38,39,50,52,55,56,57,68,69],pattern:[13,18,26,67,69],paul:68,paus:18,pave:70,pdf:[9,10],pem:[16,20],pend:[18,21],penn:68,per:[9,13,18,22,28],perform:[9,10,22,49,51],period:[18,52,68,69,70],perl:[69,70],peroid:9,persistentvolum:55,persistentvolumeclaim:[55,57],person:16,pfs:[20,25],pfsclient:20,pfspath:25,pickl:67,pid:55,piec:[9,10],pillow:[23,62],pip:[30,45,50,62,67],pixel:[3,9,13],pixels_float:3,pixels_str:3,place:[3,18],plain:[8,9,15,23,27],plan:18,platform:38,pleas:[7,9,10,11,16,18,21,22,28,30,35,57],plot:[16,62],plotcurv:62,png:62,pnpair_evalu:8,pnpairvalid:51,pod:[20,23,55,56,57],podip:57,podlist:57,point:[18,23,49],polar:13,poll:69,pool3:46,pool:[10,14,67],pool_attr:10,pool_bias_attr:10,pool_pad:10,pool_siz:[3,9,10],pool_size_i:9,pool_strid:10,pool_typ:[9,10],pooling_lay:[30,65],pooling_typ:[9,30,40,65],poolingtyp:12,port:[17,50,51,52,56,57],port_num:51,ports_num:[52,57],ports_num_for_spars:[51,52,54,57],pos:[67,69],pose:18,posit:[3,8,9,13,65],positive_label:8,posix:55,possibl:[16,21],post:[23,24],potenti:49,practic:9,pre:[9,10,13,16,61,69,70],pre_dictandmodel:61,precis:8,precision_recall_evalu:8,pred:[65,68],predetermin:9,predic:[13,68],predicate_dict:68,predicate_dict_fil:68,predicate_slot:68,predict:[3,5,8,9,11,15,30,50,61,62,63,65,67,68,69],predict_fil:[51,52],predict_output_dir:[51,52,65],predict_sampl:5,prediction1:15,prediction2:15,predin:62,prefer:55,prefetch:46,prefix:[18,20],pregrad:46,premodel:61,prepar:[23,58],preprocess:[13,50,61,62,65,67,69,70],present:[16,45],preserv:25,prev_batch_st:[51,52],prevent:[11,16,18,21],previou:[9,10,18,25],previous:9,price:13,principl:16,print:[5,7,15,16,31,36],printallstatu:49,printer:8,printstatu:49,prite:8,privat:[27,45],prob:[8,15],probabilist:[9,61],probabl:[8,9,15],problem:[9,11,16,17],proc:33,proce:[13,18,28],proceed:[9,68],process2:41,process:[3,7,9,10,16,20,21,30,31,41,44,57,65,67,68],process_predict:65,processdata:[62,63],processor:49,prod:[33,45],produc:[10,13,18,28],product:23,productgraph:56,profil:[25,49],proflier:49,prog:57,program:[13,16,20,22,28,49,57],progress:[18,21],proj:9,project:[9,10,23,27],promis:[9,10],prompt:25,prone:16,prop:68,propag:11,properli:65,proposit:68,protect:46,proto:12,protobuf:[23,30],protocol:22,provid:[9,13,16,22,23,30,31,41,51,65,67,68],prune:9,ps_desir:18,pserver:[23,50,51,52,57],pserver_config_proto:22,pserver_cpu:23,pserver_id:19,pserver_mem:23,pserver_num_thread:[51,52],pseudo:[16,23],psize:46,ptr:27,pull:29,purpos:[18,49],push:57,push_back:46,put:[18,65],pwd:[33,45],py_paddl:[5,13,47],pydataprovid:30,pydataprovider2:[3,5,31,57,67],pyramid:9,pyramid_height:9,python:[15,16,26,29,30,33,45,46,47,50,61,62,63,67,68,69,70],pythonpath:[30,62],pzo:69,queri:[9,70],question:[9,16],quick:56,quick_start:[23,56,57,58,65],quick_start_data:56,quickstart:56,r14b:38,rac:9,rais:13,ramnath:69,ran:49,rand:[49,52,54,68],random:[7,9,13,20,28,31],random_imag:20,rang:[9,13,20,28,57,65],rank:[9,16,63,65],rare:3,raspberri:39,raspberrypi:39,raspbian:39,rate:[7,8,11,13,22,50,57,67],rather:23,ratio:52,raw:9,raw_meta:67,rdma_tcp:[51,52],reach:18,read:[3,13,15,16,18,20,28,31,63,65,67],read_from_realistic_imag:16,read_from_rng:16,read_lock:19,read_mnist_imag:16,read_next_from_fil:30,read_ranking_model_data:16,reader:[15,20,36],reader_cr:20,reader_creator_bool:28,reader_creator_random_imag:[13,28],reader_creator_random_image_and_label:[13,28],readi:[18,56],readm:[27,45,67,69],real:28,real_process:3,realist:16,reason:[10,16,18,56],recal:8,receiv:[18,23],recognit:[9,63,69],recommend:[10,16,50,57,67],record:[21,67,68],recordio:[16,20,21],recov:18,recoveri:21,rectangular:9,recurr:[41,42,68,69],recurrent_group:[10,41,43,44],recurrentgradientmachin:27,recurrentgroup:8,recurs:25,reduc:11,ref:67,refer:[7,9,10,11,18,21,22,32],referenc:[9,21],reference_cblas_root:32,refine_unknown_arg:57,reflect:21,regex:67,register_gpu_profil:49,register_lay:46,register_timer_info:49,registri:[23,56],regular:[7,62,65,69],rel:10,relat:[18,23,67],relationship:13,releas:[29,35,38,39,55,68],reliabl:18,relu:9,rememb:9,remot:[7,45,50,52,54],remov:[13,25,45],renam:25,reorgan:9,replac:[17,21],replicaset:23,report:21,reportdataset:21,repositori:38,repres:[9,13,21,65],represent:[22,65],request:[18,29,56,70],requir:[8,9,16,18,22,23,25,50,67],res5_3_branch2c_bn:63,res5_3_branch2c_conv:63,res:68,research:[9,13,62],reserv:25,reserveoutput:46,reset:[9,18],reshap:28,reshape_s:9,residu:63,resnet_101:63,resnet_152:63,resnet_50:63,resolv:[45,56],respons:[9,56],rest:[9,23,24],restart:[18,22,56],restartpolici:[56,57],resu:28,result:[3,6,8,9,15,21,49,65,70],result_fil:[8,44],ret_val:67,return_seq:10,reuqest:29,reus:28,reveal:16,revers:[9,10,43,44],review:[13,45,56,69],reviews_electronics_5:56,rewrit:[17,70],rgb:9,rgen:69,rho:11,right:[9,17,23],rkt:23,rmsprop:65,rmspropoptim:67,rnn:[9,10,43,44,51,69],rnn_bias_attr:44,rnn_layer_attr:44,rnn_out:44,rnn_state:41,rnn_state_:41,rnn_step:9,rnn_use_batch:[51,52],rnnlm:13,robot:62,role:[13,16,21,22,68,69],roman:69,root:[11,12,33,50,56,57,61],root_dir:50,rot:9,routin:67,routledg:69,row:[8,9,13],row_id:9,rpc:21,rpcserver:21,rpi:39,rpi_arm_neon:39,rpi_toolchain:39,rstrip:57,rtype:[9,67],run:[16,18,23,30,33,45,49,50,56,57,58,59,67],runinitfunct:[49,57],running_on_cloud:23,runtim:[3,30],s_fusion:67,s_id:67,safe:23,same:[3,8,9,10,16,21,22,41,65],samping_id:9,sampl:[3,8,13,65,67,69,70],sample_id:8,sample_num:8,santiago:69,save:[3,9,13,18,20,21,22,23,56,67,68,69,70],save_dir:[31,52,54,56,57,62,65,67,68,69,70],save_only_on:[51,52],saving_period:[51,52,57],saving_period_by_batch:[51,52,54],saw:3,scalar:9,scale:[6,67],scalingproject:9,scan:21,scatter:9,schedul:[21,23],scheduler_factor:7,scheme:8,schmidhub:69,schwenk:70,scienc:69,score:[8,9,67],script:[13,47],seaplane_s_000978:62,search:[9,18,44,70],seat:70,second:[9,13,16,25,28,65,67],sed:69,see:[9,10,16,18,30,65],seed:[49,52],seem:17,segment:8,sel_fc:9,select:9,selectiv:9,selector:56,self:46,selfnorm:9,semant:[13,16,29,68,69],semantic_role_label:44,semat:16,sen_len:68,send:[18,22],sent:[16,22,56],sent_id:44,sentanc:30,sentenc:[3,9,13,41,44,68],sentence_last_state1:41,sentence_last_state2:41,sentiment:[3,68,69],sentiment_data:69,sentiment_net:69,sentimental_provid:3,sentimental_train:3,separ:[8,65,67],seq:[9,13,41,67],seq_pool:[9,40],seq_text_print:8,seq_to_seq_data:[61,70],seq_typ:[13,67],seqlastin:41,seqtext_printer_evalu:[8,44],seqtoseq:[9,30,44,61,70],seqtoseq_net:[9,44,61,70],sequel:3,sequenc:[3,6,8,9,10,12,13,30,36,41,43,65,67,69,70],sequence_conv_pool:65,sequence_layer_group:[9,41],sequence_nest_layer_group:[9,41],sequencegen:41,sequencestartposit:9,sequencetextprint:8,sequencetyp:[3,9,36],sequenti:[9,68],seri:[10,41,69],serial:[15,21,22],server:[16,17,50,52,55,57],serverless:18,set:[3,7,8,9,10,13,15,16,18,23,30,31,41,44,49,50,56,61,62,63,65,67,68,69,70],set_active_typ:46,set_default_parameter_nam:7,set_drop_r:46,set_input:9,set_siz:46,set_typ:46,settotalbyteslimit:30,setup:[46,65],sever:9,sgd:[11,15,16,18,23,31,36,50,51,69],shape:[9,15,36],shard:[18,19,20,21,22],share:[9,17,27,68],shared_bia:10,shared_bias:9,shared_librari:17,shared_ptr:[26,27],ship:62,shold:69,shorten:9,should:[8,9,13,15,16,22,23,28,43],should_be_fals:16,should_be_tru:16,should_shuffl:[3,41,68],show:[11,13,18,25,68,69,70],show_check_sparse_distribution_log:[51,52],show_layer_stat:[51,52],show_parameter_stats_period:[51,52,54,56,68,69,70],shown:[8,9,16],shuf:[30,67],shuffl:[13,30],side:[9,15],sigint:50,sigmoid:[9,10],sigmoidactiv:41,sign:24,similar:[9,28,67],similarli:9,simpl:[6,8,9,10,13,15,57],simple_attent:44,simple_gru:65,simple_lstm:[9,65],simple_rnn:[9,44],simpli:[9,16,22],simplifi:16,sinc:[9,18,21,28],singl:[8,10,13,18],sinlg:15,size:[3,8,9,10,11,13,18,20,28,30,31,36,41,44,46,62,63,65,67,69,70],size_a:9,size_b:9,size_t:46,skip:[28,31,45,63],sleep:57,slide:[9,11,13,18],slope:9,slot:[67,68],slot_dim:67,slot_nam:67,slottyp:67,small:13,small_messag:[51,52],small_vgg:62,smaller:[9,18],smith:69,smooth:9,snap:56,snapshot:19,sock:23,sock_recv_buf_s:[51,52],sock_send_buf_s:[51,52],socket:57,softmax:[9,10,16,30,44,46,65],softmax_param:30,softmax_param_attr:10,softmax_selfnorm_alpha:9,softmaxactiv:[30,41,44,65],solv:16,some:[7,9,13,15,16,17,21,22,23],some_c_api_funct:27,some_inst:27,some_python_class:26,somecppclass:26,somedata:15,somegotyp:26,someth:9,sometim:[11,28],somewhat:22,soon:18,sort:[9,13,57,67,69],sourc:[9,13,17,25,27,28,70],source_dict_dim:44,source_language_word:44,space:8,space_seperated_tokens_from_dictionary_according_to_seq:8,space_seperated_tokens_from_dictionary_according_to_sub_seq:8,spars:[7,9,11,13,30,46,50,52,54,57,65],sparse_binary_vector:[3,13,30,36,65],sparse_binary_vector_sequ:13,sparse_float_vector:[3,36],sparse_non_value_slot:13,sparse_upd:[7,30],sparse_value_slot:13,sparse_vector:[13,30],sparse_vector_sequ:13,sparseparam:46,sparseprefetchrowcpumatrix:46,spatial:9,spec:[56,57],special:[9,22],specif:25,specifi:[8,9,13,16,21,22,23,25,35,65],speech:9,speed:10,sphinx:[26,33,47],split:[3,9,41,50,65,67,68],split_count:57,squar:[9,11,12],squarerootnpool:9,squash:45,srand:52,src:[17,57,70],src_backward:44,src_dict:[30,44],src_dict_path:30,src_embed:44,src_forward:44,src_id:44,src_root:5,src_word_id:44,srl:[13,68],ssd:9,ssh:[33,50],sshd:33,sstabl:16,stabl:[29,55],stacked_lstm_net:69,stacked_num:69,stackexchang:9,stage:45,stake:70,stale:18,standalon:38,standard:7,stanford:[13,56],start:[9,15,18,21,22,23,30,45,52,56,57],start_paddl:57,start_pass:[51,52],start_pserv:[51,52],startpaddl:57,startup:[18,23],stat:[49,52,68,69,70],state:[9,10,18,43,56],state_act:[9,10,41],statfulset:57,staticinput:[9,43,44],statist:9,statset:49,statu:[23,45,49,56,57],status:56,std:[17,26,27,46,52],stderr:50,stdout:50,step:[9,10,12,18,22,41,43,44],stepout:41,still:21,stochast:[11,18,21],stock:69,stop:9,storag:[24,55],store:[8,9,13,65,67],str:[15,23,54,57],strategi:[12,18,52,68],street:[9,68],strict:28,stride:9,stride_i:9,stride_x:9,string:[3,8,9,21,25,46,52],strip:[30,41,65,67,68],struct:[21,22,24,27],structur:[13,21,65],stub:9,stuff:45,stun:3,style:9,sub:[8,9,13,16],sub_sequ:[3,9,36],subcommand:25,subgradi:11,subnet:16,subobjectpath:56,subseq:[40,43],subsequenceinput:[9,41],succeed:[21,56],success:[22,56],successfulcr:56,sudo:[35,62],suffic:28,suffix:23,suggest:[9,17],sum:[9,11,19],sum_:9,sum_evalu:8,sumpool:[9,30],support:[7,8,9,12,13,18,23,28,41],suppos:17,suppress:25,sure:[35,69],swagger:24,swap_channel:63,swig:[26,27],swig_paddl:[5,13],symbol:[9,27],symlink:45,sync:18,syncflag:46,synchron:[11,18,21],syntax:28,sys:63,sysroot:38,system:[17,18,22,24,30,69],t2b:61,t_i:9,tab:65,tabl:9,tableproject:9,tag:[8,13,30,33],tagtyp:8,tainer_id:57,take:[3,8,9,10,16,17],tanh:[9,10,46],tanhactiv:[41,44],target:[9,13,15,17,70],target_dict_dim:44,target_dictionary_dim:9,target_language_embed:9,target_language_word:44,target_link_librari:17,targetinlink:[9,41],task:[8,9,68],task_queu:21,taskentri:21,taskqueu:21,tbd:41,tconf:69,tcp:52,tear:49,technic:18,tee:[56,62,67,68,69,70],tell:[18,21,22],tellig:69,templat:[56,57],tempor:9,temporari:23,tensor:17,tensor_test:17,term:[9,10,18],termin:56,tesh:68,test100:13,test10:13,test1:20,test:[2,3,9,13,15,16,17,27,28,29,45,46,49,50,52,54,62,63,65,67,68,69,70],test_all_data_in_one_period:[56,62,67,68,69],test_compar:30,test_comparespars:30,test_comparetwonet:30,test_comparetwoopt:30,test_config_pars:30,test_data:[5,70],test_fcgrad:46,test_gpuprofil:49,test_layergrad:46,test_list:[3,30,31,62,65],test_networkcompar:30,test_part_000:69,test_pass:[51,52,54,70],test_period:[51,52,54],test_predict:30,test_pydataprovid:30,test_pydataprovider2:30,test_pydataproviderwrapp:30,test_ratio:67,test_recurrent_machine_gener:30,test_recurrentgradientmachin:[30,41],test_swig_api:30,test_train:30,test_traineronepass:30,test_wait:[51,52],testa:16,testb:16,testbilinearfwdbwd:49,testconfig:46,tester:[67,70],testfcgrad:46,testfclay:46,testlayergrad:46,testq:16,testresult:15,testutil:46,text1:25,text:[3,8,10,13,16,61,65,69,70],text_conv:65,text_conv_pool:67,text_fil:[13,69],tflop:49,tgz:13,than:[7,8,9,10,18,23,30],thei:[16,17,18,22,25,49],them:[10,16,18,23,28,49,65,67],therein:9,thi:[3,7,8,9,10,11,13,15,16,17,18,21,22,23,28,49,65,67,69],thing:3,think:[16,17],third:[9,18],third_parti:[38,39],those:[17,18,68],thought:17,thread:49,thread_local_rand_use_global_se:[51,52],threadid:54,threadloc:49,three:[8,9,18,28,63],threshold:[7,8,18,21,52],through:[9,18,21],throughput:49,thu:9,tier:56,time:[9,10,12,13,16,17,18,21,28,41,49,52,56,57,69],timelin:9,timeout:[18,21],timer:49,timestamp:[9,19],timestep:9,titl:[13,57,67],tls:24,tmp:3,to_your_paddle_clone_path:47,todo:[8,10,13,18,21],toend:9,togeth:[9,10,13,15],token:[8,9,16,44,69],too:13,tool:[38,39,47,57],toolchain:[38,39],top:[8,63],top_k:8,topolog:[13,16,18],topolopi:15,toronto:[13,62],total:[8,15,18,28,49,56,70],total_pass:28,touch:69,tourist:70,track:[18,21,45],tractabl:9,tradit:9,trail:13,train100:13,train10:13,train:[2,3,7,9,11,13,20,21,22,30,31,35,36,50,52,54,56,57,58,59,61,62,63,65,67,68,69,70],train_arg:57,train_args_dict:57,train_args_list:57,train_conf:[61,70],train_config_dir:57,train_data:70,train_i:36,train_list:[3,30,31,62,63,65],train_part_000:69,train_read:36,train_x:36,trainabl:9,trainer:[3,5,16,19,20,21,31,36,46,52,54,57,67,68,69,70],trainer_config:[2,3,5,31,50,56,57,65,67,69],trainer_config_help:[3,30,31,46,62,67],trainer_count:[30,51,52,54,56,57,67,68,69,70],trainer_cpu:23,trainer_cr:23,trainer_gpu:23,trainer_id:[52,57],trainer_mem:23,trainer_packag:23,trainerconfighelp:30,trainerid:57,trainerintern:[65,67,70],tran:46,transact:[18,21,69],transform:9,transform_param_attr:10,translat:[9,10,61,70],transpos:9,transposedfullmatrixproject:9,travel:3,travi:45,treat:[9,22],treatment:22,tree:[9,57],trg:70,trg_dict:44,trg_dict_path:44,trg_embed:44,trg_id:44,trg_ids_next:44,tricki:26,trn:65,truck:62,true_imag:28,true_label:28,true_read:28,truth:[8,9,70],tst:65,tune:[7,51],tupl:[9,13,15,28],ture:9,turn:[9,28,43],tutori:[56,57,58,59,61,70],tweet:69,twitter:69,two:[9,10,16,22,23,25,28,49,65],txt:[3,17,23,25,46,50,55,65,67,69],type:[3,8,9,10,12,13,16,18,21,23,24,25,26,27,28,31,36,41,46,54,56,63,65,67,68],type_nam:67,typedef:[22,26,27],typic:8,ubuntu:29,ubyt:28,uci:13,ufldl:9,uid:56,uint32:24,uint64:26,uint64_t:26,unawar:22,unconstrain:69,under:21,undeterminist:49,uniform:[7,9,13,20,28],uninstal:30,uniqu:[16,18,23],unique_ptr:46,unit:[9,10,17],unittest:[27,30],univ:70,unix:50,unk:[61,70],unk_idx:[65,68],unknown:9,unseg:9,unsup:69,unsupbow:69,until:[18,22,57],untrack:45,unus:67,unzip:38,updat:[7,9,18,21,22,24,45,50,54],update_equ:[15,36],updatecallback:46,upgrad:30,upload:[18,23,24],upstream:[30,45],url:[13,69],urls_neg:69,urls_po:69,urls_unsup:69,usag:[8,9,10,13,15,57,67],use:[7,8,9,10,12,13,15,16,17,18,45,49,57,63,65,67,68,69,70],use_global_stat:9,use_gpu:[5,30,36,51,52,54,56,57,62,63,65,67,68,69,70],use_jpeg:62,use_old_updat:[51,52],use_seq:[31,67],use_seq_or_not:67,used:[3,8,9,10,11,12,13,15,16,18,28,49,65,67,69],useful:[9,10],usegpu:46,user:[7,9,10,13,15,16,20,21,23,25,28,55,65,67],user_featur:67,user_head:67,user_id:[57,67],user_info:13,user_meta:67,user_nam:[20,67],usercert:20,userinfo:13,userkei:20,usernam:[20,45],uses:18,using:[7,9,10,13,16,17,18,21,22,23,25,28,35,68],usr:[30,32,33,35,50,52,57],usrdict:61,usrmodel:61,usual:[9,13,15,23,49],utc:66,util:[49,57,62,67],uuid:19,v28:9,v7a:38,valid:28,valu:[3,5,7,8,9,11,12,13,15,18,46,54,57,63,65,68],value1:52,value2:52,value_printer_evalu:8,value_rang:13,vanilla:44,variabl:[9,13,16,56],varianc:9,vector:[9,10,13,16,22,65,67],verb:13,verbos:25,veri:[9,12,21,62],version:[9,10,23,25,29,33,35,49,51,52],versu:16,vertic:9,vgg:[10,62],vgg_16_cifar:62,via:[18,28,35,45],view:9,vision:62,visipedia:62,visual:9,vocab:69,volum:[33,55,56,57],volumemount:[56,57],vutbr:13,wai:[9,10,16,22,70],wait:[11,18,22,57],wall:68,want:[3,9,10,16,23,28],warn:[9,25,30,57],warp:9,watch:18,wbia:63,wei:[68,69],weight:[8,9,10,11,46,63],weight_act:10,weightlist:46,weights_:46,weights_t:46,welcom:17,well:23,were:17,wether:9,wget:38,what:[7,9,10,11,17,45,65],when:[3,7,8,9,13,15,17,18,21,22,23,25,49],where:[9,10,11,16,18],wherea:21,whether:[8,9,10,28,69],which:[8,9,10,11,13,16,18,20,21,22,23,28,65,67],whoever:22,whole:[3,8,13,26,27],whole_cont:67,whose:[9,13,18],why:[10,27,49],width:[8,9,13,26,28,46,70],wiki:9,wikipedia:[9,13],wilder:3,window:[9,12,13,55],wise:9,with_avx:[32,33,35,38,45],with_c_api:[38,39],with_doc:32,with_dock:47,with_doubl:[32,35,46],with_dso:32,with_gpu:[32,33,35,38,45],with_predict_sdk:35,with_profil:49,with_python:[32,35,38],with_rdma:[32,35,38],with_style_check:[32,45],with_swig_pi:[32,38],with_test:[32,33,45],with_tim:[32,35,49],within:[9,21],without:[8,9,18,22,28],wmt14:70,wmt14_data:70,wmt14_model:70,wmt_shrinked_data:13,woboq:33,won:41,wonder:3,word2vec:[23,30],word:[3,8,9,13,30,41,43,65,68],word_dict:[41,65,68],word_dim:[41,65],word_id:[3,30],word_idx:13,word_slot:68,word_vector:65,word_vector_dim:[9,44,61],words_freq_sort:13,work:[13,16,17,18,28,33,41,45,56,57],workspac:50,would:[15,17,18,28,68],wrapper:[10,17,49],write:[13,16,18,28,68],write_lock:19,writelin:31,writer:16,wrong:28,wsj:68,wuyi:55,www:[9,13,62,70],x64:39,x86:[38,39],x86_64:38,xarg:[30,33,46],xgbe0:52,xgbe1:52,xiaojun:69,xrang:[28,31,36,46],xxbow:69,xxx:[16,63,70],xxxx:19,y_i:9,y_predict:36,yaml:[56,57],yancey1989:23,yann:13,yapf:45,yeild:15,yield:[3,13,16,20,28,30,31,36,41,44,65,67,68],you:[3,7,9,10,23,35,63,69],your:[9,16,17,23,25,30,38,39],your_param_nam:30,your_repo:57,your_source_root:27,yuyang18:[10,13],zachari:69,zeng:69,zero:[7,9,11,13,18,52],zhou:[68,69],zip:[13,38,57,66],zoo:61},titles:["\u5173\u4e8ePaddlePaddle","API","DataProvider\u7684\u4ecb\u7ecd","PyDataProvider2\u7684\u4f7f\u7528","API\u4e2d\u6587\u624b\u518c","\u57fa\u4e8ePython\u7684\u9884\u6d4b","Activation","Parameter Attribute","Evaluators","Layers","Networks","Optimizer","Pooling","Data Reader Interface and DataSets","Model Configuration","Training and Inference","PaddlePaddle Design Doc","Required CMake Function","Design Doc: Distributed Training","\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9\uff08Checkpointing\uff09","\u8bad\u7ec3\u6570\u636e\u7684\u5b58\u50a8\u548c\u5206\u53d1","Design Doc: Master Server","Design Doc: The Client Library of Parameter Server","Submit a Distributed Training Job","FileManager\u8bbe\u8ba1\u6587\u6863","PFSClient","Paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0","C-API \u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863","Python Data Reader Design Doc","Paddle\u53d1\u884c\u89c4\u8303","FAQ","\u7ecf\u5178\u7684\u7ebf\u6027\u56de\u5f52\u4efb\u52a1","PaddlePaddle\u7684\u7f16\u8bd1\u9009\u9879","PaddlePaddle\u7684Docker\u5bb9\u5668\u4f7f\u7528\u65b9\u5f0f","\u5b89\u88c5\u4e0e\u7f16\u8bd1","Ubuntu\u90e8\u7f72PaddlePaddle","\u57fa\u672c\u4f7f\u7528\u6982\u5ff5","\u65b0\u624b\u5165\u95e8","\u6784\u5efaAndroid\u5e73\u53f0\u4e0a\u7684PaddlePaddle\u5e93","\u6784\u5efaRaspberry Pi\u5e73\u53f0\u4e0a\u7684PaddlePaddle\u5e93","\u652f\u6301\u53cc\u5c42\u5e8f\u5217\u4f5c\u4e3a\u8f93\u5165\u7684Layer","\u5355\u53cc\u5c42RNN API\u5bf9\u6bd4\u4ecb\u7ecd","RNN\u76f8\u5173\u6a21\u578b","Recurrent Group\u6559\u7a0b","RNN\u914d\u7f6e","\u5982\u4f55\u8d21\u732e\u4ee3\u7801","\u5b9e\u73b0\u65b0\u7684\u7f51\u7edc\u5c42","\u5982\u4f55\u8d21\u732e/\u4fee\u6539\u6587\u6863","\u8fdb\u9636\u6307\u5357","GPU\u6027\u80fd\u5206\u6790\u4e0e\u8c03\u4f18","\u8fd0\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3","\u53c2\u6570\u6982\u8ff0","\u7ec6\u8282\u63cf\u8ff0","\u8bbe\u7f6e\u547d\u4ee4\u884c\u53c2\u6570","\u4f7f\u7528\u6848\u4f8b","Kubernetes \u7b80\u4ecb","Kubernetes\u5355\u673a\u8bad\u7ec3","Kubernetes\u5206\u5e03\u5f0f\u8bad\u7ec3","<no title>","<no title>","PaddlePaddle \u6587\u6863","\u4e2d\u6587\u8bcd\u5411\u91cf\u6a21\u578b\u7684\u4f7f\u7528","\u56fe\u50cf\u5206\u7c7b\u6559\u7a0b","Model Zoo - ImageNet","\u5b8c\u6574\u6559\u7a0b","\u5feb\u901f\u5165\u95e8\u6559\u7a0b","MovieLens\u6570\u636e\u96c6","MovieLens\u6570\u636e\u96c6\u8bc4\u5206\u56de\u5f52\u6a21\u578b","\u8bed\u4e49\u89d2\u8272\u6807\u6ce8\u6559\u7a0b","\u60c5\u611f\u5206\u6790\u6559\u7a0b","\u6587\u672c\u751f\u6210\u6559\u7a0b"],titleterms:{"\u4e00\u4e9b\u7ec6\u8282\u7684\u8865\u5145":57,"\u4e0a\u4f20\u8bad\u7ec3\u6587\u4ef6":20,"\u4e0b\u8f7d\u4e0e\u89e3\u538b\u7f29":70,"\u4e0b\u8f7d\u548c\u6570\u636e\u62bd\u53d6":61,"\u4e0b\u8f7d\u5e76\u89e3\u538b\u6570\u636e\u96c6":67,"\u4e0b\u8f7d\u6570\u636e":56,"\u4e0d\u4f7f\u7528":26,"\u4e0d\u4f7f\u7528swig\u8fd9\u79cd\u4ee3\u7801\u751f\u6210\u5668":26,"\u4e0d\u5bfc\u51fapaddle\u5185\u90e8\u7684\u7ed3\u6784\u4f53":26,"\u4e0d\u5f15\u7528\u5176\u4ed6\u52a8\u6001\u5e93":26,"\u4e2d\u6587\u5b57\u5178":61,"\u4e2d\u6587\u77ed\u8bed\u6539\u5199\u7684\u4f8b\u5b50":61,"\u4e2d\u6587\u8bcd\u5411\u91cf\u6a21\u578b\u7684\u4f7f\u7528":61,"\u4e2d\u6587\u8bcd\u5411\u91cf\u7684\u9884\u8bad\u7ec3\u6a21\u578b":61,"\u4e3a\u4ec0\u4e48\u9700\u8981\u6027\u80fd\u5206\u6790":49,"\u4ec0\u4e48\u662f\u6027\u80fd\u5206\u6790":49,"\u4ec5\u4ec5\u4f7f\u7528void":26,"\u4ecb\u7ecd":[61,63],"\u4ece\u5feb\u7167\u6062\u590d":19,"\u4ee3\u7801\u8981\u6c42":45,"\u4efb\u52a1\u7b80\u4ecb":31,"\u4f18\u5316\u7b97\u6cd5":65,"\u4f7f\u7528":[45,56],"\u4f7f\u7528\u52a8\u6001\u5e93\u6765\u5206\u53d1paddl":26,"\u4f7f\u7528\u6848\u4f8b":54,"\u4f7f\u7528\u6982\u8ff0":65,"\u4f7f\u7528\u6a21\u578b\u521d\u59cb\u5316\u7f51\u7edc":54,"\u4f7f\u7528\u73af\u5883\u53d8\u91cf":57,"\u4f7f\u7528\u7528\u6237\u6307\u5b9a\u7684\u8bcd\u5411\u91cf\u5b57\u5178":61,"\u4f7f\u7528\u8bf4\u660e":48,"\u4f7f\u7528\u8f6c\u6362\u5e93":20,"\u4f7f\u7528docker\u6784\u5efapaddlepaddle\u7684\u6587\u6863":47,"\u4f7f\u7528paddlepaddle\u751f\u6210\u6a21\u578b":70,"\u4f7f\u7528paddlepaddle\u8bad\u7ec3\u6a21\u578b":70,"\u4fdd\u6301\u672c\u5730\u4ed3\u5e93\u6700\u65b0":45,"\u4fee\u6539\u542f\u52a8\u811a\u672c":56,"\u4fee\u6539\u6587\u6863":47,"\u514b\u9686":45,"\u5173\u4e8epaddlepaddl":0,"\u5177\u4f53\u67d0\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5177\u4f53\u67d0\u79cd\u7c7b\u578b\u7684\u5b9e\u73b0\u6587\u4ef6":27,"\u5185\u5b58\u4e0d\u591f\u7528\u7684\u60c5\u51b5":3,"\u5185\u7f6e\u5b9a\u65f6\u5668":49,"\u5199\u68af\u5ea6\u68c0\u67e5\u5355\u5143\u6d4b\u8bd5":46,"\u51c6\u5907\u4ea4\u53c9\u7f16\u8bd1\u73af\u5883":[38,39],"\u51c6\u5907\u5de5\u4f5c\u7a7a\u95f4":50,"\u51c6\u5907\u5e8f\u5217\u6570\u636e":44,"\u51c6\u5907\u6570\u636e":[31,67],"\u51c6\u5907\u8bad\u7ec3\u6570\u636e":57,"\u51c6\u5907\u96c6\u7fa4\u4f5c\u4e1a\u914d\u7f6e":50,"\u51cf\u5c11\u6570\u636e\u8f7d\u5165\u7684\u8017\u65f6":30,"\u51cf\u5c11dataprovider\u7f13\u51b2\u6c60\u5185\u5b58":30,"\u5206\u5272\u8bad\u7ec3":67,"\u5206\u5757\u6587\u4ef6\u4f20\u8f93":24,"\u5206\u652f\u89c4\u8303":29,"\u521b\u5efa\u672c\u5730\u5206\u652f":45,"\u521b\u5efajob":57,"\u521b\u5efapaddl":56,"\u5220\u9664\u672c\u5730\u5206\u652f":45,"\u5220\u9664\u8fdc\u7a0b\u5206\u652f":45,"\u5229\u7528\u66f4\u591a\u7684\u8ba1\u7b97\u8d44\u6e90":30,"\u5230\u8fdc\u7a0b\u4ed3\u5e93":45,"\u5236\u4f5c\u955c\u50cf":57,"\u5236\u4f5cdocker\u955c\u50cf":56,"\u524d\u63d0\u6761\u4ef6":50,"\u52a0\u8f7dpaddlepaddl":36,"\u52a0\u901f\u6267\u884c":19,"\u52a0\u901f\u8bad\u7ec3\u901f\u5ea6":30,"\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165\u4efb\u4f55\u5176\u4ed6\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u52a8\u6001\u6269\u5bb9":19,"\u5355\u5143\u6d4b\u8bd5":52,"\u5355\u53cc\u5c42rnn":41,"\u5377\u79ef\u6a21\u578b":65,"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc":62,"\u539f\u56e0":26,"\u539f\u56e0\u5217\u8868":26,"\u53c2\u6570\u4fe1\u606f":63,"\u53c2\u6570\u5185\u5b58":30,"\u53c2\u6570\u670d\u52a1\u5668\u548c\u5206\u5e03\u5f0f\u901a\u4fe1":52,"\u53c2\u6570\u6982\u8ff0":51,"\u53c2\u6570\u8bfb\u53d6":63,"\u53c2\u8003":3,"\u53c2\u8003\u6587\u6863":[24,69],"\u53c2\u8003\u8d44\u6599":49,"\u53cc\u5411lstm":69,"\u53cc\u5c42rnn":41,"\u53cc\u5c42rnn\u4ecb\u7ecd":43,"\u53cc\u5c42rnn\u7684\u4f7f\u7528":43,"\u53ef\u80fd\u7684\u5185\u5b58\u6cc4\u9732\u95ee\u9898":3,"\u53ef\u80fd\u9047\u5230\u7684\u95ee\u9898":35,"\u53ef\u9009\u529f\u80fd":61,"\u540d\u8bcd\u89e3\u91ca":24,"\u5411\u7cfb\u7edf\u4f20\u9001\u6570\u636e":65,"\u5411\u91cf":52,"\u542f\u52a8\u4efb\u52a1":57,"\u542f\u52a8\u96c6\u7fa4\u4f5c\u4e1a":50,"\u547d\u4ee4\u884c\u53c2\u6570":65,"\u548c":40,"\u56fe\u50cf\u5206\u7c7b\u6559\u7a0b":62,"\u5728\u4e0d\u540c\u8bbe\u5907\u4e0a\u6307\u5b9a\u5c42":54,"\u5728paddlepaddle\u5e73\u53f0\u8bad\u7ec3\u6a21\u578b":61,"\u57fa\u4e8epython\u7684\u9884\u6d4b":5,"\u57fa\u672c\u4f7f\u7528\u6982\u5ff5":36,"\u57fa\u672c\u539f\u7406":43,"\u57fa\u672c\u8981\u6c42":26,"\u5982\u4f55\u4e66\u5199paddlepaddle\u7684\u6587\u6863":47,"\u5982\u4f55\u5171\u4eab\u53c2\u6570":30,"\u5982\u4f55\u51cf\u5c11\u5185\u5b58\u5360\u7528":30,"\u5982\u4f55\u521d\u59cb\u5316\u53c2\u6570":30,"\u5982\u4f55\u52a0\u901fpaddlepaddle\u7684\u8bad\u7ec3\u901f\u5ea6":30,"\u5982\u4f55\u6307\u5b9agpu\u8bbe\u5907":30,"\u5982\u4f55\u66f4\u65b0www":47,"\u5982\u4f55\u6784\u5efapaddlepaddle\u7684\u6587\u6863":47,"\u5982\u4f55\u8d21\u732e":47,"\u5982\u4f55\u8d21\u732e\u4ee3\u7801":45,"\u5982\u4f55\u8fdb\u884c\u6027\u80fd\u5206\u6790":49,"\u5982\u4f55\u9009\u62e9sgd\u7b97\u6cd5\u7684\u5b66\u4e60\u7387":30,"\u5b50\u5e8f\u5217\u95f4\u65e0memori":41,"\u5b50\u5e8f\u5217\u95f4\u6709memori":41,"\u5b57\u6bb5\u914d\u7f6e\u6587\u4ef6":67,"\u5b89\u88c5":[35,65],"\u5b89\u88c5\u4e0e\u7f16\u8bd1":34,"\u5b89\u88c5\u6d41\u7a0b":34,"\u5b89\u88c5kubectl":55,"\u5b8c\u6574\u6559\u7a0b":64,"\u5b9e\u73b0":26,"\u5b9e\u73b0\u65b0\u7684\u7f51\u7edc\u5c42":46,"\u5b9e\u73b0\u65b9\u5f0f":27,"\u5b9e\u73b0c":46,"\u5b9e\u73b0python\u5c01\u88c5":46,"\u5bfc\u51fac":26,"\u5c06\u547d\u4ee4\u53c2\u6570\u4f20\u7ed9\u7f51\u7edc\u914d\u7f6e":54,"\u5c0f\u7ed3":3,"\u5de5\u5177":49,"\u5e38\u7528\u6a21\u578b":64,"\u5e76\u5b8c\u6210":45,"\u5efa\u7acb":45,"\u5f00\u53d1\u6807\u51c6":48,"\u5f00\u59cb\u5f00\u53d1":45,"\u5f02\u6b65\u968f\u673a\u68af\u5ea6\u4e0b\u964d":52,"\u5f15\u7528":68,"\u5feb\u7167\u4fdd\u5b58\u7684\u8bbe\u8ba1\u5982\u4e0b":19,"\u5feb\u901f\u5165\u95e8\u6559\u7a0b":65,"\u6027\u80fd\u4f18\u5316":48,"\u6027\u80fd\u5206\u6790\u5c0f\u6280\u5de7":49,"\u6027\u80fd\u5206\u6790\u5de5\u5177\u4ecb\u7ecd":49,"\u6027\u80fd\u8c03\u4f18":52,"\u603b\u4f53\u6548\u679c\u603b\u7ed3":65,"\u60c5\u611f\u5206\u6790\u6559\u7a0b":69,"\u6216\u8005\u662f":30,"\u627e\u5230\u7684pythonlibs\u548cpythoninterp\u7248\u672c\u4e0d\u4e00\u81f4":30,"\u6307\u9488\u4f5c\u4e3a\u7c7b\u578b\u7684\u53e5\u67c4":26,"\u63a5\u53e3":63,"\u63a8\u5bfc\u65b9\u7a0b":46,"\u63a8\u6d4b\u6267\u884c":19,"\u63d0\u4ea4":45,"\u63d0\u4ea4\u4ee3\u7801\u7684\u4e00\u4e9b\u7ea6\u5b9a":45,"\u63d0\u4ea4\u955c\u50cf":56,"\u63d0\u53d6\u7535\u5f71\u6216\u7528\u6237\u7684\u7279\u5f81\u5e76\u751f\u6210python\u5bf9\u8c61":67,"\u642d\u5efa\u795e\u7ecf\u7f51\u7edc":36,"\u652f\u6301\u53cc\u5c42\u5e8f\u5217\u4f5c\u4e3a\u8f93\u5165\u7684layer":40,"\u652f\u6301\u7528\u6237\u81ea\u5b9a\u4e49\u7684\u6570\u636e\u9884\u5904\u7406job":20,"\u6570\u636e\u51c6\u5907":[62,67,70],"\u6570\u636e\u63cf\u8ff0":68,"\u6570\u636e\u63d0\u4f9b":68,"\u6570\u636e\u63d0\u4f9b\u811a\u672c":67,"\u6570\u636e\u652f\u6301":52,"\u6570\u636e\u683c\u5f0f\u51c6\u5907":65,"\u6570\u636e\u7684\u51c6\u5907\u548c\u9884\u5904\u7406":61,"\u6570\u636e\u96c6\u7279\u5f81":66,"\u6570\u636e\u9884\u5904\u7406":70,"\u6570\u6910\u4ecb\u7ecd":69,"\u6570\u6910\u51c6\u5907":69,"\u6574\u4f53\u65b9\u6848":57,"\u6587\u4ef6\u4f20\u8f93\u4f18\u5316":24,"\u6587\u4ef6\u8bbf\u95ee\u65b9\u5f0f":20,"\u6587\u4ef6\u8bbf\u95ee\u7684\u6743\u9650":20,"\u6587\u4ef6\u9884\u5904\u7406":20,"\u6587\u672c\u751f\u6210":70,"\u6587\u672c\u751f\u6210\u6559\u7a0b":70,"\u6587\u6863":[33,60],"\u65b0\u624b\u5165\u95e8":37,"\u65f6\u5e8f\u6a21\u578b":65,"\u65f6\u5e8f\u6a21\u578b\u7684\u4f7f\u7528\u573a\u666f":3,"\u65f6\u95f4\u5e8f\u5217":41,"\u65f6\u95f4\u6b65":41,"\u66b4\u9732\u63a5\u53e3\u539f\u5219":27,"\u672c\u5730\u6d4b\u8bd5":54,"\u672c\u5730\u8bad\u7ec3":54,"\u672f\u8bed":19,"\u6784\u5efa\u548c\u6d4b\u8bd5":45,"\u6784\u5efaandroid\u5e73\u53f0\u4e0a\u7684paddlepaddle\u5e93":38,"\u6784\u5efaraspberri":39,"\u67b6\u6784\u56fe":24,"\u67e5\u770b\u8bad\u7ec3\u7ed3\u679c":56,"\u67e5\u770b\u8f93\u51fa":57,"\u6837\u4f8b\u6570\u636e":3,"\u6846\u67b6\u751f\u6210":24,"\u6848\u4f8b\u4e00":54,"\u6848\u4f8b\u4e8c":54,"\u68c0\u67e5\u6a21\u578b\u8f93\u51fa":50,"\u68c0\u67e5\u96c6\u7fa4\u8bad\u7ec3\u7ed3\u679c":50,"\u6982\u5ff5\u89e3\u91ca":20,"\u6982\u8ff0":[40,43],"\u6a21\u5757":24,"\u6a21\u578b":63,"\u6a21\u578b\u4e0b\u8f7d":63,"\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9":19,"\u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863":27,"\u6a21\u578b\u68c0\u9a8c":31,"\u6a21\u578b\u7f51\u7edc\u7ed3\u6784":65,"\u6a21\u578b\u8bad\u7ec3":[62,70],"\u6a21\u578b\u8bc4\u4f30\u548c\u9884\u6d4b":67,"\u6a21\u578b\u914d\u7f6e":[41,48],"\u6a21\u578b\u914d\u7f6e\u7684\u6a21\u578b\u914d\u7f6e":41,"\u6ce8\u610f\u4e8b\u9879":3,"\u6d41\u7a0b\u4ecb\u7ecd":20,"\u6d4b\u8bd5":[52,68],"\u6d4b\u8bd5\u6587\u4ef6":67,"\u6d4b\u8bd5\u6a21\u578b":69,"\u7279\u5f81":68,"\u7279\u5f81\u63d0\u53d6":63,"\u751f\u6210\u5e8f\u5217":44,"\u751f\u6210\u6a21\u578b\u7684\u547d\u4ee4\u4e0e\u7ed3\u679c":70,"\u751f\u6210\u6d41\u7a0b\u7684\u4f7f\u7528\u65b9\u6cd5":43,"\u751f\u6210sparse\u6587\u4ef6":24,"\u7528\u6237\u4f7f\u7528\u6d41\u7a0b":24,"\u7528\u6237\u6587\u4ef6\u63cf\u8ff0":66,"\u7528\u6237\u81ea\u5b9a\u4e49\u6570\u636e\u96c6":70,"\u7528\u6237\u81ea\u5b9a\u4e49\u6570\u6910\u9884\u5904\u7406":69,"\u7535\u5f71\u6587\u4ef6\u63cf\u8ff0":66,"\u76ee\u5f55\u7ed3\u6784":27,"\u76ee\u6807":24,"\u76f4\u63a5\u6784\u5efapaddlepaddle\u7684\u6587\u6863":47,"\u76f8\u5173\u6982\u5ff5":43,"\u77e9\u9635":52,"\u793a\u4f8b1":41,"\u793a\u4f8b2":41,"\u793a\u4f8b3":41,"\u793a\u4f8b4":41,"\u793a\u4f8b\u7a0b\u5e8f":20,"\u795e\u7ecf\u5143\u6fc0\u6d3b\u5185\u5b58":30,"\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u914d\u7f6e":67,"\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e":68,"\u7a00\u758f\u8bad\u7ec3":54,"\u7b26\u53f7":26,"\u7b80\u4ecb":[55,70],"\u7b80\u5355\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":44,"\u7c7b":[26,46],"\u7ebf\u6027\u56de\u5f52\u5b8c\u6574\u793a\u4f8b":36,"\u7ec3\u4e60":62,"\u7ec6\u8282\u63a2\u7a76":62,"\u7ec6\u8282\u63cf\u8ff0":52,"\u7ec8\u6b62\u96c6\u7fa4\u4f5c\u4e1a":50,"\u7ecf\u5178\u7684\u7ebf\u6027\u56de\u5f52\u4efb\u52a1":31,"\u7f16\u5199yaml\u6587\u4ef6":56,"\u7f16\u8bd1\u548c\u5b89\u88c5":[38,39],"\u7f16\u8bd1\u6d41\u7a0b":34,"\u7f16\u8bd1\u9009\u9879":27,"\u7f16\u8bd1\u9009\u9879\u7684\u8bbe\u7f6e":32,"\u7f29\u5bb9":19,"\u7f51\u7edc\u53ef\u89c6\u5316":63,"\u7f51\u7edc\u914d\u7f6e\u4e2d\u7684\u8c03\u7528":3,"\u800c\u662f\u624b\u5199\u591a\u8bed\u8a00\u7ed1\u5b9a":26,"\u80cc\u666f":26,"\u81ea\u7136\u8bed\u8a00\u5904\u7406":52,"\u81f4\u8c22":0,"\u8986\u76d6\u4e0d\u4e00\u81f4\u7684\u90e8\u5206":24,"\u89c2\u6d4b\u8bcd\u5411\u91cf":61,"\u8bad\u7ec3":[52,67,68],"\u8bad\u7ec3\u5668\u914d\u7f6e\u6587\u4ef6":67,"\u8bad\u7ec3\u56e0\u6b64\u9000\u51fa\u600e\u4e48\u529e":30,"\u8bad\u7ec3\u6570\u636e\u5b58\u50a8":20,"\u8bad\u7ec3\u6570\u636e\u7684\u5b58\u50a8\u548c\u5206\u53d1":20,"\u8bad\u7ec3\u6a21\u578b":[31,36,65,69],"\u8bad\u7ec3\u6a21\u578b\u7684\u547d\u4ee4\u4e0e\u7ed3\u679c":70,"\u8bad\u7ec3\u6d41\u7a0b\u7684\u4f7f\u7528\u65b9\u6cd5":43,"\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u51fa\u73b0":30,"\u8bbe\u7f6e\u547d\u4ee4\u884c\u53c2\u6570":53,"\u8bc4\u5206\u6587\u4ef6\u63cf\u8ff0":66,"\u8bcd\u5411\u91cf\u6a21\u578b":65,"\u8bcd\u5411\u91cf\u6a21\u578b\u7684\u4fee\u6b63":61,"\u8bcd\u6c47\u8868":41,"\u8be6\u7ec6\u6559\u7a0b":49,"\u8bed\u4e49\u89d2\u8272\u6807\u6ce8\u6559\u7a0b":68,"\u8bfb\u53d6\u53cc\u5c42\u5e8f\u5217\u6570\u636e":41,"\u8f6c\u6362\u5e93":20,"\u8f93\u5165":43,"\u8f93\u5165\u4e0d\u7b49\u957f":41,"\u8f93\u5165\u793a\u4f8b":43,"\u8f93\u51fa":43,"\u8f93\u51fa\u65e5\u5fd7":65,"\u8fd0\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3":50,"\u8fd0\u884c\u5bb9\u5668":56,"\u8fd0\u884cdocker":30,"\u8fd0\u884cpaddlepaddl":33,"\u8fd9\u4e2a\u52a8\u6001\u5e93\u4f7f\u7528c99\u6807\u51c6\u7684\u5934\u6587\u4ef6\u5bfc\u51fa\u4e00\u4e9b\u51fd\u6570":26,"\u8fdb\u884c\u8bad\u7ec3":[20,56],"\u8fdb\u9636\u6307\u5357":48,"\u9009\u62e9\u5b58\u50a8\u65b9\u6848":55,"\u901a\u7528":52,"\u901a\u8fc7docker\u5bb9\u5668\u5f00\u53d1paddlepaddl":33,"\u903b\u8f91\u56de\u5f52\u6a21\u578b":65,"\u9047\u5230":30,"\u90e8\u7f72kubernetes\u96c6\u7fa4":55,"\u914d\u7f6e\u4e2d\u7684\u6570\u636e\u52a0\u8f7d\u5b9a\u4e49":65,"\u914d\u7f6e\u4ea4\u53c9\u7f16\u8bd1\u53c2\u6570":[38,39],"\u914d\u7f6e\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u67b6\u6784":44,"\u914d\u7f6e\u7f51\u7edc":36,"\u914d\u7f6ekubectl":55,"\u914d\u7f6ekubectl\u8bbf\u95ee\u4f60\u7684kubernetes\u96c6\u7fa4":55,"\u94a9\u5b50":45,"\u9644\u5f55":65,"\u968f\u673a\u6570":52,"\u96c6\u7fa4\u8bad\u7ec3":54,"\u975e\u6cd5\u6307\u4ee4":30,"\u9884\u5904\u7406":62,"\u9884\u5904\u7406\u547d\u4ee4\u548c\u7ed3\u679c":70,"\u9884\u5904\u7406\u5de5\u4f5c\u6d41\u7a0b":70,"\u9884\u6d4b":[62,63,65,68,69],"\u9884\u6d4b\u6d41\u7a0b":5,"\u9884\u6d4bdemo":5,"\u9884\u8bad\u7ec3\u7684\u6a21\u578b":70,"api\u4e2d\u6587\u624b\u518c":4,"api\u5bf9\u6bd4\u4ecb\u7ecd":41,"beam_search\u7684\u751f\u6210":41,"blas\u8def\u5f84\u76f8\u5173\u7684\u7f16\u8bd1\u9009\u9879":32,"bleu\u8bc4\u4f30":70,"book\u4e2d\u6240\u6709\u7ae0\u8282":29,"bool\u578b\u7684\u7f16\u8bd1\u9009\u9879":32,"cmake\u6e90\u7801\u7f16\u8bd1":30,"cudnn\u7684\u7f16\u8bd1\u9009\u9879":32,"dataprovider\u7684\u4ecb\u7ecd":2,"dataprovider\u7684\u4f7f\u7528":3,"filemanager\u8bbe\u8ba1\u6587\u6863":24,"float":30,"function":17,"gpu\u548ccpu\u6df7\u5408\u4f7f\u7528":54,"gpu\u6027\u80fd\u5206\u6790\u4e0e\u8c03\u4f18":49,"gpu\u955c\u50cf\u51fa\u73b0":30,"group\u6559\u7a0b":43,"kubernetes\u5206\u5e03\u5f0f\u8bad\u7ec3":57,"kubernetes\u5355\u673a\u8bad\u7ec3":56,"meta\u6587\u4ef6":67,"meta\u914d\u7f6e\u6587\u4ef6":67,"mnist\u7684\u4f7f\u7528\u573a\u666f":3,"movielens\u6570\u636e\u96c6":66,"movielens\u6570\u636e\u96c6\u8bc4\u5206\u56de\u5f52\u6a21\u578b":67,"org\u6587\u6863":47,"paddle\u52a8\u6001\u5e93\u4e2d":26,"paddle\u53d1\u884c\u89c4\u8303":29,"paddle\u56de\u5f52\u6d4b\u8bd5\u5217\u8868":29,"paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0":26,"paddle\u7248\u672c\u53f7\u4e3a0":30,"paddlepaddle\u53d1\u5e03\u7684docker\u955c\u50cf\u4f7f\u7528\u8bf4\u660e":33,"paddlepaddle\u7684\u7f16\u8bd1\u9009\u9879":32,"paddlepaddle\u7684docker\u5bb9\u5668\u4f7f\u7528\u65b9\u5f0f":33,"pi\u5e73\u53f0\u4e0a\u7684paddlepaddle\u5e93":39,"pod\u95f4\u901a\u4fe1":57,"pydataprovider2\u7684\u4f7f\u7528":3,"python\u63a5\u53e3":63,"python\u76f8\u5173\u7684\u5355\u5143\u6d4b\u8bd5\u90fd\u8fc7\u4e0d\u4e86":30,"python\u811a\u672c\u8bfb\u53d6\u6570\u636e":65,"return":28,"rnn\u76f8\u5173\u6a21\u578b":42,"rnn\u914d\u7f6e":44,"so\u627e\u4e0d\u5230":35,"ubuntu\u90e8\u7f72paddlepaddl":35,Abs:6,The:22,Using:22,With:23,activ:6,adadelta:11,adagrad:11,adam:11,adamax:11,addto:9,aggreg:9,aggregatelevel:9,algorithm:18,api:[1,4,27],applic:4,argument:[25,28],async:52,attent:44,attribut:7,auc:8,avg:12,base:23,basepool:12,batch:28,batch_norm:9,batch_siz:28,beam_search:9,becaus:30,between:16,bidirectional_gru:10,bidirectional_lstm:10,big:30,bilinear_interp:9,bla:32,block_expand:9,book:33,brelu:6,cach:3,capi:27,capi_priv:27,check:9,checkpoint:[18,19],chunk:8,cifar:13,classif:8,classification_error:8,classification_error_print:8,client:22,clone:45,cmake:17,code:23,column_sum:8,commit:45,compos:28,concat:9,config:4,configur:14,conll05:13,connect:9,content:[3,27,30,40,49],context_project:9,conv:9,conv_oper:9,conv_project:9,conv_shift:9,cos_sim:9,cost:9,cp27mu:30,creat:28,creation:21,creator:28,crf:9,crf_decod:9,cross_channel_norm:9,cross_entropy_cost:9,cross_entropy_with_selfnorm_cost:9,ctc:9,ctc_error:8,cuda:[30,32],cudnn:32,cudnnavg:12,cudnnmax:12,custom:28,dat:66,data:[9,13,18,28],datafeed:13,dataprovid:[4,52],dataset:[13,18,21],datatyp:13,decayedadagrad:11,decor:28,deploi:23,descript:25,design:[16,18,21,22,28],dictionari:28,dispatch:[18,21],distribut:[16,18,23],doc:[16,18,21,22,28],docker:23,dotmul_oper:9,dotmul_project:9,driver:30,dropout_lay:10,dylib:27,dynam:18,embed:9,entri:28,environ:23,eos:9,evalu:8,event:[15,16],exampl:[16,17,27],except:30,exp:6,expand:[9,40],expandlevel:9,faq:30,fault:18,first_seq:[9,40],fork:45,format:18,from:16,full_matrix_project:9,fulli:9,gate:44,get_output:9,gpu:52,gradient:22,gradient_print:8,group:9,gru:[10,52],gru_group:10,gru_step:9,gru_unit:10,grumemori:9,handler:[16,26],how:28,hsigmoid:9,huber_cost:9,ident:6,identity_project:9,illeg:30,imag:[9,10,23],imagenet:63,imdb:[13,69],img_cmrnorm:9,img_conv:9,img_conv_bn_pool:10,img_conv_group:10,img_pool:9,imikolov:13,implement:[17,28],infer:15,ingredi:16,ingress:24,init_hook:3,initi:22,input_typ:3,instruct:30,insuffici:30,interfac:[13,18,22,28],interpol:9,isn:28,issu:45,job:[18,23,56],join:9,kubernet:[23,55,56],lambda_cost:9,last_seq:[9,40],layer:[9,16],libcudart:35,libcudnn:35,libpaddle_capi_shar:27,libpaddle_capi_whol:27,librari:22,linear:6,linear_comb:9,linux_x86_64:30,list:[19,28],log:6,logic:21,lstm:[10,52,68,69],lstm_step:9,lstmemori:9,lstmemory_group:10,lstmemory_unit:10,map:28,master:[18,21,23],math:9,max:12,maxframe_print:8,maxid:9,maxid_print:8,maxout:9,memori:[9,41,43],messag:30,mini:28,minibatch:13,misc:10,mix:9,mnist:13,model:[4,14,16,22,44,63],momentum:11,movi:66,movielen:13,mse_cost:9,multi_binary_label_cross_entropy_cost:9,multipl:28,nce:9,need:28,network:[10,44],neural:44,nlp:[10,52],norm:9,nvprof:49,nvvp:49,object:18,onli:28,optim:[11,18,22],option:25,order:25,pad:9,paddl:[28,29],paddlejob:23,paddlepaddl:[16,47,60],parallel_nn:54,paramet:[7,15,16,18,22,23],partit:22,path:25,perform:52,persist:21,pfsclient:[24,25],pfsserver:24,platform:30,pnpair:8,point:30,pool:[9,12,40],power:9,pre:45,precision_recal:8,prefetch:28,print:8,process:[18,22,23],project:17,protocol:30,provid:[3,28],pull:45,push:45,python:[23,28],queue:[18,21],rank:8,rank_cost:9,rate:66,reader:[13,16,28],recoveri:18,recurr:[9,10,43,44],recurrent_group:9,refer:3,regular:22,reject:30,relu:6,repeat:9,request:45,requir:17,reshap:9,resnet:63,retri:21,rmsprop:11,rnn:[41,52],rotat:9,runtim:23,sampl:9,sampling_id:9,scale:[9,18],scaling_project:9,select:22,selective_fc:9,sentiment:13,seq_concat:9,seq_reshap:9,seqtext_print:8,sequenc:44,sequence_conv_pool:10,sequencesoftmax:6,server:[18,21,22,23],sgd:52,share:16,shuffl:28,sigmoid:6,simple_attent:10,simple_gru2:10,simple_gru:10,simple_img_conv_pool:10,simple_lstm:10,singl:28,slice:9,slope_intercept:9,small_vgg:10,smooth_l1_cost:9,softmax:6,softrelu:6,spars:22,spp:9,squar:6,squarerootn:12,stack:69,stanh:6,start:16,store:18,subcommond:25,submit:23,suffici:28,sum:[8,12],sum_cost:9,sum_to_one_norm:9,summar:16,support:30,synopsi:25,tabl:27,table_project:9,take:28,tanh:6,task:[18,21],tensor:9,text_conv_pool:10,thi:30,todo:[19,20],toler:18,too:30,train:[15,16,18,23,28],trainer:[15,18,22,23],tran:9,trans_full_matrix_project:9,tune:52,uci_h:13,updat:16,usag:28,use:28,user:[18,66],util:8,value_print:8,version:30,vgg_16_network:10,warp_ctc:9,wheel:30,whl:30,why:28,wmt14:13,zoo:63}})
\ No newline at end of file
+Search.setIndex({docnames:["about/index_cn","api/index_cn","api/v1/data_provider/dataprovider_cn","api/v1/data_provider/pydataprovider2_cn","api/v1/index_cn","api/v1/predict/swig_py_paddle_cn","api/v2/config/activation","api/v2/config/attr","api/v2/config/evaluators","api/v2/config/layer","api/v2/config/networks","api/v2/config/optimizer","api/v2/config/pooling","api/v2/data","api/v2/model_configs","api/v2/run_logic","design/api","design/build_system/README","design/cluster_train/README","design/cluster_train/checkpointing","design/cluster_train/data_dispatch","design/cluster_train/master_server","design/cluster_train/pserver_client","design/cluster_train/submit-job","design/file_manager/README","design/file_manager/pfs/pfsclient","design/multi_language_interface/00.why_plain_c","design/multi_language_interface/01.inference_implementation","design/parameters_in_cpp","design/reader/README","design/releasing_process","faq/index_cn","getstarted/basic_usage/index_cn","getstarted/build_and_install/cmake/build_from_source_cn","getstarted/build_and_install/docker_install_cn","getstarted/build_and_install/index_cn","getstarted/build_and_install/ubuntu_install_cn","getstarted/concepts/use_concepts_cn","getstarted/index_cn","howto/cross_compiling/cross_compiling_for_android_cn","howto/cross_compiling/cross_compiling_for_raspberry_cn","howto/deep_model/rnn/hierarchical_layer_cn","howto/deep_model/rnn/hrnn_rnn_api_compare_cn","howto/deep_model/rnn/index_cn","howto/deep_model/rnn/recurrent_group_cn","howto/deep_model/rnn/rnn_config_cn","howto/dev/contribute_to_paddle_cn","howto/dev/new_layer_cn","howto/dev/write_docs_cn","howto/index_cn","howto/optimization/gpu_profiling_cn","howto/usage/cluster/cluster_train_cn","howto/usage/cmd_parameter/arguments_cn","howto/usage/cmd_parameter/detail_introduction_cn","howto/usage/cmd_parameter/index_cn","howto/usage/cmd_parameter/use_case_cn","howto/usage/k8s/k8s_basis_cn","howto/usage/k8s/k8s_cn","howto/usage/k8s/k8s_distributed_cn","howto/usage/k8s/src/k8s_data/README","howto/usage/k8s/src/k8s_train/README","index_cn","tutorials/embedding_model/index_cn","tutorials/image_classification/index_cn","tutorials/imagenet_model/resnet_model_cn","tutorials/index_cn","tutorials/quick_start/index_cn","tutorials/rec/ml_dataset_cn","tutorials/rec/ml_regression_cn","tutorials/semantic_role_labeling/index_cn","tutorials/sentiment_analysis/index_cn","tutorials/text_generation/index_cn"],envversion:50,filenames:["about/index_cn.md","api/index_cn.rst","api/v1/data_provider/dataprovider_cn.rst","api/v1/data_provider/pydataprovider2_cn.rst","api/v1/index_cn.rst","api/v1/predict/swig_py_paddle_cn.rst","api/v2/config/activation.rst","api/v2/config/attr.rst","api/v2/config/evaluators.rst","api/v2/config/layer.rst","api/v2/config/networks.rst","api/v2/config/optimizer.rst","api/v2/config/pooling.rst","api/v2/data.rst","api/v2/model_configs.rst","api/v2/run_logic.rst","design/api.md","design/build_system/README.md","design/cluster_train/README.md","design/cluster_train/checkpointing.md","design/cluster_train/data_dispatch.md","design/cluster_train/master_server.md","design/cluster_train/pserver_client.md","design/cluster_train/submit-job.md","design/file_manager/README.md","design/file_manager/pfs/pfsclient.md","design/multi_language_interface/00.why_plain_c.md","design/multi_language_interface/01.inference_implementation.md","design/parameters_in_cpp.md","design/reader/README.md","design/releasing_process.md","faq/index_cn.rst","getstarted/basic_usage/index_cn.rst","getstarted/build_and_install/cmake/build_from_source_cn.rst","getstarted/build_and_install/docker_install_cn.rst","getstarted/build_and_install/index_cn.rst","getstarted/build_and_install/ubuntu_install_cn.rst","getstarted/concepts/use_concepts_cn.rst","getstarted/index_cn.rst","howto/cross_compiling/cross_compiling_for_android_cn.md","howto/cross_compiling/cross_compiling_for_raspberry_cn.md","howto/deep_model/rnn/hierarchical_layer_cn.rst","howto/deep_model/rnn/hrnn_rnn_api_compare_cn.rst","howto/deep_model/rnn/index_cn.rst","howto/deep_model/rnn/recurrent_group_cn.md","howto/deep_model/rnn/rnn_config_cn.rst","howto/dev/contribute_to_paddle_cn.md","howto/dev/new_layer_cn.rst","howto/dev/write_docs_cn.rst","howto/index_cn.rst","howto/optimization/gpu_profiling_cn.rst","howto/usage/cluster/cluster_train_cn.md","howto/usage/cmd_parameter/arguments_cn.md","howto/usage/cmd_parameter/detail_introduction_cn.md","howto/usage/cmd_parameter/index_cn.rst","howto/usage/cmd_parameter/use_case_cn.md","howto/usage/k8s/k8s_basis_cn.md","howto/usage/k8s/k8s_cn.md","howto/usage/k8s/k8s_distributed_cn.md","howto/usage/k8s/src/k8s_data/README.md","howto/usage/k8s/src/k8s_train/README.md","index_cn.rst","tutorials/embedding_model/index_cn.md","tutorials/image_classification/index_cn.md","tutorials/imagenet_model/resnet_model_cn.md","tutorials/index_cn.md","tutorials/quick_start/index_cn.rst","tutorials/rec/ml_dataset_cn.md","tutorials/rec/ml_regression_cn.rst","tutorials/semantic_role_labeling/index_cn.md","tutorials/sentiment_analysis/index_cn.md","tutorials/text_generation/index_cn.md"],objects:{},objnames:{},objtypes:{},terms:{"00012\u7684\u6a21\u578b\u6709\u7740\u6700\u9ad8\u7684bleu\u503c27":71,"0005\u4e58\u4ee5batch":63,"000\u4e2a\u5df2\u6807\u6ce8\u8fc7\u7684\u9ad8\u6781\u6027\u7535\u5f71\u8bc4\u8bba\u7528\u4e8e\u8bad\u7ec3":70,"000\u4e2a\u7528\u4e8e\u6d4b\u8bd5":70,"000\u4e2atxt\u6587\u4ef6":70,"000\u4f4d\u7528\u6237\u5bf94":67,"000\u5e45\u56fe\u50cf\u4e0a\u6d4b\u8bd5\u4e86\u6a21\u578b\u7684\u5206\u7c7b\u9519\u8bef\u7387":64,"000\u5f20\u7070\u5ea6\u56fe\u7247\u7684\u6570\u5b57\u5206\u7c7b\u6570\u636e\u96c6":3,"000\u6761\u8bc4\u4ef7":67,"000\u90e8\u7535\u5f71\u76841":67,"00186201e":5,"00m":50,"02595v1":9,"03m":50,"0424m":50,"0473v3":10,"05d":63,"0630u":50,"06u":50,"0810u":50,"08823112e":5,"0957m":50,"0\u53f7\u8bad\u7ec3\u8282\u70b9\u662f\u4e3b\u8bad\u7ec3\u8282\u70b9":53,"0\u5c42\u5e8f\u5217":41,"0\u8868\u793a\u7b2c\u4e00\u6b21\u7ecf\u8fc7\u8bad\u7ec3\u96c6":70,"0ab":9,"0b1":36,"0rc1":[30,34],"0rc2":30,"10000\u5f20\u4f5c\u4e3a\u6d4b\u8bd5\u96c6":63,"10007_10":70,"10014_7":70,"100m":31,"101\u5c42\u548c152\u5c42\u7684\u7f51\u7edc\u7ed3\u6784\u4e2d":64,"101\u5c42\u548c152\u5c42\u7684\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6\u53ef\u53c2\u7167":64,"101\u5c42\u7f51\u7edc\u6a21\u578b":64,"10\u4e2d\u7684\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6":63,"10\u6570\u636e\u96c6":63,"10\u6570\u636e\u96c6\u5305\u542b60000\u5f2032x32\u7684\u5f69\u8272\u56fe\u7247":63,"10\u6570\u636e\u96c6\u7684\u5b98\u65b9\u7f51\u5740":63,"10\u6570\u636e\u96c6\u8bad\u7ec3\u4e00\u4e2a\u5377\u79ef\u795e\u7ecf\u7f51\u7edc":63,"10g":23,"1150u":50,"11\u5b9e\u73b0\u4e86c":27,"11e6":57,"12194102e":5,"124n":50,"128\u7ef4\u548c256\u7ef4":62,"13m":57,"1490u":50,"14\u4ee5\u4e0a":40,"14\u6570\u636e\u96c6":71,"14\u6570\u636e\u96c6\u4e0a\u5f97\u5230\u826f\u597d\u8868\u73b0\u7684\u8bad\u7ec3\u8fc7\u7a0b":71,"14\u8fd9\u79cd\u5199\u6cd5\u5c06\u4f1a\u6d4b\u8bd5\u6a21\u578b":55,"152\u5c42\u7f51\u7edc\u6a21\u578b":64,"15501715e":5,"1550u":50,"15\u884c":42,"1636k":71,"16u":50,"173m":64,"173n":50,"1770u":50,"18\u5c81\u4ee5\u4e0b":67,"18e457ce3d362ff5f3febf8e7f85ffec852f70f3b629add10aed84f930a68750":57,"197u":50,"1\u7684\u5c42\u4e4b\u5916":55,"1\u7a00\u758f\u6570\u636e":47,"1\u8f6e\u5b58\u50a8\u7684\u6240\u6709\u6a21\u578b":55,"1\u9664\u4ee5batch":63,"1m\u6570\u636e\u96c6\u4e2d":68,"1m\u7684\u5b57\u6bb5\u914d\u7f6e\u6587\u4ef6\u5728\u76ee\u5f55":68,"200\u6570\u636e\u96c6\u4e0a\u4f7f\u7528vgg\u6a21\u578b\u8bad\u7ec3\u4e00\u4e2a\u9e1f\u7c7b\u56fe\u7247\u5206\u7c7b\u6a21\u578b":63,"210u":50,"211839e770f7b538e2d8":10,"215n":50,"228u":50,"234m":64,"24\u5c81":67,"2520u":50,"25639710e":5,"25k":66,"2680u":50,"26\u884c":42,"27787406e":5,"279n":50,"27m":50,"285m":50,"2863m":50,"28\u7684\u56fe\u7247\u50cf\u7d20\u7070\u5ea6\u503c":3,"28\u7ef4\u7684\u7a20\u5bc6\u6d6e\u70b9\u6570\u5411\u91cf\u548c\u4e00\u4e2a":3,"28m":50,"2977m":50,"29997\u4e2a\u6700\u9ad8\u9891\u5355\u8bcd\u548c3\u4e2a\u7279\u6b8a\u7b26\u53f7":71,"2\u4e09\u7c7b\u7684\u6bd4\u4f8b\u4e3a":31,"2\u4e2a\u6d6e\u70b9\u6570":32,"2\u5206\u522b\u4ee3\u88683\u4e2a\u8282\u70b9\u7684trainer":58,"2\u610f\u5473\u77400\u53f7\u548c1\u53f7gpu\u5c06\u4f1a\u4f7f\u7528\u6570\u636e\u5e76\u884c\u6765\u8ba1\u7b97fc1\u548cfc2\u5c42":55,"2\u8fd9\u51e0\u4e2a\u76ee\u5f55\u8868\u793apaddlepaddle\u8282\u70b9\u4e0etrain":58,"2nd":9,"302n":50,"30u":50,"3206326\u4e2a\u8bcd\u548c4\u4e2a\u7279\u6b8a\u6807\u8bb0":62,"32777140e":5,"328n":50,"32\u7ef4":62,"32u":50,"32x32":13,"331n":50,"3320u":50,"34\u5c81":67,"35\u65f6":71,"36540484e":5,"36u":50,"3710m":50,"3768m":50,"387u":50,"38u":50,"3920u":50,"39u":50,"3\u4e2a\u7279\u6b8a\u7b26\u53f7":71,"3\u53f7gpu":31,"4035m":50,"4090u":50,"4096mb":53,"4279m":50,"43630644e":5,"43u":50,"448a5b355b84":57,"44\u5c81":67,"4560u":50,"4563m":50,"45u":50,"4650u":50,"4726m":50,"473m":57,"48565123e":5,"48684503e":5,"49316648e":5,"49\u5c81":67,"4gb":53,"500\u4e2atxt\u6587\u4ef6":70,"500m":31,"50\u5c42":64,"50\u5c42\u7f51\u7edc\u6a21\u578b":64,"51111044e":5,"514u":50,"525n":50,"526u":50,"53018653e":5,"536u":50,"5460u":50,"5470u":50,"54u":50,"55\u5c81":67,"55g":71,"5690m":50,"573u":50,"578n":50,"5798m":50,"586u":50,"58s":57,"5969m":50,"5\u4e2a\u6d4b\u8bd5\u6837\u4f8b\u548c2\u4e2a\u751f\u6210\u5f0f\u6837\u4f8b":62,"5\u5230\u672c\u5730\u73af\u5883\u4e2d":36,"6080u":50,"6082v4":9,"6140u":50,"6305m":50,"639u":50,"64\u5e73\u53f0\u4e3a\u4f8b":[39,40],"64\u73af\u5883":40,"64\u7ef4":62,"655u":50,"6780u":50,"6810u":50,"682u":50,"6970u":50,"6\u4e07\u4ebf\u6b21\u6d6e\u70b9\u8fd0\u7b97\u6bcf\u79d2":50,"6\u4e2a\u8282\u70b9":51,"6\u5143\u4e0a\u4e0b\u6587\u4f5c\u4e3a\u8f93\u5165\u5c42":62,"704u":50,"70634608e":5,"7090u":50,"72296313e":5,"72u":50,"73u":50,"75u":50,"760u":50,"767u":50,"783n":50,"784u":50,"78m":50,"7\u7248\u672c\u5f00\u59cb":39,"7eamaa":13,"7kb":57,"8250u":50,"8300u":50,"830n":50,"849m":50,"85625684e":5,"861u":50,"864k":71,"8661m":50,"877\u4e2a\u88ab\u5411\u91cf\u5316\u7684\u8bcd":62,"877\u884c":62,"892m":50,"901n":50,"90u":50,"918u":50,"9247m":50,"924n":50,"9261m":50,"93137714e":5,"9330m":50,"94u":50,"9530m":50,"96644767e":5,"983m":50,"988u":50,"997u":50,"99982715e":5,"99m":64,"99u":50,"9\u4e2d\u7684\u4e00\u4e2a\u6570\u5b57":3,"9f18":57,"\u0233":32,"\u03b5":32,"\u4e00":42,"\u4e00\u4e2a0\u5c42\u5e8f\u5217":41,"\u4e00\u4e2a0\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u6269\u5c55\u6210\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":41,"\u4e00\u4e2a\u4f8b\u5b50\u662f\u623f\u4ea7\u4f30\u503c":32,"\u4e00\u4e2a\u5178\u578b\u7684\u795e\u7ecf\u7f51\u7edc\u5982\u4e0b\u56fe\u6240\u793a":63,"\u4e00\u4e2a\u5178\u578b\u7684chunk\u5982\u4e0b\u6240\u793a":24,"\u4e00\u4e2a\u5206\u5e03\u5f0f\u7684\u5b58\u50a8\u7cfb\u7edf":56,"\u4e00\u4e2a\u5206\u5e03\u5f0fpaddle\u8bad\u7ec3\u4efb\u52a1\u4e2d\u7684\u6bcf\u4e2a\u8fdb\u7a0b\u90fd\u53ef\u4ee5\u4ececeph\u8bfb\u53d6\u6570\u636e":57,"\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":41,"\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217\u6216\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":41,"\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u6269\u5c55\u6210\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":41,"\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217\u8fdb\u5165":44,"\u4e00\u4e2a\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5305\u542b\u5982\u4e0b\u5c42":63,"\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":41,"\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217\u6216\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":41,"\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u53d8\u6210\u4e00\u4e2a0\u5c42\u5e8f\u5217":41,"\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u53d8\u6210\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":41,"\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217\u8fdb\u5165":44,"\u4e00\u4e2a\u53cc\u5c42rnn\u7531\u591a\u4e2a\u5355\u5c42rnn\u7ec4\u6210":44,"\u4e00\u4e2a\u53ef\u8c03\u7528\u7684\u51fd\u6570":44,"\u4e00\u4e2a\u57fa\u672c\u7684\u5e94\u7528\u573a\u666f\u662f\u533a\u5206\u7ed9\u5b9a\u6587\u672c\u7684\u8912\u8d2c\u4e24\u6781\u6027":70,"\u4e00\u4e2a\u6216\u591a\u4e2a":56,"\u4e00\u4e2a\u6570\u636e\u96c6\u5927\u90e8\u5206\u5e8f\u5217\u957f\u5ea6\u662f100":31,"\u4e00\u4e2a\u6587\u4ef6":3,"\u4e00\u4e2a\u662f\u6d6e\u70b9\u8ba1\u7b97\u91cf":50,"\u4e00\u4e2a\u662f\u76f4\u63a5\u628a\u4e00\u4e2a\u5bb9\u5668\u8f6c\u6362\u6210\u955c\u50cf":34,"\u4e00\u4e2a\u72ec\u7acb\u7684\u5143\u7d20":41,"\u4e00\u4e2a\u72ec\u7acb\u7684\u8bcd\u8bed":41,"\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc\u7684\u6a21\u578b\u7531\u5927\u91cf\u7684\u53c2\u6570\u7ec4\u6210":19,"\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\u5982":70,"\u4e00\u4e2a\u7ec8\u7aef\u8fd0\u884cvi":34,"\u4e00\u4e2a\u7f51\u7edc\u5c42\u7684\u524d\u5411\u4f20\u64ad\u90e8\u5206\u628a\u8f93\u5165\u8f6c\u5316\u4e3a\u76f8\u5e94\u7684\u8f93\u51fa":47,"\u4e00\u4e2a\u7f51\u7edc\u5c42\u7684\u53c2\u6570\u662f\u5728":47,"\u4e00\u4e2a\u7f51\u7edc\u5c42\u7684c":47,"\u4e00\u4e2a\u91cd\u8981\u7684\u95ee\u9898\u662f\u9009\u62e9\u6b63\u786e\u7684learning_r":31,"\u4e00\u4e2achunk\u7531\u6240\u5728\u7684\u6587\u4ef6\u504f\u79fb":24,"\u4e00\u4e2agpu\u8bbe\u5907\u4e0a\u4e0d\u5141\u8bb8\u914d\u7f6e\u591a\u4e2a\u6a21\u578b":53,"\u4e00\u4e2alabel":42,"\u4e00\u4e2alogging\u5bf9\u8c61":3,"\u4e00\u4e2amemory\u5305\u542b":45,"\u4e00\u4e2apass\u610f\u5473\u7740paddlepaddle\u8bad\u7ec3\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u6837\u672c\u88ab\u904d\u5386\u4e00\u6b21":69,"\u4e00\u4e2apass\u8868\u793a\u8fc7\u4e00\u904d\u6240\u6709\u8bad\u7ec3\u6837\u672c":66,"\u4e00\u4e2apod\u4e2d\u7684\u6240\u6709\u5bb9\u5668\u4f1a\u88ab\u8c03\u5ea6\u5230\u540c\u4e00\u4e2anode\u4e0a":56,"\u4e00\u4e2aposix\u517c\u5bb9\u7684\u6587\u4ef6\u7cfb\u7edf":24,"\u4e00\u4eba":42,"\u4e00\u53e5\u8bdd\u662f\u7531\u8bcd\u8bed\u6784\u6210\u7684\u5e8f\u5217":44,"\u4e00\u53f0\u673a\u5668\u4e0a\u9762\u7684\u7ebf\u7a0b\u6570\u91cf":68,"\u4e00\u65e9":42,"\u4e00\u662fbatch":31,"\u4e00\u6761\u6837\u672c":3,"\u4e00\u6837\u8bbe\u7f6e":51,"\u4e00\u6b21\u4f5c\u4e1a\u79f0\u4e3a\u4e00\u4e2ajob":56,"\u4e00\u6b21\u6027\u676f\u5b50":42,"\u4e00\u6b21yield\u8c03\u7528":3,"\u4e00\u79cd\u5e38\u7528\u7684\u505a\u6cd5\u662f\u7528\u5b66\u4e60\u7684\u6a21\u578b\u5bf9\u53e6\u5916\u4e00\u7ec4\u6d4b\u8bd5\u6570\u636e\u8fdb\u884c\u9884\u6d4b":32,"\u4e00\u79cd\u5e38\u7528\u7684cmake\u914d\u7f6e\u5982\u4e0b":39,"\u4e00\u7bc7\u8bba\u6587":71,"\u4e00\u7ea7\u76ee\u5f55":[70,71],"\u4e00\u81f4":[41,42],"\u4e00\u822c\u4e0d\u5141\u8bb8\u518d\u4ece":30,"\u4e00\u822c\u4ece":46,"\u4e00\u822c\u5728paddlepaddle\u4e2d":42,"\u4e00\u822c\u60c5\u51b5\u4e0b":[2,32],"\u4e00\u822c\u63a8\u8350\u8bbe\u7f6e\u6210true":3,"\u4e00\u822c\u662f\u7531\u4e8e\u76f4\u63a5\u4f20\u9012\u5927\u5b57\u5178\u5bfc\u81f4\u7684":31,"\u4e00\u822c\u6765\u8bf4":45,"\u4e00\u822c\u800c\u8a00":71,"\u4e00\u822c\u8868\u793a":42,"\u4e00\u884c\u4e3a\u4e00\u4e2a\u6837\u672c":66,"\u4e09\u79cd\u5e8f\u5217\u6a21\u5f0f":[3,37],"\u4e09\u7ea7\u76ee\u5f55":[70,71],"\u4e0a":46,"\u4e0a\u4e0b\u6587\u5927\u5c0f\u8bbe\u7f6e\u4e3a1\u7684\u4e00\u4e2a\u6837\u672c\u7684\u7279\u5f81\u5982\u4e0b":69,"\u4e0a\u4f20\u5230cloud\u6216\u8005\u4e0b\u8f7d\u5230\u672c\u5730\u7684\u65f6\u95f4\u53ef\u80fd\u6bd4\u8f83\u957f":24,"\u4e0a\u53d1\u8868\u7684\u8bc4\u8bba\u5206\u6210\u6b63\u9762\u8bc4\u8bba\u548c\u8d1f\u9762\u8bc4\u8bba\u4e24\u7c7b":70,"\u4e0a\u56fe\u4e2d\u865a\u7ebf\u7684\u8fde\u63a5":42,"\u4e0a\u56fe\u63cf\u8ff0\u4e86\u4e00\u4e2a3\u8282\u70b9\u7684\u5206\u5e03\u5f0f\u8bad\u7ec3\u573a\u666f":58,"\u4e0a\u6ce8\u518c\u4e00\u4e0b":24,"\u4e0a\u7f16\u8bd1android\u5e73\u53f0\u4e0a\u9002\u7528\u7684paddlepaddle\u5e93":39,"\u4e0a\u7f51":42,"\u4e0a\u8fd0\u884c":34,"\u4e0a\u8ff0\u4ee3\u7801\u5c06bias\u5168\u90e8\u521d\u59cb\u5316\u4e3a1":31,"\u4e0a\u8ff0\u7684\u4ee3\u7801\u7247\u6bb5\u5305\u542b\u4e86\u4e24\u79cd\u65b9\u6cd5":50,"\u4e0a\u8ff0\u811a\u672c\u4f7f\u7528":51,"\u4e0b":63,"\u4e0b\u56fe\u4e2d\u5c31\u5c55\u793a\u4e86\u4e00\u4e9b\u5173\u4e8e\u5185\u5b58\u6570\u636e\u8fc1\u5f99\u548c\u8ba1\u7b97\u8d44\u6e90\u5229\u7528\u7387\u7684\u5efa\u8bae":50,"\u4e0b\u56fe\u5c55\u793a\u4e86\u6240\u6709\u7684\u56fe\u7247\u7c7b\u522b":63,"\u4e0b\u56fe\u5c55\u793a\u4e86\u65f6\u95f4\u6269\u5c55\u76842\u5c42":69,"\u4e0b\u56fe\u5c55\u793a\u7684\u662f\u57fa\u4e8e\u6b8b\u5dee\u7684\u8fde\u63a5\u65b9\u5f0f":64,"\u4e0b\u56fe\u662f\u4e00\u4e2a\u5168\u8fde\u63a5\u5c42\u7684\u793a\u610f\u56fe":47,"\u4e0b\u5b58\u653e\u516c\u5171\u6570\u636e\u96c6\u5408":20,"\u4e0b\u6587\u4ee5nlp\u4efb\u52a1\u4e3a\u4f8b":44,"\u4e0b\u6587\u4f7f\u7528":58,"\u4e0b\u6587\u5c31\u662f\u7528job\u7c7b\u578b\u7684\u8d44\u6e90\u6765\u8fdb\u884c\u8bad\u7ec3":57,"\u4e0b\u6b21":42,"\u4e0b\u7684":58,"\u4e0b\u7684\u4f5c\u4e3a\u7f16\u8bd1\u5de5\u5177":40,"\u4e0b\u8868\u5c55\u793a\u4e86batch":64,"\u4e0b\u8f7d":24,"\u4e0b\u8f7d\u5230\u672c\u5730":24,"\u4e0b\u8f7d\u5b8c\u6570\u636e\u540e":57,"\u4e0b\u8f7d\u5b8c\u76f8\u5173\u5b89\u88c5\u5305\u540e":36,"\u4e0b\u8f7d\u6570\u636e\u96c6":63,"\u4e0b\u8f7draspberri":40,"\u4e0b\u8f7dwmt":71,"\u4e0b\u9762\u4e3e\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50":50,"\u4e0b\u9762\u4ecb\u7ecd\u9884\u5904\u7406\u8fc7\u7a0b\u5177\u4f53\u7684\u6b65\u9aa4":68,"\u4e0b\u9762\u5148\u7b80\u8981\u4ecb\u7ecd\u4e00\u4e0b\u672c\u6587\u7528\u5230\u7684\u51e0\u4e2akubernetes\u6982\u5ff5":56,"\u4e0b\u9762\u5206\u522b\u4ecb\u7ecd\u67d0\u4e00\u7c7b\u6587\u4ef6\u7684\u5b9e\u73b0\u65b9\u5f0f":27,"\u4e0b\u9762\u5217\u51fa\u4e86":45,"\u4e0b\u9762\u5217\u51fa\u4e86\u5168\u8fde\u63a5\u5c42\u7684\u68af\u5ea6\u68c0\u67e5\u5355\u5143\u6d4b\u8bd5":47,"\u4e0b\u9762\u5c31\u6839\u636e\u8fd9\u51e0\u4e2a\u6b65\u9aa4\u5206\u522b\u4ecb\u7ecd":58,"\u4e0b\u9762\u6211\u4eec\u7ed9\u51fa\u4e86\u4e00\u4e2a\u914d\u7f6e\u793a\u4f8b":63,"\u4e0b\u9762\u662f\u4e00\u4e2a\u8bef\u5dee\u66f2\u7ebf\u56fe\u7684\u793a\u4f8b":63,"\u4e0b\u9762\u662fcifar":63,"\u4e0b\u9762\u7684\u4ee3\u7801\u7247\u6bb5\u5b9e\u73b0\u4e86":47,"\u4e0b\u9762\u7684\u4f8b\u5b50\u4f7f\u7528\u4e86":64,"\u4e0b\u9762\u7684\u4f8b\u5b50\u540c\u6837\u4f7f\u7528\u4e86":64,"\u4e0b\u9762\u7ed9\u51fa\u4e86\u4e00\u4e2a\u4f8b\u5b50":47,"\u4e0b\u9762\u7ed9\u51fa\u5728\u4e09\u7ef4\u7a7a\u95f4\u4e2d\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u62df\u5408\u4e00\u6761\u76f4\u7ebf\u7684\u4f8b\u5b50":37,"\u4e0b\u9762\u811a\u672c\u7b26\u5408paddlepaddle\u671f\u5f85\u7684\u8bfb\u53d6\u6570\u636e\u7684python\u7a0b\u5e8f\u7684\u6a21\u5f0f":32,"\u4e0b\u9762\u8fd9\u4e9blayer\u80fd\u591f\u63a5\u53d7\u53cc\u5c42\u5e8f\u5217\u4f5c\u4e3a\u8f93\u5165":41,"\u4e0d":42,"\u4e0d\u4e00\u5b9a\u548c\u65f6\u95f4\u6709\u5173\u7cfb":3,"\u4e0d\u4e00\u81f4\u7684\u7531pfsclient\u4e0b\u8f7d\u6216\u8005\u4f20\u8f93chunk\u5b8c\u6210":24,"\u4e0d\u4f1a\u518d\u4ece":31,"\u4e0d\u4f7f\u7528\u9759\u6001\u5e93":26,"\u4e0d\u4f7f\u7528\u989d\u5916\u7a7a\u95f4":47,"\u4e0d\u4f7f\u7528c":26,"\u4e0d\u4f7f\u7528swig":26,"\u4e0d\u5305\u542b\u5728\u5b57\u5178\u4e2d\u7684\u5355\u8bcd":71,"\u4e0d\u540c":69,"\u4e0d\u540c\u4e3b\u673a":56,"\u4e0d\u540c\u4ea7\u54c1":70,"\u4e0d\u540c\u53c2\u6570\u670d\u52a1\u5668\u4e0a\u6570\u636e\u5927\u5c0f\u7684\u6700\u5927\u503c\u4e0e\u6700\u5c0f\u503c\u7684\u6bd4\u7387":53,"\u4e0d\u540c\u5c42\u7684\u7279\u5f81\u7531\u5206\u53f7":64,"\u4e0d\u540c\u65f6\u95f4\u6b65\u7684\u8f93\u5165\u662f\u4e0d\u540c\u7684":45,"\u4e0d\u540c\u7248\u672c\u7684\u7f16\u8bd1\u5668\u4e4b\u95f4":26,"\u4e0d\u540c\u7684\u4f18\u5316\u7b97\u6cd5\u9700\u8981\u4f7f\u7528\u4e0d\u540c\u5927\u5c0f\u7684\u5185\u5b58":31,"\u4e0d\u540c\u7684\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf":58,"\u4e0d\u540c\u7684\u6570\u636e\u7c7b\u578b\u548c\u5e8f\u5217\u6a21\u5f0f\u8fd4\u56de\u7684\u683c\u5f0f\u4e0d\u540c":[3,37],"\u4e0d\u540c\u7a7a\u95f4\u7684\u8d44\u6e90\u540d\u53ef\u4ee5\u91cd\u590d":56,"\u4e0d\u540c\u8bed\u8a00\u7684\u63a5\u53e3\u9002\u5e94\u4e0d\u540c\u8bed\u8a00\u7684\u7279\u6027":26,"\u4e0d\u540c\u8f93\u5165\u542b\u6709\u7684\u5b50\u53e5":44,"\u4e0d\u540c\u8f93\u5165\u5e8f\u5217\u542b\u6709\u7684\u8bcd\u8bed\u6570\u5fc5\u987b\u4e25\u683c\u76f8\u7b49":44,"\u4e0d\u540cdataprovider\u5bf9\u6bd4\u5982\u4e0b":42,"\u4e0d\u540cpod\u4e4b\u95f4\u53ef\u4ee5\u901a\u8fc7ip\u5730\u5740\u8bbf\u95ee":56,"\u4e0d\u5728":27,"\u4e0d\u5bb9\u6613\u51fa\u9519":24,"\u4e0d\u5c11":42,"\u4e0d\u5d4c\u5165\u5176\u4ed6\u8bed\u8a00\u89e3\u91ca\u5668":26,"\u4e0d\u5d4c\u5165python\u89e3\u91ca\u5668":26,"\u4e0d\u5e94\u8be5\u88ab\u62c6\u89e3":44,"\u4e0d\u5fc5\u518d\u5c06\u4efb\u610f\u957f\u5ea6\u6e90\u8bed\u53e5\u4e2d\u7684\u6240\u6709\u4fe1\u606f\u538b\u7f29\u81f3\u4e00\u4e2a\u5b9a\u957f\u7684\u5411\u91cf\u4e2d":71,"\u4e0d\u6307\u5b9a\u65f6":44,"\u4e0d\u63d0\u4f9b\u5206\u5e03\u5f0f\u5b58\u50a8":56,"\u4e0d\u652f\u6301":69,"\u4e0d\u662f\u4e00\u6761\u5e8f\u5217":[3,37],"\u4e0d\u663e\u793a\u7684\u5199\u6bcf\u4e2a\u7c7b\u5177\u4f53\u5305\u542b\u4ec0\u4e48":26,"\u4e0d\u6ee1\u8db3\u94a9\u5b50\u7684":46,"\u4e0d\u7528mount\u7684\u65b9\u5f0f\u6765\u8bbf\u95ee\u6570\u636e":20,"\u4e0d\u7f13\u5b58\u4efb\u4f55\u6570\u636e":3,"\u4e0d\u80fd\u592a\u968f\u610f":46,"\u4e0d\u80fd\u88ab\u63d0\u4ea4\u5230":46,"\u4e0d\u8fc7":42,"\u4e0d\u8fdc":42,"\u4e0d\u9519":42,"\u4e0d\u9700\u8981\u5bf9\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u4efb\u4f55\u9884\u5904\u7406":45,"\u4e0e":[58,62,71],"\u4e0e\u4e4b\u76f8\u5bf9\u7684\u662flocal":24,"\u4e0e\u529f\u80fd\u5206\u652f\u4e0d\u540c\u7684\u662f":30,"\u4e0e\u5355\u5c42rnn\u7684\u914d\u7f6e\u7c7b\u4f3c":42,"\u4e0e\u53ef\u80fd\u6709\u7684":30,"\u4e0e\u5728":69,"\u4e0e\u672c\u5730\u8bad\u7ec3\u76f8\u540c":51,"\u4e0e\u6b64\u4e0d\u540c\u7684\u662f":58,"\u4e0e\u7ffb\u8bd1":71,"\u4e0e\u8bad\u7ec3\u4e0d\u540c":70,"\u4e0e\u8bad\u7ec3\u6a21\u578b\u4e0d\u540c\u7684\u662f":71,"\u4e0e\u8fd9\u4e2a\u8bad\u7ec3\u6570\u636e\u4ea4\u4e92\u7684layer":31,"\u4e0eimdb\u7f51\u7ad9\u63d0\u4f9b\u7684\u4e00\u81f4":67,"\u4e0ejob":58,"\u4e0etime":67,"\u4e14":42,"\u4e14\u4e0d\u6392\u9664commit\u4e4b\u95f4\u7684\u4fee\u6539\u5b58\u5728\u76f8\u4e92\u8986\u76d6\u7684\u60c5\u51b5":46,"\u4e14\u589e\u52a0\u4e00\u4e2a\u7b2c\u4e09\u65b9\u8bed\u8a00":26,"\u4e14\u5e8f\u5217\u7684\u6bcf\u4e00\u4e2a\u5143\u7d20\u8fd8\u662f\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217":[3,37],"\u4e14\u652f\u6301\u90e8\u7f72\u5230":56,"\u4e14\u6bcf\u4e2a\u53e5\u5b50\u8868\u793a\u4e3a\u5bf9\u5e94\u7684\u8bcd\u8868\u7d22\u5f15\u6570\u7ec4":42,"\u4e14\u8c03\u7528\u65f6\u4e0d\u80fd\u629b\u51fa\u5f02\u5e38\u6216\u51fa\u73b0\u8fd0\u884c\u65f6\u9519\u8bef":27,"\u4e14\u9ed8\u8ba4\u5728\u8bad\u7ec3\u96c6\u4e0a\u6784\u5efa\u5b57\u5178":70,"\u4e14c99\u652f\u6301bool\u7c7b\u578b\u548c\u5b9a\u957f\u6574\u6570":26,"\u4e14c99\u76f8\u5bf9\u4e8ec11\u4f7f\u7528\u66f4\u52a0\u5e7f\u6cdb":26,"\u4e24":42,"\u4e24\u4e2a\u5217\u8868\u6587\u4ef6":51,"\u4e24\u4e2a\u5b50\u76ee\u5f55\u4e0b":48,"\u4e24\u4e2a\u5d4c\u5957\u7684":44,"\u4e24\u4e2a\u64cd\u4f5c":50,"\u4e24\u4e2a\u6587\u4ef6\u5939":63,"\u4e24\u4e2a\u8f93\u5165\u7279\u5f81\u5728\u8fd9\u4e2a\u6d41\u7a0b\u4e2d\u8d77\u7740\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528":69,"\u4e24\u4e2a\u8f93\u5165\u7684\u5b50\u5e8f\u5217\u957f\u5ea6\u4e5f\u5e76\u4e0d\u76f8\u540c":42,"\u4e24\u4e2a\u90e8\u5206":48,"\u4e24\u79cd\u7c7b\u522b":66,"\u4e24\u8005\u5747\u4e3a\u7eaf\u6587\u672c\u6587\u4ef6":2,"\u4e2a":66,"\u4e2a\u5185\u5b58\u6c60\u5b9e\u9645\u4e0a\u51b3\u5b9a\u4e86shuffle\u7684\u7c92\u5ea6":31,"\u4e2a\u5355\u8bcd":71,"\u4e2a\u6027\u5316\u63a8\u8350":[30,65],"\u4e2a\u6279\u6b21\u540e\u6253\u5370\u4e00\u4e2a":68,"\u4e2a\u6279\u6b21\u7684\u53c2\u6570\u5e73\u5747\u503c\u8fdb\u884c\u6d4b\u8bd5":53,"\u4e2a\u6a21\u578b\u6d4b\u8bd5\u6570\u636e":53,"\u4e2d":[26,27,31,47,58,63,66,68,69,70],"\u4e2d\u4e0d\u8981\u6dfb\u52a0\u5927\u6587\u4ef6\u7b49":46,"\u4e2d\u4ecb\u7ecd\u7684\u65b9\u6cd5":62,"\u4e2d\u4efb\u610f\u7b2ci\u884c\u7684\u53e5\u5b50\u4e4b\u95f4\u90fd\u5fc5\u987b\u6709\u7740\u4e00\u4e00\u5bf9\u5e94\u7684\u5173\u7cfb":71,"\u4e2d\u4efb\u610f\u7b2ci\u884c\u7684\u53e5\u5b50\u4e4b\u95f4\u90fd\u6709\u7740\u4e00\u4e00\u5bf9\u5e94\u7684\u5173\u7cfb":71,"\u4e2d\u5173\u4e8e\u65f6\u95f4\u9012\u5f52\u795e\u7ecf\u7f51\u7edc\u7684\u4ecb\u7ecd":42,"\u4e2d\u5199\u5165json\u5185\u5bb9":19,"\u4e2d\u5305\u542b\u4e00\u4e2aandroid\u7248\u672c\u7684\u5e93":39,"\u4e2d\u5305\u542b\u4e00\u4e2araspberri":40,"\u4e2d\u5305\u542b\u4e86\u8bad\u7ec3\u6a21\u578b\u7684\u57fa\u672c\u547d\u4ee4":66,"\u4e2d\u5305\u542b\u5982\u4e0b\u8868\u6240\u793a\u76843\u4e2a\u6587\u4ef6\u5939":71,"\u4e2d\u5305\u542bc":[39,40],"\u4e2d\u5355\u5143\u6d4b\u8bd5\u7684\u4e00\u90e8\u5206":46,"\u4e2d\u5355\u5143\u6d4b\u8bd5\u80fd\u987a\u5229\u901a\u8fc7":46,"\u4e2d\u5b89\u88c5":51,"\u4e2d\u5b8c\u5168\u4e00\u81f4":26,"\u4e2d\u5b8c\u6210":70,"\u4e2d\u5b9a\u4e49":45,"\u4e2d\u5b9a\u4e49\u4f7f\u7528\u54ea\u79cddataprovid":2,"\u4e2d\u5b9a\u4e49\u548c\u4f7f\u7528":44,"\u4e2d\u5b9e\u73b0\u7684\u7ed3\u6784\u4f53":27,"\u4e2d\u5bfc\u51fa\u9884\u5b9a\u4e49\u7684\u7f51\u7edc":70,"\u4e2d\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528python\u6765\u63d0\u53d6\u7279\u5f81":64,"\u4e2d\u6307\u5b9a":53,"\u4e2d\u6307\u5b9a\u7684\u540d\u5b57":55,"\u4e2d\u6307\u5b9a\u7684\u5c42\u987a\u5e8f\u4e00\u81f4":64,"\u4e2d\u63d0\u51fa\u7684resnet\u7f51\u7edc\u7ed3\u6784\u57282015\u5e74imagenet\u5927\u89c4\u6a21\u89c6\u89c9\u8bc6\u522b\u7ade\u8d5b":64,"\u4e2d\u641c\u7d22\u8fd9\u51e0\u4e2a\u5e93":33,"\u4e2d\u6587\u6587\u6863":48,"\u4e2d\u6587\u6587\u6863\u76ee\u5f55":48,"\u4e2d\u6587\u7ef4\u57fa\u767e\u79d1\u9875\u9762":42,"\u4e2d\u6709\u8bb8\u591a\u7684\u7279\u5f81":67,"\u4e2d\u6bcf\u4e2apod\u7684ip\u5730\u5740":58,"\u4e2d\u6bcf\u5c42\u7684\u6570\u503c\u7edf\u8ba1":53,"\u4e2d\u7684":64,"\u4e2d\u7684\u4e00\u884c":[3,46],"\u4e2d\u7684\u5185\u5bb9":69,"\u4e2d\u7684\u5bf9\u5e94\u5206\u652f\u5373\u53ef":46,"\u4e2d\u7684\u63a5\u53e3":68,"\u4e2d\u7684\u6570\u636e":64,"\u4e2d\u7684\u6570\u636e\u662f\u5426\u4e3a\u5e8f\u5217\u6a21\u5f0f":68,"\u4e2d\u7684\u6570\u636e\u8fdb\u884c\u9884\u6d4b":64,"\u4e2d\u7684\u6570\u6910\u96c6\u7684\u7ed3\u6784\u5982\u4e0b":70,"\u4e2d\u7684\u6bcf\u4e00\u884c\u547d\u4ee4":68,"\u4e2d\u7684\u751f\u6210\u7ed3\u679c\u5982\u4e0b\u6240\u793a":71,"\u4e2d\u7684\u7528\u6237\u8bc1\u4e66":56,"\u4e2d\u7684\u7b2ci\u884c":71,"\u4e2d\u7684\u8bf4\u660e":3,"\u4e2d\u7684\u8fd9\u4e9b\u6570\u636e\u6587\u4ef6":67,"\u4e2d\u770b\u5230\u4e0b\u9762\u7684\u6587\u4ef6":70,"\u4e2d\u83b7\u53d6":58,"\u4e2d\u8ba4\u771f\u8bbe\u7f6e":51,"\u4e2d\u8bbe\u7f6e":51,"\u4e2d\u8bbe\u7f6e\u7684\u6240\u6709\u8282\u70b9":51,"\u4e2d\u8be6\u7ec6\u4ecb\u7ecd":47,"\u4e2d\u8bfb\u53d6":3,"\u4e2d\u8fd0\u884c\u4efb\u52a1\u7684\u89d2\u5ea6":20,"\u4e2d\u914d\u7f6e\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":45,"\u4e2d\u914d\u7f6e\u7684\u6548\u679c\u4e00\u81f4":3,"\u4e34\u65f6\u53d8\u91cf\u7b49\u7b49":31,"\u4e3a":[3,39,40,45],"\u4e3a0":3,"\u4e3a\u4e86\u4f7f\u7528\u63d0\u524d\u7f16\u5199\u7684\u811a\u672c":70,"\u4e3a\u4e86\u4f7f\u8bc4\u5ba1\u4eba\u5728\u8bc4\u5ba1\u4ee3\u7801\u65f6\u66f4\u597d\u5730\u4e13\u6ce8\u4e8e\u4ee3\u7801\u672c\u8eab":46,"\u4e3a\u4e86\u4fdd\u8bc1\u6548\u7387":47,"\u4e3a\u4e86\u4fdd\u8bc1gpu\u9a71\u52a8\u80fd\u591f\u5728\u955c\u50cf\u91cc\u9762\u6b63\u5e38\u8fd0\u884c":34,"\u4e3a\u4e86\u5145\u5206\u7684\u968f\u673a\u6253\u4e71\u8bad\u7ec3\u96c6":70,"\u4e3a\u4e86\u5b8c\u6210\u5206\u5e03\u5f0f\u673a\u5668\u5b66\u4e60\u8bad\u7ec3\u4efb\u52a1":56,"\u4e3a\u4e86\u5c01\u88c5\u80fd\u591f\u6b63\u786e\u5de5\u4f5c":47,"\u4e3a\u4e86\u5e94\u5bf9\u4ee5\u4e0a\u7684\u95ee\u9898":24,"\u4e3a\u4e86\u63cf\u8ff0\u65b9\u4fbf":44,"\u4e3a\u4e86\u65b9\u4fbf\u5728\u56fd":34,"\u4e3a\u4e86\u65b9\u4fbf\u5927\u5bb6":46,"\u4e3a\u4e86\u65b9\u4fbf\u8d77\u89c1":51,"\u4e3a\u4e86\u66b4\u9732\u7684\u63a5\u53e3\u5c3d\u91cf\u7b80\u5355":27,"\u4e3a\u4e86\u6ee1\u8db3\u8bad\u7ec3":51,"\u4e3a\u4e86\u7b80\u5316cmake\u914d\u7f6e":[39,40],"\u4e3a\u4e86\u8fbe\u5230\u6027\u80fd\u6700\u4f18":50,"\u4e3a\u4e86\u8fd8\u539f":32,"\u4e3a\u4f8b":66,"\u4e3a\u4f8b\u521b\u5efa\u5206\u5e03\u5f0f\u7684\u5355\u8fdb\u7a0b\u8bad\u7ec3":51,"\u4e3a\u4f8b\u8fdb\u884c\u9884\u6d4b":66,"\u4e3a\u53c2\u6570\u77e9\u9635\u7684\u5bbd\u5ea6":31,"\u4e3a\u60a8\u505a\u6027\u80fd\u8c03\u4f18\u63d0\u4f9b\u4e86\u65b9\u5411":50,"\u4e3a\u60f3\u4fee\u6b63\u8bcd\u5411\u91cf\u6a21\u578b\u7684\u7528\u6237\u63d0\u4f9b\u4e86\u5c06\u6587\u672c\u8bcd\u5411\u91cf\u6a21\u578b\u8f6c\u6362\u4e3a\u4e8c\u8fdb\u5236\u6a21\u578b\u7684\u547d\u4ee4":62,"\u4e3a\u65b9\u4fbf\u4f5c\u4e1a\u542f\u52a8\u63d0\u4f9b\u4e86\u4e24\u4e2a\u72ec\u7279\u7684\u547d\u4ee4\u9009\u9879":51,"\u4e3a\u6b64":57,"\u4e3a\u8f93\u51fa\u5206\u914d\u5185\u5b58":47,"\u4e3a\u96c6\u7fa4\u4f5c\u4e1a\u8bbe\u7f6e\u989d\u5916\u7684":51,"\u4e3ajson\u6216yaml\u683c\u5f0f":68,"\u4e3aoutput_\u7533\u8bf7\u5185\u5b58":47,"\u4e3b\u8981\u4e3a\u5f00\u53d1\u8005\u4f7f\u7528":53,"\u4e3b\u8981\u529f\u80fd\u5305\u62ec":24,"\u4e3b\u8981\u5305\u62ec\u4ee5\u4e0b\u4e94\u4e2a\u6b65\u9aa4":5,"\u4e3b\u8981\u5305\u62ec\u56db\u79cd\u7c7b\u578b":37,"\u4e3b\u8981\u539f\u56e0":42,"\u4e3b\u8981\u539f\u56e0\u5305\u62ec\u4e24\u4e2a\u65b9\u9762":31,"\u4e3b\u8981\u539f\u56e0\u662f\u589e\u52a0\u4e86\u521d\u59cb\u5316\u673a\u5236":3,"\u4e3b\u8981\u6765\u81ea\u5317\u7f8e\u6d32":63,"\u4e3b\u8981\u7684\u89e3\u51b3\u529e\u6cd5\u662f\u51cf\u5c0f\u5b66\u4e60\u5f8b\u6216\u8005\u5bf9\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406":31,"\u4e3b\u8981\u804c\u8d23\u5728\u4e8e\u5c06\u8bad\u7ec3\u6570\u636e\u4f20\u5165\u5185\u5b58\u6216\u8005\u663e\u5b58":66,"\u4e3e\u4e00\u4e2a\u4f8b\u5b50":31,"\u4e3e\u4f8b":31,"\u4e3e\u4f8b\u8bf4\u660e":42,"\u4e4b\u524d":46,"\u4e4b\u524d\u914d\u7f6e\u6587\u4ef6\u4e2d":66,"\u4e4b\u540e":[32,37,47],"\u4e4b\u540e\u4f60\u4f1a\u5f97\u5230\u8bad\u7ec3":51,"\u4e4b\u540e\u4f7f\u7528":47,"\u4e4b\u540e\u4f7f\u7528\u77e9\u9635\u8fd0\u7b97\u51fd\u6570\u6765\u8ba1\u7b97":47,"\u4e4b\u540e\u521d\u59cb\u5316\u6240\u6709\u7684\u6743\u91cd\u77e9\u9635":47,"\u4e4b\u540e\u5b9a\u4e49\u7684":63,"\u4e4b\u5916\u7684\u6240\u6709\u5934\u6587\u4ef6":27,"\u4e4b\u95f4\u7684\u8ddd\u79bb":32,"\u4e4b\u95f4\u7684\u8fd0\u7b97\u662f\u72ec\u7acb\u7684":44,"\u4e58\u4e0a\u8f93\u51fa\u7684\u68af\u5ea6":47,"\u4e58\u9664\u7b49\u65f6\u5019":31,"\u4e5d\u4e2a":69,"\u4e5f":42,"\u4e5f\u4e0d\u4f7f\u7528\u5176\u4ed6\u52a8\u6001\u5e93":26,"\u4e5f\u4e0d\u5b58\u5728\u4e00\u4e2asubseq\u76f4\u63a5\u751f\u6210\u4e0b\u4e00\u4e2asubseq\u7684\u60c5\u51b5":44,"\u4e5f\u4e0d\u5e94\u8be5\u62a5\u9519":27,"\u4e5f\u4e0d\u751f\u6210":27,"\u4e5f\u53ef\u4ee5\u4f7f\u7528":46,"\u4e5f\u53ef\u4ee5\u53bb\u6389\u8fd9\u4e9b\u8bc1\u4e66\u7684\u914d\u7f6e":56,"\u4e5f\u53ef\u4ee5\u5728\u5f00\u53d1\u955c\u50cf\u4e2d\u542f\u52a8\u4e00\u4e2asshd\u670d\u52a1":34,"\u4e5f\u53ef\u4ee5\u662f\u4e00\u4e2a\u8bcd\u8bed":44,"\u4e5f\u53ef\u4ee5\u8bf4\u662f\u67d0\u4e9b\u7279\u5b9a\u6307\u4ee4\u7684\u4f7f\u7528\u60c5\u51b5":50,"\u4e5f\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539":58,"\u4e5f\u53ef\u4ee5\u901a\u8fc7saving_period_by_batches\u8bbe\u7f6e\u6bcf\u9694\u591a\u5c11batch\u4fdd\u5b58\u4e00\u6b21\u6a21\u578b":66,"\u4e5f\u53ef\u4ee5\u914d\u7f6e\u4e0d\u540c\u7684\u91cd\u8bd5\u673a\u5236":56,"\u4e5f\u53ef\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u83b7\u53d6":[39,40],"\u4e5f\u5c31\u662f":46,"\u4e5f\u5c31\u662f\u5c06\u8bcd\u5411\u91cf\u6a21\u578b\u8fdb\u4e00\u6b65\u6f14\u5316\u4e3a\u4e09\u4e2a\u65b0\u6b65\u9aa4":66,"\u4e5f\u5c31\u662f\u81ea\u5df1\u7528\u6237\u540d\u4e0b\u7684":46,"\u4e5f\u5c31\u662f\u8bf4":[53,55,62],"\u4e5f\u5f97\u5230\u4e00\u4e2a\u7528\u6237\u7279\u5f81":68,"\u4e5f\u63cf\u8ff0\u4e86\u5bb9\u5668\u9700\u8981\u4f7f\u7528\u7684\u5b58\u50a8\u5377\u6302\u8f7d\u7684\u60c5\u51b5":58,"\u4e5f\u652f\u6301cpu\u7684\u6027\u80fd\u5206\u6790":50,"\u4e5f\u662f\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217":42,"\u4e5f\u662f\u5bb9\u5668\u4e0enode\u4e4b\u95f4\u5171\u4eab\u6587\u4ef6\u7684\u65b9\u5f0f":56,"\u4e5f\u662fdecoder\u5faa\u73af\u5c55\u5f00\u7684\u4f9d\u636e":44,"\u4e5f\u662fpaddlepaddle\u6240\u80fd\u591f\u4fdd\u8bc1\u7684shuffle\u7c92\u5ea6":3,"\u4e5f\u6ca1\u7528":31,"\u4e5f\u79f0\u4e3arnn\u6a21\u578b":66,"\u4e5f\u8bb8\u662f\u56e0\u4e3a\u9700\u8981\u5b89\u88c5":63,"\u4e5f\u9700\u8981\u4e24\u6b21\u968f\u673a\u9009\u62e9\u5230\u76f8\u540cgenerator\u7684\u65f6\u5019":3,"\u4e66\u5199":26,"\u4e7e":42,"\u4e86":42,"\u4e86\u89e3\u60a8\u7684\u786c\u4ef6":50,"\u4e86\u89e3\u66f4\u591a\u7ec6\u8282":45,"\u4e86\u89e3\u66f4\u591a\u8be6\u7ec6\u4fe1\u606f":45,"\u4e8c\u7ea7\u76ee\u5f55":[70,71],"\u4e8c\u7ef4\u77e9\u9635":64,"\u4e8c\u8005\u8bed\u610f\u4e0a\u5b8c\u5168\u4e00\u81f4":42,"\u4e8c\u8fdb\u5236":62,"\u4e92\u76f8\u901a\u4fe1":56,"\u4e92\u8054\u7f51\u7535\u5f71\u6570\u636e\u5e93":70,"\u4e94\u661f\u7ea7":42,"\u4e9a\u9a6c\u900a":70,"\u4ea4\u53c9\u7f16\u8bd1\u5de5\u5177\u94fe":40,"\u4ea4\u53c9\u7f16\u8bd1\u5de5\u5177\u94fe\u4e3a":39,"\u4ea4\u53c9\u7f16\u8bd1android\u7248\u672c\u7684paddlepaddle\u5e93\u65f6":39,"\u4ea4\u53c9\u7f16\u8bd1raspberri":40,"\u4ea4\u7531cmake\u7cfb\u7edf\u672c\u8eab\u6765\u5904\u7406":39,"\u4ea4\u901a":42,"\u4ea4\u901a\u4fbf\u5229":42,"\u4ea6\u53ef\u4ee5\u901a\u8fc7\u624b\u52a8\u8bbe\u7f6e":[39,40],"\u4eab\u53d7\u60a8\u7684\u65c5\u7a0b":34,"\u4eba\u8138\u8bc6\u522b":20,"\u4ec0\u4e48":68,"\u4ec5\u4ec5\u4f7f\u7528":26,"\u4ec5\u4ec5\u662f\u4e00\u4e9b\u5173\u952e\u8bcd":70,"\u4ec5\u4ec5\u662f\u4e24\u4e2a\u5168\u8fde\u63a5\u5c42":68,"\u4ec5\u4ec5\u662f\u7b80\u5355\u7684\u5d4c\u5165":68,"\u4ec5\u5305\u542b\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6570\u6910\u96c6":70,"\u4ec5\u5728\u8fdc\u7a0b\u7a00\u758f\u8bad\u7ec3\u65f6\u6709\u6548":47,"\u4ec5\u5bf9\u7a00\u758f\u6570\u636e\u6709\u6548":47,"\u4ec5\u9700\u8981\u77e5\u9053\u5982\u4f55\u4ece":3,"\u4ecb\u7ecd\u4e86\u4e00\u79cd\u901a\u8fc7ssh\u8fdc\u7a0b\u5206\u53d1\u4efb\u52a1":58,"\u4ecb\u7ecd\u4ea4\u53c9\u7f16\u8bd1android\u5e73\u53f0\u4e0a\u9002\u7528\u7684paddlepaddle\u5e93\u7684\u65b9\u6cd5\u548c\u6b65\u9aa4":39,"\u4ecb\u7ecd\u4ea4\u53c9\u7f16\u8bd1raspberri":40,"\u4ecb\u7ecd\u5206\u5e03\u5f0f\u8bad\u7ec3\u4e4b\u524d":56,"\u4ecb\u7ecdpaddlepaddle\u7684\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5":66,"\u4ece":[30,50,69],"\u4ece0\u5230num":53,"\u4ece\u4e00\u4e2aword\u751f\u6210\u4e0b\u4e00\u4e2aword":44,"\u4ece\u5185\u6838\u51fd\u6570\u7684\u89d2\u5ea6":50,"\u4ece\u56fe\u4e2d\u53ef\u4ee5\u770b\u5230":32,"\u4ece\u5916\u90e8\u7f51\u7ad9\u4e0a\u4e0b\u8f7d\u7684\u539f\u59cb\u6570\u6910\u96c6":70,"\u4ece\u5927\u5230\u5c0f":71,"\u4ece\u6570\u636e\u63d0\u4f9b\u7a0b\u5e8f\u52a0\u8f7d\u5b9e\u4f8b":69,"\u4ece\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u6765\u770b":42,"\u4ece\u6bcf\u4e2a\u5355\u8bcd\u5de6\u53f3\u4e24\u7aef\u5206\u522b\u83b7\u53d6k\u4e2a\u76f8\u90bb\u7684\u5355\u8bcd":66,"\u4ece\u6e90\u7801\u4ea4\u53c9\u7f16\u8bd1paddlepaddl":[39,40],"\u4ece\u78c1\u76d8\u6587\u4ef6\u4e2d\u52a0\u8f7duuid\u6587\u4ef6\u540d\u7684\u68c0\u67e5\u70b9\u5feb\u7167\u6587\u4ef6":19,"\u4ece\u7b2c0\u4e2a\u8bc4\u4f30\u5230\u5f53\u524d\u8bc4\u4f30\u4e2d":71,"\u4ece\u7b2c0\u4e2abatch\u5230\u5f53\u524dbatch\u7684\u5e73\u5747\u635f\u5931":70,"\u4ece\u7b2c0\u4e2abatch\u5230\u5f53\u524dbatch\u7684\u5e73\u5747cost":71,"\u4ece\u800c\u53ef\u4ee5\u505a\u4e00\u4e9b\u4e0e\u8ba1\u7b97\u91cd\u53e0\u7684\u5de5\u4f5c":47,"\u4ece\u800c\u53ef\u4ee5\u62df\u5408\u4efb\u610f\u7684\u51fd\u6570\u6765\u5b66\u4e60\u590d\u6742\u7684\u6570\u636e\u5173\u7cfb":32,"\u4ece\u800c\u751f\u6210\u591a\u4e2agener":3,"\u4ece\u800c\u80fd\u591f\u88abpaddlepaddl":66,"\u4ece\u800c\u9632\u6b62\u8fc7\u62df\u5408":2,"\u4ece\u8be5\u94fe\u63a5":71,"\u4ece\u8bed\u4e49\u4e0a\u770b":44,"\u4ece\u8f93\u5165\u6570\u636e\u4e0a\u770b":42,"\u4ece\u8f93\u51fa\u65e5\u5fd7\u53ef\u4ee5\u770b\u5230":32,"\u4ece\u9884\u8bad\u7ec3\u6a21\u578b\u4e2d":62,"\u4ececmake":39,"\u4eceetcd\u4e2d\u8bfb\u53d6\u8282\u70b9":19,"\u4ecestart":53,"\u4ecetest":71,"\u4ed3\u5e93\u7684\u8fdc\u7a0b\u4e3b\u673a":46,"\u4ed4\u7ec6\u89c2\u5bdf":64,"\u4ed6\u4e3b\u8981\u5305\u542b\u4e86\u5b9e\u9645\u66b4\u9732\u7684\u7c7b\u578b\u7ed3\u6784":27,"\u4ed6\u4eec\u5206\u522b\u662f":42,"\u4ed6\u4eec\u5728paddle\u7684\u6587\u6863\u548capi\u4e2d\u662f\u4e00\u4e2a\u6982\u5ff5":42,"\u4ed6\u4eec\u63d0\u51fa\u6b8b\u5dee\u5b66\u4e60\u7684\u6846\u67b6\u6765\u7b80\u5316\u7f51\u7edc\u7684\u8bad\u7ec3":64,"\u4ed6\u662f\u5c06":27,"\u4ed6\u7684\u76ee\u6807\u662f\u4f7f\u7528c":26,"\u4ee3\u66ff":58,"\u4ee3\u7801":68,"\u4ee3\u7801\u4e2d9":42,"\u4ee3\u7801\u5982\u4e0b":45,"\u4ee3\u7801\u6ce8\u91ca\u8bf7\u9075\u5b88":46,"\u4ee3\u7801\u751f\u6210\u7684\u7b26\u53f7\u53ef\u80fd\u4e0d\u4e00\u81f4":26,"\u4ee3\u8868\u5bbf\u4e3b\u673a\u76ee\u5f55":58,"\u4ee3\u8868\u7f16\u53f7":68,"\u4ee3\u8868\u8fd9\u4e2ashard\u7684\u6700\u5927index":20,"\u4ee3\u8868shard\u7684index":20,"\u4ee5\u4e0a":46,"\u4ee5\u4e0a\u4ee3\u7801\u7684reader\u8f93\u51fa\u7684data":20,"\u4ee5\u4e0a\u547d\u4ee4\u4f1a\u5728\u5f53\u524d\u76ee\u5f55\u4e0b\u751f\u6210100\u4e2a\u6587\u4ef6":20,"\u4ee5\u4e0a\u547d\u4ee4\u4f1a\u6309\u7167\u751f\u6210\u7684dockerfile\u628a\u751f\u6210\u7684\u7a0b\u5e8f\u62f7\u8d1d\u5230\u751f\u4ea7\u955c\u50cf\u4e2d\u5e76\u505a\u76f8\u5e94\u7684\u914d\u7f6e":34,"\u4ee5\u4e0a\u547d\u4ee4\u4f1a\u7f16\u8bd1paddlepaddl":34,"\u4ee5\u4e0a\u65b9\u6cd5\u5728gpu\u955c\u50cf\u91cc\u4e5f\u80fd\u7528":34,"\u4ee5\u4e0b":[20,68],"\u4ee5\u4e0b\u4ee3\u7801\u6bb5\u5b9a\u4e49\u4e86\u4e09\u4e2a\u8f93\u5165":45,"\u4ee5\u4e0b\u4ee3\u7801\u7247\u6bb5\u5b9a\u4e49":45,"\u4ee5\u4e0b\u6211\u4eec\u7ffb\u8bd1\u6570\u636e\u96c6\u7f51\u7ad9\u4e2dreadme\u6587\u4ef6\u7684\u63cf\u8ff0":67,"\u4ee5\u4e0b\u6307\u4ee4\u80fd\u68c0\u67e5linux\u7535\u8111\u662f\u5426\u652f\u6301avx":34,"\u4ee5\u4e0b\u6559\u7a0b\u5c06\u6307\u5bfc\u60a8\u63d0\u4ea4\u4ee3\u7801":46,"\u4ee5\u4e0b\u662f\u5bf9\u4e0a\u8ff0\u6570\u636e\u52a0\u8f7d\u7684\u89e3\u91ca":66,"\u4ee5\u4e0b\u6b65\u9aa4\u57fa\u4e8e":51,"\u4ee5\u4e0b\u793a\u8303\u5982\u4f55\u4f7f\u7528\u9884\u8bad\u7ec3\u7684\u4e2d\u6587\u5b57\u5178\u548c\u8bcd\u5411\u91cf\u8fdb\u884c\u77ed\u8bed\u6539\u5199":62,"\u4ee5\u4e0b\u9009\u9879\u5fc5\u987b\u5728":51,"\u4ee5\u4ea4\u4e92\u5bb9\u5668\u65b9\u5f0f\u8fd0\u884c\u5f00\u53d1\u955c\u50cf":34,"\u4ee5\u4fbf\u6211\u4eec\u53ef\u4ee5\u628a\u66f4\u591a\u7684\u7cbe\u529b\u653e\u5230\u903b\u8f91\u672c\u8eab\u4e0a":24,"\u4ee5\u4fbf\u7528\u6237":51,"\u4ee5\u4fdd\u8bc1\u68af\u5ea6\u7684\u6b63\u786e\u8ba1\u7b97":47,"\u4ee5\u4fdd\u8bc1\u68af\u5ea6\u8ba1\u7b97\u7684\u6b63\u786e\u6027":47,"\u4ee5\u5206\u7c7b\u6765\u81ea":70,"\u4ee5\u53ca":47,"\u4ee5\u53ca\u4f7f\u7528\u5b50\u5e8f\u5217\u6765\u5b9a\u4e49\u5206\u7ea7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u67b6\u6784":45,"\u4ee5\u53ca\u53cc\u5c42\u5e8f\u5217":41,"\u4ee5\u53ca\u5728wmt":71,"\u4ee5\u53ca\u5982\u4f55\u5728\u5c42\u4e4b\u95f4\u8fdb\u884c\u8fde\u63a5":63,"\u4ee5\u53ca\u751f\u6210\u521b\u5efa\u751f\u4ea7\u955c\u50cf\u7684dockerfil":34,"\u4ee5\u53ca\u76ee\u6807\u673a\u7248openblas\u5e93":40,"\u4ee5\u53ca\u8ba1\u7b97\u903b\u8f91\u5728\u5e8f\u5217\u4e0a\u7684\u5faa\u73af\u5c55\u5f00":44,"\u4ee5\u53ca\u8f93\u5165\u7684\u68af\u5ea6":47,"\u4ee5\u53caandroid":39,"\u4ee5\u53caandroid\u6240\u9700":39,"\u4ee5\u53canumpi":20,"\u4ee5\u53capaddle\u5982\u4f55\u5904\u7406\u591a\u79cd\u7c7b\u578b\u7684\u8f93\u5165":68,"\u4ee5\u53carelu":47,"\u4ee5\u63d0\u4f9b\u4e00\u4e9b\u9ed8\u8ba4\u7684\u7f16\u8bd1\u5668\u548c\u7f16\u8bd1\u53c2\u6570\u76f8\u5173\u914d\u7f6e":[39,40],"\u4ee5\u76f8\u5bf9\u8def\u5f84\u5f15\u7528":2,"\u4ee5\u786e\u4fdd\u6240\u6709\u7684\u7b2c\u4e09\u65b9\u4f9d\u8d56\u5e93\u548cpaddlepaddle\u4ee3\u7801\u90fd\u662f\u9488\u5bf9\u65b0\u7684cmake\u914d\u7f6e\u91cd\u65b0\u7f16\u8bd1\u7684":[39,40],"\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u7f51\u7edc\u6027\u80fd":51,"\u4ee5\u9017\u53f7":62,"\u4ee5\u9017\u53f7\u95f4\u9694":53,"\u4ef7\u683c":42,"\u4efb\u52a1":68,"\u4efb\u52a1\u6765\u7ec8\u6b62\u96c6\u7fa4\u4f5c\u4e1a":51,"\u4efb\u610f\u5c06\u4e00\u4e9b\u6570\u636e\u7ec4\u5408\u6210\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217":42,"\u4efb\u610f\u65f6\u523b\u53ea\u53ef\u80fd\u540c\u65f6\u6709\u4e00\u53f0\u670d\u52a1\u5668\u6545\u969c":19,"\u4f18\u5316":70,"\u4f18\u5316\u5668\u5219\u7528\u94fe\u5f0f\u6cd5\u5219\u6765\u5bf9\u6bcf\u4e2a\u53c2\u6570\u8ba1\u7b97\u635f\u5931\u51fd\u6570\u7684\u68af\u5ea6":47,"\u4f1a\u5148\u8fdb\u884c\u53c2\u6570\u7684\u521d\u59cb\u5316\u4e0e\u89e3\u6790":58,"\u4f1a\u5171\u4eab\u53c2\u6570":31,"\u4f1a\u52a0\u8f7d\u4e0a\u4e00\u8f6e\u7684\u53c2\u6570":53,"\u4f1a\u53d8\u6210\u8bcd\u8868\u4e2d\u7684\u4f4d\u7f6e":42,"\u4f1a\u542f\u52a8pserver\u4e0etrainer\u8fdb\u7a0b":58,"\u4f1a\u5728\u5f53\u524d\u76ee\u5f55\u751f\u6210\u4e24\u4e2a\u5b50\u76ee\u5f55":48,"\u4f1a\u5bf9\u6bcf\u4e00\u4e2a\u6fc0\u6d3b\u6682\u5b58\u4e00\u4e9b\u6570\u636e":31,"\u4f1a\u5bf9\u8fd9\u7c7b\u8f93\u5165\u8fdb\u884c\u62c6\u89e3":44,"\u4f1a\u5bfc\u81f4\u4e0d\u540c\u7248\u672cpython\u5728\u4e00\u4e2a\u8fdb\u7a0b\u91cc\u7684bug":26,"\u4f1a\u5c06\u6bcf\u4e2a\u65f6\u95f4\u6b65\u7684\u8f93\u51fa\u62fc\u63a5":44,"\u4f1a\u5c06\u7b2c\u4e00\u4e2a":31,"\u4f1a\u6210\u4e3astep\u51fd\u6570\u7684\u8f93\u5165":44,"\u4f1a\u6254\u5230\u8fd9\u6761\u6570\u636e":3,"\u4f1a\u62a5\u9519":44,"\u4f1a\u6839\u636e\u547d\u4ee4\u884c\u53c2\u6570\u6307\u5b9a\u7684\u6d4b\u8bd5\u65b9\u5f0f":2,"\u4f1a\u6839\u636einput_types\u68c0\u67e5\u6570\u636e\u7684\u5408\u6cd5\u6027":3,"\u4f1a\u76f4\u63a5\u62a5\u9519\u9000\u51fa":26,"\u4f1a\u76f8\u5e94\u5730\u6539\u53d8\u8f93\u51fa\u7684\u5c3a\u5bf8":47,"\u4f1a\u81ea\u52a8\u5173\u95ed\u5bf9\u5e94\u7684issu":46,"\u4f1a\u81ea\u9002\u5e94\u5730\u4ece\u8fd9\u4e9b\u5411\u91cf\u4e2d\u9009\u62e9\u4e00\u4e2a\u5b50\u96c6\u51fa\u6765":71,"\u4f1a\u83b7\u53d6\u5f53\u524dnamespace\u4e0b\u7684\u6240\u6709pod":58,"\u4f1a\u88ab\u62c6\u89e3\u4e3a\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":44,"\u4f1a\u88ab\u62c6\u89e3\u4e3a\u975e\u5e8f\u5217":44,"\u4f1a\u88abpickle\u5e8f\u5217\u5316\u6210\u5b57\u7b26\u4e32":20,"\u4f1a\u9020\u6210\u90ae\u4ef6\u707e\u96be":46,"\u4f20\u5165":[3,20],"\u4f20\u5165\u4e0a\u4e00\u6b65\u89e3\u6790\u51fa\u6765\u7684\u6a21\u578b\u914d\u7f6e\u5c31\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a":5,"\u4f20\u5165\u9884\u6d4b\u6570\u636e":5,"\u4f20\u7ed9dataprovider\u7684\u67d0\u4e00\u4e2aargs\u8fc7\u5927":31,"\u4f20\u9012\u7ed9\u914d\u7f6e\u6587\u4ef6\u7684\u53c2\u6570":53,"\u4f46":27,"\u4f46\u4e0d\u66b4\u9732":27,"\u4f46\u4e0d\u7528\u4e8e\u8ba1\u7b97\u68af\u5ea6":47,"\u4f46\u4e0d\u9700\u8981\u63d0\u524d\u521b\u5efa":53,"\u4f46\u4e8e\u53cc\u5c42\u5e8f\u5217\u7684lstm\u6765\u8bf4":42,"\u4f46\u548c\u5355\u5c42rnn\u4e0d\u540c":42,"\u4f46\u5728\u8d77\u521d\u7684\u51e0\u8f6e\u8bad\u7ec3\u4e2d\u5b83\u4eec\u90fd\u5728\u5feb\u901f\u903c\u8fd1\u771f\u5b9e\u503c":32,"\u4f46\u5b50\u53e5\u542b\u6709\u7684\u8bcd\u8bed\u6570\u53ef\u4ee5\u4e0d\u76f8\u7b49":44,"\u4f46\u5c3d\u91cf\u8bf7\u4fdd\u6301\u7f16\u8bd1\u548c\u8fd0\u884c\u4f7f\u7528\u7684cudnn\u662f\u540c\u4e00\u4e2a\u7248\u672c":33,"\u4f46\u5e76\u6ca1\u6709\u7ecf\u8fc7\u56de\u5f52\u6d4b\u8bd5":30,"\u4f46\u5e8f\u5217\u8f93\u51fa\u65f6":42,"\u4f46\u5f53\u8c03\u7528\u8fc7\u4e00\u6b21\u540e":3,"\u4f46\u6240\u6709fork\u7684\u7248\u672c\u5e93\u7684\u6240\u6709\u5206\u652f\u90fd\u76f8\u5f53\u4e8e\u7279\u6027\u5206\u652f":30,"\u4f46\u662f":[31,42],"\u4f46\u662f2008\u5e74\u4e4b\u524d\u751f\u4ea7\u7684\u65e7\u7535\u8111\u4e0d\u652f\u6301avx":34,"\u4f46\u662f\u4e5f\u6ca1\u6709\u5fc5\u8981\u5220\u9664\u65e0\u7528\u7684\u6587\u4ef6":51,"\u4f46\u662f\u53c8\u8fc7\u4e8e\u7410\u788e":27,"\u4f46\u662f\u5927\u90e8\u5206\u53c2\u6570\u662f\u4e3a\u5f00\u53d1\u8005\u63d0\u4f9b\u7684":52,"\u4f46\u662f\u5b50\u5e8f\u5217\u7684\u6570\u76ee\u5fc5\u987b\u4e00\u6837":42,"\u4f46\u662f\u6211\u4eec\u5e76\u4e0d\u63a8\u8350\u8fd9\u79cd\u65b9\u6cd5":34,"\u4f46\u662f\u652f\u6301avx\u6307\u4ee4\u96c6":46,"\u4f46\u662f\u65b9\u4fbf\u8c03\u8bd5\u548c\u6d4bbenchmark":33,"\u4f46\u662f\u6bcf\u4e2a\u6837\u672c\u4ec5\u5305\u542b\u51e0\u4e2a\u8bcd":55,"\u4f46\u662f\u7a81\u7136\u6709\u4e00\u4e2a10000\u957f\u7684\u5e8f\u5217":31,"\u4f46\u662f\u89e3\u91ca\u6027\u8bed\u8a00":26,"\u4f46\u662f\u8fd9\u79cd\u65b9\u6cd5\u5728\u6bcf\u5c42\u53ea\u4fdd\u5b58\u9884\u8bbe\u6570\u91cf\u7684\u6700\u4f18\u72b6\u6001":71,"\u4f46\u662f\u8fdc\u672a\u5b8c\u5584":0,"\u4f46\u662f\u9690\u85cf\u5c42\u4e2d\u7684\u6bcf\u4e2a\u666e\u901a\u8282\u70b9\u88ab\u4e00\u4e2a\u8bb0\u5fc6\u5355\u5143\u66ff\u6362":70,"\u4f46\u662fbatch":31,"\u4f46\u6709\u503c\u7684\u5730\u65b9\u5fc5\u987b\u4e3a1":[3,37],"\u4f46\u6709\u503c\u7684\u90e8\u5206\u53ef\u4ee5\u662f\u4efb\u4f55\u6d6e\u70b9\u6570":[3,37],"\u4f46\u8fd9\u4e2a\u5173\u7cfb\u53ef\u80fd\u4e0d\u6b63\u786e":3,"\u4f4d\u7f6e":42,"\u4f4f":42,"\u4f53\u88c1\u5b57\u5178":68,"\u4f53\u88c1\u5b57\u6bb5":68,"\u4f59\u5f26\u76f8\u4f3c\u5ea6\u56de\u5f52":68,"\u4f59\u5f26\u76f8\u4f3c\u5ea6\u5c42":68,"\u4f5c\u4e3a\u4e0b\u4e00\u4e2a\u5b50\u53e5memory\u7684\u521d\u59cb\u72b6\u6001":42,"\u4f5c\u4e3a\u4f8b\u5b50\u6f14\u793a\u5982\u4f55\u914d\u7f6e\u590d\u6742\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u6a21\u578b":45,"\u4f5c\u4e3a\u53c2\u6570\u7684id":31,"\u4f5c\u4e3a\u5b58\u50a8\u7cfb\u7edf":20,"\u4f5c\u4e3a\u5f53\u524d\u65f6\u523b\u8f93\u5165":44,"\u4f5c\u4e3a\u793a\u4f8b\u6570\u636e":67,"\u4f5c\u4e3a\u7c7b\u53e5\u67c4":26,"\u4f5c\u4e3a\u7ebf\u6027\u56de\u5f52\u7684\u8f93\u5165":32,"\u4f5c\u4e3a\u8f93\u51fa":45,"\u4f5c\u4e3a\u96c6\u7fa4\u8bad\u7ec3\u7684\u5de5\u4f5c\u7a7a\u95f4":51,"\u4f5c\u4e3aboot_layer\u4f20\u7ed9\u4e0b\u4e00\u4e2a\u5b50\u53e5\u7684memori":42,"\u4f5c\u5bb6":67,"\u4f5c\u7528":41,"\u4f60\u4e5f\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e09\u4e2a\u503c":64,"\u4f60\u4e5f\u53ef\u4ee5\u5148\u8df3\u8fc7\u672c\u6587\u7684\u89e3\u91ca\u73af\u8282":66,"\u4f60\u4e5f\u53ef\u4ee5\u7b80\u5355\u7684\u8fd0\u884c\u4ee5\u4e0b\u7684\u547d\u4ee4":62,"\u4f60\u4e5f\u53ef\u4ee5\u901a\u8fc7\u5728\u547d\u4ee4\u884c\u53c2\u6570\u4e2d\u589e\u52a0\u4e00\u4e2a\u53c2\u6570\u5982":64,"\u4f60\u4e5f\u8bb8\u53ef\u4ee5\u5c1d\u8bd5\u66f4\u8001\u7684\u65b9\u6cd5":34,"\u4f60\u53ea\u9700\u5b8c\u6210":51,"\u4f60\u53ea\u9700\u8981\u5728\u547d\u4ee4\u884c\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4":66,"\u4f60\u53ea\u9700\u8981\u6309\u7167\u5982\u4e0b\u65b9\u5f0f\u7ec4\u7ec7\u6570\u636e":71,"\u4f60\u53ef\u4ee5\u4f7f\u7528":64,"\u4f60\u53ef\u4ee5\u4f7f\u7528\u4e0b\u9762\u7684\u811a\u672c\u4e0b\u8f7d":70,"\u4f60\u53ef\u4ee5\u4f7f\u7528\u5b83\u6765\u8bbe\u7f6e":51,"\u4f60\u53ef\u4ee5\u4f7f\u7528\u672c\u5730\u8bad\u7ec3\u4e2d\u7684\u76f8\u540c\u6a21\u578b\u6587\u4ef6\u8fdb\u884c\u96c6\u7fa4\u8bad\u7ec3":51,"\u4f60\u53ef\u4ee5\u5728\u4efb\u4f55\u65f6\u5019\u7528":68,"\u4f60\u53ef\u4ee5\u5bf9\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30":68,"\u4f60\u53ef\u4ee5\u5c06\u7f51\u7edc\u914d\u7f6e\u6210\u67d0\u4e9b\u5c42\u4f7f\u7528gpu\u8ba1\u7b97":55,"\u4f60\u53ef\u4ee5\u6267\u884c\u4e0a\u8ff0\u547d\u4ee4\u6765\u4e0b\u8f7d\u6240\u6709\u7684\u6a21\u578b\u548c\u5747\u503c\u6587\u4ef6":64,"\u4f60\u53ef\u4ee5\u901a\u8fc7\u6267\u884c\u4e0b\u9762\u7684\u547d\u4ee4\u6765\u5f97\u5230resnet\u7f51\u7edc\u7684\u7ed3\u6784\u53ef\u89c6\u5316\u56fe":64,"\u4f60\u53ef\u4ee5\u9884\u6d4b\u4efb\u4f55\u7528\u6237\u5bf9\u4e8e\u4efb\u4f55\u4e00\u90e8\u7535\u5f71\u7684\u8bc4\u4ef7":68,"\u4f60\u5c06\u4f1a\u770b\u5230\u4ee5\u4e0b\u7684\u6a21\u578b\u7ed3\u6784":62,"\u4f60\u5c06\u4f1a\u770b\u5230\u5982\u4e0b\u6d88\u606f":71,"\u4f60\u5c06\u4f1a\u770b\u5230\u5982\u4e0b\u7ed3\u679c":64,"\u4f60\u5c06\u4f1a\u770b\u5230\u7279\u5f81\u5b58\u50a8\u5728":64,"\u4f60\u5c06\u4f1a\u770b\u5230\u8fd9\u6837\u7684\u6d88\u606f":71,"\u4f60\u5c06\u5728\u76ee\u5f55":70,"\u4f60\u5c06\u770b\u5230\u5982\u4e0b\u7684\u4fe1\u606f":68,"\u4f60\u7684\u5de5\u4f5c\u7a7a\u95f4\u5e94\u5982\u4e0b\u6240\u793a":51,"\u4f60\u8fd8\u53ef\u4ee5\u5c06\u7528\u6237\u548c":51,"\u4f60\u9700\u8981\u4e00\u4e9b\u66f4\u590d\u6742\u7684\u5355\u5143\u6d4b\u8bd5\u6765\u4fdd\u8bc1\u4f60\u5b9e\u73b0\u7684\u7f51\u7edc\u5c42\u662f\u6b63\u786e\u7684":47,"\u4f60\u9700\u8981\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u6307\u5b9a\u8bbe\u5907\u7684id\u53f7":55,"\u4f60\u9700\u8981\u5728\u914d\u7f6ecmake\u65f6\u5c06":47,"\u4f60\u9700\u8981\u5b89\u88c5python\u7684\u7b2c\u4e09\u65b9\u5e93":68,"\u4f60\u9700\u8981\u628a\u8be5\u6587\u4ef6\u52a0\u5165":47,"\u4f7f\u5176\u8f6c\u53d8\u4e3a\u7ef4\u5ea6\u4e3ahidden_dim\u7684\u65b0\u5411\u91cf":66,"\u4f7f\u5f97":32,"\u4f7f\u5f97\u4e24\u4e2a\u5b57\u5178\u6709\u76f8\u540c\u7684\u4e0a\u4e0b\u6587":71,"\u4f7f\u5f97\u5355\u5143\u6d4b\u8bd5\u6709\u4e00\u4e2a\u5e72\u51c0\u7684\u73af\u5883":31,"\u4f7f\u5f97\u642d\u6a21\u578b\u65f6\u66f4\u65b9\u4fbf":47,"\u4f7f\u5f97\u6700\u7ec8\u5f97\u5230\u7684\u6a21\u578b\u51e0\u4e4e\u4e0e\u771f\u5b9e\u6a21\u578b\u4e00\u81f4":32,"\u4f7f\u7528":[27,30,31,34,42,44,45,47,50,53,66,69,70],"\u4f7f\u75280\u53f7\u548c1\u53f7gpu\u8ba1\u7b97fc2\u5c42":55,"\u4f7f\u75280\u53f7gpu\u8ba1\u7b97fc2\u5c42":55,"\u4f7f\u752810\u4e2a\u88c1\u526a\u56fe\u50cf\u5757":64,"\u4f7f\u75281\u53f7gpu\u8ba1\u7b97fc3\u5c42":55,"\u4f7f\u75282\u53f7\u548c3\u53f7gpu\u8ba1\u7b97fc3\u5c42":55,"\u4f7f\u7528\u4e00\u4e2a\u5c3a\u5ea6\u4e3a":47,"\u4f7f\u7528\u4e00\u4e2a\u8bcd\u524d\u4e24\u4e2a\u8bcd\u548c\u540e\u4e24\u4e2a\u8bcd":31,"\u4f7f\u7528\u4e0a\u6587\u521b\u5efa\u7684yaml\u6587\u4ef6\u521b\u5efakubernet":57,"\u4f7f\u7528\u4e0b\u9762\u547d\u4ee4":20,"\u4f7f\u7528\u4e86\u540c\u6837\u7684parameter\u548cbia":31,"\u4f7f\u7528\u4e86\u57fa\u4e8e\u53e5\u6cd5\u7ed3\u6784\u7684\u9884\u5b9a\u4e49\u7279\u5f81\u6a21\u677f":69,"\u4f7f\u7528\u4e86avx\u6307\u4ee4\u96c6":36,"\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3":62,"\u4f7f\u7528\u5177\u6709softmax\u6fc0\u6d3b\u7684\u5168\u8fde\u63a5\u524d\u9988\u5c42\u6765\u6267\u884c\u5206\u7c7b\u4efb\u52a1":70,"\u4f7f\u7528\u52a8\u6001\u5e93":26,"\u4f7f\u7528\u540c\u6837\u7684\u8bad\u7ec3\u6570\u636eblock":19,"\u4f7f\u7528\u591a\u5757\u663e\u5361\u8bad\u7ec3":31,"\u4f7f\u7528\u591a\u7ebf\u7a0b\u8bad\u7ec3":31,"\u4f7f\u7528\u5982\u4e0b\u53c2\u6570":63,"\u4f7f\u7528\u5982\u4e0b\u547d\u4ee4":62,"\u4f7f\u7528\u5b66\u4e60\u5b8c\u6210\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u751f\u6210\u5e8f\u5217":45,"\u4f7f\u7528\u5bb9\u5668\u65b9\u5f0f\u8fd0\u884c\u8bad\u7ec3\u4efb\u52a1\u7684kubernet":58,"\u4f7f\u7528\u6211\u4eec\u4e4b\u524d\u6784\u9020\u7684\u955c\u50cf":57,"\u4f7f\u7528\u663e\u5361\u8bad\u7ec3":31,"\u4f7f\u7528\u667a\u80fd\u6307\u9488\u7684\u539f\u56e0\u662f":27,"\u4f7f\u7528\u6848\u4f8b":54,"\u4f7f\u7528\u7684":31,"\u4f7f\u7528\u7684\u7f16\u8bd1\u5668\u4e3aarm":39,"\u4f7f\u7528\u76f8\u5bf9\u8def\u5f84\u7684\u5f15\u7528\u65b9\u5f0f":27,"\u4f7f\u7528\u8005\u4e0d\u9700\u8981\u5173\u5fc3":53,"\u4f7f\u7528\u8005\u53ea\u9700\u8981\u5173\u6ce8\u4e8e\u8bbe\u8ba1rnn\u5728\u4e00\u4e2a\u65f6\u95f4\u6b65\u4e4b\u5185\u5b8c\u6210\u7684\u8ba1\u7b97":44,"\u4f7f\u7528\u8005\u53ef\u4ee5\u4f7f\u7528\u4e0b\u9762\u7684python\u811a\u672c\u6765\u8bfb\u53d6\u53c2\u6570\u503c":64,"\u4f7f\u7528\u8005\u65e0\u9700\u5173\u5fc3\u8fd9\u4e2a\u53c2\u6570":53,"\u4f7f\u7528\u8005\u901a\u5e38\u65e0\u9700\u5173\u5fc3":53,"\u4f7f\u7528\u81ea\u52a8\u7684\u66ff\u8865\u6765\u66ff\u4ee3\u7ecf\u9a8c\u4e30\u5bcc\u7684\u4eba\u5de5\u8bc4\u5224":71,"\u4f7f\u7528\u8c13\u8bcd\u4e0a\u4e0b\u6587":69,"\u4f7f\u7528\u8fd9\u4e2a\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u5b8c\u6210\u5bf9\u65b0\u6570\u636e\u7684\u9884\u6d4b":19,"\u4f7f\u7528\u8fd9\u4e2a\u811a\u672c\u524d\u8bf7\u786e\u8ba4\u5df2\u7ecf\u5b89\u88c5\u4e86pillow\u53ca\u76f8\u5173\u4f9d\u8d56\u6a21\u5757":63,"\u4f7f\u7528\u8fd9\u79cd\u65b9\u5f0f":42,"\u4f7f\u7528\u8fdc\u7a0b\u7a00\u758f\u65b9\u5f0f\u8bad\u7ec3\u65f6":47,"\u4f7f\u7528\u968f\u673a\u68af\u5ea6\u4e0b\u964d":70,"\u4f7f\u7528\u9759\u6001\u5e93\u548c\u52a8\u6001\u5e93\u96be\u5ea6\u5dee\u4e0d\u591a":26,"\u4f7f\u7528\u9884\u8bad\u7ec3\u7684\u6807\u51c6\u683c\u5f0f\u8bcd\u5411\u91cf\u6a21\u578b":62,"\u4f7f\u7528args\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u8bbe\u7f6e":3,"\u4f7f\u7528c":27,"\u4f7f\u7528c99\u505a\u63a5\u53e3":26,"\u4f7f\u7528c99\u800c\u4e0d\u4f7f\u7528c11\u7684\u539f\u56e0\u662f":26,"\u4f7f\u7528c99\u800c\u4e0d\u4f7f\u7528c89":26,"\u4f7f\u7528checkgrad\u6a21\u5f0f\u65f6\u7684\u53c2\u6570\u53d8\u5316\u5927\u5c0f":53,"\u4f7f\u7528cpu\u4e24\u7ebf\u7a0b\u8ba1\u7b97fc4\u5c42":55,"\u4f7f\u7528cpu\u8ba1\u7b97fc4\u5c42":55,"\u4f7f\u7528cpu\u8bad\u7ec3":70,"\u4f7f\u7528init":55,"\u4f7f\u7528lstm\u4f5c\u4e3aencod":42,"\u4f7f\u7528max":63,"\u4f7f\u7528memory\u7684rnn\u5b9e\u73b0\u4fbf\u5982\u4e0b\u56fe\u6240\u793a":42,"\u4f7f\u7528model":55,"\u4f7f\u7528paddlepaddl":66,"\u4f7f\u7528python\u6570\u636e\u63d0\u4f9b\u5668":63,"\u4f7f\u7528rdma\u8fd8\u662ftcp\u4f20\u8f93\u534f\u8bae":53,"\u4f7f\u7528regress":30,"\u4f7f\u7528swig\u53ea\u652f\u6301cpython\u89e3\u91ca\u5668":26,"\u4f7f\u7528swig\u9700\u8981\u591a\u8bed\u8a00\u7ed1\u5b9a\u7684\u5f00\u53d1\u4eba\u5458\u719f\u7ec3\u638c\u63e1swig\u914d\u7f6e":26,"\u4f7f\u7528void":26,"\u4f7f\u8f93\u5165\u5c42\u5230\u9690\u85cf\u5c42\u7684\u795e\u7ecf\u5143\u662f\u5168\u90e8\u8fde\u63a5\u7684":63,"\u4f86":42,"\u4f8b\u5982":[3,20,26,27,30,31,33,37,42,45,47,50,51,52,53,55,58,64,66,68,70],"\u4f8b\u5982\u4e0a\u6587\u7684pod":56,"\u4f8b\u5982\u4e0a\u9762\u7684":32,"\u4f8b\u5982\u4ee5\u592a\u7f51\u7684":51,"\u4f8b\u5982\u4f7f\u7528":31,"\u4f8b\u5982\u586b\u5145":45,"\u4f8b\u5982\u5bf9\u4e8ejava\u6216\u8005python":26,"\u4f8b\u5982\u5bf9\u4e8ejava\u6765\u8bf4":26,"\u4f8b\u5982\u5bf9\u4e8epython":26,"\u4f8b\u5982\u5c06\u7b2c\u4e00\u6761\u6570\u636e\u8f6c\u5316\u4e3a":42,"\u4f8b\u5982\u6587\u672c\u5206\u7c7b\u4e2d":42,"\u4f8b\u5982\u672c\u4f8b\u4e2d\u7684\u4e24\u4e2a\u7279\u5f81":42,"\u4f8b\u5982\u673a\u5668\u4e0a\u67094\u5757gpu":31,"\u4f8b\u5982\u7b2c300\u4e2apass\u7684\u6a21\u578b\u4f1a\u88ab\u4fdd\u5b58\u5728":63,"\u4f8b\u5982c":26,"\u4f8b\u5982hostpath":56,"\u4f8b\u5982java\u4e0epython\u7684\u9519\u8bef\u5904\u7406\u662f\u76f4\u63a5\u6254\u51fa\u6765except":26,"\u4f8b\u5982output\u76ee\u5f55\u4e0b\u5c31\u5b58\u653e\u4e86\u8f93\u51fa\u7ed3\u679c":58,"\u4f8b\u5982python\u53ef\u4ee5\u4f7f\u7528":26,"\u4f8b\u5982python\u7684":26,"\u4f8b\u5982sigmoid":47,"\u4f8b\u5982sigmoid\u53d8\u6362":66,"\u4f8b\u5b50\u4e2d\u662f":47,"\u4f8b\u5b50\u4e2d\u662f0":47,"\u4f8b\u5b50\u4e2d\u662f100":47,"\u4f8b\u5b50\u4e2d\u662f4096":47,"\u4f8b\u5b50\u4e2d\u662f8192":47,"\u4f8b\u5b50\u4e2d\u662ffc":47,"\u4f8b\u5b50\u4e2d\u662fsoftmax":47,"\u4f8b\u5b50\u4f7f\u7528":56,"\u4f9bpaddlepaddle\u52a0\u8f7d":53,"\u4f9d\u636e\u5206\u7c7b\u9519\u8bef\u7387\u83b7\u5f97\u6700\u4f73\u6a21\u578b\u8fdb\u884c\u6d4b\u8bd5":70,"\u4f9d\u6b21\u7c7b\u63a8":30,"\u4f9d\u8d56\u4e8epython\u7684":63,"\u4fbf\u4e8e\u5b58\u50a8\u8d44\u6e90\u7ba1\u7406\u548cpod\u5f15\u7528":56,"\u4fbf\u4e8e\u672c\u5730\u9a8c\u8bc1\u548c\u6d4b\u8bd5":56,"\u4fbf\u4e8e\u7528\u6237\u6d4f\u89c8c":34,"\u4fbf\u5229":42,"\u4fbf\u548c\u5355\u5c42rnn\u914d\u7f6e\u4e2d\u7684":42,"\u4fbf\u5b9c":42,"\u4fbf\u662f\u5c06\u9759\u6001\u5e93\u52a0\u5165jvm\u4e2d":26,"\u4fdd\u5b58\u6a21\u578b\u53c2\u6570\u7684\u76ee\u5f55":53,"\u4fdd\u5b58\u751f\u6210\u7ed3\u679c\u7684\u6587\u4ef6":71,"\u4fdd\u5b58\u7f51\u7edc\u5c42\u8f93\u51fa\u7ed3\u679c\u7684\u76ee\u5f55":53,"\u4fdd\u5b58\u9884\u6d4b\u7ed3\u679c\u7684\u6587\u4ef6\u540d":53,"\u4fdd\u6301\u5bbd\u9ad8\u6bd4\u7f29\u653e\u5230\u77ed\u8fb9\u4e3a256":64,"\u4fdd\u6301\u5c3d\u91cf\u5c11\u7684commit":46,"\u4fe1\u53f7\u6765\u81ea\u52a8\u7ec8\u6b62\u5b83\u542f\u52a8\u7684\u6240\u6709\u8fdb\u7a0b":51,"\u4fee\u590d\u6240\u6709bug\u540e":30,"\u4fee\u590ddocker\u7f16\u8bd1\u955c\u50cf\u95ee\u9898":30,"\u4fee\u590dubuntu":30,"\u4fee\u6539":[56,57],"\u4fee\u6539\u542f\u52a8\u811a\u672c\u540e":57,"\u4fee\u6539\u6210\u66f4\u5feb\u7684\u7248\u672c":50,"\u4fee\u6539\u6587\u6863":49,"\u503c\u5f97\u6ce8\u610f\u7684\u662f":[42,46],"\u503c\u5f97\u6df1\u5165\u5206\u6790":50,"\u503c\u7c7b\u578b":55,"\u5047\u5982\u6211\u4eec\u662f\u4e09\u5206\u7c7b\u95ee\u9898":31,"\u5047\u8bbe":47,"\u5047\u8bbe\u53d8\u91cf":32,"\u5047\u8bbe\u60a8\u5df2\u7ecf\u5b8c\u6210\u4e86\u4e00\u4e2aai\u8bad\u7ec3\u7684python\u7a0b\u5e8f":34,"\u5047\u8bbe\u635f\u5931\u51fd\u6570\u662f":47,"\u5047\u8bbe\u8bcd\u5411\u91cf\u7ef4\u5ea6\u4e3a32":62,"\u504f\u7f6e\u53c2\u6570":64,"\u504f\u7f6e\u53c2\u6570\u7684\u5927\u5c0f":47,"\u505a\u53ea\u8bfb\u6302\u8f7d":20,"\u505a\u5982\u4e0b\u51e0\u4e2a\u64cd\u4f5c":30,"\u505a\u63a5\u53e3":26,"\u505c\u6b62\u4fdd\u5b58\u68c0\u67e5\u70b9\u7684\u7ebf\u7a0b":19,"\u505c\u6b62\u52a0\u8f7d\u6570\u636e":53,"\u505c\u7535":42,"\u50cf":34,"\u513f\u7ae5\u7247":67,"\u5143\u7d20":41,"\u5143\u7d20\u4e4b\u95f4\u7684\u987a\u5e8f\u662f\u91cd\u8981\u7684\u8f93\u5165\u4fe1\u606f":41,"\u5148\u5b9e\u73b0\u6a21\u578b\u63a8\u65ad\u7684api":27,"\u5148\u8c03\u7528initializer\u51fd\u6570":66,"\u5168\u5bb6":42,"\u5168\u8fde\u63a5\u5c42":[32,62,63,68],"\u5168\u8fde\u63a5\u5c42\u4ee5\u4e00\u4e2a\u7ef4\u5ea6\u4e3a":47,"\u5168\u8fde\u63a5\u5c42\u5c06\u7535\u5f71\u7684\u6bcf\u4e2a\u7279\u5f81\u7ed3\u5408\u6210\u4e00\u4e2a\u7535\u5f71\u7279\u5f81":68,"\u5168\u8fde\u63a5\u5c42\u6743\u91cd":64,"\u5168\u8fde\u63a5\u5c42\u6ca1\u6709\u7f51\u7edc\u5c42\u914d\u7f6e\u7684\u8d85\u53c2\u6570":47,"\u5168\u8fde\u63a5\u5c42\u7684\u5b9e\u73b0\u4f4d\u4e8e":47,"\u5168\u8fde\u63a5\u5c42\u7684\u6bcf\u4e2a\u8f93\u51fa\u90fd\u8fde\u63a5\u5230\u4e0a\u4e00\u5c42\u7684\u6240\u6709\u7684\u795e\u7ecf\u5143\u4e0a":47,"\u5168\u8fde\u63a5\u5c42python\u5c01\u88c5\u7684\u4f8b\u5b50\u4e2d\u5305\u542b\u4e0b\u9762\u51e0\u6b65":47,"\u516b\u4e2a\u7279\u5f81\u5206\u522b\u8f6c\u6362\u4e3a\u5411\u91cf":69,"\u516c\u5f0f":34,"\u516c\u94a5\u5199\u5165":51,"\u516d\u4e2a\u7279\u5f81\u548c\u6807\u7b7e\u90fd\u662f\u7d22\u5f15\u69fd":69,"\u5171\u4eab\u4efb\u52a1\u4e2d\u8bbe\u7f6e\u7684\u6570\u636e\u8fdb\u884c\u8bad\u7ec3\u548c\u6d4b\u8bd5":69,"\u5171\u4eab\u5b58\u50a8\u6302\u5728\u7684\u8def\u5f84":58,"\u5171\u670932":62,"\u5173\u4e8e\u5982\u4f55\u5b9a\u4e49\u7f51\u7edc\u4e2d\u7684\u5c42":63,"\u5173\u4e8e\u65f6\u95f4\u5e8f\u5217":42,"\u5173\u4e8e\u6784\u5efa\u548c\u6d4b\u8bd5\u7684\u66f4\u591a\u4fe1\u606f":46,"\u5173\u4e8epaddlepaddle\u7684\u5206\u5e03\u5f0f\u8bad\u7ec3":58,"\u5173\u4e8epaddlepaddle\u7684\u66f4\u591a\u4f7f\u7528\u65b9\u6cd5\u8bf7\u53c2\u8003":37,"\u5173\u4e8eunbound":44,"\u5173\u4e8evgg\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u63cf\u8ff0\u53ef\u4ee5\u53c2\u8003":63,"\u5173\u952e\u8bcd\u5305\u62ec":46,"\u5176\u4e0b\u5b50\u6587\u4ef6\u5939\u7684\u7ed3\u6784\u5982\u4e0b":63,"\u5176\u4e2d":[3,26,30,31,32,37,39,40,45,47,62,63,64],"\u5176\u4e2d156\u548c285\u662f\u8fd9\u4e9b\u56fe\u50cf\u7684\u5206\u7c7b\u6807\u7b7e":64,"\u5176\u4e2d50000\u5f20\u56fe\u7247\u4f5c\u4e3a\u8bad\u7ec3\u96c6":63,"\u5176\u4e2d\u5206\u522b\u5305\u542b\u4e86cifar":63,"\u5176\u4e2d\u5305\u542b6":67,"\u5176\u4e2d\u5305\u542b\u4e86200\u79cd\u9e1f\u7c7b\u7684\u7167\u7247":63,"\u5176\u4e2d\u5305\u542b\u7b97\u6cd5\u548c\u7f51\u7edc\u914d\u7f6e":70,"\u5176\u4e2d\u5305\u62ec\u51fd\u6570":69,"\u5176\u4e2d\u5b9a\u4e49\u4e86\u6a21\u578b\u67b6\u6784\u548csolver\u914d\u7f6e":71,"\u5176\u4e2d\u6587\u672c\u8f93\u5165\u7c7b\u578b\u5b9a\u4e49\u4e3a\u6574\u6570\u65f6\u5e8f\u7c7b\u578binteger_value_sequ":66,"\u5176\u4e2d\u6bcf\u4e00\u884c\u5bf9\u5e94\u4e00\u4e2a\u6570\u636e\u6587\u4ef6\u5730\u5740":2,"\u5176\u4e2d\u6bcf\u4e2a\u5143\u7d20\u662f\u53cc\u5c42\u5e8f\u5217\u4e2d\u6bcf\u4e2asubseq\u6700\u540e\u4e00\u4e2a":41,"\u5176\u4e2d\u6bcf\u4e2a\u5411\u91cf\u5bf9\u5e94\u8f93\u5165\u8bed\u53e5\u4e2d\u7684\u4e00\u4e2a\u5143\u7d20":71,"\u5176\u4e2d\u6bcf\u6761pass\u82b1\u8d39\u4e867\u4e2a\u5c0f\u65f6":71,"\u5176\u4e2d\u6bcf\u884c\u6570\u636e\u4ee3\u8868\u4e00\u5f20\u56fe\u7247":3,"\u5176\u4e2d\u8be6\u7ec6\u8bf4\u660e\u4e86\u6a21\u578b\u67b6\u6784":71,"\u5176\u4e2d\u8f93\u5165\u56fe\u50cf\u7684\u989c\u8272\u901a\u9053\u987a\u5e8f\u4e3a":64,"\u5176\u4e2dbeam":71,"\u5176\u4e2dcheckgrad\u4e3b\u8981\u4e3a\u5f00\u53d1\u8005\u4f7f\u7528":53,"\u5176\u4e2dmean\u548cstd\u662f\u8bad\u7ec3\u914d\u7f6e\u4e2d\u7684\u53c2\u6570":53,"\u5176\u4e2dvalue\u5373\u4e3asoftmax\u5c42\u7684\u8f93\u51fa":5,"\u5176\u4e2dx\u8868\u793a\u8f93\u5165\u6570\u636e\u662f\u4e00\u4e2a\u7ef4\u5ea6\u4e3a2\u7684\u7a20\u5bc6\u5411\u91cf":37,"\u5176\u4ed6":67,"\u5176\u4ed6\u4eba\u5f88\u5bb9\u6613\u770b\u61c2\u955c\u50cf\u751f\u6210\u8fc7\u7a0b":34,"\u5176\u4ed6\u516d\u884c\u5217\u51fa\u4e86\u96c6\u675f\u641c\u7d22\u7684\u7ed3\u679c":71,"\u5176\u4ed6\u5185\u5b58\u6742\u9879":31,"\u5176\u4ed6\u5185\u5b58\u6742\u9879\u662f\u6307paddlepaddle\u672c\u8eab\u6240\u7528\u7684\u4e00\u4e9b\u5185\u5b58":31,"\u5176\u4ed6\u51fd\u6570\u5747\u8fd4\u56de":27,"\u5176\u4ed6\u53c2\u6570\u4f7f\u7528":3,"\u5176\u4ed6\u53c2\u6570\u8bf7\u53c2\u8003":66,"\u5176\u4ed6\u6240\u6709\u5c42\u90fd\u4f1a\u4f7f\u7528gpu\u8ba1\u7b97":55,"\u5176\u4ed6\u7528\u6237\u7684fork\u7248\u672c\u5e93\u5e76\u4e0d\u9700\u8981\u4e25\u683c\u9075\u5b88":30,"\u5176\u4ed6\u914d\u7f6e\u53c2\u6570":[39,40],"\u5176\u4ed6\u9ad8\u7ea7\u529f\u80fd\u5305\u62ec\u5b9a\u4e49\u591a\u4e2amemori":45,"\u5176\u4f1a\u81ea\u52a8\u88ab\u52a0\u5165\u7f16\u8bd1\u5217\u8868":47,"\u5176\u4f59\u884c\u662f":62,"\u5176\u4f5c\u7528\u662f\u5c06\u6570\u636e\u4f20\u5165\u5185\u5b58\u6216\u663e\u5b58":2,"\u5176\u5177\u4f53\u8bf4\u660e\u4e86\u5b57\u6bb5\u7c7b\u578b\u548c\u6587\u4ef6\u540d\u79f0":68,"\u5176\u5185\u90e8\u7684\u6587\u4ef6\u4e5f\u4f1a\u968f\u4e4b\u6d88\u5931":56,"\u5176\u5305\u62ec\u4e24\u4e2a\u51fd\u6570":66,"\u5176\u53c2\u6570\u5982\u4e0b":3,"\u5176\u5b83\u90e8\u5206\u548c\u903b\u8f91\u56de\u5f52\u7f51\u7edc\u7ed3\u6784\u4e00\u81f4":66,"\u5176\u5b83layer\u7684\u8f93\u51fa":44,"\u5176\u5b9e\u4e5f\u662f\u548c\u6bcf\u4e2amini":31,"\u5176\u63d0\u4f9b\u5e94\u7528\u90e8\u7f72":56,"\u5176\u6b21":[3,42,66],"\u5176\u76ee\u7684\u662f\u5728\u7ed9\u5b9a\u7684\u8f93\u5165\u53e5\u5b50\u4e2d\u53d1\u73b0\u6bcf\u4e2a\u8c13\u8bcd\u7684\u8c13\u8bcd\u8bba\u5143\u7ed3\u6784":69,"\u5176\u8bf4\u660e\u5982\u4e0b":42,"\u5176\u8f93\u5165\u53c2\u6570\u5982\u4e0b":71,"\u5176\u8f93\u51fa\u88ab\u7528\u4f5cmemory\u7684\u521d\u59cb\u503c":45,"\u5177\u4f53\u4f7f\u7528\u65b9\u6cd5\u4e3a":[27,31],"\u5177\u4f53\u539f\u56e0\u53c2\u8003":27,"\u5177\u4f53\u53ef\u4ee5\u53c2\u8003":[3,47],"\u5177\u4f53\u53ef\u53c2\u8003\u6587\u6863":44,"\u5177\u4f53\u5982\u4e0b":34,"\u5177\u4f53\u60c5\u51b5\u56e0\u4eba\u800c\u5f02":50,"\u5177\u4f53\u64cd\u4f5c\u5982\u4e0b":31,"\u5177\u4f53\u6d41\u7a0b\u5982\u4e0b":66,"\u5177\u4f53\u7684\u683c\u5f0f\u8bf4\u660e":3,"\u5177\u4f53\u7684\u89e3\u51b3\u65b9\u6cd5\u662f":31,"\u5177\u4f53\u8bf7\u53c2\u7167\u793a\u4f8b":64,"\u5177\u4f53\u8bf7\u53c2\u8003":[3,27,46],"\u5177\u4f53\u8bf7\u89c1":46,"\u5177\u6709\u76f8\u540c\u7684\u7ed3\u679c\u4e86":42,"\u5177\u6709\u81ea\u5faa\u73af\u8fde\u63a5\u7684\u795e\u7ecf\u5143":70,"\u517c\u5907\u6613\u7528\u6027":0,"\u5185":45,"\u5185\u5b58":50,"\u5185\u5b58\u5bb9\u9650\u9608\u503c":53,"\u5185\u5bb9":66,"\u5185\u5bb9\u5982\u4e0b":57,"\u5185\u5c42inner_step\u7684recurrent_group\u548c\u5355\u5c42\u5e8f\u5217\u7684\u51e0\u4e4e\u4e00\u6837":42,"\u5185\u5df2\u7ecf\u5305\u542bpaddlepaddle\u7684\u6267\u884c\u7a0b\u5e8f\u4f46\u662f\u8fd8\u6ca1\u4e0a\u8ff0\u529f\u80fd":58,"\u5185\u7684\u5f00\u53d1\u8005\u4e0b\u8f7ddocker\u955c\u50cf":34,"\u5185\u90e8":58,"\u5185\u90e8\u9a71\u52a8python\u89e3\u91ca\u5668\u8fdb\u884c\u6a21\u578b\u914d\u7f6e\u89e3\u6790\u548c\u6570\u636e\u8bfb\u53d6":26,"\u518d\u4e3apaddle\u7684\u8bad\u7ec3\u8fc7\u7a0b\u63d0\u4f9b\u6587\u4ef6\u5217\u8868":68,"\u518d\u5728\u6bcf\u4e00\u4e2aapi\u4e2d\u81ea\u5df1\u68c0\u67e5\u7c7b\u578b":26,"\u518d\u57fa\u4e8e":30,"\u518d\u5bf9\u6bcf\u4e00\u4e2a\u5355\u5c42\u65f6\u95f4\u5e8f\u5217\u8fdb\u884c\u5904\u7406":42,"\u518d\u5bf9\u6bcf\u4e00\u53e5\u8bdd\u7684\u7f16\u7801\u5411\u91cf\u7528lstm\u7f16\u7801\u6210\u4e00\u4e2a\u6bb5\u843d\u7684\u5411\u91cf":42,"\u518d\u5bf9\u8fd9\u4e2a\u6bb5\u843d\u5411\u91cf\u8fdb\u884c\u5206\u7c7b":42,"\u518d\u6307\u5b9a":33,"\u518d\u6b21\u5bf9\u4ee3\u7801\u8fdb\u884c\u6027\u80fd\u5206\u6790":50,"\u518d\u7528\u8fd9\u4e2a\u68af\u5ea6\u53bb\u548c":47,"\u518d\u901a\u8fc7\u51fd\u6570":58,"\u5192\u9669\u7247":67,"\u5197\u4f59\u7b49\u529f\u80fd":56,"\u5199\u4ee3\u7801":26,"\u5199\u5165\u5feb\u7167\u6570\u636e":19,"\u5199\u5b8c\u6a21\u578b\u914d\u7f6e\u4e4b\u540e":71,"\u5199\u68af\u5ea6\u68c0\u67e5\u5355\u5143\u6d4b\u8bd5\u662f\u4e00\u4e2a\u9a8c\u8bc1\u65b0\u5b9e\u73b0\u7684\u5c42\u662f\u5426\u6b63\u786e\u7684\u76f8\u5bf9\u7b80\u5355\u7684\u529e\u6cd5":47,"\u519c\u6c11":67,"\u51c6\u5907":42,"\u51c6\u5907\u597d\u6570\u636e":68,"\u51c6\u5907\u7528\u6765\u5b66\u4e60\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u5e8f\u5217\u6570\u636e":45,"\u51c6\u5907\u9884\u6d4b\u6570\u636e":5,"\u51cf\u5c0f\u5e8f\u5217\u7684\u957f\u5ea6":31,"\u51cf\u5c0f\u8fd9\u4e2a\u5185\u5b58\u6c60\u5373\u53ef\u51cf\u5c0f\u5185\u5b58\u5360\u7528":31,"\u51cf\u5c0fbatch":31,"\u51fa\u53bb\u73a9":42,"\u51fa\u5dee":42,"\u51fa\u6765":42,"\u51fa\u73b0":31,"\u51fa\u73b0\u4ee5\u4e0b\u9519\u8bef":31,"\u51fa\u73b0\u8fd9\u4e2a\u95ee\u9898\u7684\u4e3b\u8981\u539f\u56e0\u662f":31,"\u51fd\u6570":[3,32,45,47,50,69,70],"\u51fd\u6570\u4e2d":45,"\u51fd\u6570\u4e2d\u4f7f\u7528":3,"\u51fd\u6570\u4e2d\u8bbe\u7f6e\u7684":51,"\u51fd\u6570\u5047\u8bbe":45,"\u51fd\u6570\u52a0\u5230\u4ee3\u7801\u4e2d":50,"\u51fd\u6570\u5373\u53ef\u5b8c\u6210\u8f6c\u6362":20,"\u51fd\u6570\u53ea\u5173\u6ce8\u4e8ernn\u4e00\u4e2a\u65f6\u95f4\u6b65\u4e4b\u5185\u7684\u8ba1\u7b97":44,"\u51fd\u6570\u540d\u4e3a":27,"\u51fd\u6570\u547d\u540d":26,"\u51fd\u6570\u5c06\u8fd4\u56de\u4e09\u4e2a\u6574\u6570\u5217\u8868":45,"\u51fd\u6570\u5c31\u662f\u6839\u636e\u8be5\u673a\u5236\u914d\u7f6e\u7684":3,"\u51fd\u6570\u5f97\u5230\u7684\u68af\u5ea6\u53bb\u5bf9\u6bd4":47,"\u51fd\u6570\u5fc5\u987b\u5148\u8c03\u7528\u57fa\u7c7b\u4e2d\u7684\u51fd\u6570":47,"\u51fd\u6570\u5fc5\u987b\u8fd4\u56de\u4e00\u4e2a\u6216\u591a\u4e2alayer\u7684\u8f93\u51fa":44,"\u51fd\u6570\u6307\u51fa\u4e86\u5728\u8bad\u7ec3\u65f6\u9700\u8981\u4ece\u53c2\u6570\u670d\u52a1\u5668\u53d6\u51fa\u7684\u884c":47,"\u51fd\u6570\u6765\u5c06\u4fe1\u606f\u8f93\u51fa\u5230\u754c\u9762\u4e2d":50,"\u51fd\u6570\u67e5\u8be2\u8f6f\u4ef6\u5305\u76f8\u5173api\u8bf4\u660e":5,"\u51fd\u6570\u7684":3,"\u51fd\u6570\u7684\u5b9e\u73b0\u662f\u6b63\u786e\u7684":47,"\u51fd\u6570\u7684\u5f00\u5934\u5fc5\u987b\u8c03\u7528":47,"\u5206\u4e3a\u597d\u8bc4":66,"\u5206\u522b\u4e3a":62,"\u5206\u522b\u4e3atrain":71,"\u5206\u522b\u4ece\u8bcd\u8bed\u548c\u53e5\u5b50\u7ea7\u522b\u7f16\u7801\u8f93\u5165\u6570\u636e":44,"\u5206\u522b\u4f7f\u7528\u5355\u53cc\u5c42rnn\u4f5c\u4e3a\u7f51\u7edc\u914d\u7f6e\u7684\u6a21\u578b":42,"\u5206\u522b\u5305\u542b\u4e86\u6cd5\u8bed\u5230\u82f1\u8bed\u7684\u5e73\u884c\u8bed\u6599\u5e93\u7684\u8bad\u7ec3\u6570\u636e":71,"\u5206\u522b\u5b9a\u4e49\u5b50\u53e5\u7ea7\u522b\u548c\u8bcd\u8bed\u7ea7\u522b\u4e0a\u9700\u8981\u5b8c\u6210\u7684\u8fd0\u7b97":44,"\u5206\u522b\u5bf9\u5e94\u4e8e\u53d8\u91cf":32,"\u5206\u522b\u662f":41,"\u5206\u522b\u662frnn\u72b6\u6001\u548c\u8f93\u5165\u7684\u53d8\u6362\u77e9\u9635":45,"\u5206\u522b\u662fsentences\u548clabel":42,"\u5206\u522b\u662fwords\u548clabel":42,"\u5206\u522b\u8ba1\u7b97\u6bcf\u4e2a\u53c2\u6570\u7684\u68af\u5ea6":47,"\u5206\u522b\u8fdb\u884c\u5e8f\u5217\u64cd\u4f5c":42,"\u5206\u5272":[67,69],"\u5206\u5272\u6587\u4ef6\u7684\u65b9\u6cd5\u662f":68,"\u5206\u5e03\u5f0f\u5b58\u50a8\u670d\u52a1":19,"\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf":56,"\u5206\u6210\u4e24\u90e8\u5206":3,"\u5206\u652f":[30,46],"\u5206\u652f\u4e00\u65e6\u5efa\u7acb":30,"\u5206\u652f\u4e0a":46,"\u5206\u652f\u4e0a\u521b\u5efa\u65b0\u5206\u652f":46,"\u5206\u652f\u4e2d":30,"\u5206\u652f\u4e3a\u5f00\u53d1":30,"\u5206\u652f\u4e3a\u6bcf\u4e00\u6b21release\u65f6\u5efa\u7acb\u7684\u4e34\u65f6\u5206\u652f":30,"\u5206\u652f\u4e3a\u7a33\u5b9a":30,"\u5206\u652f\u529f\u80fd\u7684\u5c01\u95ed":30,"\u5206\u652f\u5408\u5165":30,"\u5206\u652f\u5408\u5165master\u5206\u652f":30,"\u5206\u652f\u540c\u6b65\u4e3b\u7248\u672c\u5e93\u7684":30,"\u5206\u652f\u540d":46,"\u5206\u652f\u540d\u4e3a":30,"\u5206\u652f\u5b58\u5728\u7684\u65f6\u5019":30,"\u5206\u652f\u6d3e\u751f\u51fa\u65b0\u7684\u5206\u652f":30,"\u5206\u652f\u7684\u7248\u672c\u90fd\u662f\u7ecf\u8fc7\u5355\u5143\u6d4b\u8bd5\u548c\u56de\u5f52\u6d4b\u8bd5\u7684\u7248\u672c":30,"\u5206\u652f\u7684\u7248\u672c\u90fd\u7ecf\u8fc7\u5355\u5143\u6d4b\u8bd5":30,"\u5206\u652f\u7684\u76ee\u6807\u673a\u7248openblas\u5e93":39,"\u5206\u652f\u89c4\u8303":46,"\u5206\u6790\u5f97\u5230\u7684\u4fe1\u606f\u7528\u4e8e\u534f\u52a9\u8fdb\u884c\u7a0b\u5e8f\u7684\u4f18\u5316":50,"\u5206\u7247":19,"\u5206\u7c7b\u6210\u6b63\u9762\u60c5\u7eea\u548c\u8d1f\u9762\u60c5\u7eea\u4e24\u7c7b":3,"\u5206\u7c7b\u8bef\u5dee\u662f0":70,"\u5206\u7c7b\u9519\u8bef\u7387\u548c\u6a21\u578b\u5927\u5c0f\u7531\u4e0b\u8868\u7ed9\u51fa":64,"\u5206\u8bcd\u5e8f\u5217\u7684\u5f00\u59cb":62,"\u5206\u8bcd\u5e8f\u5217\u7684\u7ed3\u675f":62,"\u5206\u8bcd\u98ce\u683c\u5982\u4e0b":62,"\u5206\u914d\u5230\u5f53\u524d\u6570\u636e\u5757\u6837\u672c\u6570\u7684\u56db\u5206\u4e4b\u4e00":53,"\u5206\u9694":[62,68],"\u5206\u9694\u7b26\u4e3a":67,"\u5207\u6362\u5230":46,"\u5207\u6362\u5230\u6240\u5efa\u5206\u652f":46,"\u5217\u8868":68,"\u5217\u8868\u5982\u4e0b":[3,37],"\u5219\u4e0d\u5728\u4e4e\u5185\u5b58\u6682\u5b58\u591a\u5c11\u6761\u6570\u636e":3,"\u5219\u4e0d\u9700\u8981\u91cd\u5199\u8be5\u51fd\u6570":47,"\u5219\u4f1a\u9884\u5148\u8bfb\u53d6\u5168\u90e8\u6570\u636e\u5230\u5185\u5b58\u4e2d":3,"\u5219\u4f7f\u7528\u533a\u57df\u6807\u8bb0":69,"\u5219\u4f7f\u7528\u540c\u6b65\u8bad\u7ec3":53,"\u5219\u4f7f\u7528\u542f\u52a8\u53c2\u6570\u5b9a\u4e49\u7684\u521d\u59cb\u5316\u65b9\u6cd5\u521d\u59cb\u5316\u53c2\u6570":19,"\u5219\u4f7f\u7528\u8be5\u53c2\u6570\u4f5c\u4e3a\u9ed8\u8ba4\u503c":53,"\u5219\u5148\u505a\u5d4c\u5165":68,"\u5219\u53ef\u4ee5\u50cf":51,"\u5219\u53ef\u8bbe\u7f6e":[39,40],"\u5219\u5b57\u4e0e\u5b57\u4e4b\u95f4\u7528\u7a7a\u683c\u5206\u9694":66,"\u5219\u5ffd\u7565":19,"\u5219\u603b\u4f1a\u663e\u793a\u963b\u9694\u6458\u8981\u4fe1\u606f":53,"\u5219\u628a\u53e6\u4e00\u4e2a\u6162\u901f\u7684kill\u6389":19,"\u5219\u63a8\u8350\u5927\u4e8e\u8bad\u7ec3\u65f6batch":3,"\u5219\u662f\u5e26gui\u7684nvidia\u53ef\u89c6\u5316\u6027\u80fd\u5206\u6790\u5de5\u5177":50,"\u5219\u663e\u793a\u963b\u9694\u6027\u80fd\u7684\u6458\u8981\u4fe1\u606f":53,"\u5219\u76f4\u63a5\u5f15\u5165\u53e6\u4e00\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5219\u8bbe\u7f6e\u6210":[39,40],"\u5219\u9700\u8981\u4f7f\u7528\u7b49\u4e8e\u6743\u91cd\u53c2\u6570\u89c4\u6a21\u5927\u7ea65\u500d\u7684\u5185\u5b58":31,"\u5219\u9700\u8981\u56de\u6eda\u5230\u4e0a\u4e00\u4e2a\u68c0\u67e5\u70b9":19,"\u5219\u9700\u8981\u914d\u7f6e":56,"\u5219\u9700\u9009\u7528":40,"\u521b\u5efa\u4e00\u4e2akubernet":58,"\u521b\u5efa\u548c\u53d1\u5e03\u81ea\u5df1\u7684ai\u7a0b\u5e8f\u955c\u50cf":34,"\u521b\u5efa\u5e76\u5207\u6362\u5230\u65b0\u5206\u652f":46,"\u521b\u5efa\u6210\u529f\u540e":58,"\u521b\u5efa\u8bad\u7ec3\u6570\u636e\u7684":71,"\u521b\u5efagener":3,"\u521d\u59cb\u5316\u4e4b\u540e":5,"\u521d\u59cb\u5316\u504f\u7f6e\u5411\u91cf":47,"\u521d\u59cb\u5316\u65f6\u8c03\u7528\u7684\u51fd\u6570":3,"\u521d\u59cb\u5316\u6743\u91cd\u8868":47,"\u521d\u59cb\u5316\u6a21\u578b\u7684\u8def\u5f84":53,"\u521d\u59cb\u5316\u6a21\u578b\u7684\u8def\u5f84\u914d\u7f6e\u4e3a":62,"\u521d\u59cb\u5316\u7236\u7c7b":47,"\u521d\u59cb\u5316biases_":47,"\u521d\u59cb\u5316paddlepaddle\u73af\u5883":5,"\u521d\u59cb\u72b6\u6001":44,"\u5220\u9664":46,"\u5220\u9664\u78c1\u76d8\u76ee\u5f55\u4e2d\u4e0d\u662f\u5f53\u524duuid\u7684\u5feb\u7167\u6587\u4ef6":19,"\u5229\u7528\u5206\u5e03\u5f0f\u8bad\u7ec3\u9a7e\u9a6d\u66f4\u591a\u7684\u8ba1\u7b97\u8d44\u6e90":31,"\u5229\u7528\u5355\u8bcdid\u67e5\u627e\u8be5\u5355\u8bcd\u5bf9\u5e94\u7684\u8fde\u7eed\u5411\u91cf":66,"\u5229\u7528\u66f4\u591a\u7684\u8ba1\u7b97\u8d44\u6e90\u53ef\u4ee5\u5206\u4e3a\u4e00\u4e0b\u51e0\u4e2a\u65b9\u5f0f\u6765\u8fdb\u884c":31,"\u5229\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u6765\u89e3\u6790\u8be5\u7279\u5f81":68,"\u5229\u7528\u8bad\u7ec3\u96c6\u751f\u6210\u7684\u5b57\u5178":70,"\u5229\u7528\u8fd9\u79cd\u7279\u6027":44,"\u5229\u7528\u903b\u8f91\u56de\u5f52\u6a21\u578b\u5bf9\u8be5\u5411\u91cf\u8fdb\u884c\u5206\u7c7b":66,"\u5229\u7528kubernetes\u80fd\u65b9\u4fbf\u5730\u7ba1\u7406\u8de8\u673a\u5668\u8fd0\u884c\u5bb9\u5668\u5316\u7684\u5e94\u7528":56,"\u5229\u843d":42,"\u522b\u4eba\u5e2e\u4e86\u5fd9":46,"\u5230":[19,31,45],"\u5230\u6240\u6709\u8282\u70b9\u800c\u4e0d\u7528\u5bc6\u7801":51,"\u5230\u672c\u5730":46,"\u5230\u6b64":34,"\u5230\u76ee\u524d\u4e3a\u6b62":69,"\u5236\u4f5c\u65b0\u955c\u50cf\u6765\u5b8c\u6210\u4ee5\u4e0a\u7684\u5de5\u4f5c":58,"\u5236\u4f5cpaddlepaddle\u5f00\u53d1\u955c\u50cf":34,"\u5236\u4f5cpaddlepaddle\u751f\u4ea7\u955c\u50cf":34,"\u5236\u4f5cpaddlepaddle\u955c\u50cf":58,"\u5237\u7259":42,"\u524d\u4e00\u7bc7\u6587\u7ae0\u4ecb\u7ecd\u4e86\u5982\u4f55\u5728kubernetes\u96c6\u7fa4\u4e0a\u542f\u52a8\u4e00\u4e2a\u5355\u673apaddlepaddle\u8bad\u7ec3\u4f5c\u4e1a":58,"\u524d\u4e09\u884cimport\u4e86\u5b9a\u4e49network":71,"\u524d\u53f0":42,"\u524d\u5411\u4f20\u64ad":47,"\u524d\u5411\u4f20\u64ad\u7ed9\u5b9a\u8f93\u5165":47,"\u524d\u5411\u548c\u540e\u5411":47,"\u524d\u81ea\u52a8\u68c0\u67e5\u4e00\u4e9b\u57fa\u672c\u4e8b\u5b9c":46,"\u5269\u4e0b\u7684pass\u4f1a\u76f4\u63a5\u4ece\u5185\u5b58\u91cc":3,"\u529f\u80fd":24,"\u529f\u80fd\u7684\u6b63\u786e\u6027\u5305\u62ec\u9a8c\u8bc1paddle\u76ee\u524d\u7684":30,"\u52a0\u4e0a\u504f\u7f6e\u5411\u91cf":47,"\u52a0\u4e86l2\u6b63\u5219\u548c\u68af\u5ea6\u622a\u65ad":66,"\u52a0\u5165":50,"\u52a0\u6743\u548c\u7528\u6765\u751f\u6210":45,"\u52a0\u6743\u7f16\u7801\u5411\u91cf":45,"\u52a0\u8f7d\u6570\u636e":69,"\u52a0\u8f7d\u6a21\u578b":69,"\u52a0\u8f7d\u6a21\u578b\u53c2\u6570":71,"\u52a0\u8f7dtest":53,"\u52a0\u901fpaddlepaddle\u8bad\u7ec3\u53ef\u4ee5\u8003\u8651\u4ece\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762":31,"\u52a8\u4f5c\u7247":67,"\u52a8\u6001\u5e93":26,"\u52a8\u753b\u7247":67,"\u52a8\u8bcd":69,"\u52a9\u624b":47,"\u5305\u542b12":70,"\u5305\u542b20\u4e2a\u8bad\u7ec3\u6837\u4f8b":62,"\u5305\u542b3\u4e2a\u5c5e\u6027":62,"\u5305\u542b50":70,"\u5305\u542b\u4e86\u67d0\u79cd\u7c7b\u578b\u7684\u7c7b\u578b\u5b9a\u4e49\u548c\u66b4\u9732\u7684\u5168\u90e8\u51fd\u6570":27,"\u5305\u542b\u7684\u6240\u6709\u4f9d\u8d56\u5047\u8bbe\u90fd\u53ef\u4ee5\u5728paddle\u7684\u8fd0\u884c\u5bb9\u5668\u4e2d":34,"\u5305\u5e76\u91cd\u65b0\u7f16\u8bd1paddlepaddl":31,"\u5305\u62ec":[20,53,66,69,71],"\u5305\u62ec\u4ee5\u4e0b\u4e24\u79cd":3,"\u5305\u62ec\u53d1\u884c\u65f6\u95f4":67,"\u5305\u62ec\u5b57\u7b26\u4e32\u5206\u914d":31,"\u5305\u62ec\u6570\u636e\u8f93\u5165":32,"\u5305\u62ec\u6743\u91cdw\u548c\u504f\u7f6eb":19,"\u5305\u62ec\u751f\u6210cpu":33,"\u5305\u62ec\u795e\u7ecf\u7f51\u7edc\u62d3\u6251\u7ed3\u6784":37,"\u5305\u62ec\u7b80\u5355\u7684":66,"\u5305\u62ecbool":55,"\u5305\u62ecdocker\u955c\u50cf":35,"\u5305\u62eclinux":34,"\u5305\u662f\u6700\u65b0\u7684":31,"\u5305\u6bd4\u8f83\u8001":31,"\u5305\u7684\u65b9\u6cd5\u662f":31,"\u533a\u522b\u662f\u540c\u65f6\u5904\u7406\u4e86\u4e24\u4e2a\u8f93\u5165":42,"\u533a\u522b\u662frnn\u4f7f\u7528\u4e24\u5c42\u5e8f\u5217\u6a21\u578b":42,"\u533b\u751f":67,"\u533b\u7597\u4fdd\u5065":67,"\u5341\u4e00":42,"\u5347\u5e8f\u6392\u5217":71,"\u534e\u6da6\u4e07\u5bb6":42,"\u534f\u540c\u5b8c\u6210releas":30,"\u5355\u4e2a\u503c":20,"\u5355\u4f4d\u662fmb":53,"\u5355\u5143\u6d4b\u8bd5\u4f1a\u5f15\u7528site":31,"\u5355\u5143\u6d4b\u8bd5checkgrad_ep":52,"\u5355\u53cc\u5c42\u5e8f\u5217\u7684\u53e5\u5b50\u662f\u4e00\u6837\u7684":42,"\u5355\u53cc\u5c42rnn":43,"\u5355\u53d8\u91cf\u7684\u7ebf\u6027\u56de\u5f52":32,"\u5355\u5c42":44,"\u5355\u5c42\u4e0d\u7b49\u957frnn":42,"\u5355\u5c42\u548c\u53cc\u5c42\u5e8f\u5217\u7684\u4f7f\u7528\u548c\u793a\u4f8b2\u4e2d\u7684\u793a\u4f8b\u7c7b\u4f3c":42,"\u5355\u5c42\u5e8f\u5217":41,"\u5355\u5c42\u5e8f\u5217\u7684\u6bcf\u4e2a\u5143\u7d20":41,"\u5355\u5c42\u5e8f\u5217\u7b2ci\u4e2a\u5143\u7d20":41,"\u5355\u5c42\u6216\u53cc\u5c42":41,"\u5355\u5c42\u65f6\u95f4\u5e8f\u5217":42,"\u5355\u5c42rnn":[42,44],"\u5355\u5c42rnn\u548c\u53cc\u5c42rnn\u7684\u7f51\u7edc\u914d\u7f6e":42,"\u5355\u673acpu\u8bad\u7ec3":31,"\u5355\u673agpu\u8bad\u7ec3":31,"\u5355\u6b65\u51fd\u6570":45,"\u5355\u6b65\u51fd\u6570\u548c\u8f93\u51fa\u51fd\u6570\u5728":45,"\u5355\u6b65\u51fd\u6570\u548c\u8f93\u51fa\u51fd\u6570\u90fd\u975e\u5e38\u7b80\u5355":45,"\u5355\u6b65\u51fd\u6570\u7684\u5b9e\u73b0\u5982\u4e0b\u6240\u793a":45,"\u5355\u70b9\u6545\u969c":19,"\u5355\u8fdb\u5355\u51fa":44,"\u5360\u4f4d\u7b26":62,"\u536b\u751f":42,"\u5373":[27,31,32,48,58,66,70],"\u5373\u4e00\u4e2a\u5c06\u5355\u8bcd\u5b57\u7b26\u4e32\u6620\u5c04\u5230\u5355\u8bcdid\u7684\u5b57\u5178":3,"\u5373\u4e0a\u8ff0\u4ee3\u7801\u4e2d\u7684\u7b2c19\u884c":42,"\u5373\u4e0d\u8981\u5c06\u6bcf\u4e00\u4e2a\u6837\u672c\u90fd\u653e\u5165train":3,"\u5373\u4e0d\u9700\u8981\u4f7f\u7528memori":42,"\u5373\u4e3a\u4e00\u4e2a\u65f6\u95f4\u6b65":42,"\u5373\u4e3a\u5355\u5c42rnn\u5e8f\u5217\u7684\u4f7f\u7528\u4ee3\u7801":42,"\u5373\u4e3a\u65f6\u95f4\u5e8f\u5217\u7684\u8f93\u5165":42,"\u5373\u4e3a\u8fd9\u4e2a\u53cc\u5c42rnn\u7684\u7f51\u7edc\u7ed3\u6784":42,"\u5373\u4e3a\u8fd9\u4e2a\u6570\u636e\u6587\u4ef6\u7684\u540d\u5b57":3,"\u5373\u4e8c\u7ef4\u6570\u7ec4":42,"\u5373\u4f7f\u7528":27,"\u5373\u4f7f\u7528\u6237\u76f4\u63a5\u5f15\u7528\u67d0\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5373\u4f7f\u95f4\u9694\u5f88\u5c0f":53,"\u5373\u4f7fc":27,"\u5373\u4f7fprocess\u51fd\u6570\u91cc\u9762\u53ea\u6709\u4e00\u4e2ayield":3,"\u5373\u4f8b\u5982":27,"\u5373\u4fbf\u8bbe\u7f6e":31,"\u5373\u4fbfpaddl":27,"\u5373\u521d\u59cb\u72b6\u6001\u4e3a0":44,"\u5373\u5305\u542b\u65f6\u95f4\u6b65\u4fe1\u606f":3,"\u5373\u5355\u65f6\u95f4\u6b65\u6267\u884c\u7684\u51fd\u6570":45,"\u5373\u53cc\u5411lstm\u548c\u4e09\u5c42\u5806\u53e0lstm":70,"\u5373\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217":42,"\u5373\u53cc\u5c42rnn\u7684\u6bcf\u4e2a\u72b6\u6001":44,"\u5373\u53ef":[32,46],"\u5373\u53ef\u4ee5\u6781\u5927\u7684\u52a0\u901f\u6570\u636e\u8f7d\u5165\u6d41\u7a0b":31,"\u5373\u53ef\u8fdb\u884c\u4e0b\u4e00\u6b65\u64cd\u4f5c":48,"\u5373\u5728\u53cc\u5c42\u5e8f\u5217\u7684\u539f\u59cb\u6570\u636e\u4e2d":42,"\u5373\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d":31,"\u5373\u5927\u90e8\u5206\u503c\u4e3a0":[3,37],"\u5373\u5b8c\u6210\u67d0\u4e00\u4e2a\u4efb\u52a1\u7684\u6700\u5c11\u51fd\u6570":27,"\u5373\u5bf9\u7b2c\u4e09\u6b65\u8fdb\u884c\u66ff\u6362":66,"\u5373\u5c06\u4e00\u6bb5\u82f1\u6587\u6587\u672c\u6570\u636e":3,"\u5373\u5c06\u4e00\u6bb5\u8bdd\u8fdb\u884c\u5206\u7c7b":42,"\u5373\u5f53\u524d\u65f6\u95f4\u6b65\u4e0b\u7684\u795e\u7ecf\u7f51\u7edc\u4f9d\u8d56\u524d\u4e00\u4e2a\u65f6\u95f4\u6b65\u795e\u7ecf\u7f51\u7edc\u4e2d\u67d0\u4e00\u4e2a\u795e\u7ecf\u5143\u8f93\u51fa":42,"\u5373\u6211\u4eec\u7684\u8bad\u7ec3\u76ee\u6807":32,"\u5373\u628a\u5355\u5c42rnn\u751f\u6210\u540e\u7684subseq\u7ed9\u62fc\u63a5\u6210\u4e00\u4e2a\u65b0\u7684\u53cc\u5c42seq":44,"\u5373\u6574\u4e2a\u53cc\u5c42group\u662f\u5c06\u524d\u4e00\u4e2a\u5b50\u53e5\u7684\u6700\u540e\u4e00\u4e2a\u5411\u91cf":42,"\u5373\u6574\u4e2a\u8f93\u5165\u5e8f\u5217":41,"\u5373\u6574\u6570\u6570\u7ec4":42,"\u5373\u65f6\u95f4\u9012\u5f52\u795e\u7ecf\u7f51\u7edc":42,"\u5373\u662f\u8de8\u8d8a\u65f6\u95f4\u6b65\u7684\u7f51\u7edc\u8fde\u63a5":42,"\u5373\u66b4\u9732":27,"\u5373\u6b63\u9762\u548c\u8d1f\u9762":70,"\u5373\u6b63\u9762\u8bc4\u4ef7\u6807\u7b7e\u548c\u8d1f\u9762\u8bc4\u4ef7\u6807\u7b7e":70,"\u5373\u7279\u5f81\u7684\u6570\u7ec4":42,"\u5373\u7f51\u5361\u540d":58,"\u5373\u82e5\u5e72\u6570\u636e\u6587\u4ef6\u8def\u5f84\u7684\u67d0\u4e00\u4e2a":3,"\u5373\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u51fa\u73b0nan\u6216\u8005inf":31,"\u5373\u8bbe\u7f6e":31,"\u5373\u8fd9\u4e2a\u52a8\u6001\u5e93\u662f\u4e0d\u4f9d\u8d56\u4e8e\u5176\u4ed6\u4efb\u4f55\u6587\u4ef6\u7684":26,"\u5373define_py_data_sources2\u5e94\u6539\u4e3a":31,"\u5373input":44,"\u5373rnn\u4e4b\u95f4\u6709\u4e00\u6b21\u5d4c\u5957\u5173\u7cfb":42,"\u5377\u79ef\u5c42":63,"\u5377\u79ef\u5c42\u6743\u91cd":64,"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u8fa8\u8bc6\u56fe\u7247\u4e2d\u7684\u4e3b\u4f53":63,"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5728\u56fe\u7247\u5206\u7c7b\u4e0a\u6709\u7740\u60ca\u4eba\u7684\u6027\u80fd":63,"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u662f\u4e00\u79cd\u4f7f\u7528\u5377\u79ef\u5c42\u7684\u524d\u5411\u795e\u7ecf\u7f51\u7edc":63,"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u80fd\u591f\u5f88\u597d\u7684\u8868\u793a\u8fd9\u4e24\u7c7b\u4fe1\u606f":63,"\u5377\u79ef\u7f51\u7edc\u662f\u4e00\u79cd\u7279\u6b8a\u7684\u4ece\u8bcd\u5411\u91cf\u8868\u793a\u5230\u53e5\u5b50\u8868\u793a\u7684\u65b9\u6cd5":66,"\u5378\u8f7dpaddlepaddle\u5305":31,"\u538b\u7f29\u6210\u4e00\u4e2a\u5411\u91cf":42,"\u539f\u56e0":46,"\u539f\u56e0\u5728\u4e8e\u6ca1\u6709\u628a\u673a\u5668\u4e0acuda\u76f8\u5173\u7684\u9a71\u52a8\u548c\u5e93\u6620\u5c04\u5230\u5bb9\u5668\u5185\u90e8":31,"\u539f\u56e0\u662f\u672a\u8bbe\u7f6ecuda\u8fd0\u884c\u65f6\u73af\u5883\u53d8\u91cf":36,"\u539f\u56e0\u662f\u6bcf\u4e2a\u56de\u590d\u90fd\u4f1a\u53d1\u9001\u4e00\u5c01\u90ae\u4ef6":46,"\u53bb\u8fc7":42,"\u53c2\u6570":[3,7,8,9,10,11,12,13,15,26,47,52,58,62,64,70],"\u53c2\u6570\u5171\u4eab\u7684\u914d\u7f6e\u793a\u4f8b\u4e3a":31,"\u53c2\u6570\u521d\u59cb\u5316\u8def\u5f84":69,"\u53c2\u6570\u5373\u53ef":70,"\u53c2\u6570\u540d":64,"\u53c2\u6570\u6570\u91cf":66,"\u53c2\u6570\u670d\u52a1\u5668":52,"\u53c2\u6570\u670d\u52a1\u5668\u7684\u53c2\u6570\u5206\u5757\u5927\u5c0f":53,"\u53c2\u6570\u670d\u52a1\u5668\u7684\u76d1\u542c\u7aef\u53e3":53,"\u53c2\u6570\u670d\u52a1\u5668\u7684\u7f51\u7edc\u8bbe\u5907\u540d\u79f0":53,"\u53c2\u6570\u670d\u52a1\u5668\u7684ip\u5730\u5740":53,"\u53c2\u6570\u670d\u52a1\u5668\u7a00\u758f\u66f4\u65b0\u7684\u53c2\u6570\u5206\u5757\u5927\u5c0f":53,"\u53c2\u6570\u6765\u63a7\u5236\u7f13\u5b58\u65b9\u6cd5":31,"\u53c2\u6570\u6982\u8ff0":54,"\u53c2\u6570\u7684\u89e3\u6790":58,"\u53c2\u6570\u7ef4\u5ea6":62,"\u53c2\u6570\u884c":62,"\u53c2\u6570\u8bbe\u7f6e\u4e86\u5916\u5c42":42,"\u53c2\u6570\u9700\u8981\u5b9e\u73b0":45,"\u53c2\u7167\u4e0a\u8ff0\u6b65\u9aa4\u66f4\u65b0":46,"\u53c2\u8003":[24,26,56],"\u53c2\u8003\u5f3a\u8c03\u90e8\u5206":50,"\u53c2\u8003\u6587\u732e":71,"\u53c2\u8003\u65f6\u95f4\u5e8f\u5217":42,"\u53c2\u8003\u955c\u50cf\u7684":58,"\u53c8":42,"\u53c8\u662f\u4e00\u4e2a\u5355\u5c42\u7684\u5e8f\u5217":41,"\u53c8\u8981\u4fdd\u8bc1\u6570\u636e\u662f\u968f\u673a\u7684":31,"\u53ca":47,"\u53cc\u5411\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u9690\u85cf\u72b6\u6001":45,"\u53cc\u5411\u9a8c\u8bc1":24,"\u53cc\u5c42":44,"\u53cc\u5c42\u4e0d\u7b49\u957frnn":42,"\u53cc\u5c42\u5e8f\u5217":41,"\u53cc\u5c42\u5e8f\u5217\u6216\u5355\u5c42\u5e8f\u5217":41,"\u53cc\u5c42\u5e8f\u5217\u6570\u636e\u4e00\u5171\u67094\u4e2a\u6837\u672c":42,"\u53cc\u5c42\u5e8f\u5217\u662f\u4e00\u4e2a\u5d4c\u5957\u7684\u5e8f\u5217":41,"\u53cc\u5c42\u5e8f\u5217\u662fpaddlepaddle\u652f\u6301\u7684\u4e00\u79cd\u975e\u5e38\u7075\u6d3b\u7684\u6570\u636e\u7ec4\u7ec7\u65b9\u5f0f":44,"\u53cc\u5c42\u5e8f\u5217\u6bcf\u4e2asubseq\u4e2d\u6bcf\u4e2a\u5143\u7d20":41,"\u53cc\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u53d8\u6210\u4e00\u4e2a0\u5c42\u5e8f\u5217":41,"\u53cc\u5c42\u6216\u8005\u5355\u5c42":41,"\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217\u7684dataprovider\u7684\u4ee3\u7801":42,"\u53cc\u5c42rnn":44,"\u53cc\u5c42rnn\u6570\u636e\u968f\u610f\u52a0\u4e86\u4e00\u4e9b\u9694\u65ad":42,"\u53cc\u5c42rnn\u987e\u540d\u601d\u4e49":42,"\u53cc\u8fdb\u5355\u51fa":44,"\u53cc\u8fdb\u53cc\u51fa":44,"\u53cd\u4e4b\u5219":69,"\u53cd\u5411\u4f20\u64ad":47,"\u53cd\u5411\u4f20\u64ad\u6839\u636e\u8f93\u51fa\u7684\u68af\u5ea6":47,"\u53d1\u5e03":34,"\u53d1\u5e03\u5230dockerhub":30,"\u53d1\u5e03\u5230github":30,"\u53d1\u6563\u5230\u4e86\u4e00\u4e2a\u6570\u503c\u7279\u522b\u5927\u7684\u5730\u65b9":31,"\u53d1\u884c\u548c\u7ef4\u62a4":46,"\u53d1\u9001\u53c2\u6570\u7684\u7aef\u53e3\u53f7":53,"\u53d6\u51b3\u4e8e\u662f\u5426\u5bfb\u627e\u5230cuda\u5de5\u5177\u94fe":33,"\u53d6\u51b3\u4e8e\u662f\u5426\u5bfb\u627e\u5230gtest":33,"\u53d6\u51b3\u4e8e\u662f\u5426\u5bfb\u627e\u5230swig":33,"\u53d8\u6362\u77e9\u9635":47,"\u53d8\u91cf\u6765\u8bbe\u7f6e\u5185\u5b58\u4e2d\u6682\u5b58\u7684\u6570\u636e\u6761":3,"\u53e3\u5934":42,"\u53e3\u7edf\u8ba1\u5b66\u4fe1\u606f\u7684\u7528\u6237\u624d\u88ab\u5305\u542b\u5728\u6570\u636e\u96c6\u4e2d":67,"\u53e5\u5b50":70,"\u53e5\u5b50\u4e2d\u7684\u7ec4\u5757\u5c06\u4f1a\u626e\u6f14\u67d0\u4e9b\u8bed\u4e49\u89d2\u8272":69,"\u53e5\u5b50\u8868\u793a\u7684\u8ba1\u7b97\u66f4\u65b0\u4e3a\u4e24\u6b65":66,"\u53e6\u4e00\u4e2a\u4f8b\u5b50\u662f\u901a\u8fc7\u5206\u6790\u6bcf\u65e5twitter\u535a\u5ba2\u7684\u6587\u672c\u5185\u5bb9\u6765\u9884\u6d4b\u80a1\u7968\u53d8\u52a8":70,"\u53e6\u4e00\u4e2a\u597d\u5904\u662f\u6211\u4eec\u53ef\u4ee5\u628apaddlepaddle\u5bb9\u5668\u8fd0\u884c\u5728\u8fdc\u7a0b\u670d\u52a1\u5668\u4e0a":34,"\u53e6\u4e00\u4e2a\u662f\u5185\u5b58\u64cd\u4f5c\u91cf":50,"\u53e6\u4e00\u4e2a\u662f\u521b\u5efadockerfile\u5e76\u8fd0\u884cdock":34,"\u53e6\u4e00\u4e2a\u662f\u6bcf\u6761\u5e8f\u5217":31,"\u53e6\u4e00\u4e2a\u7ec8\u7aef\u8fd0\u884cpython":34,"\u53e6\u4e00\u65b9\u9762":70,"\u53e6\u4e00\u79cd\u65b9\u5f0f\u662f\u5c06\u7f51\u7edc\u5c42\u5212\u5206\u5230\u4e0d\u540c\u7684gpu\u4e0a\u53bb\u8ba1\u7b97":55,"\u53e6\u5916":42,"\u53e6\u5916\u4e24\u4e2a\u5206\u522b\u662f\u6ed1\u52a8\u5747\u503c\u548c\u65b9\u5dee":64,"\u53ea\u4f5c\u4e3aread":44,"\u53ea\u4fdd\u5b58\u6700\u540e\u4e00\u8f6e\u7684\u53c2\u6570":53,"\u53ea\u5141\u8bb8\u6574\u6570\u7684\u661f\u7ea7":67,"\u53ea\u5728\u7b2c\u4e00\u6b21cmake\u7684\u65f6\u5019\u6709\u6548":33,"\u53ea\u5bf9\u7279\u6b8a\u5728\u7ebf\u7cfb\u7edf\u8003\u8651\u4e24\u53f0\u4ee5\u4e0a\u540c\u65f6\u6545\u969c\u7684\u5bb9\u707e":19,"\u53ea\u622a\u53d6\u4e2d\u5fc3\u65b9\u5f62\u7684\u56fe\u50cf\u533a\u57df":64,"\u53ea\u662f\u53cc\u5c42\u5e8f\u5217\u5c06\u5176\u53c8\u505a\u4e86\u5b50\u5e8f\u5217\u5212\u5206":42,"\u53ea\u662f\u5c06\u53e5\u5b50\u7528\u8fde\u7eed\u5411\u91cf\u8868\u793a\u66ff\u6362\u4e3a\u7528\u7a00\u758f\u5411\u91cf\u8868\u793a":66,"\u53ea\u662f\u8bf4\u660e\u6570\u636e\u7684\u987a\u5e8f\u662f\u91cd\u8981\u7684":3,"\u53ea\u662f\u8bf7\u4e0d\u8981\u5fd8\u8bb0\u63d0\u524d\u5728\u7269\u7406\u673a\u4e0a\u5b89\u88c5gpu\u6700\u65b0\u9a71\u52a8":34,"\u53ea\u66b4\u9732\u6982\u5ff5\u7684\u63a5\u53e3":27,"\u53ea\u6709":42,"\u53ea\u67092\u4e2a\u914d\u7f6e\u4e0d\u4e00\u6837":62,"\u53ea\u6709\u542b\u6709\u4eba":67,"\u53ea\u6709\u5f53\u8bbe\u7f6e\u4e86spars":53,"\u53ea\u7528\u4e8e\u5728\u5e8f\u5217\u751f\u6210\u4efb\u52a1\u4e2d\u6307\u5b9a\u8f93\u5165\u6570\u636e":44,"\u53ea\u80fd\u6d4b\u8bd5\u5355\u4e2a\u6a21\u578b":55,"\u53ea\u80fd\u8c03\u7528paddle\u7684\u52a8\u6001\u5e93":26,"\u53ea\u8981\u4e00\u7cfb\u5217\u7279\u5f81\u6570\u636e\u4e2d\u7684":42,"\u53ea\u8981\u51fa\u73b0\u6d6e\u70b9\u6570\u5f02\u5e38":31,"\u53ea\u8981\u5728docker\u91cc\u542f\u52a8paddlepaddle\u7684\u65f6\u5019\u7ed9\u5b83\u4e00\u4e2a\u540d\u5b57":34,"\u53ea\u8bfbmemory\u8f93\u5165":44,"\u53ea\u9488\u5bf9\u5185\u5b58":31,"\u53ea\u9700\u4e2d\u65ad":51,"\u53ea\u9700\u4f7f\u7528":51,"\u53ea\u9700\u5220\u9664\u6700\u540e\u4e00\u884c\u4e2d\u7684\u6ce8\u91ca\u5e76\u628a":70,"\u53ea\u9700\u5728linux\u4e0b\u8fd0\u884c\u5982\u4e0b\u547d\u4ee4":71,"\u53ea\u9700\u7528\u4f60\u5b9a\u4e49\u7684\u76ee\u5f55\u4fee\u6539":51,"\u53ea\u9700\u77e5\u9053\u8fd9\u662f\u4e00\u4e2a\u6807\u8bb0\u5c5e\u6027\u7684\u65b9\u6cd5\u5c31\u53ef\u4ee5\u4e86":3,"\u53ea\u9700\u8981":45,"\u53ea\u9700\u8981\u4e00\u884c\u4ee3\u7801\u5c31\u53ef\u4ee5\u8c03\u7528\u8fd9\u4e2apydataprovider2":3,"\u53ea\u9700\u8981\u5728\u51fd\u6570\u4e2d\u8c03\u7528\u591a\u6b21yield\u5373\u53ef":3,"\u53ea\u9700\u8981\u6062\u590d\u8fd9\u53f0\u8282\u70b9":19,"\u53ea\u9700\u8981\u7b80\u5355\u5730\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4":71,"\u53ea\u9700\u8981\u7b80\u5355\u7684\u8fd0\u884c\u4e0b\u9762\u7684\u547d\u4ee4\u5373\u53ef":68,"\u53ea\u9700\u8981\u8fd0\u884c":68,"\u53ef\u4ee5":[42,46,51],"\u53ef\u4ee5\u4ee5\u540e\u53f0\u8fdb\u7a0b\u65b9\u5f0f\u8fd0\u884c\u5bb9\u5668":34,"\u53ef\u4ee5\u4f20\u5165\u4e00\u4e2a\u51fd\u6570":3,"\u53ef\u4ee5\u4f30\u8ba1\u51fa\u5982\u679c\u6a21\u578b\u91c7\u7528\u4e0d\u53d8\u7684\u8f93\u51fa\u6700\u5c0f\u7684cost0\u662f\u591a\u5c11":31,"\u53ef\u4ee5\u4f7f\u7528":31,"\u53ef\u4ee5\u4f7f\u7528\u547d\u4ee4":36,"\u53ef\u4ee5\u4f7f\u7528\u5982\u4e0b\u4ee3\u7801":31,"\u53ef\u4ee5\u4f7f\u7528\u8be5\u53c2\u6570":53,"\u53ef\u4ee5\u4f7f\u7528kubernetes\u7684\u547d\u4ee4\u884c\u5de5\u5177\u521b\u5efajob":58,"\u53ef\u4ee5\u4f7f\u7528python\u7684":5,"\u53ef\u4ee5\u51cf\u5c0f\u7cfb\u7edf\u590d\u6742\u6027":19,"\u53ef\u4ee5\u51cf\u5c11\u7f13\u5b58\u6c60\u7684\u5927\u5c0f":31,"\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a":57,"\u53ef\u4ee5\u53c2\u7167\u4e0b\u9762\u7684\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5":63,"\u53ef\u4ee5\u53c2\u8003":[42,45,46,56,58,71],"\u53ef\u4ee5\u53c2\u8003\u4fdd\u5b58\u5728":62,"\u53ef\u4ee5\u53c2\u8003paddlepaddl":37,"\u53ef\u4ee5\u544a\u8bc9\u60a8\u67d0\u4e2a\u64cd\u4f5c\u5230\u5e95\u82b1\u4e86\u591a\u957f\u65f6\u95f4":50,"\u53ef\u4ee5\u5728":34,"\u53ef\u4ee5\u5728\u4efb\u4f55\u673a\u5668\u4e0a\u6267\u884c\u7684":26,"\u53ef\u4ee5\u5728\u5171\u4eab\u5b58\u50a8\u4e0a\u67e5\u770b\u8f93\u51fa\u7684\u65e5\u5fd7\u548c\u6a21\u578b":58,"\u53ef\u4ee5\u5728\u5f88\u5927\u7a0b\u5ea6\u4e0a\u6d88\u9664\u6b67\u4e49":69,"\u53ef\u4ee5\u5728\u7f51\u7ad9\u4e0a\u627e\u5230":69,"\u53ef\u4ee5\u5728\u8fd9\u4e2a":46,"\u53ef\u4ee5\u5728kubernetes\u4e2d\u6309\u7167":56,"\u53ef\u4ee5\u5c06\u67d0\u4e00\u4e2a\u51fd\u6570\u6807\u8bb0\u6210\u4e00\u4e2apydataprovider2":3,"\u53ef\u4ee5\u5c06\u78c1\u76d8\u4e0a\u67d0\u4e2a\u76ee\u5f55\u5171\u4eab\u7ed9\u7f51\u7edc\u4e2d\u5176\u4ed6\u673a\u5668\u8bbf\u95ee":56,"\u53ef\u4ee5\u5c06memory\u7406\u89e3\u4e3a\u4e00\u4e2a\u65f6\u5ef6\u64cd\u4f5c":44,"\u53ef\u4ee5\u5e2e\u60a8\u63d0\u4f9b\u4e00\u4e9b\u5b9a\u4f4d\u6027\u80fd\u74f6\u9888\u7684\u5efa\u8bae":50,"\u53ef\u4ee5\u5feb\u901f\u8fed\u4ee3":34,"\u53ef\u4ee5\u6267\u884c\u4ee5\u4e0b\u547d\u4ee4\u7f16\u8bd1\u751f\u6210\u6587\u6863":48,"\u53ef\u4ee5\u628a\u672c\u5730\u7684\u6570\u636e\u4e0a\u4f20\u5230\u5b58\u50a8\u96c6\u7fa4\u4e2d":20,"\u53ef\u4ee5\u6307\u5b9a\u54ea\u4e00\u4e2a\u8f93\u5165\u548c\u8f93\u51fa\u5e8f\u5217\u4fe1\u606f\u4e00\u81f4":42,"\u53ef\u4ee5\u6309\u5982\u4e0b\u7684\u7ed3\u6784\u6765\u51c6\u5907\u6570\u6910":70,"\u53ef\u4ee5\u662f\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":[41,44],"\u53ef\u4ee5\u662f\u4e00\u4e2a\u975e\u5e8f\u5217":44,"\u53ef\u4ee5\u662f\u4ee5\u4e0b\u51e0\u79cd":47,"\u53ef\u4ee5\u663e\u793a\u5730\u6307\u5b9a\u4e00\u4e2alayer\u7684\u8f93\u51fa\u7528\u4e8e\u521d\u59cb\u5316memori":44,"\u53ef\u4ee5\u6709\u4ee5\u4e0b\u4e24\u79cd":44,"\u53ef\u4ee5\u6709\u6548\u51cf\u5c0f\u7f51\u7edc\u7684\u963b\u585e":53,"\u53ef\u4ee5\u6709\u6548\u7684\u907f\u514dparamet":19,"\u53ef\u4ee5\u67e5\u770b":58,"\u53ef\u4ee5\u67e5\u770b\u6b64pod\u8fd0\u884c\u7684\u5bbf\u4e3b\u673a":57,"\u53ef\u4ee5\u6d4b\u8bd5\u591a\u4e2a\u6a21\u578b":55,"\u53ef\u4ee5\u7528":24,"\u53ef\u4ee5\u7528\u4e8e\u4ece\u5b98\u65b9\u7f51\u7ad9\u4e0a\u4e0b\u8f7dcifar":63,"\u53ef\u4ee5\u7528\u4e8e\u5c0f\u91cf\u6570\u636e\u7684\u9a8c\u8bc1":56,"\u53ef\u4ee5\u7528\u4e8e\u63a5\u6536\u548cpydataprovider2\u4e00\u6837\u7684\u8f93\u5165\u6570\u636e\u5e76\u8f6c\u6362\u6210\u9884\u6d4b\u63a5\u53e3\u6240\u9700\u7684\u6570\u636e\u7c7b\u578b":5,"\u53ef\u4ee5\u7528\u4ee5\u4e0b\u6307\u4ee4":20,"\u53ef\u4ee5\u7528\u5982\u4e0b\u547d\u4ee4":46,"\u53ef\u4ee5\u7528\u6765\u8ba1\u7b97cpu\u51fd\u6570\u6216cuda\u5185\u6838\u7684\u65f6\u95f4\u6d88\u8017":50,"\u53ef\u4ee5\u770b\u4f5c\u662f\u4e00\u4e2a\u975e\u5e8f\u5217\u8f93\u5165":41,"\u53ef\u4ee5\u7cbe\u786e\u8bf4\u660e\u4e00\u4e2a\u957f\u8017\u65f6\u64cd\u4f5c\u7684\u5177\u4f53\u539f\u56e0":50,"\u53ef\u4ee5\u7ee7\u7eed\u5728\u81ea\u5df1\u7684\u529f\u80fd\u5206\u652f\u63d0\u4ea4\u4ee3\u7801":30,"\u53ef\u4ee5\u7f16\u5199":34,"\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u4e00\u4e9b\u4f18\u5316\u7b97\u6cd5":31,"\u53ef\u4ee5\u8bbe\u7f6e":[39,40,63],"\u53ef\u4ee5\u8f7b\u677e\u5730\u5b8c\u6210\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e":37,"\u53ef\u4ee5\u8fd0\u884c\u4e0b\u9762\u7684\u547d\u4ee4\u6765\u751f\u6210":68,"\u53ef\u4ee5\u8fd0\u884c\u811a\u672ctrain":63,"\u53ef\u4ee5\u9009\u62e9\u662f\u5426\u4f7f\u7528\u53c2\u6570":55,"\u53ef\u4ee5\u901a\u8fc7":[46,56],"\u53ef\u4ee5\u901a\u8fc7\u4fee\u6539\u8fd9\u4e24\u4e2a\u51fd\u6570\u6765\u5b9e\u73b0\u590d\u6742\u7684\u7f51\u7edc\u914d\u7f6e":45,"\u53ef\u4ee5\u901a\u8fc7\u8c03\u7528":5,"\u53ef\u4ee5\u901a\u8fc7\u9636\u6bb5\u6027\u7684\u4fdd\u5b58\u6bcf\u4e2aparamet":19,"\u53ef\u4ee5\u901a\u8fc7show_parameter_stats_period\u8bbe\u7f6e\u6253\u5370\u53c2\u6570\u4fe1\u606f\u7b49":66,"\u53ef\u7528\u4e8e\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u89e3\u6790\u8fd9\u4e9b\u53c2\u6570":55,"\u53ef\u7528\u5728\u6d4b\u8bd5\u6216\u8bad\u7ec3\u65f6\u6307\u5b9a\u521d\u59cb\u5316\u6a21\u578b":66,"\u53ef\u80fd\u4f1a\u5bfc\u81f4\u51fa\u9519":58,"\u53ef\u80fd\u4f1a\u9020\u6210\u7f51\u7edc\u62e5\u585e":19,"\u53ef\u80fd\u7684\u4ee3\u7801\u4e3a":31,"\u53ef\u80fd\u7684\u539f\u56e0\u662f":31,"\u53ef\u80fd\u7684\u60c5\u51b5\u4e0b":50,"\u53ef\u8bbe\u7f6e":39,"\u53ef\u9009":[3,47],"\u53f3\u8fb9\u662f":64,"\u5403":42,"\u5403\u996d":42,"\u5404\u65b9\u9762":42,"\u5404\u9879\u53c2\u6570\u7684\u8be6\u7ec6\u8bf4\u660e\u53ef\u4ee5\u5728\u547d\u4ee4\u884c\u53c2\u6570\u76f8\u5173\u6587\u6863\u4e2d\u627e\u5230":63,"\u5408":42,"\u5408\u5e76":71,"\u5408\u5e76\u6bcf\u4e2a":71,"\u5408\u7406":42,"\u540c\u65f6":[31,50],"\u540c\u65f6\u4e5f\u4f1a\u8bfb\u53d6\u76f8\u5173\u8def\u5f84\u53d8\u91cf\u6765\u8fdb\u884c\u641c\u7d22":33,"\u540c\u65f6\u4e5f\u53ef\u4ee5\u52a0\u901f\u5f00\u59cb\u8bad\u7ec3\u524d\u6570\u636e\u8f7d\u5165\u7684\u8fc7\u7a0b":31,"\u540c\u65f6\u4e5f\u53ef\u4ee5\u901a\u8fc7":46,"\u540c\u65f6\u4e5f\u80fd\u591f\u5f15\u5165\u66f4\u52a0\u590d\u6742\u7684\u8bb0\u5fc6\u673a\u5236":44,"\u540c\u65f6\u4f1a\u8ba1\u7b97\u5206\u7c7b\u51c6\u786e\u7387":66,"\u540c\u65f6\u4f60\u53ef\u4ee5\u4f7f\u7528":64,"\u540c\u65f6\u4f7f\u7528\u4e86l2\u6b63\u5219":66,"\u540c\u65f6\u5176\u5185\u90e8\u5b9e\u73b0\u53ef\u4ee5\u907f\u514d\u7eafcpu\u7248\u672cpaddlepaddle\u5728\u6267\u884c\u672c\u8bed\u53e5\u65f6\u53d1\u751f\u5d29\u6e83":50,"\u540c\u65f6\u518d\u5c06":30,"\u540c\u65f6\u53ef\u4ee5\u4f7f\u7528\u6237\u53ea\u5173\u6ce8\u5982\u4f55\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6bcf\u4e00\u6761\u6570\u636e":3,"\u540c\u65f6\u5728\u5185\u5b58\u91cc\u76f4\u63a5\u968f\u5373\u9009\u53d6\u6570\u636e\u6765\u505ashuffl":31,"\u540c\u65f6\u5c06\u53c2\u6570\u521d\u59cb\u5316\u4e3a":31,"\u540c\u65f6\u6211\u4eec\u5e0c\u671b\u5e7f\u5927\u5f00\u53d1\u8005\u79ef\u6781\u63d0\u4f9b\u53cd\u9988\u548c\u8d21\u732e\u6e90\u4ee3\u7801":0,"\u540c\u65f6\u63d0\u8d77":30,"\u540c\u65f6\u6b22\u8fce\u8d21\u732e\u66f4\u591a\u7684\u5b89\u88c5\u5305":35,"\u540c\u65f6\u7528\u6237\u9700\u8981\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u6307\u5b9a":55,"\u540c\u65f6\u8bbe\u7f6e\u5185\u5b58\u7f13\u5b58\u529f\u80fd":31,"\u540c\u65f6\u8bbe\u7f6e\u5b83\u7684input_types\u5c5e\u6027":3,"\u540c\u65f6\u9884\u6d4b\u7f51\u7edc\u901a\u5e38\u76f4\u63a5\u8f93\u51fa\u6700\u540e\u4e00\u5c42\u7684\u7ed3\u679c\u800c\u4e0d\u662f\u50cf\u8bad\u7ec3\u7f51\u7edc\u4e00\u6837\u518d\u63a5\u4e00\u5c42cost":5,"\u540c\u6837":37,"\u540c\u6837\u4e5f\u53ef\u4ee5\u5728\u6d4b\u8bd5\u6a21\u5f0f\u4e2d\u6307\u5b9a\u6a21\u578b\u8def\u5f84":53,"\u540c\u6837\u53ef\u4ee5\u6269\u5c55\u5230\u53cc\u5c42\u5e8f\u5217\u7684\u5904\u7406\u4e0a":44,"\u540c\u6b65\u6267\u884c\u64cd\u4f5c\u7684\u7ebf\u7a0b\u6570":53,"\u540d\u5b57\u4fee\u9970":26,"\u540d\u79f0":66,"\u540e":[31,33,39,40,46,58,70],"\u540e\u5411\u4f20\u64ad":47,"\u540e\u5411\u4f20\u64ad\u7ed9\u5b9a\u8f93\u51fa\u7684\u68af\u5ea6":47,"\u540e\u9762\u8fde\u5168\u8fde\u63a5\u5c42\u548csoftmax\u5c42":70,"\u5411\u6307\u5b9a\u7684\u76ee\u5f55\u4e2d\u4e00\u4e2a\u65b0\u7684\u6587\u4ef6":19,"\u5411\u91cfenable_parallel_vector":52,"\u5411paddle\u7684\u4e3b\u7248\u672c\u5e93\u63d0\u4ea4":30,"\u5426":33,"\u5426\u5219":[2,39,40,51,68],"\u5426\u5219\u4f1a\u628a":46,"\u5426\u5219\u4f60\u9700\u8981\u81ea\u5df1\u4e0b\u8f7d":71,"\u5426\u5219\u4f7f\u7528\u591a\u673a\u8bad\u7ec3":53,"\u5426\u5219\u4f7f\u7528cpu\u6a21\u5f0f":53,"\u5426\u5219\u4f7f\u7528gpu":55,"\u5426\u5219\u5b83\u4ee5\u4e00\u4e2a\u5e8f\u5217\u8f93\u5165":45,"\u5426\u5219\u5f97\u628apaddle\u9759\u6001\u5e93\u94fe\u63a5\u5230\u89e3\u91ca\u5668\u91cc":26,"\u5426\u5219\u9891\u7e41\u7684\u591a\u8282\u70b9\u5de5\u4f5c\u7a7a\u95f4\u90e8\u7f72\u53ef\u80fd\u4f1a\u5f88\u9ebb\u70e6":51,"\u5426\u5b9a":69,"\u542b\u4e49":[64,70],"\u542b\u6709":67,"\u542b\u6709\u5e8f\u5217\u4fe1\u606f\u548c\u5b50\u5e8f\u5217\u4fe1\u606f\u7684\u7a20\u5bc6\u5411\u91cf":47,"\u542b\u6709\u5e8f\u5217\u4fe1\u606f\u7684\u6574\u6570":47,"\u542b\u6709\u5e8f\u5217\u4fe1\u606f\u7684\u7a20\u5bc6\u5411\u91cf":47,"\u542f\u52a8\u4e00\u4e2a\u65b0\u7684\u7ebf\u7a0b\u5f00\u59cb\u4fdd\u5b58\u68c0\u67e5\u70b9":19,"\u542f\u52a8\u5bb9\u5668\u5f00\u59cb\u8bad\u7ec3":58,"\u542f\u52a8\u5e76\u884c\u5411\u91cf\u7684\u9608\u503c":53,"\u542f\u52a8\u5feb\u901f\u5e94\u7b54":53,"\u542f\u7528\u68af\u5ea6\u53c2\u6570\u7684\u9608\u503c":53,"\u5440":42,"\u544a\u8bc9paddle\u54ea\u4e2a\u6587\u4ef6\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u914d\u7f6e\u6587\u4ef6":68,"\u544a\u8bc9paddle\u5c06\u6a21\u578b\u4fdd\u5b58\u5728":68,"\u5468\u56f4":42,"\u547d\u4ee4":51,"\u547d\u4ee4\u4e3a":[31,57],"\u547d\u4ee4\u521b\u5efa\u65b0\u955c\u50cf":57,"\u547d\u4ee4\u5220\u9664":[39,40],"\u547d\u4ee4\u53ef\u4ee5\u8bbe\u7f6e":33,"\u547d\u4ee4\u6307\u5b9a\u7684\u53c2\u6570\u4f1a\u4f20\u5165\u7f51\u7edc\u914d\u7f6e\u4e2d":66,"\u547d\u4ee4\u884c\u53c2\u6570\u6587\u6863":66,"\u547d\u4ee4\u8bbe\u7f6e\u8be5\u7c7b\u7f16\u8bd1\u9009\u9879":33,"\u547d\u4ee4\u9009\u9879\u5e76\u4e14":51,"\u547d\u540d\u4e3a":46,"\u547d\u540d\u7a7a\u95f4":56,"\u547d\u540d\u7a7a\u95f4\u4e3b\u8981\u4e3a\u4e86\u5bf9\u8c61\u8fdb\u884c\u903b\u8f91\u4e0a\u7684\u5206\u7ec4\u4fbf\u4e8e\u7ba1\u7406":56,"\u548c":[20,26,27,30,31,32,33,39,40,42,45,46,47,48,50,51,55,56,62,63,66,68,71],"\u548c\u4e00\u4e2a\u5df2\u7ecf\u5206\u8bcd\u540e\u7684\u53e5\u5b50":42,"\u548c\u4e09\u79cd\u5e8f\u5217\u6a21\u5f0f":[3,37],"\u548c\u4e2d\u6587\u6587\u6863":48,"\u548c\u4e4b\u524d\u51cf\u5c0f\u901a\u8fc7\u51cf\u5c0f\u7f13\u5b58\u6c60\u6765\u51cf\u5c0f\u5185\u5b58\u5360\u7528\u7684\u539f\u7406\u4e00\u81f4":31,"\u548c\u504f\u7f6e\u5411\u91cf":47,"\u548c\u533a\u57df\u6807\u8bb0":69,"\u548c\u53cc\u5c42\u5e8f\u5217\u542b\u6709subseq":41,"\u548c\u5728":3,"\u548c\u5bf9\u8c61\u5b58\u50a8api":56,"\u548c\u5dee\u8bc4":66,"\u548c\u5e8f\u5217\u4e2d\u542b\u6709\u5143\u7d20\u7684\u6570\u76ee\u540c":41,"\u548c\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u8f93\u5165":45,"\u548c\u68af\u5ea6\u622a\u65ad":66,"\u548c\u6a21\u578b\u8def\u5f84":70,"\u548c\u771f\u5b9e":32,"\u548c\u793a\u4f8b2\u4e2d\u7684\u914d\u7f6e\u7c7b\u4f3c":42,"\u548c\u79bb\u7ebf\u6570\u636e\u7684\u65b9\u5f0f":20,"\u548c\u7b2c6\u884c\u7684":71,"\u548c\u90e8\u5206layer":44,"\u548cadam\u5b66\u4e60\u65b9\u6cd5":71,"\u548cargument":69,"\u548ccudnn":36,"\u548cpool":41,"\u548cpython\u63a5\u53e3\u6765\u63d0\u53d6\u7279\u5f81":64,"\u54c1\u8d28":42,"\u54ea\u4e2atrainer\u5148\u5b8c\u6210block\u7684\u8bad\u7ec3":19,"\u54ea\u4e9b\u4e0d\u662f":42,"\u552f\u4e00\u9700\u8981\u505a\u7684\u662f\u5c06\u76f8\u5e94\u7c7b\u578b\u8bbe\u7f6e\u4e3a\u8f93\u5165":45,"\u5546\u52a1":42,"\u554a":42,"\u559c\u5267\u7247":67,"\u5668":66,"\u56db\u79cd\u6570\u636e\u7c7b\u578b":[3,37],"\u56de\u5f52\u8bef\u5dee\u4ee3\u4ef7\u5c42":32,"\u56e0\u4e3a\u5168\u8fde\u63a5\u5c42\u7684\u6fc0\u6d3b\u53ef\u4ee5\u662fsoftmax":47,"\u56e0\u4e3a\u5355\u4e2a\u8c13\u8bcd\u4e0d\u80fd\u7cbe\u786e\u5730\u63cf\u8ff0\u8c13\u8bcd\u4fe1\u606f":69,"\u56e0\u4e3a\u53c2\u6570":55,"\u56e0\u4e3a\u5b83\u4eec\u7684\u8ba1\u7b97\u6548\u7387\u6bd4":45,"\u56e0\u4e3a\u5b83\u6bd4":45,"\u56e0\u4e3a\u5b98\u65b9\u955c\u50cf":58,"\u56e0\u4e3a\u5bb9\u5668\u5185\u7684\u6587\u4ef6\u90fd\u662f\u6682\u65f6\u5b58\u5728\u7684":56,"\u56e0\u4e3a\u8be5\u6587\u4ef6\u53ef\u9002\u7528\u4e8e\u9884\u6d4b":63,"\u56e0\u4e3a\u8fd9\u6837\u505a\u4e5f\u6ca1\u6cd5\u4fdd\u8bc1\u6d88\u9664\u968f\u673a\u6027":19,"\u56e0\u4e3adocker\u80fd\u5728\u6240\u6709\u4e3b\u8981\u64cd\u4f5c\u7cfb\u7edf":34,"\u56e0\u4e3apaddlepaddle\u7684v2":48,"\u56e0\u4e3apython\u7684\u641c\u7d22\u8def\u5f84\u662f\u4f18\u5148\u5df2\u7ecf\u5b89\u88c5\u7684python\u5305":31,"\u56e0\u4e3aswig\u5728\u7b2c\u4e09\u65b9\u8bed\u8a00\u4e2d\u66b4\u9732\u7684\u51fd\u6570\u540d":26,"\u56e0\u6b64":[2,3,42,44,47],"\u56e0\u6b64\u4f7f\u7528":3,"\u56e0\u6b64\u53cc\u5c42\u5e8f\u5217\u7684\u914d\u7f6e\u4e2d":42,"\u56e0\u6b64\u53ef\u4ee5\u4f7f\u7528\u8be5\u9009\u9879":62,"\u56e0\u6b64\u53ef\u80fd\u4f1a\u6709\u4e00\u4e9b\u9519\u8bef\u548c\u4e0d\u4e00\u81f4\u53d1\u751f":67,"\u56e0\u6b64\u5982\u679c\u8fd9\u4e2a\u811a\u672c\u8fd0\u884c\u5931\u8d25":63,"\u56e0\u6b64\u5b83\u662finteger_value_sub_sequ":42,"\u56e0\u6b64\u6211\u4eec\u91c7\u7528\u8f93\u51fa\u7684\u52a0\u6743\u548c":47,"\u56e0\u6b64\u6709\u4e24\u79cd\u89e3\u51b3\u65b9\u6848":3,"\u56e0\u6b64\u7528\u6237\u5e76\u4e0d\u9700\u8981\u5173\u5fc3\u5b83\u4eec":52,"\u56e0\u6b64\u8be5\u5c42\u4e2d\u6ca1\u6709\u504f\u7f6e":64,"\u56e0\u6b64\u9519\u8bef\u7684\u4f7f\u7528\u4e8c\u8fdb\u5236\u53d1\u884c\u7248\u53ef\u80fd\u4f1a\u5bfc\u81f4\u8fd9\u79cd\u9519\u8bef":31,"\u56e0\u6b64init_hook\u5c3d\u91cf\u4f7f\u7528":3,"\u56e2\u8d2d\u7f51\u7ad9":70,"\u56fe":[64,70],"\u56fe2\u662f\u53cc\u5411lstm\u7f51\u7edc":70,"\u56fe3\u662f\u4e09\u5c42lstm\u7ed3\u6784":70,"\u56fe\u50cf\u5206\u7c7b":[30,65],"\u56fe\u50cf\u5927\u5c0f\u4e3a3":64,"\u56fe\u50cf\u63cf\u8ff0":71,"\u56fe\u7247\u5206\u4e3a10\u7c7b":63,"\u56fe\u7684\u5e95\u90e8\u662fword":70,"\u56fe\u8868":[34,70],"\u5728":[3,27,30,36,41,42,45,46,51,64,66,67,69],"\u5728\u4e00\u4e2a\u4e0d\u53ef\u4e2d\u65ad\u5e76\u7f3a\u5c11\u5907\u4efd\u7684\u8bad\u7ec3\u4efb\u52a1\u4e2d":19,"\u5728\u4e00\u4e2a\u529f\u80fd\u9f50\u5168\u7684kubernetes\u673a\u7fa4\u91cc":57,"\u5728\u4e00\u4e2a\u53c2\u6570\u7684\u68af\u5ea6\u88ab\u66f4\u65b0\u540e":47,"\u5728\u4e00\u4e2a\u5468\u671f\u5185\u6d4b\u8bd5\u6240\u6709\u6570\u636e":69,"\u5728\u4e00\u8f6e\u4e2d\u6bcfsave":53,"\u5728\u4e0a\u56fe\u4e2d\u663e\u793a\u4e86\u5728\u4e00\u4e2a\u5b9e\u9645\u751f\u4ea7\u73af\u5883\u4e2d\u7684\u5e94\u7528":20,"\u5728\u4e0a\u9762\u4ee3\u7801\u4e2d":42,"\u5728\u4e0b\u4e00\u7bc7\u4e2d":57,"\u5728\u4e0b\u9762\u4f8b\u5b50\u91cc":66,"\u5728\u4e0b\u9762\u7684\u4f8b\u5b50\u4e2d":63,"\u5728\u4e0d\u540c\u64cd\u4f5c\u7cfb\u7edf":56,"\u5728\u4e4b\u540e\u7684":31,"\u5728\u4efb\u610f\u957f\u5ea6\u8bed\u53e5\u7ffb\u8bd1\u7684\u573a\u666f\u4e0b\u90fd\u53ef\u4ee5\u89c2\u5bdf\u5230\u5176\u6548\u679c\u7684\u63d0\u5347":71,"\u5728\u4f7f\u7528\u5b83\u4e4b\u524d\u8bf7\u5b89\u88c5paddlepaddle\u7684python":70,"\u5728\u4f7f\u7528\u8be5\u6587\u6863\u4e4b\u524d":37,"\u5728\u5168\u8fde\u63a5\u5c42\u4e2d":47,"\u5728\u51c6\u5907\u53d1\u8d77":46,"\u5728\u51fa\u73b0\u5355\u70b9\u6545\u969c\u65f6":19,"\u5728\u51fd\u6570":58,"\u5728\u5206\u5e03\u5f0f\u73af\u5883\u4e2d\u6d4b\u8bd5":53,"\u5728\u5206\u5e03\u5f0f\u8bad\u7ec3\u4e2d":53,"\u5728\u5355\u5c42\u6570\u636e\u7684\u57fa\u7840\u4e0a":42,"\u5728\u53c2\u6570\u670d\u52a1\u5668\u4e0a\u52a0\u8f7d\u548c\u4fdd\u5b58\u53c2\u6570":53,"\u5728\u53c2\u6570\u670d\u52a1\u5668\u7ec8\u7aef\u6bcflog":53,"\u5728\u53cc\u5c42rnn\u4e2d\u7684\u7ecf\u5178\u60c5\u51b5\u662f\u5c06\u5185\u5c42\u7684\u6bcf\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u6570\u636e":42,"\u5728\u53cd\u5411\u4f20\u9012\u7684\u65f6\u5019":31,"\u5728\u53d8\u6362\u65f6\u9700\u8981\u5c06\u8f93\u5165\u5e8f\u5217\u4f20\u5165":42,"\u5728\u540c\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4\u4e2d":56,"\u5728\u542f\u52a8job\u4e4b\u524d":58,"\u5728\u56de\u590d\u8bc4\u5ba1\u4eba\u610f\u89c1\u65f6":46,"\u5728\u56fd\u5185":34,"\u5728\u58f0\u660edataprovider\u7684\u65f6\u5019\u4f20\u5165dictionary\u4f5c\u4e3a\u53c2\u6570":3,"\u5728\u591acpu\u8bad\u7ec3\u65f6\u5171\u4eab\u8be5\u53c2\u6570":53,"\u5728\u5b8c\u6210\u795e\u7ecf\u7f51\u7edc\u7684\u642d\u5efa\u4e4b\u540e":37,"\u5728\u5b9a\u4e49\u8f93\u5165layer\u4e4b\u540e":37,"\u5728\u5b9e\u73b0\u8fc7\u7a0b\u4e2d":27,"\u5728\u5bb9\u5668\u521b\u5efa\u540e":58,"\u5728\u5bf9\u5bb9\u5668\u7684\u63cf\u8ff0":58,"\u5728\u5c42\u4e2d\u6307\u5b9a":55,"\u5728\u5e8f\u5217\u751f\u6210\u4efb\u52a1\u4e2d":44,"\u5728\u5f00\u59cb\u8bad\u7ec3\u4e4b\u524d":[20,63],"\u5728\u5f02\u6784\u96c6\u7fa4\u4e2d":19,"\u5728\u5f15\u5165\u5176\u4ed6\u7c7b\u578b\u7684\u5934\u6587\u4ef6\u65f6":27,"\u5728\u5f53\u524d\u7684\u5b9e\u73b0\u65b9\u5f0f\u4e0b":47,"\u5728\u5f97\u5230":58,"\u5728\u5feb\u7167\u5199\u5165\u5b8c\u6210\u540e":19,"\u5728\u60a8\u7684\u5b9e\u9645\u73af\u5883\u4e2d":19,"\u5728\u6211\u4eec\u7684\u4f8b\u5b50\u4e2d":45,"\u5728\u6211\u4eec\u7684\u6d4b\u8bd5\u4e2d":70,"\u5728\u63d0\u4ea4":46,"\u5728\u642d\u5efa\u795e\u7ecf\u7f51\u7edc\u7684\u8fc7\u7a0b\u4e2d":37,"\u5728\u6570\u636e\u52a0\u8f7d\u548c\u7f51\u7edc\u914d\u7f6e\u5b8c\u6210\u4e4b\u540e":66,"\u5728\u6587\u4ef6":68,"\u5728\u6709\u65b0\u7684\u5355\u8bcd\u6765\u4e34\u7684\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u9aa4\u5185":70,"\u5728\u672c\u4f8b\u4e2d":[42,46,55],"\u5728\u672c\u4f8b\u4e2d\u6ca1\u6709\u4f7f\u7528":3,"\u5728\u672c\u6559\u7a0b\u4e2d":[45,63],"\u5728\u672c\u6587\u4e2d":51,"\u5728\u672c\u6587\u4e2d\u4f7f\u7528\u7684":51,"\u5728\u672c\u6587\u6863\u4e2d":24,"\u5728\u672c\u6f14\u793a\u4e2d":70,"\u5728\u672c\u793a\u4f8b\u4e2d":[42,70],"\u5728\u672c\u8282\u4e2d":45,"\u5728\u673a\u7fa4\u4e0a\u8fd0\u884c\u8f6c\u6362\u7a0b\u5e8f":20,"\u5728\u6811\u7684\u6bcf\u4e00\u5c42\u4e0a":53,"\u5728\u6837\u4f8b\u4e2d":27,"\u5728\u6a21\u578b\u6587\u4ef6\u7684":51,"\u5728\u6a21\u578b\u914d\u7f6e\u4e2d\u901a\u8fc7":66,"\u5728\u6b64":[0,52,55],"\u5728\u6b64\u4e3a\u65b9\u4fbf\u5bf9\u6bd4\u4e0d\u540c\u7f51\u7edc\u7ed3\u6784":66,"\u5728\u6b64\u611f\u8c22":62,"\u5728\u6bcf\u4e2a\u65f6\u95f4\u6b65\u4e2d":45,"\u5728\u6bcf\u4e2a\u65f6\u95f4\u6b65\u7684\u5b50\u5e8f\u5217\u957f\u5ea6\u53ef\u4ee5\u4e0d\u76f8\u7b49":42,"\u5728\u6bcf\u4e2a\u65f6\u95f4\u6b65\u957f":45,"\u5728\u6bcf\u4e2apod\u4e0a\u90fd\u901a\u8fc7volume\u65b9\u5f0f\u6302\u8f7d\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf\u7684\u4e00\u4e2a\u76ee\u5f55\u7528\u4e8e\u4fdd\u5b58\u8bad\u7ec3\u6570\u636e\u548c\u8f93\u51fa\u7ed3\u679c":58,"\u5728\u6bcf\u8bad\u7ec3":68,"\u5728\u6d4b\u8bd5\u9636\u6bb5":53,"\u5728\u6d4b\u8bd5\u9636\u6bb5\u5b83\u4eec\u5c06\u4f1a\u88ab\u52a0\u8f7d\u5230\u6a21\u578b\u4e2d":64,"\u5728\u6e90\u7801\u76ee\u5f55\u6811\u7684\u6839\u76ee\u5f55\u4e2d\u8fd0\u884c":46,"\u5728\u6f14\u793a\u4e2d":69,"\u5728\u7269\u7406\u673a\u4e0a\u624b\u52a8\u90e8\u7f72":56,"\u5728\u751f\u6210\u65f6":45,"\u5728\u751f\u6210\u8fc7\u7a0b\u4e2d":71,"\u5728\u751f\u6210\u8fc7\u7a0b\u4e2d\u6211\u4eec\u4f7f\u7528sgd\u8bad\u7ec3\u7b97\u6cd5":71,"\u5728\u7528\u6237\u4f7f\u7528c":27,"\u5728\u7528\u6237\u6587\u4ef6user":68,"\u5728\u7528\u6237\u719f\u6089\u7684\u5f00\u53d1\u5e73\u53f0":39,"\u5728\u7535\u5f71\u6587\u4ef6movi":68,"\u5728\u793a\u4f8b\u4e2d\u6211\u4eec\u4f7f\u7528attention\u7248\u672c\u7684gru\u7f16\u89e3\u7801\u7f51\u7edc":71,"\u5728\u793a\u4f8b\u4e2d\u6211\u4eec\u4f7f\u7528sgd\u8bad\u7ec3\u7b97\u6cd5":71,"\u5728\u793a\u4f8b\u4e2d\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u5e8f\u5217\u5230\u5e8f\u5217\u7684\u751f\u6210\u6570\u636e":71,"\u5728\u793a\u4f8b\u4e2d\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u5e8f\u5217\u5230\u5e8f\u5217\u7684\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6570\u636e":71,"\u5728\u7a0b\u5e8f\u5f00\u59cb\u9636\u6bb5":5,"\u5728\u7b2c\u4e00\u884c\u4e2d\u6211\u4eec\u8f7d\u5165\u7528\u4e8e\u5b9a\u4e49\u7f51\u7edc\u7684\u51fd\u6570":63,"\u5728\u7ebf\u6a21\u578b\u9884\u6d4b\u670d\u52a1":20,"\u5728\u7ec4\u5408\u65f6":37,"\u5728\u7f16\u8bd1\u5b89\u88c5\u6b63\u786e\u4e4b\u540e":48,"\u5728\u7f16\u8bd1\u5bbf\u4e3b\u673a\u7248protoc\u53ef\u6267\u884c\u6587\u4ef6\u548c\u76ee\u6807\u673a\u7248openblas\u5e93\u65f6\u9700\u8981\u7528\u5230":[39,40],"\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d":47,"\u5728\u7f51\u7edc\u914d\u7f6e\u91cc":3,"\u5728\u7ffb\u8bd1\u6cd5\u8bed\u53e5\u5b50\u4e4b\u524d":71,"\u5728\u811a\u672c":68,"\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u4e2d":41,"\u5728\u8bad\u7ec3\u4e2d":45,"\u5728\u8bad\u7ec3\u4e4b\u524d":58,"\u5728\u8bad\u7ec3\u4e86":68,"\u5728\u8bad\u7ec3\u4e86\u51e0\u4e2a\u8f6e\u6b21\u4ee5\u540e":68,"\u5728\u8bad\u7ec3\u5b8c\u6210\u540e":63,"\u5728\u8bad\u7ec3\u6570\u96c6\u4e0a\u8bad\u7ec3\u751f\u6210\u8bcd\u5411\u91cf\u5b57\u5178":62,"\u5728\u8bad\u7ec3\u65f6":57,"\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d":[58,71],"\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u6bcfshow":53,"\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u8fdb\u884c\u6d4b\u8bd5":2,"\u5728\u8bbe\u7f6e":[39,40],"\u5728\u8bc4\u5ba1\u8fc7\u7a0b\u4e2d":30,"\u5728\u8be5\u914d\u7f6e\u76847":42,"\u5728\u8bed\u8a00\u751f\u6210\u9886\u57df\u4e2d":71,"\u5728\u8d2d\u7269\u7f51\u7ad9\u4e0a":66,"\u5728\u8f6f\u4ef6\u5de5\u7a0b\u7684\u8303\u7574\u91cc":50,"\u5728\u8f93\u51fa\u7684\u8fc7\u7a0b\u4e2d":44,"\u5728\u8fd0\u884c":70,"\u5728\u8fd9\u4e2a":30,"\u5728\u8fd9\u4e2a\u4efb\u52a1\u4e2d":71,"\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d":[32,70],"\u5728\u8fd9\u4e2a\u4f8b\u5b50\u91cc":[47,57],"\u5728\u8fd9\u4e2a\u51fd\u6570\u4e2d":42,"\u5728\u8fd9\u4e2a\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165\u4efb\u4f55\u5176\u4ed6\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u5728\u8fd9\u4e2a\u6559\u7a0b\u4e2d":50,"\u5728\u8fd9\u4e2a\u6a21\u578b\u4e2d":45,"\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d":69,"\u5728\u8fd9\u4e2a\u9636\u6bb5\u7684\u4ee3\u7801\u6b63\u5728\u7ecf\u5386\u56de\u5f52\u6d4b\u8bd5":30,"\u5728\u8fd9\u4e9b\u5934\u6587\u4ef6\u4e2d":27,"\u5728\u8fd9\u4e9b\u6587\u4ef6\u4e2d":27,"\u5728\u8fd9\u4e9b\u7f51\u7edc\u4e2d":68,"\u5728\u8fd9\u4e9blayer\u4e2d":42,"\u5728\u8fd9\u6b65\u4efb\u52a1\u4e2d":70,"\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b":[45,47],"\u5728\u8fd9\u79cd\u7ed3\u6784\u4e2d":44,"\u5728\u8fd9\u7bc7\u6587\u6863\u91cc":57,"\u5728\u8fd9\u7bc7\u6587\u7ae0\u91cc":58,"\u5728\u8fd9\u91cc":44,"\u5728\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528\u5168\u8fde\u63a5\u5c42\u4f5c\u4e3a\u4f8b\u5b50\u6765\u5c55\u793a\u5b9e\u73b0\u65b0\u7f51\u7edc\u5c42\u6240\u9700\u8981\u7684\u56db\u4e2a\u6b65\u9aa4":47,"\u5728\u8fdb\u884c\u7f51\u7edc\u914d\u7f6e\u4e4b\u524d":37,"\u5728\u914d\u7f6e\u4e2d\u9700\u8981\u8bfb\u53d6\u5916\u90e8\u5b57\u5178":3,"\u5728\u914d\u7f6e\u6587\u4ef6\u4e2d\u7684":64,"\u5728\u91c7\u7528sgd":31,"\u5728\u96c6\u7fa4\u4e0a\u8bad\u7ec3\u4e00\u4e2a\u7a00\u758f\u6a21\u578b\u9700\u8981\u52a0\u4e0a\u4e0b\u9762\u7684\u53c2\u6570":55,"\u5728\u9884\u5904\u7406\u542b\u6709\u591a\u884c\u6570\u6910\u7684\u6587\u672c\u6587\u4ef6\u65f6\u53c2\u6570\u8bbe\u7f6e\u7a0d\u6709\u4e0d\u540c":70,"\u5728\u9884\u6d4b\u5e8f\u5217\u6216\u6bb5\u843d\u7684\u60c5\u611f\u4e2d\u8d77\u4e3b\u8981\u4f5c\u7528":70,"\u5728android\u5e73\u53f0\u4e0a\u4e0d\u652f\u6301\u901a\u8fc7swig\u8c03\u7528\u6765\u8bad\u7ec3\u6216\u8005\u9884\u6d4b":39,"\u5728android\u5e73\u53f0\u4e0a\u53ea\u652f\u6301\u4f7f\u7528c":39,"\u5728aws\u4e0a\u5feb\u901f\u90e8\u7f72\u96c6\u7fa4":56,"\u5728c":26,"\u5728c\u7684\u5934\u6587\u4ef6":26,"\u5728cub":63,"\u5728generator\u7684\u4e0a\u4e0b\u6587\u4e2d\u5c3d\u91cf\u7559\u4e0b\u975e\u5e38\u5c11\u7684\u53d8\u91cf\u5f15\u7528":3,"\u5728kubernetes\u4e2d\u521b\u5efa\u7684\u6240\u6709\u8d44\u6e90\u5bf9\u8c61":56,"\u5728linux\u4e0b":71,"\u5728meta\u6587\u4ef6\u4e2d\u6709\u4e24\u79cd\u7279\u5f81":68,"\u5728movielen":68,"\u5728paddl":58,"\u5728paddle\u4e2d":55,"\u5728paddle\u4e4b\u4e0a\u8fd0\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u8f93\u51fa\u7684\u6a21\u578b\u4f1a\u63d0\u4f9b\u7ed9\u5728\u7ebf\u4eba\u8138\u8bc6\u522b\u7684\u5e94\u7528\u4f7f\u7528":20,"\u5728paddlepaddle\u4e2d":[37,44],"\u5728paddlepaddle\u7684\u6587\u6863\u4e2d":42,"\u5728paddlepaddle\u91cc":32,"\u5728paramet":19,"\u5728step\u51fd\u6570\u4e2d\u5b9a\u4e49":44,"\u5728step\u51fd\u6570\u4e2d\u5b9a\u4e49memori":44,"\u5728trainer":55,"\u5730\u5740\u4e5f\u53ef\u4ee5\u4e3ahdfs\u6587\u4ef6\u8def\u5f84":2,"\u5730\u6bb5":42,"\u5730\u7406\u4f4d\u7f6e":42,"\u5730\u94c1\u7ad9":42,"\u5747\u4f1a\u88ab\u5b89\u88c5\u5230includ":27,"\u5747\u503c\u56fe\u50cf\u6587\u4ef6":64,"\u5747\u5300\u5206\u5e03":31,"\u5747\u5300\u5206\u5e03\u7684\u8303\u56f4\u662f":53,"\u5747\u662f\u5728":27,"\u5747\u6709\u4e09\u4e2a\u5b50\u5e8f\u5217":42,"\u5747\u6709\u4e24\u7ec4\u7279\u5f81":42,"\u57fa\u4e8e\u53cc\u5c42\u5e8f\u5217\u8f93\u5165":44,"\u57fa\u4e8e\u5b57\u6bcd\u7684\u8bcd\u5d4c\u5165\u7279\u5f81":68,"\u57fa\u4e8e\u7c98\u6027\u4f1a\u8bdd\u7684\u8d1f\u8f7d\u5747\u8861\u529f\u80fd":24,"\u57fa\u4e8epython\u7684\u6a21\u578b\u9884\u6d4b":5,"\u57fa\u4e8epython\u7684\u9884\u6d4b":[4,66],"\u57fa\u672c\u4e0a\u548cmnist\u6837\u4f8b\u4e00\u81f4":3,"\u57fa\u672c\u4f7f\u7528\u6982\u5ff5":38,"\u57fa\u672c\u76f8\u540c":62,"\u586b\u5199":46,"\u589e\u52a0\u4e86\u4e00\u6761cd\u547d\u4ee4":57,"\u589e\u52a0\u5982\u4e0b\u53c2\u6570":55,"\u589e\u52a0\u68af\u5ea6\u68c0\u6d4b\u7684\u5355\u5143\u6d4b\u8bd5":47,"\u58f0\u660epython\u6570\u636e\u6e90":68,"\u5904\u7406\u5668\u6709\u4e24\u4e2a\u5173\u952e\u6027\u80fd\u9650\u5236":50,"\u5904\u7406\u6570\u636e\u7684python\u811a\u672c\u6587\u4ef6":66,"\u5904\u7406\u7684\u8f93\u5165\u5e8f\u5217\u4e3b\u8981\u5206\u4e3a\u4ee5\u4e0b\u4e09\u79cd\u7c7b\u578b":44,"\u5904\u7406\u76f8\u4f3c\u5ea6\u56de\u5f52":68,"\u5904\u7406\u8fc7\u7a0b\u4e2d\u6570\u636e\u5b58\u50a8\u683c\u5f0f":63,"\u5907\u6ce8":50,"\u590d\u6742\u5ea6\u6216\u65f6\u95f4\u590d\u6742\u5ea6":50,"\u5916\u5c42memory\u662f\u4e00\u4e2a\u5143\u7d20":42,"\u5916\u5c42outer_step\u4e2d":42,"\u591a\u4e2a\u503c":20,"\u591a\u4e2ainput\u4ee5list\u65b9\u5f0f\u8f93\u5165":66,"\u591a\u4e2aparamet":19,"\u591a\u53e5\u8bdd\u8fdb\u4e00\u6b65\u6784\u6210\u4e86\u6bb5\u843d":44,"\u591a\u673a\u8bad\u7ec3":31,"\u591a\u7ebf\u7a0b\u7684\u6570\u636e\u8bfb\u53d6":3,"\u591a\u8f6e\u5bf9\u8bdd\u7b49\u66f4\u4e3a\u590d\u6742\u7684\u8bed\u8a00\u6570\u636e":44,"\u5927\u578b\u7535\u5f71\u8bc4\u8bba\u6570\u636e\u96c6":70,"\u5927\u591a\u6570\u5c42\u4e0d\u9700\u8981\u8fdc\u7a0b\u7a00\u758f\u8bad\u7ec3\u51fd\u6570":47,"\u5927\u591a\u6570\u5c42\u9700\u8981\u8bbe\u7f6e\u4e3a":47,"\u5927\u591a\u6570\u6210\u529f\u7684srl\u7cfb\u7edf\u662f\u5efa\u7acb\u5728\u67d0\u79cd\u5f62\u5f0f\u7684\u53e5\u6cd5\u5206\u6790\u7ed3\u679c\u4e4b\u4e0a\u7684":69,"\u5927\u591a\u6570\u7f51\u7edc\u5c42\u4e0d\u9700\u8981\u652f\u6301\u8fdc\u7a0b\u7a00\u758f\u66f4\u65b0":47,"\u5927\u591a\u6570\u8bed\u8a00\u90fd\u652f\u6301\u4f7f\u7528c\u8bed\u8a00api":26,"\u5927\u5b66\u751f":67,"\u5927\u5bb6\u53ef\u4ee5\u901a\u8fc7\u5b83\u5236\u4f5c\u548c\u5206\u4eab\u5e26\u6709\u4ee3\u7801":34,"\u5927\u5c0f":51,"\u5929":42,"\u5929\u4e00\u5e7f\u573a":42,"\u5929\u4e00\u9601":42,"\u5929\u732b":70,"\u5934\u6587\u4ef6\u4e2d\u628a\u53c2\u6570\u5b9a\u4e49\u4e3a\u7c7b\u7684\u6210\u5458\u53d8\u91cf":47,"\u5934\u6587\u4ef6\u5982\u4e0b":47,"\u5947\u5e7b\u7247":67,"\u597d":42,"\u597d\u5403":42,"\u597d\u8bc4":66,"\u5982":[3,45,46,51,55],"\u59822":51,"\u5982\u4e0b":[3,68,70],"\u5982\u4e0b\u56fe\u6240\u793a":[42,50,63],"\u5982\u4e0b\u6240\u793a":[55,64,68],"\u5982\u4e0b\u662f\u4e00\u6bb5\u4f7f\u7528mnist":5,"\u5982\u4e0b\u8868\u683c":66,"\u5982\u4f55":68,"\u5982\u4f55\u5b58\u50a8\u7b49\u7b49":3,"\u5982\u4f55\u89e3\u6790\u8be5\u5730\u5740\u4e5f\u662f\u7528\u6237\u81ea\u5b9a\u4e49dataprovider\u65f6\u9700\u8981\u8003\u8651\u7684\u5730\u65b9":2,"\u5982\u4f55\u8d21\u732e":49,"\u5982\u4f55\u8d21\u732e\u4ee3\u7801":49,"\u5982\u4f55\u8fdb\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3":66,"\u5982\u4fe1\u606f\u63d0\u53d6":69,"\u5982\u56fe2\u6240\u793a":70,"\u5982\u56fe\u4e2dtrainer":19,"\u5982\u5f62\u5bb9\u8bcd\u548c\u526f\u8bcd":70,"\u5982\u60f3\u4e86\u89e3\u66f4\u591a\u8be6\u7ec6\u7684\u89e3\u91ca":71,"\u5982\u672c\u4f8b\u4e2d":3,"\u5982\u672c\u4f8b\u7684":3,"\u5982\u679c\u4e00\u4e2a\u7f51\u7edc\u5c42\u9700\u8981\u914d\u7f6e\u7684\u8bdd":47,"\u5982\u679c\u4e0a\u9762\u4e24\u6b65\u51fa\u73b0\u9519\u8bef":19,"\u5982\u679c\u4e0b\u8f7d\u6210\u529f":64,"\u5982\u679c\u4e0d\u4e3a0":53,"\u5982\u679c\u4e0d\u4e86\u89e3":3,"\u5982\u679c\u4e0d\u5207\u8bcd":66,"\u5982\u679c\u4e0d\u6536\u655b":31,"\u5982\u679c\u4e0d\u662f\u5e8f\u5217":68,"\u5982\u679c\u4e3a":3,"\u5982\u679c\u4e3a0":53,"\u5982\u679c\u4e3afals":53,"\u5982\u679c\u4e3atrue":[3,53],"\u5982\u679c\u4e4b\u540e\u60f3\u8981\u91cd\u65b0\u8bbe\u7f6e":33,"\u5982\u679c\u4ec5\u4ec5\u4fee\u6539\u4e00\u4e2a\u6587\u4ef6\u4f46\u63d0\u4ea4\u4e86\u5341\u51e0\u4e2acommit":46,"\u5982\u679c\u4ed4\u7ec6\u8bbe\u7f6e\u7684\u8bdd":53,"\u5982\u679c\u4f60\u4e0d\u9700\u8981\u8fd9\u4e2a\u64cd\u4f5c":70,"\u5982\u679c\u4f60\u53ea\u9700\u8981\u4f7f\u7528\u7b80\u5355\u7684rnn":45,"\u5982\u679c\u4f60\u5b89\u88c5gpu\u7248\u672c\u7684paddlepaddl":70,"\u5982\u679c\u4f60\u60f3\u4f7f\u7528\u8fd9\u4e9b\u7279\u6027":55,"\u5982\u679c\u4f60\u60f3\u8981\u4fdd\u5b58\u67d0\u4e9b\u5c42\u7684\u7279\u5f81\u56fe":53,"\u5982\u679c\u4f60\u60f3\u8fdb\u884c\u8bf8\u5982\u8bed\u4e49\u8f6c\u8ff0":71,"\u5982\u679c\u4f60\u6267\u884c\u5176\u5b83\u7684\u7528\u60c5\u611f\u5206\u6790\u6765\u5206\u7c7b\u6587\u672c\u7684\u4efb\u52a1":70,"\u5982\u679c\u4f60\u66fe\u7ecf\u5728\u6e90\u7801\u76ee\u5f55\u4e0b\u7f16\u8bd1\u8fc7\u5176\u4ed6\u5e73\u53f0\u7684paddlepaddle\u5e93":[39,40],"\u5982\u679c\u4f60\u6b63\u5728\u5904\u7406\u5e8f\u5217\u6807\u8bb0\u4efb\u52a1":45,"\u5982\u679c\u4f60\u6ca1\u6709gpu\u73af\u5883":63,"\u5982\u679c\u4f60\u8981\u4e3a\u4e86\u6d4b\u8bd5\u800c\u589e\u52a0\u65b0\u7684\u6587\u4ef6":47,"\u5982\u679c\u4f7f\u7528":[51,62],"\u5982\u679c\u4f7f\u7528gpu\u7248\u672c\u7684paddlepaddl":36,"\u5982\u679c\u4f7f\u7528nvidia":34,"\u5982\u679c\u4f7f\u7528ssl\u8ba4\u8bc1":56,"\u5982\u679c\u4f7f\u7528swig\u6211\u4eec\u9700\u8981\u5c06\u5728interface\u6587\u4ef6\u91cc":26,"\u5982\u679c\u51fa\u73b0\u4ee5\u4e0bpython\u76f8\u5173\u7684\u5355\u5143\u6d4b\u8bd5\u90fd\u8fc7\u4e0d\u4e86\u7684\u60c5\u51b5":31,"\u5982\u679c\u53c2\u6570\u4fdd\u5b58\u4e0b\u6765\u7684\u6a21\u578b\u76ee\u5f55":31,"\u5982\u679c\u53c2\u6570\u6a21\u578b\u6587\u4ef6\u7f3a\u5931":62,"\u5982\u679c\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u672a\u8bbe\u7f6easync":53,"\u5982\u679c\u5728\u8bad\u7ec3\u671f\u95f4\u540c\u65f6\u53d1\u8d77\u53e6\u5916\u4e00\u4e2a\u8fdb\u7a0b\u8fdb\u884c\u6d4b\u8bd5":53,"\u5982\u679c\u5728\u8bad\u7ec3\u914d\u7f6e\u4e2d\u8bbe\u7f6ebatch":53,"\u5982\u679c\u5728\u8bad\u7ec3nlp\u76f8\u5173\u6a21\u578b\u65f6":31,"\u5982\u679c\u5931\u8d25":30,"\u5982\u679c\u5b58\u5728\u67d0\u4e9btrainer\u6267\u884c\u901f\u5ea6\u8fc7\u6162\u4f1a\u5f71\u54cd\u6574\u4f53\u96c6\u7fa4\u7684\u901f\u5ea6":19,"\u5982\u679c\u5b83\u4f4d\u4e8e\u8c13\u8bcd\u4e0a\u4e0b\u6587\u533a\u57df\u4e2d":69,"\u5982\u679c\u5c06\u8fd9\u4e2a\u5185\u5b58\u6c60\u51cf\u5c0f":31,"\u5982\u679c\u5df2\u5b89\u88c5":69,"\u5982\u679c\u5df2\u7ecf\u6709pod\u8fd0\u884c":58,"\u5982\u679c\u5df2\u7ecf\u6b63\u5728\u6267\u884c\u4fdd\u5b58\u68c0\u67e5\u70b9\u7684\u7ebf\u7a0b":19,"\u5982\u679c\u5f00\u542f\u4f1a\u5bfc\u81f4\u8fd0\u884c\u7565\u6162":33,"\u5982\u679c\u60a8":34,"\u5982\u679c\u60a8\u60f3\u8981\u66f4\u6df1\u5165\u4e86\u89e3deep":34,"\u5982\u679c\u60a8\u6709\u597d\u7684\u5efa\u8bae\u6765":68,"\u5982\u679c\u60a8\u7684gpu\u7406\u8bba\u53ef\u4ee5\u8fbe\u52306":50,"\u5982\u679c\u60f3\u4e3a\u4e00\u4e2a\u6570\u636e\u6587\u4ef6\u8fd4\u56de\u591a\u6761\u6837\u672c":3,"\u5982\u679c\u60f3\u4f7f\u7528\u53ef\u89c6\u5316\u7684\u5206\u6790\u5668":50,"\u5982\u679c\u60f3\u5f88\u597d\u7684\u7406\u89e3\u7a0b\u5e8f\u7684\u884c\u4e3a":50,"\u5982\u679c\u60f3\u8981\u4e86\u89e3\u53cc\u5c42rnn\u5728\u5177\u4f53\u95ee\u9898\u4e2d\u7684\u4f7f\u7528":42,"\u5982\u679c\u60f3\u8981\u542f\u7528paddlepaddle\u7684\u5185\u7f6e\u5b9a\u65f6\u5668":50,"\u5982\u679c\u6211\u77e5\u9053\u5185\u6838\u82b1\u4e8610ms\u6765\u79fb\u52a81gb\u6570\u636e":50,"\u5982\u679c\u6267\u884c\u5931\u8d25":56,"\u5982\u679c\u6267\u884c\u6210\u529f":64,"\u5982\u679c\u63d0\u793a\u6b63\u786e":48,"\u5982\u679c\u63d0\u793a\u9519\u8bef":48,"\u5982\u679c\u6570\u636e\u6587\u4ef6\u5b58\u4e8e\u672c\u5730\u78c1\u76d8":2,"\u5982\u679c\u6570\u6910\u83b7\u53d6\u6210\u529f":70,"\u5982\u679c\u662f\u4f7f\u7528\u975essl\u65b9\u5f0f\u8bbf\u95ee":56,"\u5982\u679c\u662f\u5176\u5b83\u7c7b\u578b":20,"\u5982\u679c\u662f\u5e8f\u5217":68,"\u5982\u679c\u6709\u591a\u4e2a\u8f93\u5165":44,"\u5982\u679c\u6709\u591a\u4e2a\u8f93\u5165\u5e8f\u5217":44,"\u5982\u679c\u6709\u66f4\u590d\u6742\u7684\u4f7f\u7528":2,"\u5982\u679c\u6709\u9700\u8981\u4fee\u6539\u7684\u5730\u65b9":46,"\u5982\u679c\u6709bugfix\u7684\u884c\u4e3a":30,"\u5982\u679c\u672a\u8bbe\u7f6e":53,"\u5982\u679c\u672a\u8bbe\u7f6egpu":55,"\u5982\u679c\u67d0\u4e00\u4e2a\u7c7b\u578b\u9700\u8981\u5f15\u7528\u53e6\u4e00\u4e2a\u7c7b\u578b":27,"\u5982\u679c\u67d0\u4e00\u4e2apaddl":27,"\u5982\u679c\u67d0\u4e00\u4e2apaddle\u6982\u5ff5\u5fc5\u987b\u8981\u66b4\u9732":27,"\u5982\u679c\u67d0\u4e00\u5757\u6839\u672c\u5c31\u4e0d\u600e\u4e48\u8017\u65f6":50,"\u5982\u679c\u68c0\u67e5\u5230\u5206\u914d\u5728\u4e0d\u540c\u53c2\u6570\u670d\u52a1\u5668\u4e0a\u7684\u53c2\u6570\u7684\u5206\u5e03\u4e0d\u5747\u5300\u6b21\u6570\u5927\u4e8echeck":53,"\u5982\u679c\u6ca1\u6709\u5b9a\u4e49memori":44,"\u5982\u679c\u6ca1\u6709\u8bbe\u7f6e":71,"\u5982\u679c\u6ca1\u6709\u8bbe\u7f6etest":2,"\u5982\u679c\u6ca1\u8fc7":46,"\u5982\u679c\u6d88\u606f\u6570\u636e\u592a\u5c0f":53,"\u5982\u679c\u6ee1\u8db3\u6761\u4ef6":19,"\u5982\u679c\u7528\u6237\u4e0d\u663e\u793a\u6307\u5b9a\u8fd4\u56de\u6570\u636e\u7684\u5bf9\u5e94\u5173\u7cfb":3,"\u5982\u679c\u7528\u6237\u60f3\u8981\u4e86\u89e3\u8be6\u7ec6\u7684\u6570\u636e\u96c6\u7684\u683c\u5f0f":62,"\u5982\u679c\u7528\u6237\u60f3\u8981\u81ea\u5b9a\u4e49\u521d\u59cb\u5316\u65b9\u5f0f":31,"\u5982\u679c\u7528\u6237\u8981\u628apaddle\u7684\u9759\u6001\u5e93":26,"\u5982\u679c\u771f\u60f3\u6316\u6398\u5185\u6838\u6df1\u5904\u7684\u67d0\u4e2a\u79d8\u5bc6":50,"\u5982\u679c\u7a0b\u5e8f\u5d29\u6e83\u4f60\u4e5f\u53ef\u4ee5\u624b\u52a8\u7ec8\u6b62":51,"\u5982\u679c\u7cfb\u7edf\u5b89\u88c5\u4e86\u591a\u4e2apython\u7248\u672c":31,"\u5982\u679c\u7f51\u7edc\u5c42\u4e0d\u9700\u8981\u8fdc\u7a0b\u7a00\u758f\u66f4\u65b0":47,"\u5982\u679c\u7f51\u7edc\u67b6\u6784\u7b80\u5355":45,"\u5982\u679c\u8981\u4f7f\u7528\u53cc\u5411lstm":70,"\u5982\u679c\u8981\u4f7f\u7528gpu":34,"\u5982\u679c\u8981\u542f\u7528gpu":51,"\u5982\u679c\u8981\u8fd0\u884c\u6240\u6709\u7684\u5355\u5143\u6d4b\u8bd5":46,"\u5982\u679c\u89e3\u51b3\u4e86\u67d0\u4e2aissue\u7684\u95ee\u9898":46,"\u5982\u679c\u8bad\u7ec3\u4e00\u4e2apass":31,"\u5982\u679c\u8bad\u7ec3\u8fc7\u7a0b\u542f\u52a8\u6210\u529f\u7684\u8bdd":68,"\u5982\u679c\u8bad\u7ec3\u8fc7\u7a0b\u7684\u7684cost\u660e\u663e\u9ad8\u4e8e\u8fd9\u4e2a\u5e38\u6570\u8f93\u51fa\u7684cost":31,"\u5982\u679c\u8bbe\u7f6e":3,"\u5982\u679c\u8bbe\u7f6e\u8be5\u53c2\u6570":53,"\u5982\u679c\u8bc4\u5ba1\u610f\u89c1\u6bd4\u8f83\u591a":46,"\u5982\u679c\u8c03\u7528\u9759\u6001\u5e93\u53ea\u80fd\u5c06\u9759\u6001\u5e93\u4e0e\u89e3\u91ca\u5668\u94fe\u63a5":26,"\u5982\u679c\u8f93\u51fa\u662fno":34,"\u5982\u679c\u8fd0\u884c":31,"\u5982\u679c\u8fd0\u884c\u6210\u529f":[64,70],"\u5982\u679c\u96c6\u7fa4\u8282\u70b9\u6570\u91cf\u5c11":51,"\u5982\u679c\u9700\u8981\u5305\u542b\u66f4\u591a\u7684\u4f9d\u8d56":34,"\u5982\u679c\u9700\u8981\u6269\u5927\u77e9\u9635":47,"\u5982\u679c\u9700\u8981\u7f29\u51cf\u77e9\u9635":47,"\u5982\u679clearning_rate\u592a\u5927":31,"\u5982\u679clearning_rate\u592a\u5c0f":31,"\u5982\u679cpaddlepaddle\u5305\u5df2\u7ecf\u5728python\u7684sit":31,"\u5982\u679cparamet":19,"\u5982\u6bcf\u4e2a\u6587\u4ef6\u53ea\u6709\u4e00\u4e2a":46,"\u5982\u795e\u7ecf\u5143\u6fc0\u6d3b\u503c\u7b49":31,"\u5982\u8981build\u8fd9\u4e2a\u5f00\u53d1\u955c\u50cf":46,"\u5982\u9ad8\u4eae\u90e8\u5206":50,"\u5b50":42,"\u5b50\u53e5":44,"\u5b50\u53e5\u7684\u5355\u8bcd\u6570\u548c\u6307\u5b9a\u7684\u4e00\u4e2a\u8f93\u5165\u5e8f\u5217\u4e00\u81f4":44,"\u5b57\u5178":71,"\u5b57\u5178\u4f1a\u5305\u542b\u8f93\u5165\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u5355\u8bcd":71,"\u5b57\u5178\u5171\u5305\u542b":62,"\u5b57\u5178\u6587\u4ef6":[69,70],"\u5b57\u5178\u91c7\u7528utf8\u7f16\u7801":62,"\u5b57\u5178imdb":70,"\u5b57\u6bb5\u4e2d":58,"\u5b57\u6bb5\u8868\u793a\u5bb9\u5668\u7684\u73af\u5883\u53d8\u91cf":58,"\u5b57\u6bb5\u8868\u793a\u8fd9\u4e2ajob\u4f1a\u540c\u65f6\u5f00\u542f3\u4e2apaddlepaddle\u8282\u70b9":58,"\u5b57\u7b26\u4e32":20,"\u5b58\u50a8":20,"\u5b58\u50a8\u5377":56,"\u5b58\u50a8\u5728\u8bb0\u5fc6\u5355\u5143\u533a\u5757\u7684\u5386\u53f2\u4fe1\u606f\u88ab\u66f4\u65b0\u7528\u6765\u8fed\u4ee3\u7684\u5b66\u4e60\u5355\u8bcd\u4ee5\u5408\u7406\u7684\u5e8f\u5217\u7a0b\u73b0":70,"\u5b58\u50a8\u6a21\u578b\u7684\u8def\u5f84":71,"\u5b58\u50a8\u7740\u7535\u5f71\u6216\u7528\u6237\u4fe1\u606f":68,"\u5b58\u5165settings\u5bf9\u8c61":3,"\u5b58\u5728\u6216\u66f4\u6539\u4e3a\u5176\u5b83\u6a21\u578b\u8def\u5f84":70,"\u5b66\u4e60\u6210\u672c\u9ad8":26,"\u5b66\u4e60\u7b97\u6cd5":32,"\u5b66\u672f":67,"\u5b81\u6ce2":42,"\u5b83\u4e0d\u4ec5\u80fd\u591f\u5904\u7406imdb\u6570\u636e":70,"\u5b83\u4eec\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4f5c\u4e3a\u7f51\u7edc\u7684\u51fa\u53e3":32,"\u5b83\u4eec\u7684\u5927\u5c0f\u662f":45,"\u5b83\u4eec\u7684\u6587\u4ef6\u540d\u662f":20,"\u5b83\u4eec\u8fd8\u53ef\u4ee5\u4f9b\u90a3\u4e9b\u8fd0\u884c\u66f4\u590d\u6742\u7684\u96c6\u7fa4\u7ba1\u7406\u7cfb\u7edf":51,"\u5b83\u4eec\u90fd\u662f\u5e8f\u5217":45,"\u5b83\u4f1a\u5728dataprovider\u521b\u5efa\u7684\u65f6\u5019\u6267\u884c":3,"\u5b83\u4f7f\u752850\u5c42\u7684resnet\u6a21\u578b\u6765\u5bf9":64,"\u5b83\u5305\u542b\u4ee5\u4e0b\u51e0\u6b65":47,"\u5b83\u5305\u542b\u4ee5\u4e0b\u53c2\u6570":47,"\u5b83\u5305\u542b\u56db\u4e2a\u7248\u672c":36,"\u5b83\u5305\u542b\u7684\u5c5e\u6027\u53c2\u6570\u5982\u4e0b":3,"\u5b83\u5305\u62ec\u4e86\u4e00\u4e2a\u53cc\u5411\u7684gru\u4f5c\u4e3a\u7f16\u7801\u5668\u548c\u89e3\u7801\u5668":71,"\u5b83\u53eb\u505a":45,"\u5b83\u53ef\u4ee5\u5728\u53e5\u5b50\u7ea7\u522b\u5229\u7528\u53ef\u6269\u5c55\u7684\u4e0a\u4e0b\u6587":70,"\u5b83\u53ef\u4ee5\u5e2e\u52a9\u51cf\u5c11\u5206\u53d1\u5ef6\u8fdf":51,"\u5b83\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u683c\u5f0f\u5316\u6e90\u4ee3\u7801":46,"\u5b83\u53ef\u4ee5\u6307\u6d4b\u91cf\u4e00\u4e2a\u7a0b\u5e8f\u7684\u7a7a\u95f4":50,"\u5b83\u53ef\u4ee5\u88ab\u5e94\u7528\u4e8e\u8fdb\u884c\u673a\u5668\u7ffb\u8bd1":71,"\u5b83\u53ef\u80fd\u6709\u4e0d\u6b62\u4e00\u4e2a\u6743\u91cd":47,"\u5b83\u540c\u65f6\u5b66\u4e60\u6392\u5217":71,"\u5b83\u548c\u6570\u636e\u4f20\u5165\u51fd\u6570\u7684\u7b2c\u4e00\u4e2a\u53c2\u6570":3,"\u5b83\u5b58\u50a8\u5f53\u524d\u8282\u70b9\u6240\u6709\u8bad\u7ec3":51,"\u5b83\u5b9a\u4e49\u4e86":45,"\u5b83\u5b9a\u4e49\u4e86\u6a21\u578b\u53c2\u6570\u6539\u53d8\u7684\u89c4\u5219":32,"\u5b83\u5b9a\u4e49\u89e3\u7801\u7f51\u7edc\u7684":45,"\u5b83\u5c06\u88ab\u5206\u53d1\u5230":51,"\u5b83\u5c06\u8f93\u5165\u8bed\u53e5\u7f16\u7801\u4e3a\u5411\u91cf\u7684\u5e8f\u5217":71,"\u5b83\u5c06\u8fd4\u56de\u5982\u4e0b\u7684\u5b57\u5178":64,"\u5b83\u5c31\u4f1a\u5728\u6e90\u8bed\u53e5\u4e2d\u641c\u7d22\u51fa\u6700\u76f8\u5173\u4fe1\u606f\u7684\u4f4d\u7f6e\u7684\u96c6\u5408":71,"\u5b83\u652f\u6301\u591a\u7ebf\u7a0b\u66f4\u65b0":47,"\u5b83\u662finteger_value\u7c7b\u578b\u7684":42,"\u5b83\u662finteger_value_sequence\u7c7b\u578b\u7684":42,"\u5b83\u6709\u52a9\u4e8e\u5e2e\u52a9\u9891\u7e41\u4fee\u6539\u548c\u8bbf\u95ee\u5de5\u4f5c\u533a\u6587\u4ef6\u7684\u7528\u6237\u51cf\u5c11\u8d1f\u62c5":51,"\u5b83\u6a21\u62df\u4e86\u89e3\u7801\u7ffb\u8bd1\u8fc7\u7a0b\u4e2d\u5728\u6e90\u8bed\u53e5\u4e2d\u7684\u641c\u7d22":71,"\u5b83\u7684":45,"\u5b83\u7684\u6536\u655b\u901f\u5ea6\u6bd4":70,"\u5b83\u7684\u6bcf\u4e00\u4e2a\u5143\u7d20":41,"\u5b83\u7684\u76ee\u7684\u662f\u9884\u6d4b\u5728\u4e00\u4e2a\u5e8f\u5217\u4e2d\u8868\u8fbe\u7684\u60c5\u611f\u6001\u5ea6":70,"\u5b83\u7684\u8f93\u5165\u4e0e\u7ecf\u8fc7\u5b66\u4e60\u7684\u53c2\u6570\u505a\u5185\u79ef\u5e76\u52a0\u4e0a\u504f\u7f6e":47,"\u5b83\u76f4\u63a5\u5b66\u4e60\u6bb5\u843d\u8868\u793a":70,"\u5b83\u80fd\u591f\u4ece\u8bcd\u7ea7\u5230\u5177\u6709\u53ef\u53d8\u4e0a\u4e0b\u6587\u957f\u5ea6\u7684\u4e0a\u4e0b\u6587\u7ea7\u522b\u6765\u603b\u7ed3\u8868\u793a":70,"\u5b83\u8bfb\u5165\u6570\u636e\u5e76\u5c06\u5b83\u4eec\u4f20\u8f93\u5230\u63a5\u4e0b\u6765\u7684\u7f51\u7edc\u5c42":32,"\u5b83\u8fd4\u56degen":71,"\u5b83\u8fd4\u56detrain":71,"\u5b83\u9700\u8981\u5728\u8fd9\u91cc\u6307\u5b9a":70,"\u5b83\u9996\u5148\u8c03\u7528\u57fa\u6784\u9020\u51fd\u6570":47,"\u5b89\u6392":42,"\u5b89\u88c5\u4e0e\u7f16\u8bd1":38,"\u5b89\u88c5\u5305\u7684\u4e0b\u8f7d\u5730\u5740\u662f":36,"\u5b89\u88c5\u540e\u7684\u76ee\u5f55\u7ed3\u6784\u4e3a":27,"\u5b89\u88c5\u597ddocker\u4e4b\u540e\u53ef\u4ee5\u4f7f\u7528\u6e90\u7801\u76ee\u5f55\u4e0b\u7684\u811a\u672c\u6784\u5efa\u6587\u6863":48,"\u5b89\u88c5\u5b8c\u6210\u540e":36,"\u5b89\u88c5\u6587\u6863":37,"\u5b89\u88c5\u6d41\u7a0b":66,"\u5b89\u88c5\u8be5\u8f6f\u4ef6\u5305\u5c31\u53ef\u4ee5\u5728python\u73af\u5883\u4e0b\u5b9e\u73b0\u6a21\u578b\u9884\u6d4b":5,"\u5b89\u88c5paddlepaddl":66,"\u5b89\u88c5pillow":63,"\u5b89\u9759":42,"\u5b8c\u6210":46,"\u5b8c\u6210\u4e00\u4e2a\u4f20\u8f93\u52a8\u4f5c\u5b8c\u6210\u7684\u65f6\u95f4\u4e5f\u6bd4\u8f83\u77ed":24,"\u5b8c\u6210\u4efb\u610f\u7684\u8fd0\u7b97\u903b\u8f91":44,"\u5b8c\u6210\u540evolume\u4e2d\u7684\u6587\u4ef6\u5185\u5bb9\u5927\u81f4\u5982\u4e0b":58,"\u5b8c\u6210\u5f00\u53d1":34,"\u5b8c\u6210\u6570\u636e\u7684\u9884\u5904\u7406":20,"\u5b8c\u6210\u76f8\u5e94\u7684\u8ba1\u7b97":41,"\u5b8c\u6210paddlepaddle\u7684\u5b89\u88c5":37,"\u5b8c\u6574\u6e90\u7801\u53ef\u53c2\u8003":31,"\u5b8c\u6574\u7684\u4ee3\u7801\u89c1":5,"\u5b8c\u6574\u7684\u53c2\u6570\u77e9\u9635\u88ab\u5206\u5e03\u5728\u4e0d\u540c\u7684\u53c2\u6570\u670d\u52a1\u5668\u4e0a":47,"\u5b8c\u6574\u7684\u6570\u636e\u63d0\u4f9b\u6587\u4ef6\u5728":45,"\u5b8c\u6574\u7684\u914d\u7f6e\u6587\u4ef6\u5728":45,"\u5b98\u65b9\u6587\u6863":33,"\u5b9a\u4e49\u4e00\u4e2a\u65f6\u95f4\u6b65\u4e4b\u5185rnn\u5355\u5143\u5b8c\u6210\u7684\u8ba1\u7b97":44,"\u5b9a\u4e49\u4e00\u4e2apython\u7684":3,"\u5b9a\u4e49\u4e86\u4e00\u4e2a\u53ea\u8bfb\u7684memori":44,"\u5b9a\u4e49\u4e86\u7f51\u7edc\u7684\u6570\u636e\u69fd":69,"\u5b9a\u4e49\u4e86\u7f51\u7edc\u7ed3\u6784":63,"\u5b9a\u4e49\u4e86\u7f51\u7edc\u7ed3\u6784\u5e76\u4fdd\u5b58\u4e3a":32,"\u5b9a\u4e49\u5728\u5916\u5c42":44,"\u5b9a\u4e49\u5f02\u6b65\u8bad\u7ec3\u7684\u957f\u5ea6":53,"\u5b9a\u4e49\u6570\u636e\u6765\u6e90":32,"\u5b9a\u4e49\u6e90\u8bed\u53e5\u7684\u6570\u636e\u5c42":45,"\u5b9a\u4e49\u89e3\u7801\u5668\u7684memori":45,"\u5b9a\u4e49\u8bad\u7ec3\u6570\u6910\u548c\u6d4b\u8bd5\u6570\u6910\u63d0\u4f9b\u8005":70,"\u5b9a\u4e49\u8f93\u5165\u6570\u636e\u7684\u7c7b\u578b":32,"\u5b9a\u4e49\u8f93\u51fa\u51fd\u6570":45,"\u5b9a\u4e49\u95e8\u63a7\u5faa\u73af\u5355\u5143\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u5355\u6b65\u51fd\u6570":45,"\u5b9d\u5854\u7684\u5e95\u7aef\u9700\u8981\u575a\u5b9e\u7684\u57fa\u5ea7\u6765\u652f\u6491":37,"\u5b9e\u4f8b\u5982\u4e0b":69,"\u5b9e\u73b0\u4e24\u4e2a\u5b8c\u5168\u7b49\u4ef7\u7684\u5168\u8fde\u63a5rnn":42,"\u5b9e\u73b0\u524d\u5411\u4f20\u64ad\u7684\u90e8\u5206\u6709\u4e0b\u9762\u51e0\u4e2a\u6b65\u9aa4":47,"\u5b9e\u73b0\u5355\u6b65\u51fd\u6570":45,"\u5b9e\u73b0\u540e\u5411\u4f20\u64ad\u7684\u90e8\u5206\u6709\u4e0b\u9762\u51e0\u4e2a\u6b65\u9aa4":47,"\u5b9e\u73b0\u6570\u636e\u8f93\u5165\u51fd\u6570":3,"\u5b9e\u73b0\u6784\u9020\u51fd\u6570":47,"\u5b9e\u73b0\u7b80\u5355":26,"\u5b9e\u73b0\u7ec6\u8282":47,"\u5b9e\u73b0\u7f51\u7edc\u5c42\u7684\u524d\u5411\u4f20\u64ad":47,"\u5b9e\u73b0\u7f51\u7edc\u5c42\u7684\u540e\u5411\u4f20\u64ad":47,"\u5b9e\u73b0\u8bcd\u8bed\u548c\u53e5\u5b50\u4e24\u4e2a\u7ea7\u522b\u7684\u53cc\u5c42rnn\u7ed3\u6784":44,"\u5b9e\u73b0\u8be5\u5c42\u7684c":47,"\u5b9e\u9645\u4e0a\u53ea\u6709":64,"\u5b9e\u9645\u4e0a\u662fcsv\u6587\u4ef6":67,"\u5ba2\u6237":42,"\u5ba2\u6237\u670d\u52a1":67,"\u5bb6":42,"\u5bb9\u5668":56,"\u5bb9\u5668\u4e0d\u4f1a\u4fdd\u7559\u5728\u8fd0\u884c\u65f6\u751f\u6210\u7684\u6570\u636e":56,"\u5bb9\u5668\u8fd0\u884c\u90fd\u8fd0\u884c":58,"\u5bbf\u4e3b\u673a\u662flinux":40,"\u5bbf\u4e3b\u673a\u7684c":[39,40],"\u5bbf\u4e3b\u673a\u76ee\u5f55":56,"\u5bc4\u5b58\u5668\u4f7f\u7528\u60c5\u51b5\u548c\u5171\u4eab\u5185\u5b58\u4f7f\u7528\u60c5\u51b5\u80fd\u8ba9\u6211\u4eec\u5bf9gpu\u7684\u6574\u4f53\u4f7f\u7528\u6709\u66f4\u597d\u7684\u7406\u89e3":50,"\u5bf9":42,"\u5bf9\u4e00\u4e2a5\u7ef4\u975e\u5e8f\u5217\u7684\u7a00\u758f01\u5411\u91cf":[3,37],"\u5bf9\u4e00\u4e2a5\u7ef4\u975e\u5e8f\u5217\u7684\u7a00\u758f\u6d6e\u70b9\u5411\u91cf":[3,37],"\u5bf9\u4e8e":45,"\u5bf9\u4e8e\u4e0d\u540c\u8bed\u8a00":26,"\u5bf9\u4e8e\u4e24\u79cd\u4e0d\u540c\u7684\u8f93\u5165\u6570\u636e\u7c7b\u578b":42,"\u5bf9\u4e8e\u5185\u5b58\u8f83\u5c0f\u7684\u673a\u5668":3,"\u5bf9\u4e8e\u5355\u5c42rnn":42,"\u5bf9\u4e8e\u5355\u5c42rnn\u7684\u6570\u636e\u4e00\u5171\u6709\u4e24\u4e2a\u6837\u672c":42,"\u5bf9\u4e8e\u53cc\u5c42rnn":42,"\u5bf9\u4e8e\u540c\u4e00\u6bb5c":26,"\u5bf9\u4e8e\u540c\u6837\u7684\u6570\u636e":42,"\u5bf9\u4e8e\u591a\u8bed\u8a00\u63a5\u53e3":26,"\u5bf9\u4e8e\u5927\u591a\u6570\u8bed\u8a00":26,"\u5bf9\u4e8e\u6211\u4eec\u652f\u6301\u7684\u5168\u90e8\u77e9\u9635\u64cd\u4f5c":47,"\u5bf9\u4e8e\u6811\u7684\u6bcf\u4e00\u5c42":71,"\u5bf9\u4e8e\u6bb5\u843d\u7684\u6587\u672c\u5206\u7c7b":42,"\u5bf9\u4e8e\u6bcf\u4e00\u4e2a\u5355\u5c42rnn\u7684\u6570\u636e":42,"\u5bf9\u4e8e\u6bcf\u4f4d\u7528\u6237":68,"\u5bf9\u4e8e\u6bcf\u79cd\u7c7b\u578b":27,"\u5bf9\u4e8e\u6bcf\u79cdc":27,"\u5bf9\u4e8e\u7ed9\u5b9a\u7684\u4e00\u6761\u6587\u672c":66,"\u5bf9\u4e8e\u914d\u5907\u6709\u6ce8\u610f\u529b\u673a\u5236\u7684\u89e3\u7801\u5668":45,"\u5bf9\u4e8eamazon":66,"\u5bf9\u4e8erasspberri":40,"\u5bf9\u4ee3\u7801\u8fdb\u884c\u6027\u80fd\u5206\u6790":50,"\u5bf9\u5168\u8fde\u63a5\u5c42\u6765\u8bf4":47,"\u5bf9\u56fe\u7247\u8fdb\u884c\u9884\u5904\u7406":63,"\u5bf9\u5df2\u7ecfpush\u5230\u8fdc\u7a0b\u4ed3\u5e93\u7684\u591a\u4e2acommit":46,"\u5bf9\u5e94\u4e00\u4e2a\u5b50\u53e5":44,"\u5bf9\u5e94\u4e00\u4e2a\u8bcd":44,"\u5bf9\u5e94\u4e8e\u5b57\u5178":62,"\u5bf9\u5e94\u7684":3,"\u5bf9\u6570\u636e\u96c6\u8fdb\u884c\u9884\u5904\u7406\u7684\u57fa\u672c\u547d\u4ee4\u662f":71,"\u5bf9\u6574\u4e2a\u65b0\u5411\u91cf\u96c6\u5408\u7684\u6bcf\u4e00\u4e2a\u7ef4\u5ea6\u53d6\u6700\u5927\u503c\u6765\u8868\u793a\u6700\u540e\u7684\u53e5\u5b50":66,"\u5bf9\u6587\u6863\u5904\u7406\u540e\u5f62\u6210\u7684\u5355\u8bcd\u5411\u91cf":70,"\u5bf9\u673a\u5668\u7ffb\u8bd1\u7684\u4eba\u5de5\u8bc4\u4f30\u5de5\u4f5c\u5f88\u5e7f\u6cdb\u4f46\u4e5f\u5f88\u6602\u8d35":71,"\u5bf9\u6bcf\u4e2a\u8f93\u5165":47,"\u5bf9\u6bcf\u4e2a\u8f93\u5165\u4e58\u4e0a\u53d8\u6362\u77e9\u9635":47,"\u5bf9\u6bd4":26,"\u5bf9\u6fc0\u6d3b\u6c42\u5bfc":47,"\u5bf9\u7528\u6237\u6765\u8bf4":3,"\u5bf9\u8bad\u7ec3\u6570\u636e\u8fdb\u884cshuffl":3,"\u5bf9\u8bc4\u5ba1\u610f\u89c1\u4e0d\u540c\u610f\u7684":46,"\u5bf9\u8bc4\u5ba1\u610f\u89c1\u540c\u610f\u4e14\u6309\u5176\u4fee\u6539\u5b8c\u7684":46,"\u5bf9\u8be5\u5411\u91cf\u8fdb\u884c\u975e\u7ebf\u6027\u53d8\u6362":66,"\u5bf9\u8c61":31,"\u5bf9\u8c61\u5b58\u50a8\u4e3a\u6587\u4ef6":68,"\u5bf9\u8f93\u5165\u53c2\u6570\u7684\u5b89\u5168\u6027\u8fdb\u884c\u4e86\u5fc5\u8981\u7684\u5224\u65ad":27,"\u5bf9\u8f93\u51fa\u7684\u5408\u5e76":44,"\u5bf9\u8fd9\u4e2a\u7248\u672c\u7684\u63d0\u4ea4":30,"\u5bf9\u9762":42,"\u5bf9check":3,"\u5bf9sparse_binary_vector\u548csparse_float_vector":[3,37],"\u5bfc\u51fa\u8fd9\u4e9b\u63a5\u53e3":27,"\u5bfc\u81f4\u4e86\u6d6e\u70b9\u6570\u6ea2\u51fa":31,"\u5bfc\u81f4\u53c2\u6570\u6536\u655b\u5230\u4e86\u4e00\u4e9b\u5947\u5f02\u7684\u60c5\u51b5":31,"\u5bfc\u81f4\u53c2\u6570\u7d2f\u52a0":31,"\u5bfc\u81f4\u7f16\u8bd1paddlepaddle\u5931\u8d25":31,"\u5bfc\u81f4\u8bad\u7ec3\u65f6\u95f4\u8fc7\u957f":31,"\u5c01\u88c5\u4e86":50,"\u5c01\u88c5\u8be5\u5c42\u7684python\u63a5\u53e3":47,"\u5c06":[3,30,31,50,66],"\u5c06\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc\u53c2\u6570\u62c6\u5206\u6210\u591a\u4efd":19,"\u5c06\u4e0a\u4e00\u65f6\u95f4\u6b65\u6240\u751f\u6210\u7684\u8bcd\u7684\u5411\u91cf\u6765\u4f5c\u4e3a\u5f53\u524d\u65f6\u95f4\u6b65\u7684\u8f93\u5165":45,"\u5c06\u4ed6\u4eec\u79fb\u52a8\u5230\u76ee\u5f55":68,"\u5c06\u4f1a\u5c06\u7528\u6237\u4f20\u8fdb\u6765\u7684\u914d\u7f6e\u53c2\u6570\u4f20\u9012cmake\u7cfb\u7edf":39,"\u5c06\u4f1a\u81ea\u52a8\u8ba1\u7b97\u51fa\u4e00\u4e2a\u5408\u9002\u7684\u503c":53,"\u5c06\u5176\u8bbe\u7f6e\u6210":31,"\u5c06\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5148\u53d8\u6362\u6210\u5355\u5c42\u65f6\u95f4\u5e8f\u5217\u6570\u636e":42,"\u5c06\u542b\u6709\u5b50\u53e5":44,"\u5c06\u542b\u6709\u8bcd\u8bed\u7684\u53e5\u5b50\u5b9a\u4e49\u4e3a\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":44,"\u5c06\u56fe\u7247\u6309\u7167\u4e0a\u8ff0\u7ed3\u6784\u5b58\u50a8\u597d\u4e4b\u540e":63,"\u5c06\u5728":63,"\u5c06\u5728\u8fd0\u884c\u65f6\u62a5\u9519":51,"\u5c06\u5916\u90e8\u7684\u5b58\u50a8\u670d\u52a1\u5728kubernetes\u4e2d\u63cf\u8ff0\u6210\u4e3a\u7edf\u4e00\u7684\u8d44\u6e90\u5f62\u5f0f":56,"\u5c06\u591a\u53e5\u8bdd\u770b\u6210\u4e00\u4e2a\u6574\u4f53\u540c\u65f6\u4f7f\u7528encoder\u538b\u7f29":42,"\u5c06\u591a\u53f0\u673a\u5668\u7684\u6d4b\u8bd5\u7ed3\u679c\u5408\u5e76":53,"\u5c06\u5927\u91cf\u7684":26,"\u5c06\u5b57\u5178\u7684\u5730\u5740\u4f5c\u4e3aargs\u4f20\u7ed9dataprovid":31,"\u5c06\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u524d\u5411\u548c\u53cd\u5411\u90e8\u5206\u6df7\u5408\u5728\u4e00\u8d77":45,"\u5c06\u6570\u636e\u5904\u7406\u6210\u89c4\u8303\u683c\u5f0f":62,"\u5c06\u6570\u636e\u7ec4\u5408\u6210batch\u8fdb\u884c\u8bad\u7ec3":3,"\u5c06\u6570\u636e\u8f6c\u6362\u4e3apaddle\u7684\u683c\u5f0f":63,"\u5c06\u65b0\u5206\u652f\u7684\u7248\u672c\u6253\u4e0atag":30,"\u5c06\u65b0\u5efa\u7684\u6743\u91cd\u52a0\u5165\u6743\u91cd\u8868":47,"\u5c06\u65e5\u5fd7\u5199\u5165\u6587\u4ef6":68,"\u5c06\u672c\u5730\u7684\u4fee\u6539\u63a8\u9001\u5230":46,"\u5c06\u6837\u672c\u7684\u5730\u5740\u653e\u5165\u53e6\u4e00\u4e2a\u6587\u672c\u6587\u4ef6":3,"\u5c06\u6b64\u76ee\u5f55\u6302\u8f7d\u4e3a\u5bb9\u5668\u7684":58,"\u5c06\u6bcf\u4e2a\u6e90\u8bed\u8a00\u5230\u76ee\u6807\u8bed\u8a00\u7684\u5e73\u884c\u8bed\u6599\u5e93\u6587\u4ef6\u5408\u5e76\u4e3a\u4e00\u4e2a\u6587\u4ef6":71,"\u5c06\u73af\u5883\u53d8\u91cf\u8f6c\u6362\u6210paddle\u7684\u547d\u4ee4\u884c\u53c2\u6570":58,"\u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u6b64\u76ee\u5f55\u91cc":58,"\u5c06\u884c\u4e2d\u7684\u6570\u636e\u8f6c\u6362\u6210\u4e0einput_types\u4e00\u81f4\u7684\u683c\u5f0f":3,"\u5c06\u88ab\u5206\u4e3a":62,"\u5c06\u8bad\u7ec3\u6587\u4ef6\u4e0e\u5207\u5206\u597d\u7684\u6570\u636e\u4e0a\u4f20\u5230\u5171\u4eab\u5b58\u50a8":58,"\u5c06\u8be5\u53e5\u8bdd\u5305\u542b\u7684\u6240\u6709\u5355\u8bcd\u5411\u91cf\u6c42\u5e73\u5747":66,"\u5c06\u8df3\u8fc7\u5206\u53d1\u9636\u6bb5\u76f4\u63a5\u542f\u52a8\u6240\u6709\u8282\u70b9\u7684\u96c6\u7fa4\u4f5c\u4e1a":51,"\u5c06\u8fd9\u79cd\u8de8\u8d8a\u65f6\u95f4\u6b65\u7684\u8fde\u63a5\u7528\u4e00\u4e2a\u7279\u6b8a\u7684\u795e\u7ecf\u7f51\u7edc\u5355\u5143\u5b9e\u73b0":42,"\u5c06\u8fdc\u7a0b\u4ed3\u5e93":46,"\u5c06\u900f\u660e":51,"\u5c06ip\u6392\u5e8f\u751f\u6210\u7684\u5e8f\u53f7\u4f5c\u4e3atrain":58,"\u5c06master\u5206\u652f\u7684\u5408\u5165commit\u6253\u4e0atag":30,"\u5c11\u4e8e5":51,"\u5c1a\u53ef":42,"\u5c31":42,"\u5c31\u4f1a\u751f\u6210\u975e\u5e38\u591a\u7684gener":3,"\u5c31\u53ef\u4ee5\u518d\u8fd0\u884c\u53e6\u4e00\u4e2anginx":34,"\u5c31\u53ef\u4ee5\u5c06\u6570\u636e\u4f20\u9001\u7ed9paddlepaddle\u4e86":3,"\u5c31\u53ef\u4ee5\u5c06\u8fd9\u4e9b\u6587\u4ef6\u6301\u4e45\u5316\u5b58\u50a8":56,"\u5c31\u5f88\u5bb9\u6613\u5bfc\u81f4\u5185\u5b58\u8d85\u9650":31,"\u5c31\u662f":42,"\u5c31\u662f\u6a21\u578b\u7684\u53c2\u6570":32,"\u5c31\u662f\u7528\u4e8e\u5c55\u793a\u4e0a\u8ff0\u5206\u6790\u5de5\u5177\u7684\u7528\u6cd5":50,"\u5c31\u80fd\u591f\u5f88\u65b9\u4fbf\u7684\u5b8c\u6210\u6570\u636e\u4e0b\u8f7d\u548c\u76f8\u5e94\u7684\u9884\u5904\u7406\u5de5\u4f5c":66,"\u5c31\u8fd9\u4e48\u7b80\u5355":34,"\u5c31\u901a\u5e38\u7684gpu\u6027\u80fd\u5206\u6790\u6765\u8bf4":50,"\u5c31\u9700\u8981\u5bf9\u8fd9\u4e2a\u7b2c\u4e09\u65b9\u8bed\u8a00\u589e\u52a0\u4e00\u4e9b\u5b9a\u4e49":26,"\u5c31\u9700\u8981\u9009\u62e9\u4f7f\u7528no":34,"\u5c3a\u5bf8":64,"\u5c40\u90e8\u5173\u8054\u6027\u8d28\u548c\u7a7a\u95f4\u4e0d\u53d8\u6027\u8d28":63,"\u5c42\u540e\u5f97\u5230\u6df1\u5ea6":69,"\u5c42\u548c\u8f93\u5165\u7684\u914d\u7f6e":47,"\u5c42\u6743\u91cd":64,"\u5c42\u6b21\u5316\u7684rnn":44,"\u5c42\u7279\u5f81":64,"\u5c42\u7684\u540d\u79f0\u4e0e":45,"\u5c42\u7684\u5927\u5c0f":47,"\u5c42\u7684\u7279\u5f81":64,"\u5c42\u7684\u7c7b\u578b":47,"\u5c42\u7684\u8f93\u5165":69,"\u5c42\u7684\u8f93\u5165\u548c\u8f93\u51fa\u4f5c\u4e3a\u4e0b\u4e00\u4e2a":69,"\u5c42\u7684\u8f93\u51fa\u88ab\u7528\u4f5c\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684":45,"\u5c42\u7ec4\u6210\u4e00\u5bf9":69,"\u5c45\u7136":42,"\u5c55\u793a\u4e86\u4e00\u79cd\u65b9\u6cd5":71,"\u5c55\u793a\u4e86\u4e0a\u8ff0\u7f51\u7edc\u6a21\u578b\u7684\u8bad\u7ec3\u6548\u679c":66,"\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u6bcf\u4e2a\u7279\u5f81\u6620\u5c04\u5230\u4e00\u4e2a\u5411\u91cf":68,"\u5c5e\u6027":69,"\u5d4c\u5165\u5c42":68,"\u5d4c\u5165\u7279\u5f81\u5b57\u5178":68,"\u5d4c\u5165\u7f16\u53f7\u4f1a\u6839\u636e\u5355\u8bcd\u6392\u5e8f":68,"\u5de5\u4f5c\u6a21\u5f0f":53,"\u5de5\u4f5c\u7a7a\u95f4":51,"\u5de5\u4f5c\u7a7a\u95f4\u4e2d\u7684":51,"\u5de5\u4f5c\u7a7a\u95f4\u6839\u76ee\u5f55":51,"\u5de5\u4f5c\u7a7a\u95f4\u76ee\u5f55\u7684\u5de5\u4f5c\u7a7a\u95f4":51,"\u5de5\u4f5c\u7a7a\u95f4\u914d\u7f6e":51,"\u5de5\u5177":70,"\u5de5\u5177\u4e2d\u7684\u811a\u672c":70,"\u5de5\u5177\u6765\u7ba1\u7406":46,"\u5de5\u5177\u94fe\u7684android":39,"\u5de5\u7a0b\u5e08":67,"\u5de6\u56fe\u6784\u9020\u7f51\u7edc\u6a21\u5757\u7684\u65b9\u5f0f\u88ab\u7528\u4e8e34\u5c42\u7684\u7f51\u7edc\u4e2d":64,"\u5de6\u8fb9\u662f":64,"\u5dee\u8bc4":66,"\u5df2\u6253\u5f00":46,"\u5df2\u7ecf\u5728\u96c6\u7fa4\u63d0\u4ea4\u73af\u5883\u4e2d\u5b8c\u6210\u8bbe\u7f6e":53,"\u5df2\u7ecf\u63d0\u4f9b\u4e86\u811a\u672c\u6765\u5e2e\u52a9\u60a8\u521b\u5efa\u8fd9\u4e24\u4e2a\u6587\u4ef6":51,"\u5e02\u573a":67,"\u5e02\u9762\u4e0a\u5df2\u7ecf\u6709nvidia\u6216\u7b2c\u4e09\u65b9\u63d0\u4f9b\u7684\u4f17\u591a\u5de5\u5177":50,"\u5e0c\u671b\u80fd\u8ba9\u6211\u4eec\u77e5\u6653":68,"\u5e2e\u52a9\u6211\u4eec\u5b8c\u6210\u5bf9\u8f93\u5165\u5e8f\u5217\u7684\u62c6\u5206":44,"\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u63cf\u8ff0\u6bb5\u843d":44,"\u5e2e\u52a9\u6211\u4eec\u6784\u9020\u4e00\u4e9b\u590d\u6742\u7684\u8f93\u5165\u4fe1\u606f":41,"\u5e38\u5e38\u51fa\u73b0":31,"\u5e38\u7528\u4f18\u5316\u7b97\u6cd5\u5305\u62ecmomentum":66,"\u5e38\u89c1\u7684\u53ef\u9009\u5b58\u50a8\u670d\u52a1\u5305\u62ec":56,"\u5e72\u51c0":42,"\u5e73\u53f0\u4e3a\u60f3\u89c2\u6d4b\u8bcd\u5411\u91cf\u7684\u7528\u6237\u63d0\u4f9b\u4e86\u5c06\u4e8c\u8fdb\u5236\u8bcd\u5411\u91cf\u6a21\u578b\u8f6c\u6362\u4e3a\u6587\u672c\u6a21\u578b\u7684\u529f\u80fd":62,"\u5e73\u5747\u6545\u969c\u4fee\u590d\u65f6\u95f4":19,"\u5e73\u5747\u6545\u969c\u7387":19,"\u5e73\u5747\u7279\u5f81\u56fe\u7684\u9ad8\u5ea6\u53ca\u5bbd\u5ea6":63,"\u5e74\u9f84":67,"\u5e74\u9f84\u4ece\u4e0b\u5217\u5217\u8868\u8303\u56f4\u4e2d\u9009\u53d6":67,"\u5e74\u9f84\u548c\u804c\u4e1a":68,"\u5e76\u4e0d\u4fdd\u8bc1":47,"\u5e76\u4e0d\u662f\u4f7f\u7528\u53cc\u5c42rnn\u89e3\u51b3\u5b9e\u9645\u7684\u95ee\u9898":42,"\u5e76\u4e0d\u662fkubernetes\u4e2d\u7684node\u6982\u5ff5":58,"\u5e76\u4e0d\u771f\u6b63\u7684\u548c":42,"\u5e76\u4e14":[3,45],"\u5e76\u4e14\u4f7f\u7528":27,"\u5e76\u4e14\u5185\u5c42\u7684\u5e8f\u5217\u64cd\u4f5c\u4e4b\u95f4\u72ec\u7acb\u65e0\u4f9d\u8d56":42,"\u5e76\u4e14\u5206\u522b\u91cd\u547d\u540d\u6587\u4ef6\u540e\u7f00":71,"\u5e76\u4e14\u52a0\u4e0a\u4e0b\u9762\u7684\u547d\u4ee4\u884c\u53c2\u6570":55,"\u5e76\u4e14\u5305\u62ecunit":46,"\u5e76\u4e14\u53ea\u6709\u4e00\u4e2a\u6743\u91cd":64,"\u5e76\u4e14\u53ef\u80fd\u4f1a\u52a0\u901f\u8bad\u7ec3\u8fc7\u7a0b":31,"\u5e76\u4e14\u540e\u7eed\u4ecd\u5728\u4e0d\u65ad\u6539\u8fdb":32,"\u5e76\u4e14\u542f\u52a8\u8bad\u7ec3":58,"\u5e76\u4e14\u5728\u5185\u5b58\u8db3\u591f\u7684\u60c5\u51b5\u4e0b\u8d8a\u5927\u8d8a\u597d":3,"\u5e76\u4e14\u5728\u5e38\u89c1\u7684\u5e73\u53f0\u4e0a":26,"\u5e76\u4e14\u5728\u968f\u540e\u7684\u8bfb\u53d6\u6570\u636e\u8fc7\u7a0b\u4e2d\u586b\u5145\u8bcd\u8868":66,"\u5e76\u4e14\u5728dataprovider\u4e2d\u5b9e\u73b0\u5982\u4f55\u8bbf\u95ee\u8bad\u7ec3\u6587\u4ef6\u5217\u8868":2,"\u5e76\u4e14\u5b83\u4eec\u7684\u987a\u5e8f\u4e0e":64,"\u5e76\u4e14\u5bf9\u7528\u6237\u7684\u7279\u5f81\u505a\u540c\u6837\u7684\u64cd\u4f5c":68,"\u5e76\u4e14\u5c06\u9884\u5904\u7406\u597d\u7684\u6570\u636e\u96c6\u5b58\u653e\u5728":71,"\u5e76\u4e14\u5c55\u793a\u4e86\u5982\u4f55\u5229\u7528paddlepaddle\u6765\u89e3\u51b3\u4e00\u4e2a\u7ecf\u5178\u7684\u7ebf\u6027\u56de\u5f52\u95ee\u9898":37,"\u5e76\u4e14\u628a\u7cfb\u7edf\u751f\u6210\u7684ca":24,"\u5e76\u4e14\u628a\u7ed3\u679c\u8fd4\u56depfsclient\u7aef":24,"\u5e76\u4e14\u67e5\u8be2paddlepaddle\u5355\u5143\u6d4b\u8bd5\u7684\u65e5\u5fd7":31,"\u5e76\u4e14\u7b2c\u4e8c\u4e2a\u662f\u53cd\u5411lstm":70,"\u5e76\u4e14\u7f16\u8bd1\u80fd\u901a\u8fc7\u4ee3\u7801\u6837\u5f0f\u68c0\u67e5":46,"\u5e76\u4e14\u8ba9\u63a5\u53e3\u8131\u79bb\u5b9e\u73b0\u7ec6\u8282":26,"\u5e76\u4e14\u8f93\u51fa\u4e00\u4e2a":46,"\u5e76\u4e14\u901a\u8fc7\u7ed9\u51fa\u5f53\u524d\u76ee\u6807\u5355\u8bcd\u6765\u9884\u6d4b\u4e0b\u4e00\u4e2a\u76ee\u6807\u5355\u8bcd":71,"\u5e76\u4e14\u96c6\u7fa4\u4f5c\u4e1a\u4e2d\u7684\u6240\u6709\u8282\u70b9\u5c06\u5728\u6b63\u5e38\u60c5\u51b5\u4e0b\u5904\u7406\u5177\u6709\u76f8\u540c\u903b\u8f91\u4ee3\u7801\u7684\u6587\u4ef6":51,"\u5e76\u4e14\u9700\u8981\u91cd\u5199\u57fa\u7c7b\u4e2d\u7684\u4ee5\u4e0b\u51e0\u4e2a\u865a\u51fd\u6570":47,"\u5e76\u4e14softmax\u5c42\u7684\u4e24\u4e2a\u8f93\u5165\u4e5f\u4f7f\u7528\u4e86\u540c\u6837\u7684\u53c2\u6570":31,"\u5e76\u4f20\u5165\u76f8\u5e94\u7684\u547d\u4ee4\u884c\u53c2\u6570\u521d\u59cb\u5316paddlepaddl":5,"\u5e76\u4f7f\u7528":69,"\u5e76\u4f7f\u7528\u4e86dropout":66,"\u5e76\u4f7f\u7528\u8fd9\u4e2a\u795e\u7ecf\u7f51\u7edc\u6765\u5bf9\u56fe\u7247\u8fdb\u884c\u5206\u7c7b":63,"\u5e76\u521b\u5efa\u4e86\u4e00\u4e2a\u65b0\u6587\u4ef6":46,"\u5e76\u521b\u5efaoptim":37,"\u5e76\u5220\u9664":30,"\u5e76\u5220\u9664\u66f4\u65e9\u7684\u5feb\u7167":19,"\u5e76\u52a0\u8f7d\u5176\u4e2d\u7684\u53c2\u6570":19,"\u5e76\u5728\u4e58\u79ef\u7ed3\u679c\u4e0a\u518d\u52a0\u4e0a\u7ef4\u5ea6\u4e3a":47,"\u5e76\u5728\u6700\u5f00\u59cb\u521d\u59cb\u5316\u4e3a\u8d77\u59cb\u8bcd":45,"\u5e76\u5728\u7b14\u8bb0\u672c\u4e0a\u901a\u8fc7ssh\u4e0e\u5176\u8fde\u63a5":34,"\u5e76\u5728\u7c7b\u6784\u5efa\u51fd\u6570\u4e2d\u628a\u5b83\u653e\u5165\u4e00\u4e2a\u7c7b\u6210\u5458\u53d8\u91cf\u91cc":47,"\u5e76\u5728\u96c6\u7fa4\u4e2d\u8fd0\u884c\u591a\u4e2a\u5206\u5e03\u5f0f\u6570\u636e\u5904\u7406\u4efb\u52a1":20,"\u5e76\u5bf9\u76f8\u5e94\u7684\u53c2\u6570\u8c03\u7528":47,"\u5e76\u5c06\u5176\u6295\u5c04\u5230":45,"\u5e76\u5c06\u5b83\u4eec\u6309\u7167\u542f\u53d1\u4ee3\u4ef7":71,"\u5e76\u5c06\u5b83\u4eec\u653e\u5728":71,"\u5e76\u5c06\u6bcf\u8f6e\u7684\u6a21\u578b\u7ed3\u679c\u5b58\u653e\u5728":32,"\u5e76\u5c06c":27,"\u5e76\u5c06develop\u548ctest\u6570\u636e\u5206\u522b\u653e\u5165\u4e0d\u540c\u7684\u6587\u4ef6\u5939":71,"\u5e76\u60f3\u4f7f\u7528gpu\u6765\u8bad\u7ec3\u8bbe\u7f6e\u4e3atru":70,"\u5e76\u628a\u5feb\u7167\u4fdd\u5b58\u5230\u8fd9\u4e2a\u76ee\u5f55\u4e0b":19,"\u5e76\u6307\u5b9abatch":71,"\u5e76\u63d0\u4f9b\u4e86\u7b80\u5355\u7684cache\u529f\u80fd":3,"\u5e76\u6b22\u8fce\u60a8\u6765\u53c2\u4e0e\u8d21\u732e":70,"\u5e76\u6ca1\u6709paddle\u7279\u522b\u9700\u8981\u7684\u7279\u6027":26,"\u5e76\u7531":69,"\u5e76\u7ed9\u51fa\u5206\u7c7b\u7ed3\u679c":63,"\u5e76\u7ed9\u51fa\u7684\u76f8\u5173\u6a21\u578b\u683c\u5f0f\u7684\u5b9a\u4e49":62,"\u5e76\u81ea\u52a8\u7f16\u8bd1\u5bbf\u4e3b\u673a\u7248protoc\u53ef\u6267\u884c\u6587\u4ef6":[39,40],"\u5e76\u88ab\u53cd\u5411\u5904\u7406":69,"\u5e76\u88ab\u5b58\u50a8\u5728\u8bf8\u5982hadoop":20,"\u5e76\u89c2\u5bdf\u7ed3\u679c":50,"\u5e76\u8bb0\u5f55\u5b83\u7684\u7f16\u53f7":46,"\u5e76\u8bbe\u7f6e":[36,51],"\u5e76\u8fdb\u884c\u521d\u59cb\u5316\u64cd\u4f5c":37,"\u5e76\u9002\u5e94github\u7684\u7279\u6027\u505a\u4e86\u4e00\u4e9b\u533a\u522b":30,"\u5e76\u9010\u6e10\u5c55\u793a\u66f4\u52a0\u6df1\u5165\u7684\u529f\u80fd":66,"\u5e8a\u4e0a\u7528\u54c1":42,"\u5e8a\u57ab":42,"\u5e8f\u5217\u4e2d\u542b\u6709\u5143\u7d20\u7684\u6570\u76ee\u540c":41,"\u5e8f\u5217\u5230\u5e8f\u5217":71,"\u5e8f\u5217\u6570\u636e\u662f\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u9762\u5bf9\u7684\u4e00\u79cd\u4e3b\u8981\u8f93\u5165\u6570\u636e\u7c7b\u578b":44,"\u5e8f\u5217\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u6570\u636e\u7c7b\u578b":41,"\u5e8f\u5217\u751f\u6210\u4efb\u52a1\u5927\u591a\u9075\u5faaencod":44,"\u5e8f\u5217\u751f\u6210\u4efb\u52a1\u7684\u8f93\u5165":44,"\u5e8f\u5217\u7684\u5f00\u59cb":71,"\u5e8f\u5217\u7684\u6bcf\u4e2a\u5143\u7d20\u662f\u539f\u6765\u53cc\u5c42\u5e8f\u5217\u6bcf\u4e2asubseq\u5143\u7d20\u7684\u5e73\u5747\u503c":41,"\u5e8f\u5217\u7684\u7ed3\u5c3e":71,"\u5e8f\u5217\u7684\u7ed3\u675f":71,"\u5e93":51,"\u5e93\u7684\u8def\u5f84":51,"\u5e94\u7528\u524d\u5411\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":45,"\u5e94\u7528\u53cd\u5411\u9012\u5f52\u795e\u7ecf\u7f51\u7edc":45,"\u5e94\u7528\u6a21\u578b":66,"\u5e94\u80fd\u53cd\u6620\u5f53\u524dcommit\u7684\u5185\u5bb9":46,"\u5e94\u8be5":42,"\u5e94\u8be5\u4e0e\u5b83\u7684memory\u540d\u5b57\u76f8\u540c":45,"\u5e94\u8be5\u8bf4\u8c22\u8c22":46,"\u5e94\u8be5\u964d\u4f4e\u5b66\u4e60\u7387":31,"\u5e95\u5c42\u8fdb\u7a0b":51,"\u5efa\u7acb\u4e00\u4e2a":46,"\u5efa\u7acb\u4e00\u4e2a\u6d3b\u8dc3\u7684\u5f00\u6e90\u793e\u533a":0,"\u5efa\u8bae":[30,46],"\u5efa\u8bae\u5c06\u5176\u8bbe\u7f6e\u4e3a\u8f83\u5927":51,"\u5efa\u8bae\u5c06\u8be5\u53c2\u6570\u8bbe\u4e3atrue":53,"\u5f00\u53d1\u4e86\u6a21\u578b\u9884\u6d4b\u7684\u6837\u4f8b\u4ee3\u7801":27,"\u5f00\u53d1\u4eba\u5458\u4f7f\u7528":46,"\u5f00\u53d1\u4eba\u5458\u53ef\u4ee5\u5728docker\u5f00\u53d1\u955c\u50cf\u4e2d\u5f00\u53d1paddlepaddl":34,"\u5f00\u53d1\u8005\u4fee\u6539\u81ea\u5df1\u7684\u4ee3\u7801":30,"\u5f00\u53d1\u8005\u7ed9paddlepaddle\u4ee3\u7801\u589e\u52a0\u7684\u6ce8\u91ca\u4ee5pr\u7684\u5f62\u5f0f\u63d0\u4ea4\u5230github\u4e2d":48,"\u5f00\u53d1\u8005fork\u7684\u7248\u672c\u5e93\u4e2d":30,"\u5f00\u53d1\u8005fork\u7684\u7248\u672c\u5e93\u4f7f\u7528":30,"\u5f00\u53d1\u955c\u50cf":[34,46],"\u5f00\u53d1\u955c\u50cf\u5305\u542b\u4e86\u4ee5\u4e0b\u5de5\u5177":34,"\u5f00\u59cb":32,"\u5f00\u59cb\u63d0\u4f9b\u670d\u52a1":19,"\u5f00\u59cb\u6807\u8bb0":45,"\u5f00\u59cb\u8bad\u7ec3\u6a21\u578b":66,"\u5f00\u59cb\u9636\u6bb5":50,"\u5f02\u6b65\u968f\u673a\u68af\u5ea6\u4e0b\u964d":52,"\u5f15\u5165\u4e86\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5f15\u5165lstm\u6a21\u578b\u4e3b\u8981\u662f\u4e3a\u4e86\u514b\u670d\u6d88\u5931\u68af\u5ea6\u7684\u95ee\u9898":70,"\u5f15\u5165paddlepaddle\u7684pydataprovider2\u5305":3,"\u5f15\u53d1":13,"\u5f15\u5bfc\u5c42":45,"\u5f15\u7528":51,"\u5f15\u7528memory\u5f97\u5230\u8fd9layer\u4e0a\u4e00\u65f6\u523b\u8f93\u51fa":44,"\u5f3a\u70c8\u63a8\u8350":42,"\u5f3a\u70c8\u63a8\u8350\u4f7f\u7528virtualenv\u6765\u521b\u9020\u4e00\u4e2a\u5e72\u51c0\u7684python\u73af\u5883":68,"\u5f52\u4e00\u5316\u6982\u7387\u5411\u91cf":45,"\u5f53":55,"\u5f53\u4f20\u9012\u76f8\u540c\u7684\u6837\u672c\u6570\u65f6":70,"\u5f53\u4f60\u6267\u884c\u547d\u4ee4":47,"\u5f53\u51fd\u6570\u8fd4\u56de\u7684\u65f6\u5019":3,"\u5f53\u524d\u5355\u8bcd\u5728\u76f8\u6bd4\u4e4b\u4e0b\u603b\u662f\u88ab\u5f53\u4f5c\u771f\u503c":71,"\u5f53\u524d\u5355\u8bcd\u662f\u89e3\u7801\u5668\u6700\u540e\u4e00\u6b65\u7684\u8f93\u51fa":71,"\u5f53\u524d\u65f6\u95f4\u6b65\u5904\u7684memory\u7684\u8f93\u51fa\u4f5c\u4e3a\u4e0b\u4e00\u65f6\u95f4\u6b65memory\u7684\u8f93\u5165":45,"\u5f53\u524d\u7684\u5b9e\u73b0\u65b9\u5f0f\u4e0b":47,"\u5f53\u524d\u7684\u8f93\u5165y\u548c\u4e0a\u4e00\u4e2a\u65f6\u95f4\u6b65\u7684\u8f93\u51farnn_state\u505a\u4e86\u4e00\u4e2a\u5168\u94fe\u63a5":42,"\u5f53\u524d\u8bc4\u4f30\u4e2d":71,"\u5f53\u524dbatch\u7684cost":71,"\u5f53\u524dlog_period\u4e2abatch\u6240\u6709\u6837\u672c\u7684\u5e73\u5747\u5206\u7c7b\u9519\u8bef\u7387":66,"\u5f53\u524dlog_period\u4e2abatch\u6240\u6709\u6837\u672c\u7684\u5e73\u5747cost":66,"\u5f53\u529f\u80fd\u5206\u652f\u5f00\u53d1\u5b8c\u6bd5\u540e":30,"\u5f53\u5728\u7f51\u7edc\u5c42\u914d\u7f6e\u4e2d\u8bbe\u7f6e":53,"\u5f53\u5728\u8bad\u7ec3\u914d\u7f6e\u4e2d\u8bbe\u7f6e":53,"\u5f53\u5bb9\u5668\u56e0\u4e3a\u5404\u79cd\u539f\u56e0\u88ab\u9500\u6bc1\u65f6":56,"\u5f53\u6240\u6709pod\u90fd\u5904\u4e8erunning\u72b6\u6001":58,"\u5f53\u6a21\u578b\u53c2\u6570\u4e0d\u5b58\u5728\u65f6":53,"\u5f53\u6a21\u578b\u8bad\u7ec3\u597d\u4e86\u4e4b\u540e":66,"\u5f53\u6a21\u5f0f\u4e3a":53,"\u5f53\u7136":50,"\u5f53\u7528\u6237\u4f7f\u7528\u5b8c\u8fd9\u4e2a\u53c2\u6570\u540e":27,"\u5f53\u7f51\u7edc\u5c42\u7528\u4e00\u4e2a\u6279\u6b21\u505a\u8bad\u7ec3\u65f6":47,"\u5f53\u89e3\u8bfb\u6bcf\u4e00\u4e2a":45,"\u5f53\u8bad\u7ec3\u6570\u636e\u975e\u5e38\u591a\u65f6":3,"\u5f53\u8d85\u8fc7\u8be5\u9608\u503c\u65f6":53,"\u5f53\u8f93\u5165\u662f\u7ef4\u5ea6\u5f88\u9ad8\u7684\u7a00\u758f\u6570\u636e\u65f6":55,"\u5f53\u9700\u8981\u5feb\u901f\u6216\u8005\u9891\u7e41\u7684\u8bc4\u4f30\u65f6":71,"\u5f53classif":71,"\u5f53destination\u6587\u4ef6\u4e0d\u5b58\u5728\u6216\u8005\u5927\u5c0f\u548csource\u6587\u4ef6\u4e0d\u4e00\u81f4\u65f6":24,"\u5f62\u6210recurr":44,"\u5f62\u6210recurrent\u8fde\u63a5":44,"\u5f62\u72b6":64,"\u5f88":[42,66],"\u5f88\u591a":42,"\u5f88\u591a\u5f00\u53d1\u8005\u4f1a\u4f7f\u7528\u8fdc\u7a0b\u7684\u5b89\u88c5\u6709gpu\u7684\u670d\u52a1\u5668\u5de5\u4f5c":34,"\u5f88\u5b89\u9759":42,"\u5f88\u5e72\u51c0":42,"\u5f88\u65b9\u4fbf":42,"\u5f88\u6709\u53ef\u80fd\u5b9e\u9645\u5e94\u7528\u5c31\u662f\u6ca1\u6709\u6309\u7167\u60a8\u7684\u9884\u671f\u60c5\u51b5\u8fd0\u884c":50,"\u5f88\u9002\u5408\u6784\u5efa\u7528\u4e8e\u7406\u89e3\u56fe\u7247\u5185\u5bb9\u7684\u6a21\u578b":63,"\u5f88\u96be\u4fdd\u8bc1\u591a\u8bed\u8a00\u4ee3\u7801\u98ce\u683c\u7684\u4e00\u81f4\u6027":26,"\u5f88\u96be\u6574\u4f53\u4fee\u6b63":3,"\u5f8b\u5e08":67,"\u5f97":42,"\u5f97\u4f7f\u7528":26,"\u5f97\u5230\u53e5\u5b50\u7684\u8868\u793a":66,"\u5f97\u5230\u6700\u597d\u8f6e\u6b21\u4e0b\u7684\u6a21\u578b":68,"\u5faa\u73af\u5c55\u5f00\u7684\u6bcf\u4e2a\u65f6\u95f4\u6b65\u603b\u662f\u80fd\u591f\u5f15\u7528\u6240\u6709\u8f93\u5165":44,"\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u4e2d":45,"\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u4f5c\u4e3a\u4f7f\u7528":45,"\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u548c":45,"\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u5728\u6bcf\u4e2a\u65f6\u95f4\u6b65\u9aa4\u987a\u5e8f\u5730\u5904\u7406\u5e8f\u5217":45,"\u5faa\u73af\u7f51\u7edc\u4ece":45,"\u5fc5\u8981":27,"\u5fc5\u987b":47,"\u5fc5\u987b\u4e00\u81f4":3,"\u5fc5\u987b\u4f7f\u7528python\u5173\u952e\u8bcd":3,"\u5fc5\u987b\u5c06\u524d\u4e00\u4e2a\u5b50\u53e5\u7684\u6700\u540e\u4e00\u4e2a\u5143\u7d20":42,"\u5fc5\u987b\u6307\u5411\u4e00\u4e2apaddlepaddle\u5b9a\u4e49\u7684lay":44,"\u5fc5\u987b\u662f\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":44,"\u5fc5\u987b\u662f\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":41,"\u5fc5\u987b\u7531\u53ea\u8bfbmemory\u7684":45,"\u5fc5\u987b\u8bbe\u7f6e\u4e3a":39,"\u5fc5\u987b\u914d\u7f6e\u4e3a":40,"\u5fd8\u8bb0\u95e8\u548c\u8f93\u51fa\u95e8":70,"\u5feb":[42,70],"\u5feb\u901f\u5165\u95e8":65,"\u5feb\u901f\u5728\u672c\u5730\u542f\u52a8\u4e00\u4e2a\u5355\u673a\u7684kubernetes\u670d\u52a1\u5668":56,"\u5feb\u901f\u90e8\u7f72\u96c6\u7fa4":56,"\u6027\u4ef7\u6bd4":42,"\u6027\u522b":[67,68],"\u6027\u80fd\u5206\u6790":50,"\u6027\u80fd\u5206\u6790\u5de5\u5177\u662f\u7528\u4e8e\u7ed9\u5e94\u7528\u7a0b\u5e8f\u7684\u6027\u80fd\u505a\u5b9a\u91cf\u5206\u6790\u7684":50,"\u6027\u80fd\u5206\u6790\u662f\u6027\u80fd\u4f18\u5316\u7684\u5173\u952e\u4e00\u6b65":50,"\u6027\u80fd\u8c03\u4f18":52,"\u603b\u4f53\u6765\u8bf4":42,"\u603b\u8ba1\u7684\u53c2\u6570\u4e2a\u6570":62,"\u603b\u8bc4\u520610\u5206":70,"\u6050\u6016\u7247":67,"\u60a8\u4f1a\u5728\u63a5\u4e0b\u6765\u7684\u90e8\u5206\u4e2d\u83b7\u5f97\u66f4\u591a\u7684\u7ec6\u8282\u4ecb\u7ecd":50,"\u60a8\u53ef\u4ee5\u4efb\u610f\u4f7f\u7528\u4e00\u4e2a\u6216\u4e24\u4e2a\u6765\u5bf9\u611f\u5174\u8da3\u7684\u4ee3\u7801\u6bb5\u505a\u6027\u80fd\u5206\u6790":50,"\u60a8\u53ef\u4ee5\u5bfc\u5165":50,"\u60a8\u53ef\u4ee5\u91c7\u7528\u4e0b\u9762\u4e94\u4e2a\u6b65\u9aa4":50,"\u60a8\u5c06\u4e86\u89e3\u5982\u4f55":45,"\u60a8\u5c31\u80fd\u83b7\u5f97\u5982\u4e0b\u7684\u5206\u6790\u7ed3\u679c":50,"\u60a8\u6309\u5982\u4e0b\u6b65\u9aa4\u64cd\u4f5c\u5373\u53ef":50,"\u60a8\u6700\u597d\u5148\u786e\u8ba4":50,"\u60a8\u9700\u8981\u66f4\u6539":34,"\u60a8\u9996\u5148\u9700\u8981\u5728\u76f8\u5173\u4ee3\u7801\u6bb5\u4e2d\u52a0\u5165":50,"\u60ac\u7591\u7247":67,"\u60c5\u6001\u52a8\u8bcd":69,"\u60c5\u611f\u5206\u6790":[30,65],"\u60c5\u611f\u5206\u6790\u4e5f\u5e38\u7528\u4e8e\u57fa\u4e8e\u5927\u91cf\u8bc4\u8bba\u548c\u4e2a\u4eba\u535a\u5ba2\u6765\u76d1\u63a7\u793e\u4f1a\u5a92\u4f53":70,"\u60c5\u611f\u5206\u6790\u662f\u81ea\u7136\u8bed\u8a00\u7406\u89e3\u4e2d\u6700\u5178\u578b\u7684\u95ee\u9898\u4e4b\u4e00":70,"\u60c5\u611f\u5206\u6790\u6709\u8bb8\u591a\u5e94\u7528\u573a\u666f":70,"\u60ca\u9669\u7535\u5f71":67,"\u60f3\u4e86\u89e3\u66f4\u591a\u7ec6\u8282\u53ef\u4ee5\u53c2\u8003pydataprovider\u90e8\u5206\u7684\u6587\u6863":70,"\u610f\u5473\u7740\u4e0d\u540c\u65f6\u95f4\u6b65\u7684\u8f93\u5165\u90fd\u662f\u76f8\u540c\u7684\u503c":45,"\u610f\u601d\u662f\u4e0d\u4f7f\u7528\u5e73\u5747\u53c2\u6570\u6267\u884c\u6d4b\u8bd5":53,"\u610f\u601d\u662f\u4e0d\u4fdd\u5b58\u7ed3\u679c":53,"\u610f\u601d\u662f\u4f7f\u7528\u7b2ctest":53,"\u610f\u601d\u662f\u5728gpu\u6a21\u5f0f\u4e0b\u4f7f\u75284\u4e2agpu":53,"\u611f\u89c9":42,"\u620f\u5267":67,"\u6210\u529f\u8bad\u7ec3\u4e14\u9000\u51fa\u7684pod\u6570\u76ee\u4e3a3\u65f6":58,"\u6211\u4eec\u4e0d\u80fd\u901a\u8fc7\u5e38\u89c4\u7684\u68af\u5ea6\u68c0\u67e5\u7684\u65b9\u5f0f\u6765\u8ba1\u7b97\u68af\u5ea6":47,"\u6211\u4eec\u4e3b\u8981\u4f1a\u4ecb\u7ecdnvprof\u548cnvvp":50,"\u6211\u4eec\u4e5f\u53ef\u4ee5\u786e\u5b9a\u6bcf\u4e00\u4e2a\u53c2\u6570\u7684\u7c7b\u578b":27,"\u6211\u4eec\u4ec5\u4ec5\u5bf9\u795e\u7ecf\u7f51\u7edc\u7684\u8f93\u5165\u8fdb\u884c\u4e86\u63cf\u8ff0":37,"\u6211\u4eec\u4ec5\u4ec5\u662f\u5c06\u6bcf\u4e2a\u7279\u5f81\u79cd\u7c7b\u6620\u5c04\u5230\u4e00\u4e2a\u7279\u5f81\u5411\u91cf\u4e2d":68,"\u6211\u4eec\u4ec5\u6709\u4e00\u4e2a\u8f93\u5165":47,"\u6211\u4eec\u4ec5\u7528":68,"\u6211\u4eec\u4ecb\u7ecd\u5982\u4f55\u5728":57,"\u6211\u4eec\u4ecb\u7ecd\u5982\u4f55\u5728kubernetes\u96c6\u7fa4\u4e0a\u8fdb\u884c\u5206\u5e03\u5f0fpaddlepaddle\u8bad\u7ec3\u4f5c\u4e1a":58,"\u6211\u4eec\u4ece\u63d0\u524d\u7ed9\u5b9a\u7684\u7c7b\u522b\u96c6\u5408\u4e2d\u9009\u62e9\u5176\u6240\u5c5e\u7c7b\u522b":66,"\u6211\u4eec\u4ee5mnist\u624b\u5199\u8bc6\u522b\u4e3a\u4f8b":3,"\u6211\u4eec\u4f1a\u53d1\u73b0\u6570\u636e\u96c6":71,"\u6211\u4eec\u4f1a\u5728":34,"\u6211\u4eec\u4f1a\u7ee7\u7eed\u4f7f\u7528\u73b0\u6709\u7684\u5185\u5b58\u5757":47,"\u6211\u4eec\u4f1a\u91cd\u65b0\u5206\u914d\u5185\u5b58":47,"\u6211\u4eec\u4f7f\u7528":[47,51,70],"\u6211\u4eec\u4f7f\u7528\u4e0d\u540c\u7684layer\u8fdb\u884c\u7ec4\u5408":37,"\u6211\u4eec\u4f7f\u7528\u4e86":42,"\u6211\u4eec\u4f7f\u7528\u4e86\u4e00\u4e2a\u7f16\u89e3\u7801\u6a21\u578b\u7684\u6269\u5c55":71,"\u6211\u4eec\u4f7f\u7528\u4e86\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":70,"\u6211\u4eec\u4f7f\u7528\u5176\u4e2d\u7684":71,"\u6211\u4eec\u4f7f\u7528\u52a8\u6001\u5e93\u6765\u5206\u53d1paddl":26,"\u6211\u4eec\u4f7f\u7528\u6700\u5927\u6982\u7387\u7684\u6807\u7b7e\u4f5c\u4e3a\u7ed3\u679c":69,"\u6211\u4eec\u4f7f\u7528\u96c6\u675f\u641c\u7d22":71,"\u6211\u4eec\u4f7f\u7528paddlepaddle\u5728ilsvrc\u7684\u9a8c\u8bc1\u96c6\u517150":64,"\u6211\u4eec\u5047\u8bbe\u4e00\u53f0\u673a\u5668\u4e0a\u67094\u4e2agpu":55,"\u6211\u4eec\u5047\u8bbe\u623f\u4ea7\u7684\u4ef7\u683c":32,"\u6211\u4eec\u5148\u4ece\u4e00\u6761\u968f\u673a\u7684\u76f4\u7ebf":32,"\u6211\u4eec\u51c6\u5907\u4e86\u4e00\u4e2a\u811a\u672c":63,"\u6211\u4eec\u5373\u53ef\u5b8c\u6210\u795e\u7ecf\u7f51\u7edc\u7684\u642d\u5efa":37,"\u6211\u4eec\u53ea\u4f7f\u7528\u5df2\u7ecf\u6807\u6ce8\u8fc7\u7684\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6":70,"\u6211\u4eec\u53ea\u6240\u4ee5\u4f7f\u7528lstm\u6765\u6267\u884c\u8fd9\u4e2a\u4efb\u52a1\u662f\u56e0\u4e3a\u5176\u6539\u8fdb\u7684\u8bbe\u8ba1\u5e76\u4e14\u5177\u6709\u95e8\u673a\u5236":70,"\u6211\u4eec\u53ea\u6f14\u793a\u4e00\u4e2a\u5355\u673a\u4f5c\u4e1a":57,"\u6211\u4eec\u53ea\u9700\u8981\u4f7f\u7528lstm":42,"\u6211\u4eec\u53ea\u9700\u8981\u8fd0\u884c":66,"\u6211\u4eec\u53ea\u9700\u8981\u8fd0\u884c\u4e0b\u9762\u547d\u4ee4\u628a\u7f16\u8bd1\u597d\u7684paddlepaddle\u6253\u5305\u6210\u4e00\u4e2a":46,"\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528":63,"\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5176\u4ed6layer\u8fdb\u884c\u7ec4\u5408":37,"\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5b83\u6765\u751f\u6210\u5e8f\u5217":45,"\u6211\u4eec\u53ef\u4ee5\u521b\u5efatrainer\u6765\u5bf9\u7f51\u7edc\u8fdb\u884c\u8bad\u7ec3":37,"\u6211\u4eec\u53ef\u4ee5\u5728":46,"\u6211\u4eec\u53ef\u4ee5\u5b9a\u4e49\u5982\u4e0b\u7684layer\u7ec4\u5408":37,"\u6211\u4eec\u53ef\u4ee5\u5b9a\u4e49\u5982\u4e0blayer\u6765\u63cf\u8ff0\u795e\u7ecf\u7f51\u7edc\u7684\u8f93\u5165":37,"\u6211\u4eec\u53ef\u4ee5\u5c06":51,"\u6211\u4eec\u53ef\u4ee5\u6309\u7167\u5982\u4e0b\u5c42\u6b21\u5b9a\u4e49\u975e\u5e8f\u5217":41,"\u6211\u4eec\u53ef\u4ee5\u751f\u6210":68,"\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u89c2\u5bdf\u6a21\u578b\u7684\u53c2\u6570\u662f\u5426\u7b26\u5408\u9884\u671f\u6765\u8fdb\u884c\u68c0\u9a8c":32,"\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u5728\u76ee\u5f55":70,"\u6211\u4eec\u53ef\u4ee5\u8bbe\u8ba1\u642d\u5efa\u4e00\u4e2a\u7075\u6d3b\u7684":44,"\u6211\u4eec\u53ef\u4ee5\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\u6765\u505ableu\u8bc4\u4f30":71,"\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u6765\u8bad\u7ec3\u6a21\u578b":71,"\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u6765\u8fdb\u884c\u4ece\u6cd5\u8bed\u5230\u82f1\u8bed\u7684\u6587\u672c\u7ffb\u8bd1":71,"\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5982\u4e0b\u547d\u4ee4\u8fdb\u884c\u9884\u5904\u7406\u5de5\u4f5c":63,"\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u65e5\u5fd7\u67e5\u770b\u5bb9\u5668\u8bad\u7ec3\u7684\u60c5\u51b5":58,"\u6211\u4eec\u57285\u5929\u91cc\u8bad\u7ec3\u4e8616\u4e2apass":71,"\u6211\u4eec\u5728\u51fd\u6570\u7684\u7ed3\u5c3e\u8fd4\u56de":45,"\u6211\u4eec\u5728\u62e5\u670950\u4e2a\u8282\u70b9\u7684\u96c6\u7fa4\u4e2d\u8bad\u7ec3\u6a21\u578b":71,"\u6211\u4eec\u5728\u8bad\u7ec3\u4e4b\u524d\u9700\u8981\u5e38\u89c1\u4e00\u4e2a\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":71,"\u6211\u4eec\u5728initialzier\u51fd\u6570\u91cc\u521d\u59cb\u5316\u8bcd\u8868":66,"\u6211\u4eec\u5bf9\u6a21\u578b\u8fdb\u884c\u4e86\u4ee5\u4e0b\u66f4\u6539":45,"\u6211\u4eec\u5c06":[58,68],"\u6211\u4eec\u5c06\u4e00\u6bb5\u8bdd\u770b\u6210\u53e5\u5b50\u7684\u6570\u7ec4":42,"\u6211\u4eec\u5c06\u4ecb\u7ecd\u5982\u4f55\u542f\u52a8\u5206\u5e03\u5f0f\u8bad\u7ec3\u4f5c\u4e1a":57,"\u6211\u4eec\u5c06\u4ee5":[51,66],"\u6211\u4eec\u5c06\u4ee5\u6700\u57fa\u672c\u7684\u903b\u8f91\u56de\u5f52\u7f51\u7edc\u4f5c\u4e3a\u8d77\u70b9":66,"\u6211\u4eec\u5c06\u4f7f\u7528":45,"\u6211\u4eec\u5c06\u4f7f\u7528\u7b80\u5355\u7684":45,"\u6211\u4eec\u5c06\u4f7f\u7528cifar":63,"\u6211\u4eec\u5c06\u539f\u59cb\u6570\u636e\u7684\u6bcf\u4e00\u7ec4":42,"\u6211\u4eec\u5c06\u5728\u540e\u9762\u4ecb\u7ecd\u8bad\u7ec3\u548c\u9884\u6d4b\u6d41\u7a0b\u7684\u811a\u672c":66,"\u6211\u4eec\u5c06\u5b83\u4eec\u5212\u5206\u4e3a\u4e0d\u540c\u7684\u7c7b\u522b":52,"\u6211\u4eec\u5c06\u5bf9\u8fd9\u4e24\u4e2a\u6b65\u9aa4\u7ed9\u51fa\u4e86\u8be6\u7ec6\u7684\u89e3\u91ca":66,"\u6211\u4eec\u5c06\u653e\u7f6e\u4f9d\u8d56\u5e93":51,"\u6211\u4eec\u5c06\u8bc4\u5206\u5206\u6210\u4e24\u90e8\u5206":68,"\u6211\u4eec\u5c06\u9610\u91ca\u5982\u4f55\u5728\u96c6\u7fa4\u4e0a\u8fd0\u884c\u5206\u5e03\u5f0f":51,"\u6211\u4eec\u5c31\u53ef\u4ee5\u7740\u624b\u5bf9\u5206\u7c7b\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u4e86":63,"\u6211\u4eec\u5c31\u53ef\u4ee5\u8bad\u7ec3\u6a21\u578b\u4e86":66,"\u6211\u4eec\u5c31\u53ef\u4ee5\u8fdb\u884c\u9884\u6d4b\u4e86":66,"\u6211\u4eec\u5c31\u5b8c\u6210\u4e86\u4e00\u6b21\u4ee3\u7801\u8d21\u732e\u7684\u8fc7\u7a0b":46,"\u6211\u4eec\u5c55\u793a\u5982\u4f55\u7528paddlepaddle\u89e3\u51b3":32,"\u6211\u4eec\u5df2\u7ecf\u5b9e\u73b0\u4e86\u5927\u591a\u6570\u5e38\u7528\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u67b6\u6784":45,"\u6211\u4eec\u5e0c\u671b\u80fd\u5728\u8fd9\u4e2a\u57fa\u7840\u4e0a\u4e0d\u65ad\u7684\u6539\u8fdb":0,"\u6211\u4eec\u5e0c\u671b\u80fd\u591f\u68c0\u9a8c\u6a21\u578b\u7684\u597d\u574f":32,"\u6211\u4eec\u5e94\u5f53\u4f1a\u5f97\u5230\u4e00\u4e2a\u540d\u4e3acifar":63,"\u6211\u4eec\u5efa\u8bae\u4f60\u4e3a\u4f60\u7684python\u5c01\u88c5\u5b9e\u73b0\u4e00\u4e2a":47,"\u6211\u4eec\u5efa\u8bae\u4f60\u5728\u5199\u65b0\u7f51\u7edc\u5c42\u65f6\u628a\u6d4b\u8bd5\u4ee3\u7801\u653e\u5165\u65b0\u7684\u6587\u4ef6\u4e2d":47,"\u6211\u4eec\u603b\u7ed3\u4e86\u5404\u4e2a\u7f51\u7edc\u7684\u590d\u6742\u5ea6\u548c\u6548\u679c":66,"\u6211\u4eec\u611f\u8c22":71,"\u6211\u4eec\u628apaddlepaddle\u7684\u7f16\u8bd1\u73af\u5883\u6253\u5305\u6210\u4e00\u4e2a\u955c\u50cf":34,"\u6211\u4eec\u63a8\u8350\u4f7f\u7528":34,"\u6211\u4eec\u63a8\u8350\u4f7f\u7528\u6700\u65b0\u7248\u672c\u7684cudnn":33,"\u6211\u4eec\u63a8\u8350\u4f7f\u7528docker\u955c\u50cf\u6765\u90e8\u7f72\u73af\u5883":35,"\u6211\u4eec\u63d0\u4f9b\u4e24\u4e2a\u7f51\u7edc":70,"\u6211\u4eec\u63d0\u4f9b\u4e24\u4e2a\u8f6c\u6362\u65b9\u5f0f":20,"\u6211\u4eec\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6570\u636e\u9884\u5904\u7406\u811a\u672c":70,"\u6211\u4eec\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6784\u5efa\u811a\u672cbuild_doc":48,"\u6211\u4eec\u63d0\u4f9b\u4e86\u4e00\u4e2a\u793a\u4f8b\u811a\u672c":64,"\u6211\u4eec\u63d0\u4f9b\u4e86\u56fd\u5185\u7684\u955c\u50cf\u670d\u52a1\u5668\u4f9b\u5927\u5bb6\u4f7f\u7528":34,"\u6211\u4eec\u63d0\u4f9b\u4e86\u811a\u672c\u6765\u6784\u5efa\u5b57\u5178\u548c\u9884\u5904\u7406\u6570\u6910":70,"\u6211\u4eec\u63d0\u4f9b\u4e86c":64,"\u6211\u4eec\u63d0\u4f9b\u53ef\u4ee5\u76f4\u63a5\u8fd0\u884cpaddlepaddl":34,"\u6211\u4eec\u63d0\u51fa\u4e86chunk\u7684\u6982\u5ff5":24,"\u6211\u4eec\u662f\u5bf9\u6bcf\u4e00\u4e2a\u5b50\u5e8f\u5217\u53d6\u6700\u540e\u4e00\u4e2a\u5143\u7d20":42,"\u6211\u4eec\u6700\u7ec8\u7684\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165python\u6216\u8005\u5176\u4ed6\u4efb\u4f55\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u6211\u4eec\u6709\u4e00\u4e2a\u5e8f\u5217\u4f5c\u4e3a\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u72b6\u6001":45,"\u6211\u4eec\u7528":67,"\u6211\u4eec\u7528\u4ee5\u4e0b\u7684\u4e00\u4e9b":68,"\u6211\u4eec\u7528\u7f16\u53f7\u4f5c\u4e3akei":68,"\u6211\u4eec\u7528paddlepaddle\u89e3\u51b3\u4e86\u5355\u53d8\u91cf\u7ebf\u6027\u56de\u5f52\u95ee\u9898":32,"\u6211\u4eec\u7684\u5b57\u5178\u4f7f\u7528\u5185\u90e8\u7684\u5206\u8bcd\u5de5\u5177\u5bf9\u767e\u5ea6\u77e5\u9053\u548c\u767e\u5ea6\u767e\u79d1\u7684\u8bed\u6599\u8fdb\u884c\u5206\u8bcd\u540e\u4ea7\u751f":62,"\u6211\u4eec\u7684\u6807\u51c6\u5f00\u53d1\u6d41\u7a0b\u662f\u628a\u8fd9\u4e9b\u5de5\u5177\u90fd\u88c5\u8fdb\u4e00\u4e2adocker":46,"\u6211\u4eec\u7684\u8bad\u7ec3\u66f2\u7ebf\u5982\u4e0b":69,"\u6211\u4eec\u770b\u4e00\u4e0b\u5355\u5c42rnn\u7684\u914d\u7f6e":42,"\u6211\u4eec\u770b\u4e00\u4e0b\u8bed\u4e49\u76f8\u540c\u7684\u53cc\u5c42rnn\u7684\u7f51\u7edc\u914d\u7f6e":42,"\u6211\u4eec\u771f\u8bda\u5730\u611f\u8c22\u60a8\u7684\u5173\u6ce8":70,"\u6211\u4eec\u771f\u8bda\u5730\u611f\u8c22\u60a8\u7684\u8d21\u732e":46,"\u6211\u4eec\u79f0\u4e4b\u4e3a\u4e00\u4e2a0\u5c42\u7684\u5e8f\u5217":41,"\u6211\u4eec\u8bbe\u8ba1\u8bf4\u660e\u4e86\u540d\u4e3afilemanager\u7cfb\u7edf":24,"\u6211\u4eec\u8fd8\u53ef\u4ee5\u767b\u5f55\u5230\u5bbf\u4e3b\u673a\u4e0a\u67e5\u770b\u8bad\u7ec3\u7ed3\u679c":57,"\u6211\u4eec\u8fd8\u5c06\u7f16\u7801\u5411\u91cf\u6295\u5c04\u5230":45,"\u6211\u4eec\u9009\u53d6\u5355\u53cc\u5c42\u5e8f\u5217\u914d\u7f6e\u4e2d\u7684\u4e0d\u540c\u90e8\u5206":42,"\u6211\u4eec\u9009\u62e9":20,"\u6211\u4eec\u901a\u5e38\u5728\u6240\u6709\u8282\u70b9\u4e0a\u521b\u5efa\u4e00\u4e2a":51,"\u6211\u4eec\u901a\u5e38\u5c06\u4e00\u53e5\u8bdd\u7406\u89e3\u6210\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217":42,"\u6211\u4eec\u901a\u8fc7\u8bfb\u53d6":58,"\u6211\u4eec\u9075\u5faa":71,"\u6211\u4eec\u90fd\u63d0\u4f9bpython\u7684\u8f6c\u6362\u5e93":20,"\u6211\u4eec\u91c7\u7528\u4e0a\u9762\u7684\u7279\u5f81\u6a21\u677f":69,"\u6211\u4eec\u91c7\u7528\u5355\u5c42lstm\u6a21\u578b":66,"\u6211\u4eec\u91c7\u7528\u7b2c\u4e8c\u4e2a\u65b9\u6cd5":34,"\u6211\u4eec\u9700\u8981\u5148\u521b\u5efa\u4e00\u4e2a\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":63,"\u6211\u4eec\u9700\u8981\u521b\u5efa\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":71,"\u6211\u4eec\u9700\u8981\u5236\u4f5c\u4e00\u4e2a\u5305\u542b\u8bad\u7ec3\u6570\u636e\u7684paddle\u955c\u50cf":57,"\u6211\u4eec\u9700\u8981\u5728\u96c6\u7fa4\u7684\u6240\u6709\u8282\u70b9\u4e0a\u5b89\u88c5":51,"\u6211\u4eec\u9700\u8981\u8ba1\u7b97":47,"\u6211\u4eec\u9700\u8981\u8bbe\u7f6e":68,"\u6211\u4eec\u9700\u8981\u9884\u5904\u7406\u6570\u6910\u5e76\u6784\u5efa\u4e00\u4e2a\u5b57\u5178":70,"\u6211\u4eec\u975e\u5e38\u6b22\u8fce\u60a8\u7528paddlepaddle\u6784\u5efa\u66f4\u597d\u7684\u793a\u4f8b":68,"\u6211\u4eec\u9884\u8bad\u7ec3\u5f97\u52304\u79cd\u4e0d\u540c\u7ef4\u5ea6\u7684\u8bcd\u5411\u91cf":62,"\u6211\u4eec\u9996\u5148\u5904\u7406\u7535\u5f71\u6216\u7528\u6237\u7684\u7279\u5f81\u6587\u4ef6":68,"\u6211\u4eec\u9996\u5148\u9700\u8981\u6839\u636e\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u6765\u521b\u5efa\u6240\u9700\u8981\u4f18\u5316\u7684paramet":37,"\u6211\u4eec\u9ed8\u8ba4\u4f7f\u7528imdb\u7684\u6d4b\u8bd5\u6570\u636e\u96c6\u4f5c\u4e3a\u9a8c\u8bc1":70,"\u6211\u5220\u9664\u4e86":46,"\u6216":[3,50,63,69],"\u6216\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":41,"\u6216\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u53d8\u6210\u4e00\u4e2a0\u5c42\u5e8f\u5217":41,"\u6216\u4e00\u4e2a\u5411\u91cf":44,"\u6216\u4e0d\u786e\u5b9a":67,"\u6216\u5355\u5c42\u5e8f\u5217\u7ecf\u8fc7\u8fd0\u7b97\u53d8\u6210\u4e00\u4e2a0\u5c42\u5e8f\u5217":41,"\u6216\u662f\u624b\u52a8\u7f16\u8f91\u751f\u6210":68,"\u6216\u6700\u5927\u503c":41,"\u6216\u6d4b\u8bd5\u6587\u4ef6\u5217\u8868":2,"\u6216\u7b2c\u4e00\u4e2a":41,"\u6216\u7b2c\u4e00\u4e2a\u5143\u7d20":41,"\u6216\u8005":[26,27,31,34,36,41,42,50],"\u6216\u8005\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":44,"\u6216\u8005\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":[41,44],"\u6216\u8005\u4ece\u5de5\u5177\u7684\u754c\u9762\u91cc\u8fd0\u884c\u60a8\u7684\u5e94\u7528":50,"\u6216\u8005\u53cd\u5411\u5730\u4ece":45,"\u6216\u8005\u5728cpu\u6a21\u5f0f\u4e0b\u4f7f\u75284\u4e2a\u7ebf\u7a0b":53,"\u6216\u8005\u5c06\u8fd9\u53f0\u8282\u70b9\u8fc1\u79fb\u5230\u53e6\u4e00\u4e2a\u8282\u70b9\u5e76\u542f\u52a8\u5373\u53ef\u6062\u590d\u8bad\u7ec3\u4efb\u52a1":19,"\u6216\u8005\u5df2\u7ecf\u5728\u96c6\u7fa4\u63d0\u4ea4\u73af\u5883\u4e2d\u81ea\u52a8\u8bbe\u7f6e":52,"\u6216\u8005\u6570\u636e\u5e93\u8fde\u63a5\u8def\u5f84\u7b49":2,"\u6216\u8005\u6570\u7ec4\u7684\u6570\u7ec4\u8fd9\u4e2a\u6982\u5ff5":42,"\u6216\u8005\u662f\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":41,"\u6216\u8005\u662f\u4e00\u4e2a\u5c0f\u7684\u6587\u672c\u7247\u6bb5":70,"\u6216\u8005\u662f\u51fd\u6570\u8c03\u7528\u7684\u9891\u7387\u548c\u8017\u65f6\u7b49":50,"\u6216\u8005\u66f4\u65e9":31,"\u6216\u8005\u6bcf\u4e00\u4e2a\u7cfb\u5217\u91cc\u7684\u7279\u5f81\u6570\u636e":42,"\u6216\u8005\u7528tuple\u8868\u793a\u7684\u591a\u4e2a\u503c":20,"\u6216\u8005\u7531\u5b83\u4eec\u7ec4\u6210\u7684list":20,"\u6216\u8005\u76f4\u63a5\u4f7f\u7528\u4e0b\u9762\u7684shell\u547d\u4ee4":64,"\u6216\u8005\u76f4\u63a5\u6254\u6389\u975e\u5e38\u957f\u7684\u5e8f\u5217":31,"\u6216\u8005\u76f8\u5bf9\u4e8e\u6784\u5efa\u76ee\u5f55\u7684\u76f8\u5bf9\u8def\u5f84":[39,40],"\u6216\u8005\u8f93\u5165\u6570\u636e\u5c3a\u5ea6\u8fc7\u5927":31,"\u6216\u8005\u8fd0\u884c":31,"\u6216\u8005\u91c7\u7528\u5e76\u884c\u8ba1\u7b97\u6765\u52a0\u901f\u67d0\u4e9b\u5c42\u7684\u66f4\u65b0":55,"\u6216\u8005\u9700\u8981\u53d1\u5e03\u60a8\u7684\u5e94\u7528\u7684\u955c\u50cf":34,"\u6216\u8005\u9700\u8981\u66f4\u9ad8\u7684\u6548\u7387":2,"\u6216\u8bbe\u7f6e\u4e3anone":2,"\u6216gpu":53,"\u6216gpu\u4e2a\u6570":70,"\u6218\u4e89\u7247":67,"\u623f":42,"\u623f\u95f4":42,"\u6240\u4ee5":[3,31],"\u6240\u4ee5\u4e00\u822c\u9700\u8981\u5bf9\u8bad\u7ec3\u7528\u7684\u6a21\u578b\u914d\u7f6e\u6587\u4ef6\u7a0d\u4f5c\u76f8\u5e94\u4fee\u6539\u624d\u80fd\u5728\u9884\u6d4b\u65f6\u4f7f\u7528":5,"\u6240\u4ee5\u4f60\u53ea\u7528\u6309\u4e0b\u9762\u7684\u7ed3\u6784\u6765\u7ec4\u7ec7\u6570\u6910\u5c31\u884c\u4e86":70,"\u6240\u4ee5\u505a\u6cd5\u53ef\u4ee5\u6709\u4e24\u79cd":31,"\u6240\u4ee5\u53ef\u4ee5\u5229\u7528\u5982\u4e0b\u65b9\u6cd5\u8bfb\u53d6\u6a21\u578b\u7684\u53c2\u6570":32,"\u6240\u4ee5\u53ef\u4ee5\u7b80\u5316\u5bf9\u73af\u5883\u7684\u8981\u6c42":57,"\u6240\u4ee5\u5728\u5199\u5165\u5feb\u7167\u7684\u8fc7\u7a0b\u4e2d":19,"\u6240\u4ee5\u5916\u5c42\u8f93\u51fa\u7684\u5e8f\u5217\u5f62\u72b6":42,"\u6240\u4ee5\u5982\u679c\u9700\u8981\u81ea\u884c\u914d\u7f6e\u5f00\u53d1\u73af\u5883\u9700\u8981\u8003\u8651\u7248\u672c\u7684\u56e0\u7d20":34,"\u6240\u4ee5\u5b83\u4eec\u4f7f\u7528\u540c\u4e00\u4e2aip\u5730\u5740":56,"\u6240\u4ee5\u5bf9":42,"\u6240\u4ee5\u5f88\u591a\u65f6\u5019\u4f60\u9700\u8981\u505a\u7684\u53ea\u662f\u5b9a\u4e49\u6b63\u786e\u7684\u7f51\u7edc\u5c42\u5e76\u628a\u5b83\u4eec\u8fde\u63a5\u8d77\u6765":32,"\u6240\u4ee5\u6027\u80fd\u4e5f\u5c31\u9010\u6b65\u53d8\u6210\u4e86\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u6700\u91cd\u8981\u7684\u6307\u6807":50,"\u6240\u4ee5\u6211\u4eec\u4f7f\u7528\u8fd9\u4e2a\u955c\u50cf\u6765\u4e0b\u8f7d\u8bad\u7ec3\u6570\u636e\u5230docker":57,"\u6240\u4ee5\u6211\u4eec\u53ef\u4ee5\u5728\u8fd9\u4e2a\u57fa\u7840\u4e0a":58,"\u6240\u4ee5\u6211\u4eec\u63a8\u8350\u4f7f\u7528\u57fa\u4e8edocker\u6765\u6784\u5efapaddlepaddle\u7684\u6587\u6863":48,"\u6240\u4ee5\u6211\u4eec\u9700\u8981\u5c06\u8f93\u5165\u6570\u636e\u6807\u8bb0\u6210":42,"\u6240\u4ee5\u63a8\u8350\u4f7f\u7528\u663e\u5f0f\u6307\u5b9a\u7684\u65b9\u5f0f\u6765\u8bbe\u7f6einput_typ":3,"\u6240\u4ee5\u653e\u4e00\u4e2a\u7a7a\u5217\u8868":32,"\u6240\u4ee5\u7528\u6237\u9700\u8981\u9996\u5148\u5728":24,"\u6240\u4ee5\u8bad\u7ec3":51,"\u6240\u4ee5\u8f93\u51fa\u7684value\u5305\u542b\u4e24\u4e2a\u5411\u91cf":5,"\u6240\u4ee5\u8fd9\u4e00\u6b65\u662f\u5fc5\u8981\u7684":47,"\u6240\u4f7f\u7528\u7684\u7f16\u8bd1\u5668\u4e3aarm":40,"\u6240\u5bf9\u5e94\u7684\u8bcd\u8868index\u6570\u7ec4":42,"\u6240\u6709\u4e0e\u7c7b\u578b\u76f8\u5173\u7684\u51fd\u6570":27,"\u6240\u6709\u4ee3\u7801\u5fc5\u987b\u5177\u6709\u5355\u5143\u6d4b\u8bd5":46,"\u6240\u6709\u53c2\u6570\u7f6e\u4e3a\u96f6":53,"\u6240\u6709\u540c\u76ee\u5f55\u4e0b\u7684\u6587\u672c\u5b9e\u4f8b\u6587\u4ef6\u90fd\u662f\u540c\u7ea7\u522b\u7684":70,"\u6240\u6709\u547d\u4ee4\u884c\u9009\u9879\u53ef\u4ee5\u8bbe\u7f6e\u4e3a":51,"\u6240\u6709\u6587\u4ef6\u5217\u8868":3,"\u6240\u6709\u672c\u5730\u8bad\u7ec3":51,"\u6240\u6709\u67b6\u6784":39,"\u6240\u6709\u6807\u8bb0\u7684\u6d4b\u8bd5\u96c6\u548c\u8bad\u7ec3\u96c6":70,"\u6240\u6709\u751f\u6210\u7684\u7684\u6587\u4ef6\u90fd\u5728build\u76ee\u5f55\u4e0b":34,"\u6240\u6709\u7684":[46,47],"\u6240\u6709\u7684\u4eba\u53e3\u7edf\u8ba1\u5b66\u4fe1\u606f\u7531\u7528\u6237\u81ea\u613f\u63d0\u4f9b":67,"\u6240\u6709\u7684\u5355\u6d4b\u90fd\u4f1a\u88ab\u6267\u884c\u4e00\u6b21":47,"\u6240\u6709\u7684\u63a5\u53e3\u5747\u4e3ac\u63a5\u53e3":27,"\u6240\u6709\u7684\u64cd\u4f5c\u90fd\u662f\u9488\u5bf9\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u6765\u8fdb\u884c\u7684":42,"\u6240\u6709\u7684\u7528\u6237\u4fe1\u606f\u90fd\u5305\u542b\u5728":67,"\u6240\u6709\u7684\u7535\u5f71\u4fe1\u606f\u90fd\u5305\u542b\u5728":67,"\u6240\u6709\u7684\u8bc4\u5206\u6570\u636e\u90fd\u5305\u542b\u5728":67,"\u6240\u6709\u7684python\u5c01\u88c5\u90fd\u4f7f\u7528":47,"\u6240\u6709\u7684python\u5c01\u88c5\u90fd\u5728":47,"\u6240\u6709\u7c7b\u578b\u540d\u4e3a":27,"\u6240\u6709\u7f51\u7edc\u5c42\u7684\u68af\u5ea6\u68c0\u67e5\u5355\u6d4b\u90fd\u4f4d\u4e8e":47,"\u6240\u6709\u8282\u70b9\u8fd0\u884c\u96c6\u7fa4\u4f5c\u4e1a\u7684\u4e3b\u673a\u540d\u6216":51,"\u6240\u6709\u8d21\u732e\u8005":0,"\u6240\u6709\u8f93\u5165\u5e8f\u5217\u5e94\u8be5\u6709\u76f8\u540c\u7684\u957f\u5ea6":45,"\u6240\u6709\u914d\u7f6e\u90fd\u80fd\u5728":66,"\u6240\u6784\u5efa\u7f51\u7edc\u7ed3\u6784\u7684\u7684\u6df1\u5ea6\u6bd4\u4e4b\u524d\u4f7f\u7528\u7684\u7f51\u7edc\u6709\u5927\u5e45\u5ea6\u7684\u63d0\u9ad8":64,"\u6240\u793a":69,"\u6240\u8c13\u65f6\u95f4\u6b65\u4fe1\u606f":3,"\u6240\u9700\u652f\u6301\u7684\u6700\u4f4eandroid":39,"\u624b\u5199\u591a\u8bed\u8a00\u7ed1\u5b9a":26,"\u624b\u5de5\u827a\u8005":67,"\u624d\u4f1a\u91ca\u653e\u8be5\u6bb5\u5185\u5b58":3,"\u624d\u4f1astop":3,"\u624d\u80fd\u4fdd\u8bc1\u548c\u5355\u5c42\u5e8f\u5217\u7684\u914d\u7f6e\u4e2d":42,"\u624d\u80fd\u5145\u5206\u5229\u7528mac":34,"\u624d\u80fd\u53d1\u6325\u5176\u5168\u90e8\u80fd\u529b":50,"\u6253\u5370\u5728\u5c4f\u5e55\u4e0a":68,"\u6253\u5370\u7684\u65e5\u5fd7\u53d8\u591a":33,"\u6253\u5f00":50,"\u6253\u5f00\u6587\u672c\u6587\u4ef6":3,"\u6253\u5f00\u6d4f\u89c8\u5668\u8bbf\u95ee\u5bf9\u5e94\u76ee\u5f55\u4e0b\u7684index":48,"\u6253\u5f00\u8fd9\u4e2a\u7f16\u8bd1\u9009\u9879":27,"\u6267\u884c":[36,69,70],"\u6267\u884c\u4e0b\u9762\u7684\u547d\u4ee4\u5c31\u53ef\u4ee5\u9884\u5904\u7406\u6570\u6910":70,"\u6267\u884c\u4ee5\u4e0b\u547d\u4ee4":[39,40],"\u6267\u884c\u4ee5\u4e0b\u64cd\u4f5c":45,"\u6267\u884c\u5b8c\u5b89\u88c5\u547d\u4ee4\u540e":[39,40],"\u6267\u884c\u60a8\u7684\u4ee3\u7801":50,"\u6267\u884c\u7684\u547d\u4ee4\u5982\u4e0b":64,"\u6269\u5c55\u548c\u5ef6\u4f38":0,"\u6269\u5c55\u673a\u5236\u7b49\u529f\u80fd":56,"\u6279\u6b21\u540e\u6253\u5370\u65e5\u5fd7":68,"\u6279\u6b21\u5bf9\u5e73\u5747\u53c2\u6570\u8fdb\u884c\u6d4b\u8bd5":69,"\u6279\u6b21\u7684\u6570\u636e":68,"\u627e\u5230":45,"\u627e\u5230\u8fd0\u884c\u6162\u7684\u539f\u56e0":50,"\u627e\u5230\u8fd0\u884c\u6162\u7684\u90e8\u5206":50,"\u6280\u672f\u5458":67,"\u628a":[20,47],"\u628a\u4e4b\u524d\u793a\u4f8b\u4e2d\u8f6c\u6362\u5b8c\u6bd5\u7684random":20,"\u628a\u7528\u6237\u5728\u8d2d\u7269\u7f51\u7ad9":70,"\u628a\u7f16\u8bd1\u51fa\u6765\u7684paddlepaddle\u4e5f\u6253\u5305\u6210\u4e00\u4e2a\u955c":34,"\u628a\u8bad\u7ec3\u6570\u636e\u76f4\u63a5\u653e\u5728":57,"\u6293\u53d6\u4ea7\u54c1\u7684\u7528\u6237\u8bc4\u8bba\u5e76\u5206\u6790\u4ed6\u4eec\u7684\u60c5\u611f":70,"\u6295\u5c04\u53cd\u5411rnn\u7684\u7b2c\u4e00\u4e2a\u5b9e\u4f8b\u5230":45,"\u6295\u5c04\u7f16\u7801\u5411\u91cf\u5230":45,"\u62a5\u9519":36,"\u62bd\u53d6\u51fa\u7684\u65b0\u8bcd\u8868\u7684\u4fdd\u5b58\u8def\u5f84":62,"\u62bd\u53d6\u5bf9\u5e94\u7684\u8bcd\u5411\u91cf\u6784\u6210\u65b0\u7684\u8bcd\u8868":62,"\u62c6\u5206\u5230\u4e0d\u540c\u6587\u4ef6\u5939":71,"\u62c6\u89e3":44,"\u62c6\u89e3\u6210\u7684\u6bcf\u4e00\u53e5\u8bdd\u518d\u901a\u8fc7\u4e00\u4e2alstm\u7f51\u7edc":42,"\u62f7\u8d1d\u8bad\u7ec3\u6587\u4ef6\u5230\u5bb9\u5668\u5185":58,"\u62fc\u63a5\u6210\u4e00\u4e2a\u65b0\u7684\u5411\u91cf":66,"\u6301\u7eed\u96c6\u6210\u7cfb\u7edf\u4e5f\u53ef\u4ee5\u7b80\u5355\u5730\u590d\u73b0\u8fd9\u4e2a\u8fc7\u7a0b":34,"\u6307\u5411\u4e00\u4e2alayer":44,"\u6307\u5b9a":[31,44,45],"\u6307\u5b9a\u4e00\u53f0\u673a\u5668\u4e0a\u4f7f\u7528\u7684\u7ebf\u7a0b\u6570":53,"\u6307\u5b9a\u4f7f\u75282":31,"\u6307\u5b9a\u521d\u59cb\u5316\u6a21\u578b\u8def\u5f84":66,"\u6307\u5b9a\u52a0\u8f7d\u7684\u65b9\u5f0f":53,"\u6307\u5b9a\u5de5\u4f5c\u6a21\u578b\u8fdb\u884c\u9884\u6d4b":64,"\u6307\u5b9a\u5de5\u4f5c\u6a21\u5f0f\u6765\u63d0\u53d6\u7279\u5f81":64,"\u6307\u5b9a\u63d0\u53d6\u7279\u5f81\u7684\u5c42":64,"\u6307\u5b9a\u662f\u5426\u4f7f\u7528gpu":64,"\u6307\u5b9a\u751f\u6210\u6570\u636e\u7684\u51fd\u6570":66,"\u6307\u5b9a\u7684\u5b57\u5178\u5355\u8bcd\u6570":71,"\u6307\u5b9a\u7684\u6570\u636e\u5c06\u4f1a\u88ab\u6d4b\u8bd5":66,"\u6307\u5b9a\u7684\u8f93\u5165\u4e0d\u4f1a\u88ab":44,"\u6307\u5b9a\u8bad\u7ec3\u6570\u636e\u548c\u6d4b\u8bd5\u6570\u636e":66,"\u6307\u5b9abatch":71,"\u6307\u5b9acudnn\u7684\u6700\u5927\u5de5\u4f5c\u7a7a\u95f4\u5bb9\u9650":53,"\u6307\u5bf9\u4e8e\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u8f93\u5165\u6570\u636e":42,"\u6307\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u4e4b\u540e\u5f97\u5230\u7684\u6240\u6709\u53c2\u6570":19,"\u6307\u793a\u4f7f\u7528\u54ea\u4e2agpu\u6838":53,"\u6307\u793a\u5728\u7b80\u5355\u7684recurrentlayer\u5c42\u7684\u8ba1\u7b97\u4e2d\u662f\u5426\u4f7f\u7528\u6279\u5904\u7406\u65b9\u6cd5":53,"\u6307\u793a\u5f53\u6307\u5b9a\u8f6e\u7684\u6d4b\u8bd5\u6a21\u578b\u4e0d\u5b58\u5728\u65f6":53,"\u6307\u793a\u662f\u5426\u4f7f\u7528\u591a\u7ebf\u7a0b\u6765\u8ba1\u7b97\u4e00\u4e2a\u795e\u7ecf\u7f51\u7edc":53,"\u6307\u793a\u662f\u5426\u5f00\u542f\u53c2\u6570\u670d\u52a1\u5668":53,"\u6307\u793a\u662f\u5426\u663e\u793a\u53c2\u6570\u670d\u52a1\u5668\u4e0a\u7684\u7a00\u758f\u53c2\u6570\u5206\u5e03\u7684\u65e5\u5fd7\u7ec6\u8282":53,"\u6307\u793a\u662f\u5426\u68c0\u67e5\u6240\u6709\u53c2\u6570\u670d\u52a1\u5668\u4e0a\u7684\u7a00\u758f\u53c2\u6570\u7684\u5206\u5e03\u662f\u5747\u5300\u7684":53,"\u6307\u793a\u6d4b\u8bd5\u4efb\u52a1":69,"\u6307\u793a\u6d4b\u8bd5\u4efb\u52a1\u7684\u6807\u8bb0":69,"\u6309\u542f\u53d1\u5f0f\u635f\u5931\u7684\u5927\u5c0f\u9012\u589e\u6392\u5e8f":53,"\u6309\u7167":40,"\u6309\u7167\u4e0b\u9762\u6b65\u9aa4\u5373\u53ef":58,"\u6309\u94ae":46,"\u633a":42,"\u633a\u597d":42,"\u6355\u83b7":66,"\u6362":42,"\u6392\u6210\u4e00\u5217\u7684\u591a\u4e2a\u5143\u7d20":41,"\u63a5\u4e0a\u4e00\u4e2a\u5168\u8fde\u63a5\u5c42":37,"\u63a5\u4e0a\u5747\u65b9\u8bef\u5dee\u5c42":37,"\u63a5\u4e0b\u6765":[66,70],"\u63a5\u4e0b\u6765\u53ef\u4ee5\u8003\u8651\u4e0b\u65f6\u95f4\u7ebf\u7684\u5206\u6790":50,"\u63a5\u4e0b\u6765\u5c31\u53ef\u4ee5\u4f7f\u7528":50,"\u63a5\u4e0b\u6765\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u539f\u59cb":46,"\u63a5\u4e0b\u6765\u6211\u4eec\u53d6\u6d88\u5bf9":46,"\u63a5\u4e0b\u6765\u6211\u4eec\u5c06\u5c55\u793a\u5982\u4f55\u7528paddlepaddle\u8bad\u7ec3\u4e00\u4e2a\u6587\u672c\u5206\u7c7b\u6a21\u578b":66,"\u63a5\u4e0b\u6765\u7b49\u5f85":46,"\u63a5\u53d7":69,"\u63a5\u53d7\u7684\u4e1c\u897f":69,"\u63a5\u53d7\u8005":69,"\u63a5\u53e3":[26,27],"\u63a5\u53e3\u540d\u79f0":51,"\u63a5\u53e3\u5c42\u505a\u8fc7\u591a\u5c01\u88c5":27,"\u63a5\u53e3\u63d0\u53d6\u7684\u7ed3\u679c\u662f\u4e00\u81f4\u7684":64,"\u63a5\u53e3\u662f":20,"\u63a5\u53e3\u6709\u4e00\u4e2a":31,"\u63a5\u53e3\u6765\u52a0\u8f7d\u6570\u636e":66,"\u63a5\u53e3\u6765\u52a0\u8f7d\u8be5\u6587\u4ef6":64,"\u63a5\u53e3\u6765\u6253\u5f00\u8be5\u6587\u4ef6":64,"\u63a5\u6536\u5904\u7406pfsclient\u7aef\u7684\u6587\u4ef6\u7ba1\u7406\u8bf7\u6c42":24,"\u63a5\u7740\u6211\u4eec\u5c31\u80fd\u591f\u6253\u5f00\u6d4f\u89c8\u5668\u5728":34,"\u63a7\u5236":53,"\u63a7\u5236\u5982\u4f55\u6539\u53d8\u6a21\u578b\u53c2\u6570":32,"\u63a7\u5236\u662f\u5426\u751f\u6210\u5355\u5143\u6d4b\u8bd5":34,"\u63a7\u5236\u751f\u6210\u7684\u751f\u4ea7\u955c\u50cf\u662f\u5426\u652f\u6301avx":34,"\u63a7\u5236\u751f\u6210\u7684\u751f\u4ea7\u955c\u50cf\u662f\u5426\u652f\u6301gpu":34,"\u63a7\u5236\u7528\u6237\u6743\u9650":20,"\u63a8\u5bfc\u8be5\u5c42\u524d\u5411\u548c\u540e\u5411\u4f20\u9012\u7684\u65b9\u7a0b":47,"\u63a8\u8350":42,"\u63a8\u8350\u4f7f\u7528":3,"\u63a8\u8350\u6e05\u7406\u6574\u4e2a\u7f16\u8bd1\u76ee\u5f55":33,"\u63a8\u8350\u76f4\u63a5\u5b58\u653e\u5230\u8bad\u7ec3\u76ee\u5f55":2,"\u63a8\u8350\u7cfb\u7edf":51,"\u63a8\u9001\u5230\u8fdc\u7a0b\u4ed3\u5e93":46,"\u63a8\u9500\u5458":67,"\u63cf\u8ff0":33,"\u63cf\u8ff0\u7f51\u7edc\u7ed3\u6784\u548c\u4f18\u5316\u7b97\u6cd5":66,"\u63cf\u8ff0\u95ee\u9898":46,"\u63cf\u8ff0kubernetes\u4e0a\u8fd0\u884c\u7684\u4f5c\u4e1a":56,"\u63d0\u4ea4\u65b9\u5f0f\u53ef\u53c2\u89c1":48,"\u63d0\u4ea4pull":46,"\u63d0\u4f9b":51,"\u63d0\u4f9b\u4e03\u5c42\u534f\u8bae\u7684\u53cd\u5411\u4ee3\u7406":24,"\u63d0\u4f9b\u4e86\u4e00\u4e2a\u542f\u52a8\u811a\u672c":58,"\u63d0\u4f9b\u4e86\u547d\u4ee4\u6837\u4f8b\u6765\u8fd0\u884c":51,"\u63d0\u4f9b\u4e86\u81ea\u52a8\u5316\u811a\u672c\u6765\u542f\u52a8\u4e0d\u540c\u8282\u70b9\u4e2d\u7684\u6240\u6709":51,"\u63d0\u4f9b\u51e0\u4e4e\u6240\u6709\u8bad\u7ec3\u7684\u5185\u90e8\u8f93\u51fa\u65e5\u5fd7":51,"\u63d0\u4f9b\u5e38\u7528\u7684\u547d\u4ee4\u884c\u7ba1\u7406\u547d\u4ee4\u7ba1\u7406\u6587\u4ef6\u548c\u76ee\u5f55":24,"\u63d0\u4f9b\u6269\u5c55\u7684\u957f\u5ea6\u4fe1\u606f":41,"\u63d0\u4f9b\u6700\u65b0":34,"\u63d0\u4f9b\u7528\u6237\u7ba1\u7406\u6587\u4ef6\u7684\u547d\u4ee4":24,"\u63d0\u4f9b\u7ed9paddle\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e":20,"\u63d0\u4f9b\u8bad\u7ec3\u8fc7\u7a0b\u7684":51,"\u63d0\u51fa\u7684\u4ee3\u7801\u9700\u6c42":62,"\u63d0\u793a":31,"\u642d\u5efa\u795e\u7ecf\u7f51\u7edc\u5c31\u50cf\u4f7f\u7528\u79ef\u6728\u642d\u5efa\u5b9d\u5854\u4e00\u6837":37,"\u64cd\u4f5c":42,"\u64cd\u6301\u5bb6\u52a1\u8005":67,"\u652f\u6301\u4e3b\u6d41x86\u5904\u7406\u5668\u5e73\u53f0":36,"\u652f\u6301\u53cc\u5c42\u5e8f\u5217\u4f5c\u4e3a\u8f93\u5165\u7684layer":[43,44],"\u652f\u6301\u5927\u6587\u4ef6\u7684\u65ad\u70b9\u4e0a\u4f20":24,"\u652f\u6301\u7684\u6700\u5c0f\u7684android":39,"\u652f\u6301nvidia":36,"\u652f\u6301rbd":56,"\u653e\u5728\u8fd9\u4e2a\u76ee\u5f55\u91cc\u7684\u6587\u4ef6\u5176\u5b9e\u662f\u4fdd\u5b58\u5230\u4e86mfs\u4e0a":58,"\u653e\u5fc3":42,"\u6545\u800c\u662f\u4e00\u4e2a\u5355\u5c42\u65f6\u95f4\u5e8f\u5217":42,"\u6548\u679c\u603b\u7ed3":66,"\u6559\u7a0b\u6587\u6863\u5230":51,"\u6559\u80b2\u5de5\u4f5c\u8005":67,"\u6570":[44,69],"\u6570\u5fc5\u987b\u4e25\u683c\u76f8\u7b49":44,"\u6570\u636e":[24,71],"\u6570\u636e\u4e0b\u8f7d\u4e4b\u540e":63,"\u6570\u636e\u4e2d0":31,"\u6570\u636e\u5217\u8868":64,"\u6570\u636e\u5c06\u4fdd\u5b58\u5728":62,"\u6570\u636e\u5c42":[32,68],"\u6570\u636e\u5e94\u8be5\u5728\u542f\u52a8\u96c6\u7fa4\u4f5c\u4e1a\u4e4b\u524d\u51c6\u5907\u597d":51,"\u6570\u636e\u63d0\u4f9b\u5668":52,"\u6570\u636e\u63d0\u4f9b\u811a\u672c\u4ec5\u4ec5\u662f\u8bfb\u53d6meta":68,"\u6570\u636e\u63d0\u4f9b\u811a\u672c\u7684\u7ec6\u8282\u6587\u6863\u53ef\u4ee5\u53c2\u8003":68,"\u6570\u636e\u7684\u6574\u6570id":45,"\u6570\u636e\u76ee\u5f55\u4e2d\u7684\u6240\u6709\u6587\u4ef6\u88ab":51,"\u6570\u636e\u7c7b\u578b":5,"\u6570\u636e\u7f13\u5b58\u7684\u7b56\u7565":3,"\u6570\u636e\u8bbf\u95ee":1,"\u6570\u636e\u8bfb\u53d6\u5747\u4ea4\u7531\u5176\u4ed6\u8bed\u8a00\u5b8c\u6210":26,"\u6570\u636e\u8f93\u5165":44,"\u6570\u636e\u8f93\u5165\u683c\u5f0f":3,"\u6570\u636e\u957f\u5ea6\u53ca\u6821\u9a8c\u503c\u7ec4\u6210":24,"\u6570\u636e\u96c6":67,"\u6570\u636e\u96c6\u63cf\u8ff0":68,"\u6570\u636e\u96c6\u6587\u4ef6\u5939\u540d\u79f0":71,"\u6570\u636e\u96c6\u9700\u8981\u9884\u5148\u88ab\u8f6c\u6362\u6210paddlepaddle\u5206\u5e03\u5f0f\u8bad\u7ec3\u4f7f\u7528\u7684\u5b58\u50a8\u683c":20,"\u6570\u636e\u9884\u5904\u7406\u4efb\u52a1":20,"\u6570\u636e\u9884\u5904\u7406\u5b8c\u6210\u4e4b\u540e":66,"\u6570\u636e\u9884\u6d4b":69,"\u6570\u6910\u5b9a\u4e49":70,"\u6570\u6910\u8bf4\u660e\u6587\u6863":70,"\u6570\u6910\u96c6\u548c":70,"\u6570\u76ee":55,"\u6574\u4f53":42,"\u6574\u4f53\u6570\u636e\u548c\u539f\u59cb\u6570\u636e\u5b8c\u5168\u4e00\u6837":42,"\u6574\u4f53\u7684\u7ed3\u6784\u56fe\u5982\u4e0b":58,"\u6574\u6570":47,"\u6574\u6570\u6807\u7b7e":[3,37],"\u6574\u6d01":42,"\u6587\u4e66\u5de5\u4f5c":67,"\u6587\u4ef6":[26,46,57,69],"\u6587\u4ef6\u4e2d":[58,64,67,69],"\u6587\u4ef6\u4e2d\u6307\u5b9a\u6a21\u578b\u8def\u5f84\u548c\u8f93\u51fa\u7684\u76ee\u5f55":64,"\u6587\u4ef6\u4e2d\u6307\u5b9a\u8981\u63d0\u53d6\u7279\u5f81\u7684\u7f51\u7edc\u5c42\u7684\u540d\u5b57":64,"\u6587\u4ef6\u4e2d\u7684":64,"\u6587\u4ef6\u4e2d\u7684\u6bcf\u884c\u90fd\u5fc5\u987b\u662f\u4e00\u4e2a\u53e5\u5b50":71,"\u6587\u4ef6\u4e3a":[31,71],"\u6587\u4ef6\u4e4b\u5916":46,"\u6587\u4ef6\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5bf9\u6837\u672c\u8fdb\u884c\u9884\u6d4b":64,"\u6587\u4ef6\u4f20\u8f93\u7684\u7684\u5173\u952e\u5728\u4e8e\u9700\u8981pfsclient\u7aef\u5bf9\u6bd4source\u548cdestination\u7684\u6587\u4ef6chunks\u7684checksum\u662f\u5426\u4fdd\u6301\u4e00\u81f4":24,"\u6587\u4ef6\u5185\u5bb9\u4e3a":26,"\u6587\u4ef6\u5206\u5272\u4e3a\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6587\u4ef6":68,"\u6587\u4ef6\u540d\u4e3a\u6b64uuid":19,"\u6587\u4ef6\u540d\u79f0\u4e3a":68,"\u6587\u4ef6\u59390":58,"\u6587\u4ef6\u5939\u4e2d\u7684\u6bcf\u4e2a\u6587\u4ef6\u7684\u6bcf\u4e00\u884c\u5305\u542b\u4e24\u90e8\u5206":71,"\u6587\u4ef6\u5bf9\u5e94\u7684data":20,"\u6587\u4ef6\u7684\u4e0a\u4f20\u548c\u4e0b\u8f7d\u90fd\u662f\u901a\u8fc7\u5bf9chunk\u7684\u64cd\u4f5c\u6765\u5b9e\u73b0\u7684":24,"\u6587\u4ef6\u7684\u5206\u9694\u7b26\u4e3a":68,"\u6587\u4ef6\u7684\u6539\u53d8":46,"\u6587\u4ef6\u7684\u683c\u5f0f\u53ef\u4ee5":68,"\u6587\u4ef6\u7a0d\u6709\u5dee\u522b":63,"\u6587\u4ef6\u7d22\u5f15":51,"\u6587\u4ef6\u7ed9\u51fa\u4e86\u5b8c\u6574\u4f8b\u5b50":66,"\u6587\u4ef6model":55,"\u6587\u5b57\u7684\u4ea4\u4e92\u5f0f\u6587\u6863":34,"\u6587\u672c\u4e2d\u7684\u5355\u8bcd\u7528\u7a7a\u683c\u5206\u9694":66,"\u6587\u672c\u4fe1\u606f\u5c31\u662f\u4e00\u4e2a\u5e8f\u5217\u6570\u636e":3,"\u6587\u672c\u5206\u7c7b\u95ee\u9898":66,"\u6587\u672c\u5377\u79ef\u5206\u53ef\u4e3a\u4e09\u4e2a\u6b65\u9aa4":66,"\u6587\u672c\u5377\u79ef\u91c7\u6837\u5c42":68,"\u6587\u672c\u6295\u5f71\u5c42":68,"\u6587\u672c\u683c\u5f0f\u7684\u5b9e\u4f8b\u6587\u4ef6":70,"\u6587\u6863":31,"\u6587\u6863\u7f16\u5199\u7b49\u5de5\u4f5c":34,"\u6587\u6863\u81ea\u52a8\u5206\u7c7b\u548c\u95ee\u7b54":69,"\u6587\u6863\u90fd\u662f\u901a\u8fc7":48,"\u6587\u7ae0":58,"\u65b0":42,"\u65b0\u5efa\u4e00\u4e2a\u6743\u91cd":47,"\u65b0\u624b\u5165\u95e8":61,"\u65b0\u624b\u5165\u95e8\u7ae0\u8282":30,"\u65b9\u4fbf":42,"\u65b9\u4fbf\u4eca\u540e\u7684\u5d4c\u5165\u5f0f\u79fb\u690d\u5de5\u4f5c":33,"\u65b9\u4fbf\u5f00\u53d1\u8005\u76f4\u63a5\u767b\u5f55\u5230\u955c\u50cf\u4e2d\u8fdb\u884c\u5f00\u53d1":34,"\u65b9\u4fbf\u6d4b\u8bd5\u4eba\u5458\u6d4b\u8bd5paddle\u7684\u884c\u4e3a":30,"\u65b9\u4fbf\u7528\u6237\u4e0a\u4f20\u81ea\u5df1\u7684\u8bad\u7ec3\u6570\u636e\u4ee5\u8fdb\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3":24,"\u65b9\u5f0f1":31,"\u65b9\u5f0f2":31,"\u65b9\u6848\u6765\u6807\u8bb0\u6bcf\u4e2a\u53c2\u6570":69,"\u65b9\u6cd5\u4e00":55,"\u65b9\u6cd5\u4e09":55,"\u65b9\u6cd5\u4e8c":55,"\u65c1\u8fb9":42,"\u65c5\u6e38\u7f51\u7ad9":70,"\u65e0":42,"\u65e0\u4e1a\u4eba\u58eb":67,"\u65e0\u5ef6\u8fdf":53,"\u65e0\u6cd5\u505a\u5230\u5bf9\u4e8e\u5404\u79cd\u8bed\u8a00\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u7684\u9002\u914d":26,"\u65e0\u8bba\u5728\u672c\u5730\u8fd8\u662f\u5728\u4e91\u7aef":20,"\u65e0\u8bba\u662f\u4ece":20,"\u65e0\u8bba\u662f\u5728\u672c\u5730\u6216\u662f\u4e91\u7aef\u8f6c\u6362":20,"\u65e0\u9ed8\u8ba4\u503c":[39,40],"\u65e5\u5fd7\u5c06\u4fdd\u5b58\u5728":70,"\u65e8\u5728\u5efa\u7acb\u4e00\u4e2a\u53ef\u4ee5\u88ab\u534f\u540c\u8c03\u81f3\u6700\u4f18\u7ffb\u8bd1\u6548\u679c\u7684\u5355\u795e\u7ecf\u5143\u7f51\u7edc":71,"\u65e9\u9910":42,"\u65f6":[19,31,41,45,47,53,58],"\u65f6\u5019":42,"\u65f6\u52a0\u4e0a":70,"\u65f6\u5e8f\u6a21\u578b\u5747\u4f7f\u7528\u8be5\u811a\u672c":66,"\u65f6\u5e8f\u6a21\u578b\u662f\u6307\u6570\u636e\u7684\u67d0\u4e00\u7ef4\u5ea6\u662f\u4e00\u4e2a\u5e8f\u5217\u5f62\u5f0f":3,"\u65f6\u76ee\u6807\u8bed\u8a00\u7684\u6587\u4ef6":71,"\u65f6\u88ab\u8bad\u7ec3\u7684":47,"\u65f6\u8bbe\u5907id\u53f7\u7684\u5206\u914d":55,"\u65f6\u95f4":42,"\u65f6\u95f4\u6233":67,"\u65f6\u95f4\u6233\u8868\u793a\u4e3a\u4ece1970":67,"\u65f6\u95f4\u6b65\u7684\u6982\u5ff5":42,"\u6620\u5c04\u5230\u4e00\u4e2a\u7ef4\u5ea6\u4e3a":47,"\u662f":[24,33,42],"\u662f\u4e00\u4e2a\u51681\u7684\u5411\u91cf":47,"\u662f\u4e00\u4e2a\u5185\u7f6e\u7684\u5b9a\u65f6\u5668\u5c01\u88c5":50,"\u662f\u4e00\u4e2a\u52a8\u6001\u7a0b\u5e8f\u5206\u6790\u7684\u672f\u8bed":50,"\u662f\u4e00\u4e2a\u5305\u88c5\u6570\u636e\u7684":69,"\u662f\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":41,"\u662f\u4e00\u4e2a\u53cc\u5c42\u7684\u5e8f\u5217":41,"\u662f\u4e00\u4e2a\u591a\u8bed\u8a00\u63a5\u53e3\u7684\u4ee3\u7801\u751f\u6210\u5668":26,"\u662f\u4e00\u4e2a\u5c01\u88c5\u5bf9\u8c61":50,"\u662f\u4e00\u4e2a\u5f88\u6709\u7528\u7684\u53c2\u6570":55,"\u662f\u4e00\u4e2a\u7b26\u5408\u9ad8\u65af\u5206\u5e03\u7684\u968f\u673a\u53d8\u91cf":32,"\u662f\u4e00\u4e2a\u7c7b\u578b\u7684\u6807\u5fd7":27,"\u662f\u4e00\u4e2a\u7edf\u8ba1\u5b66\u7684\u673a\u5668\u7ffb\u8bd1\u7cfb\u7edf":71,"\u662f\u4e00\u4e2a\u914d\u7f6e\u6587\u4ef6\u7684\u4f8b\u5b50":70,"\u662f\u4e00\u4e2a\u975e\u7ebf\u6027\u7684":47,"\u662f\u4e00\u4e2apython\u7684":3,"\u662f\u4e00\u4e2aunbound":44,"\u662f\u4e00\u6761\u65f6\u95f4\u5e8f\u5217":[3,37],"\u662f\u4e00\u79cd\u4efb\u610f\u590d\u6742\u7684rnn\u5355\u5143":44,"\u662f\u4e00\u7ec4":56,"\u662f\u4e0d\u5305\u62ec\u6e90\u7801\u7684":57,"\u662f\u4e0d\u5e38\u89c1\u7684\u505a\u6cd5":26,"\u662f\u4e0d\u662f\u5f88\u7b80\u5355\u5462":3,"\u662f\u4e0d\u662f\u8981\u5bf9\u6570\u636e\u505ashuffl":3,"\u662f\u4e3b\u8981\u7684\u53ef\u6267\u884cpython\u811a\u672c":69,"\u662f\u4ec0\u4e48\u4e5f\u6ca1\u5173\u7cfb":3,"\u662f\u4f17\u591a\u8bef\u5dee\u4ee3\u4ef7\u51fd\u6570\u5c42\u7684\u4e00\u79cd":32,"\u662f\u4f7f\u5f97\u8981\u5171\u4eab\u7684\u53c2\u6570\u4f7f\u7528\u540c\u6837\u7684":31,"\u662f\u504f\u5dee":45,"\u662f\u5176\u5927\u5c0f":32,"\u662f\u51e0\u4e4e\u4e0d\u5360\u5185\u5b58\u7684":3,"\u662f\u539f\u59cb\u6cd5\u8bed\u6587\u4ef6":71,"\u662f\u5404\u4e2a\u5b9e\u73b0\u4e2d\u5171\u4eab\u7684\u5934\u6587\u4ef6":27,"\u662f\u5411\u91cf":47,"\u662f\u5426\u4ee5\u9006\u5e8f\u5904\u7406\u8f93\u5165\u5e8f\u5217":44,"\u662f\u5426\u4f7f\u7528\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u6570":33,"\u662f\u5426\u4f7f\u7528\u65e7\u7684remoteparameterupdat":53,"\u662f\u5426\u4f7f\u7528\u6743\u91cd":47,"\u662f\u5426\u4f7f\u7528arm\u6a21\u5f0f":39,"\u662f\u5426\u4f7f\u7528gpu":68,"\u662f\u5426\u4f7f\u7528gpu\u8bad\u7ec3":71,"\u662f\u5426\u4f7f\u7528neon\u6307\u4ee4":[39,40],"\u662f\u5426\u5141\u8bb8\u6682\u5b58\u7565\u5fae\u591a\u4f59pool_size\u7684\u6570\u636e":3,"\u662f\u5426\u5185\u5d4cpython\u89e3\u91ca\u5668":33,"\u662f\u5426\u5c06\u5168\u5c40\u79cd\u5b50\u5e94\u7528\u4e8e\u672c\u5730\u7ebf\u7a0b\u7684\u968f\u673a\u6570":53,"\u662f\u5426\u5f00\u542f\u5355\u5143\u6d4b\u8bd5":33,"\u662f\u5426\u5f00\u542f\u8ba1\u65f6\u529f\u80fd":33,"\u662f\u5426\u5f00\u542frdma":33,"\u662f\u5426\u6253\u5370\u7248\u672c\u4fe1\u606f":53,"\u662f\u5426\u652f\u6301gpu":33,"\u662f\u5426\u663e\u793a":53,"\u662f\u5426\u7a00\u758f":47,"\u662f\u5426\u7f16\u8bd1\u4e2d\u82f1\u6587\u6587\u6863":33,"\u662f\u5426\u7f16\u8bd1\u542b\u6709avx\u6307\u4ee4\u96c6\u7684paddlepaddle\u4e8c\u8fdb\u5236\u6587\u4ef6":33,"\u662f\u5426\u7f16\u8bd1\u65f6\u8fdb\u884c\u4ee3\u7801\u98ce\u683c\u68c0\u67e5":33,"\u662f\u5426\u7f16\u8bd1python\u7684swig\u63a5\u53e3":33,"\u662f\u5426\u8fd0\u884c\u65f6\u52a8\u6001\u52a0\u8f7dcuda\u52a8\u6001\u5e93":33,"\u662f\u5426\u9700\u8981\u7b49\u5f85\u8be5\u8f6e\u6a21\u578b\u53c2\u6570":53,"\u662f\u56e0\u4e3ac99\u652f\u6301":26,"\u662f\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u52a0\u8f7d\u5b57\u5178\u5e76\u5b9a\u4e49\u6570\u636e\u63d0\u4f9b\u7a0b\u5e8f\u6a21\u5757\u548c\u7f51\u7edc\u67b6\u6784\u7684\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6":69,"\u662f\u5728paddlepaddle\u4e2d\u6784\u9020\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u65f6\u6700\u91cd\u8981\u7684\u6982\u5ff5":45,"\u662f\u57fa\u7840\u7684\u8ba1\u7b97\u5355\u5143":32,"\u662f\u5b58\u6709\u4e00\u7cfb\u5217\u53d8\u6362\u77e9\u9635\u7684\u6743\u91cd":47,"\u662f\u5b58\u6709\u504f\u7f6e\u5411\u91cf\u7684\u6743\u91cd":47,"\u662f\u5bf9\u7528\u6237\u6587\u4ef6\u5b58\u50a8\u7a7a\u95f4\u7684\u62bd\u8c61":24,"\u662f\u5e8f\u5217":68,"\u662f\u5f85\u6269\u5c55\u7684\u6570\u636e":41,"\u662f\u6211\u4eec":46,"\u662f\u6307":27,"\u662f\u6307\u4e00\u7cfb\u5217\u7684\u7279\u5f81\u6570\u636e":42,"\u662f\u6307recurrent_group\u7684\u591a\u4e2a\u8f93\u5165\u5e8f\u5217":42,"\u662f\u6570\u636e\u8f93\u5165":45,"\u662f\u6574\u4e2a\u7684\u8bcd\u5d4c\u5165":68,"\u662f\u6709\u610f\u4e49\u7684":42,"\u662f\u6784\u5efa\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u6700\u91cd\u8981\u7684\u5de5\u5177":45,"\u662f\u6a21\u578b\u53c2\u6570\u4f18\u5316\u7684\u76ee\u6807\u51fd\u6570":32,"\u662f\u6d45\u5c42\u8bed\u4e49\u89e3\u6790\u7684\u4e00\u79cd\u5f62\u5f0f":69,"\u662f\u6e90\u8bed\u8a00\u7684\u6587\u4ef6":71,"\u662f\u7528\u6237\u4f7f\u7528c":27,"\u662f\u76ee\u6807\u82f1\u8bed\u6587\u4ef6":71,"\u662f\u77e9\u9635":47,"\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u5165\u53e3":32,"\u662f\u7f51\u7edc\u548c\u6570\u636e\u914d\u7f6e\u6587\u4ef6":63,"\u662f\u7f51\u7edc\u5c42\u5b9e\u4f8b\u7684\u540d\u5b57\u6807\u8bc6\u7b26":47,"\u662f\u7f51\u7edc\u5c42\u7684\u6807\u8bc6\u7b26":47,"\u662f\u7f51\u7edc\u5c42\u7684\u7c7b\u578b":47,"\u662f\u7f51\u7edc\u5c42\u8f93\u51fa\u7684\u5927\u5c0f":47,"\u662f\u8be5\u5c42\u7684\u6807\u8bc6\u7b26":47,"\u662f\u8be5\u5c42\u7684\u7c7b\u540d":47,"\u662f\u8be5\u7f51\u7edc\u5c42\u7684":47,"\u662f\u8f93\u5165":45,"\u662f\u901a\u7528\u7269\u4f53\u5206\u7c7b\u9886\u57df\u4e00\u4e2a\u4f17\u6240\u5468\u77e5\u7684\u6570\u636e\u5e93":64,"\u662f\u9700\u8981\u4e86\u89e3\u54ea\u4e9b\u6b65\u9aa4\u62d6\u6162\u4e86\u6574\u4f53":50,"\u662fc":27,"\u662fdecoder\u7684\u6570\u636e\u8f93\u5165":44,"\u662fgoogle\u5f00\u6e90\u7684\u5bb9\u5668\u96c6\u7fa4\u7ba1\u7406\u7cfb\u7edf":56,"\u662fnvidia\u6027\u80fd\u5206\u6790\u5de5\u5177":50,"\u662fpaddlepaddle\u652f\u6301\u7684\u4e00\u79cd\u4efb\u610f\u590d\u6742\u7684rnn\u5355\u5143":44,"\u662fpaddlepaddle\u8d1f\u8d23\u63d0\u4f9b\u6570\u636e\u7684\u6a21\u5757":66,"\u662fpod\u5185\u7684\u5bb9\u5668\u90fd\u53ef\u4ee5\u8bbf\u95ee\u7684\u5171\u4eab\u76ee\u5f55":56,"\u662fpython\u5c01\u88c5\u7684\u7c7b\u540d":47,"\u662frnn\u72b6\u6001":45,"\u663e":66,"\u663e\u5f0f\u6307\u5b9a\u8fd4\u56de\u7684\u662f\u4e00\u4e2a28":3,"\u665a":42,"\u666e\u901a\u7528\u6237\u8bf7\u8d70\u5b89\u88c5\u6d41\u7a0b":35,"\u6682\u4e0d\u8003\u8651\u5728\u5185":31,"\u6682\u65f6\u4e0d\u8003\u8651\u591a\u4e2aparamet":19,"\u66b4\u9732\u8fd9\u4e2a\u6982\u5ff5\u5fc5\u8981\u51fd\u6570":27,"\u66f4\u591a\u5173\u4e8edocker\u7684\u5b89\u88c5\u4e0e\u4f7f\u7528":31,"\u66f4\u591a\u5185\u5bb9\u53ef\u67e5\u770b\u53c2\u8003\u6587\u732e":70,"\u66f4\u591a\u7684\u7ec6\u8282\u53ef\u4ee5\u5728\u6587\u732e\u4e2d\u627e\u5230":70,"\u66f4\u591a\u7684\u7f16\u8bd1\u914d\u7f6e\u89c1":40,"\u66f4\u597d\u5730\u5b8c\u6210\u4e00\u4e9b\u590d\u6742\u7684\u8bed\u8a00\u7406\u89e3\u4efb\u52a1":44,"\u66f4\u5feb":45,"\u66f4\u65b0":31,"\u66f4\u65b0\u6a21\u5f0f":31,"\u66f4\u65b9\u4fbf\u7684\u8bbe\u7f6e\u65b9\u5f0f":31,"\u66f4\u8be6\u7ec6\u6570\u636e\u683c\u5f0f\u548c\u7528\u4f8b\u8bf7\u53c2\u8003":66,"\u66f4\u8be6\u7ec6\u7684\u7f51\u7edc\u914d\u7f6e\u8fde\u63a5\u8bf7\u53c2\u8003":66,"\u66f4\u8be6\u7ec6\u7684\u8bf4\u660e":66,"\u66f4\u8fdb\u4e00\u6b65":44,"\u66f4\u9ad8":45,"\u66ff\u6211\u4eec\u5b8c\u6210\u4e86\u539f\u59cb\u8f93\u5165\u6570\u636e\u7684\u62c6\u5206":44,"\u6700":42,"\u6700\u4e0d\u540c\u7684\u7279\u8272\u662f\u5b83\u5e76\u6ca1\u6709\u5c06\u8f93\u5165\u8bed\u53e5\u7f16\u7801\u4e3a\u4e00\u4e2a\u5355\u72ec\u7684\u5b9a\u957f\u5411\u91cf":71,"\u6700\u4e3b\u8981\u7684\u5de5\u4f5c\u5c31\u662f\u89e3\u6790\u51fa":58,"\u6700\u4f73\u63a8\u8350":3,"\u6700\u540e":[3,46,47,51,66,70],"\u6700\u540e\u4e00\u4e2a":41,"\u6700\u540e\u4e00\u5c42cost\u4e2d\u8bb0\u5f55\u4e86\u795e\u7ecf\u7f51\u7edc\u7684\u6240\u6709\u62d3\u6251\u7ed3\u6784":37,"\u6700\u540e\u4e00\u90e8\u5206\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u914d\u7f6e":32,"\u6700\u540e\u5220\u9664":30,"\u6700\u540e\u6211\u4eec\u4f7f\u7528\u94fe\u5f0f\u6cd5\u5219\u8ba1\u7b97":47,"\u6700\u597d\u7684\u6a21\u578b\u662f":70,"\u6700\u5c11\u663e\u793a\u591a\u5c11\u4e2a\u8282\u70b9":53,"\u6700\u5e38\u89c1\u7684\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u662fexcept":26,"\u6700\u65b0\u7684\u4ee3\u7801":46,"\u6700\u65b0log":70,"\u6700\u7ec8":47,"\u6700\u7ec8\u5b9e\u73b0\u4e00\u4e2a\u5c42\u6b21\u5316\u7684\u590d\u6742rnn":44,"\u6700\u7ec8\u6211\u4eec\u53ef\u4ee5\u8c03\u7528trainer\u7684train\u65b9\u6cd5\u542f\u52a8\u8bad\u7ec3":37,"\u6700\u7ec8\u751f\u6210\u540d\u4e3apaddl":34,"\u6700\u7ec8\u7684\u8f93\u51fa\u7ed3\u679c":44,"\u6700\u7ec8\u8d8b\u4e8e\u63a5\u8fd1":32,"\u6708\u6e56":42,"\u6709":42,"\u6709\u4e00\u4e2a\u57fa\u672c\u7684\u8ba4\u8bc6":56,"\u6709\u4e00\u4e9b\u5fc5\u987b\u914d\u7f6e\u7684\u53c2\u6570":[39,40],"\u6709\u4e24\u4e2a\u7279\u6b8a\u6807\u8bc6":71,"\u6709\u4e86meta\u914d\u7f6e\u6587\u4ef6\u4e4b\u540e":68,"\u6709\u4e9b\u5c42\u53ef\u80fd\u9700\u8981\u9ad8\u7cbe\u5ea6\u6765\u4fdd\u8bc1\u68af\u5ea6\u68c0\u67e5\u5355\u6d4b\u6b63\u786e\u6267\u884c":47,"\u6709\u4e9b\u5c42\u6216\u8005\u6fc0\u6d3b\u9700\u8981\u505a\u5f52\u4e00\u5316\u4ee5\u4fdd\u8bc1\u5b83\u4eec\u7684\u8f93\u51fa\u7684\u548c\u662f\u4e00\u4e2a\u5e38\u6570":47,"\u6709\u4e9b\u7279\u5f81\u7684\u53d6\u503c\u8fbe\u5230\u6570\u767e\u4e07":31,"\u6709\u4e9b\u7535\u5f71id\u53ef\u80fd\u4e0e\u5b9e\u9645\u7535\u5f71\u4e0d\u76f8\u7b26\u5408":67,"\u6709\u5173":42,"\u6709\u5173\u53c2\u6570\u914d\u7f6e\u7684\u8be6\u7ec6\u8bf4\u660e\u89c1":39,"\u6709\u5173\u5982\u4f55\u7f16\u5199\u6570\u636e\u63d0\u4f9b\u7a0b\u5e8f\u7684\u66f4\u591a\u7ec6\u8282\u63cf\u8ff0":45,"\u6709\u5173\u7ebf\u6027\u56de\u5f52\u7684\u5b9e\u9645\u5e94\u7528":37,"\u6709\u5173kubernetes\u76f8\u5173\u6982\u5ff5\u4ee5\u53ca\u5982\u4f55\u642d\u5efa\u548c\u914d\u7f6ekubernetes\u96c6\u7fa4":58,"\u6709\u52a9\u4e8e\u7406\u89e3\u7528\u6237\u5bf9\u4e0d\u540c\u516c\u53f8":70,"\u6709\u52a9\u4e8e\u8bca\u65ad\u5206\u5e03\u5f0f\u9519\u8bef":51,"\u6709\u65f6\u79f0\u4e3a":70,"\u6709\u6807\u51c6\u7684":26,"\u6709\u7684\u65f6\u5019":26,"\u6709\u7684\u65f6\u5019\u7b80\u7b80\u5355\u5355\u7684\u6539\u53d8\u5c31\u80fd\u5728\u6027\u80fd\u4e0a\u4ea7\u751f\u660e\u663e\u7684\u4f18\u5316\u6548\u679c":50,"\u670d\u52a1":42,"\u670d\u52a1\u5458":42,"\u672a\u5305\u542b\u5728\u5b57\u5178\u4e2d\u7684\u5355\u8bcd":71,"\u672a\u6807\u8bb0\u7684\u8bc4\u4ef7\u6837\u672c":70,"\u672a\u77e5\u8bcd":62,"\u672c\u4f8b\u4e2d\u4e3a0":62,"\u672c\u4f8b\u4e2d\u4e3a32":62,"\u672c\u4f8b\u4e2d\u4e3a4":62,"\u672c\u4f8b\u4e2d\u4f7f\u7528for\u5faa\u73af\u8fdb\u884c\u591a\u6b21\u8c03\u7528":3,"\u672c\u4f8b\u4e2d\u7684\u539f\u59cb\u6570\u636e\u4e00\u5171\u670910\u4e2a\u6837\u672c":42,"\u672c\u4f8b\u4e2d\u7684\u8f93\u5165\u7279\u5f81\u662f\u8bcdid\u7684\u5e8f\u5217":3,"\u672c\u4f8b\u6839\u636e\u7f51\u7edc\u914d\u7f6e\u4e2d":3,"\u672c\u4f8b\u6bcf\u884c\u4fdd\u5b58\u4e00\u6761\u6837\u672c":66,"\u672c\u4f8b\u7531\u6613\u5230\u96be\u5c55\u793a4\u79cd\u4e0d\u540c\u7684\u6587\u672c\u5206\u7c7b\u7f51\u7edc\u914d\u7f6e":66,"\u672c\u4f8b\u7684":3,"\u672c\u4f8b\u7684\u6240\u6709\u5b57\u7b26\u90fd\u5c06\u8f6c\u6362\u4e3a\u8fde\u7eed\u6574\u6570\u8868\u793a\u7684id\u4f20\u7ed9\u6a21\u578b":66,"\u672c\u4f8b\u91c7\u7528\u82f1\u6587\u60c5\u611f\u5206\u7c7b\u7684\u6570\u636e":3,"\u672c\u4f8b\u91c7\u7528adam\u4f18\u5316\u65b9\u6cd5":66,"\u672c\u5217\u8868\u8bf4\u660epaddle\u53d1\u7248\u4e4b\u524d\u9700\u8981\u6d4b\u8bd5\u7684\u529f\u80fd\u70b9":30,"\u672c\u5730\u6d4b\u8bd5":52,"\u672c\u5730\u8bad\u7ec3":52,"\u672c\u5730\u8bad\u7ec3\u7684\u5b9e\u9a8c":55,"\u672c\u5b9e\u4f8b\u4e2d":62,"\u672c\u5c0f\u8282\u6211\u4eec\u5c06\u4ecb\u7ecd\u6a21\u578b\u7f51\u7edc\u7ed3\u6784":66,"\u672c\u5c42\u5c3a\u5bf8":64,"\u672c\u5c42\u6709\u56db\u4e2a\u53c2\u6570":64,"\u672c\u6559\u7a0b\u4e2d\u6211\u4eec\u7ed9\u51fa\u4e86\u4e09\u4e2aresnet\u6a21\u578b":64,"\u672c\u6559\u7a0b\u5c06\u4ecb\u7ecd\u4f7f\u7528\u6df1\u5ea6\u53cc\u5411\u957f\u77ed\u671f\u8bb0\u5fc6":69,"\u672c\u6559\u7a0b\u5c06\u6307\u5bfc\u4f60\u5982\u4f55\u5728":45,"\u672c\u6559\u7a0b\u5c06\u6307\u5bfc\u60a8\u5b8c\u6210\u957f\u671f\u77ed\u671f\u8bb0\u5fc6":70,"\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7528\u4e8eimagenet\u4e0a\u7684\u5377\u79ef\u5206\u7c7b\u7f51\u7edc\u6a21\u578b":64,"\u672c\u6587\u4e2d\u6240\u6709\u7684\u4f8b\u5b50":42,"\u672c\u6587\u4e2d\u7531\u4e8e\u8f93\u5165\u6570\u636e\u662f\u968f\u673a\u751f\u6210\u7684\u4e0d\u9700\u8981\u8bfb\u8f93\u5165\u6587\u4ef6":32,"\u672c\u6587\u4e2d\u793a\u4f8b\u6240\u4f7f\u7528\u7684\u5355\u5143\u6d4b\u8bd5\u6587\u4ef6\u662f":42,"\u672c\u6587\u4ee5paddlepaddle\u7684\u53cc\u5c42rnn\u5355\u5143\u6d4b\u8bd5\u4e3a\u793a\u4f8b":42,"\u672c\u6587\u53ea\u4f7f\u7528\u4e86\u9ed8\u8ba4\u547d\u540d\u7a7a\u95f4":56,"\u672c\u6587\u5c06\u4ecb\u7ecd\u5728kubernetes\u5bb9\u5668\u7ba1\u7406\u5e73\u53f0\u4e0a\u5feb\u901f\u6784\u5efapaddlepaddle\u5bb9\u5668\u96c6\u7fa4":58,"\u672c\u6587\u6863\u4ecb\u7ecd\u5982\u4f55\u5728paddlepaddle\u5e73\u53f0\u4e0a":62,"\u672c\u6587\u6863\u5185\u4e0d\u91cd\u590d\u4ecb\u7ecd":56,"\u672c\u6587\u6863\u5c06\u4ee5linux":39,"\u672c\u6587\u6863\u63cf\u8ff0paddl":27,"\u672c\u6765":42,"\u672c\u6b21\u8bad\u7ec3\u6587\u4ef6\u6240\u5728\u76ee\u5f55":58,"\u672c\u6b21\u8bad\u7ec3\u7684yaml\u6587\u4ef6\u53ef\u4ee5\u5199\u6210":58,"\u672c\u6b21\u8bad\u7ec3\u8981\u6c42\u67093\u4e2apaddlepaddle\u8282\u70b9":58,"\u672c\u6b21\u8bd5\u9a8c":66,"\u672c\u793a\u4f8b\u4e2d\u4f7f\u7528\u7684\u539f\u59cb\u6570\u636e\u5982\u4e0b":42,"\u672c\u793a\u4f8b\u610f\u56fe\u4f7f\u7528\u5355\u5c42rnn\u548c\u53cc\u5c42rnn\u5b9e\u73b0\u4e24\u4e2a\u5b8c\u5168\u7b49\u4ef7\u7684\u5168\u8fde\u63a5rnn":42,"\u672c\u793a\u4f8b\u7684\u9884\u6d4b\u7ed3\u679c":70,"\u672c\u7bc7\u6559\u7a0b\u5728paddlepaddle\u4e2d\u91cd\u73b0\u4e86\u8fd9\u4e00\u826f\u597d\u7684\u8bad\u7ec3\u7ed3\u679c":71,"\u672c\u7bc7\u6559\u7a0b\u5c06\u4f1a\u6307\u5bfc\u4f60\u901a\u8fc7\u8bad\u7ec3\u4e00\u4e2a":71,"\u672c\u8d28\u4e0a\u4e0e\u8bad\u7ec3\u6a21\u578b\u4e00\u6837":71,"\u673a\u5668\u7684\u8bbe\u5907":55,"\u673a\u5668\u7ffb\u8bd1":[30,65],"\u6743\u91cd\u66f4\u65b0\u7684\u68af\u5ea6":53,"\u6761\u4ef6\u4e0b":56,"\u6765":42,"\u6765\u4ee3\u66ff":46,"\u6765\u4fdd\u8bc1\u8bad\u7ec3\u8fc7\u7a0b\u53ef\u4ee5\u4ece\u4e2d\u95f4\u72b6\u6001\u91cd\u65b0\u542f\u52a8":19,"\u6765\u505a\u68af\u5ea6\u68c0\u67e5":47,"\u6765\u505ableu\u8bc4\u4f30":71,"\u6765\u505c\u6b62\u8bad\u7ec3":68,"\u6765\u5206\u6790\u6267\u884c\u6587\u4ef6":50,"\u6765\u5206\u79bb\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6587\u4ef6":68,"\u6765\u5206\u9694\u6bcf\u4e00\u884c":68,"\u6765\u521d\u59cb\u5316\u53c2\u6570":31,"\u6765\u5b89\u88c5":51,"\u6765\u5b8c\u6210\u7f51\u7edc\u7684\u8bad\u7ec3":37,"\u6765\u5b9a\u4e49\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":45,"\u6765\u5bf9\u6bd4\u5206\u6790\u4e24\u8005\u8bed\u4e49\u76f8\u540c\u7684\u539f\u56e0":42,"\u6765\u5e2e\u52a9\u4f60\u7406\u89e3paddlepaddle\u7684\u5185\u90e8\u8fd0\u884c\u673a\u5236":66,"\u6765\u5f00\u542f\u672c\u5730\u7684\u8bad\u7ec3":70,"\u6765\u5f71\u54cdpaddlepaddle\u7684\u7f16\u8bd1\u8fc7\u7a0b":[39,40],"\u6765\u5f97\u5230\u67d0\u4e2a\u7279\u5b9a\u53c2\u6570\u7684\u68af\u5ea6\u77e9\u9635":47,"\u6765\u6307\u5b9a\u7f51\u7edc\u5c42\u7684\u6570\u76ee":64,"\u6765\u63a5\u53d7\u4e0d\u4f7f\u7528\u7684\u51fd\u6570\u4ee5\u4fdd\u8bc1\u517c\u5bb9\u6027":3,"\u6765\u642d\u5efa\u795e\u7ecf\u7f51\u7edc":37,"\u6765\u6ce8\u518c\u8be5\u5c42":47,"\u6765\u6df7\u5408\u4f7f\u7528gpu\u548ccpu\u8ba1\u7b97\u7f51\u7edc\u5c42\u7684\u53c2\u6570":55,"\u6765\u751f\u6210\u5e8f\u5217":71,"\u6765\u7684\u79d2\u6570":67,"\u6765\u786e\u4fdd\u628a":26,"\u6765\u786e\u5b9a\u5bf9\u5e94\u5173\u7cfb":3,"\u6765\u81ea\u5b9a\u4e49\u4f20\u6570\u636e\u7684\u8fc7\u7a0b":2,"\u6765\u83b7\u5f97\u8f93\u51fa\u7684\u68af\u5ea6":47,"\u6765\u8868\u793a":45,"\u6765\u8868\u793a\u53c2\u6570\u4f4d\u7f6e":69,"\u6765\u8868\u793a\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":45,"\u6765\u8868\u793apaddle\u5185\u90e8\u7c7b":26,"\u6765\u8ba1\u7b97\u68af\u5ea6":47,"\u6765\u8bb2\u89e3\u5982\u4f55\u4f7f\u7528\u53cc\u5c42rnn":42,"\u6765\u8bbe\u7f6e":31,"\u6765\u8bbf\u95ee\u7528\u6237\u81ea\u5df1\u7684\u6570\u636e":20,"\u6765\u8bf4\u660epydataprovider2\u7684\u7b80\u5355\u4f7f\u7528\u573a\u666f":3,"\u6765\u8c03\u6574":46,"\u6765\u8fd0\u884c":51,"\u6765\u8fd0\u884c\u6027\u80fd\u5206\u6790\u548c\u8c03\u4f18":50,"\u6765\u8fd0\u884c\u955c\u50cf":34,"\u6765\u8fdb\u884c\u8ba8\u8bba":27,"\u6765\u9884\u6d4b\u8fd9\u4e2a\u4e2d\u95f4\u7684\u8bcd":31,"\u676f\u5b50":42,"\u6784\u5efa":39,"\u6784\u6210\u4e86\u8f93\u51fa\u53cc\u5c42\u5e8f\u5217\u7684\u7b2ci\u4e2a":41,"\u6784\u9020":58,"\u6784\u9020paddl":5,"\u67b6\u6784\u6765\u8bad\u7ec3\u60c5\u611f\u5206\u6790\u6a21\u578b":70,"\u67d0\u4e00\u4e2a\u795e\u7ecf\u5143\u7684\u4e00\u4e2a\u8f93\u5165\u4e3a\u4e0a\u4e00\u4e2a\u65f6\u95f4\u6b65\u7f51\u7edc\u4e2d\u67d0\u4e00\u4e2a\u795e\u7ecf\u5143\u7684\u8f93\u51fa":42,"\u67d0\u4e9b\u53c2\u6570\u53ea\u53ef\u7528\u4e8e\u7279\u5b9a\u7684\u5c42\u4e2d":52,"\u67e5\u770b":[46,66],"\u67e5\u770b\u5b89\u88c5\u540e\u7684paddl":36,"\u67e5\u770b\u5f53\u524d\u72b6\u6001":46,"\u67e5\u770b\u5f53\u524d\u8fdc\u7a0b\u4ed3\u5e93\u7684\u540d\u5b57":46,"\u67e5\u770b\u6587\u4ef6\u5177\u4f53\u88ab\u4fee\u6539\u7684\u5185\u5bb9":46,"\u67e5\u770bjob\u7684\u8be6\u7ec6\u60c5\u51b5":57,"\u6807\u51c6\u5dee\u4e3a":31,"\u6807\u51c6\u8868\u793apaddle\u7248\u672c\u53f7":30,"\u6807\u51c6lstm\u4ee5\u6b63\u5411\u5904\u7406\u8be5\u5e8f\u5217":69,"\u6807\u793a\u56fe\u7247\u662f\u5f69\u8272\u56fe\u6216\u7070\u5ea6\u56fe":63,"\u6807\u793a\u662f\u5426\u4e3a\u5f69\u8272\u56fe\u7247":63,"\u6807\u7b7e0\u8868\u793a\u8d1f\u9762\u7684\u8bc4\u8bba":70,"\u6807\u7b7e1\u8868\u793a\u6b63\u9762\u7684\u8bc4\u8bba":70,"\u6807\u7b7e\u4e0b\u627e\u5230\u6700\u65b0\u7684paddle\u955c\u50cf\u7248\u672c":34,"\u6807\u7b7e\u6587\u4ef6":69,"\u6807\u7b7e\u65b9\u6848\u6765\u81ea":69,"\u6807\u8bc6\u662f\u5426\u4e3a\u8fde\u7eed\u7684batch\u8ba1\u7b97":53,"\u6839\u636e\u4f60\u7684\u4efb\u52a1":55,"\u6839\u636e\u524d\u6587\u7684\u63cf\u8ff0":58,"\u6839\u636e\u5728\u6a21\u578b\u914d\u7f6e\u6587\u4ef6\u4e2d\u4f7f\u7528\u7684\u540d\u4e3a":51,"\u6839\u636e\u6570\u636e\u91cf\u89c4\u6a21":67,"\u6839\u636e\u7528\u6237\u6307\u5b9a\u7684\u5b57\u5178":62,"\u6839\u636e\u7d22\u5f15\u77e9\u9635\u548c\u5b57\u5178\u6253\u5370\u6587\u672c":45,"\u6839\u636e\u7f51\u7edc\u914d\u7f6e\u4e2d\u7684":53,"\u6839\u636e\u8fd9\u4e9b\u53c2\u6570\u7684\u4f7f\u7528\u573a\u5408":52,"\u6839\u636e\u9ed8\u8ba4\u503c\u9012\u589e":53,"\u6839\u636e\u9ed8\u8ba4\u7aef\u53e3\u53f7\u9012\u589e":53,"\u6839\u636ecpu":34,"\u6839\u636ejob\u5bf9\u5e94\u7684pod\u4fe1\u606f":57,"\u683c\u5f0f":53,"\u683c\u5f0f\u5982\u4e0b":66,"\u683c\u5f0f\u8bf4\u660e":62,"\u68af\u5ea6\u4f1a\u5c31\u5730":47,"\u68af\u5ea6\u53c2\u6570\u7684\u5206\u5757\u6570\u76ee":53,"\u68af\u5ea6\u5c31\u53ef\u4ee5\u901a\u8fc7\u8fd9\u4e2a\u65b9\u7a0b\u8ba1\u7b97\u5f97\u5230":47,"\u68af\u5ea6\u670d\u52a1\u5668\u7684\u6570\u91cf":53,"\u68af\u5ea6\u68c0\u67e5\u5355\u5143\u6d4b\u8bd5\u901a\u8fc7\u6709\u9650\u5dee\u5206\u6cd5\u6765\u9a8c\u8bc1\u4e00\u4e2a\u5c42\u7684\u68af\u5ea6":47,"\u68af\u5ea6\u68c0\u67e5\u7684\u8f93\u5165\u6570\u636e\u7684\u6279\u6b21\u5927\u5c0f":47,"\u68c0\u67e5\u70b9\u4fdd\u5b58\u7a0b\u5e8f\u6d41\u7a0b":19,"\u68d2":66,"\u697c\u5c42":42,"\u6a21\u5757":63,"\u6a21\u5757\u4e2d\u7684":3,"\u6a21\u578b":69,"\u6a21\u578b\u4e00\u76f4\u4e0d\u6536\u655b":31,"\u6a21\u578b\u4fdd\u5b58\u5728\u76ee\u5f55":70,"\u6a21\u578b\u5171\u5305\u542b1":62,"\u6a21\u578b\u5217\u8868\u6587\u4ef6":69,"\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9\u901a\u8fc7\u5b9a\u671f\u5411\u78c1\u76d8\u4e0a\u4fdd\u5b58\u4e00\u4efd\u5b58\u50a8\u5728paramet":19,"\u6a21\u578b\u53ca\u53c2\u6570\u4f1a\u88ab\u4fdd\u5b58\u5728\u8def\u5f84":63,"\u6a21\u578b\u5b58\u50a8\u8def\u5f84":66,"\u6a21\u578b\u5c06\u4fdd\u5b58\u5728\u76ee\u5f55":69,"\u6a21\u578b\u5c31\u8bad\u7ec3\u6210\u529f\u4e86":71,"\u6a21\u578b\u6570\u636e\u68c0\u67e5\u70b9\u7684\u5b9e\u73b0":19,"\u6a21\u578b\u6587\u4ef6\u5c06\u88ab\u5199\u5165\u8282\u70b9":51,"\u6a21\u578b\u6765\u5c06\u6cd5\u8bed\u7ffb\u8bd1\u6210\u82f1\u8bed":71,"\u6a21\u578b\u6765\u6307\u5bfc\u4f60\u5b8c\u6210\u8fd9\u4e9b\u6b65\u9aa4":45,"\u6a21\u578b\u6f14\u793a\u5982\u4f55\u914d\u7f6e\u590d\u6742\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u6a21\u578b":45,"\u6a21\u578b\u7684\u4ee3\u7801\u53ef\u4ee5\u5728":45,"\u6a21\u578b\u7684\u7ed3\u6784\u548c\u8bad\u7ec3\u8fc7\u7a0b":62,"\u6a21\u578b\u7684\u7f16\u7801\u5668\u90e8\u5206\u5982\u4e0b\u6240\u793a":45,"\u6a21\u578b\u88ab\u4fdd\u5b58\u5728":68,"\u6a21\u578b\u8bad\u7ec3\u4f1a\u770b\u5230\u7c7b\u4f3c\u4e0a\u9762\u8fd9\u6837\u7684\u65e5\u5fd7\u4fe1\u606f":66,"\u6a21\u578b\u8bad\u7ec3\u548c\u6700\u540e\u7684\u7ed3\u679c\u9a8c\u8bc1":32,"\u6a21\u578b\u8bad\u7ec3\u7b49\u4efb\u52a1":37,"\u6a21\u578b\u8def\u5f84":[64,69],"\u6a21\u578b\u8f93\u51fa\u8def\u5f84":69,"\u6a21\u578b\u914d\u7f6e":1,"\u6a21\u578b\u914d\u7f6e\u89e3\u6790":26,"\u6a21\u578b\u91c7\u7528":62,"\u6a21\u578b\u9884\u6d4b":5,"\u6b21":42,"\u6b22\u8fce\u901a\u8fc7":46,"\u6b63\u6837\u672c":66,"\u6b63\u786e\u7684\u89e3\u51b3\u65b9\u6cd5\u662f":31,"\u6b63\u8d1f\u5bf9\u9a8c\u8bc1":52,"\u6b63\u9762\u7684\u8bc4\u8bba\u7684\u5f97\u5927\u4e8e\u7b49\u4e8e7":70,"\u6b63\u9762\u8bc4\u4ef7\u6837\u672c":70,"\u6b64\u547d\u4ee4\u5c06\u5728your":39,"\u6b64\u5904":62,"\u6b64\u5904\u90fd\u4e3a2":42,"\u6b64\u5916":[39,46],"\u6b64\u6559\u7a0b\u5c06\u5411\u60a8\u5206\u6b65\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u5185\u7f6e\u7684\u5b9a\u65f6\u5de5\u5177":50,"\u6b64\u6570\u636e\u96c6\u5305\u542b\u7535\u5f71\u8bc4\u8bba\u53ca\u5176\u76f8\u5173\u8054\u7684\u7c7b\u522b\u6807\u7b7e":70,"\u6b64\u65f6\u60a8\u53ef\u4ee5\u8fd0\u884c\u8fd9\u4e2a\u547d\u4ee4\u5728\u5f00\u53d1\u673a\u4e0a\u8fdb\u884c\u6d4b\u8bd5\u8fd0\u884c":34,"\u6b64\u65f6master\u5c06\u8d1f\u8d23\u542f\u52a8\u4e00\u4e2a\u65b0\u7684train":19,"\u6bb5\u843d\u53ef\u4ee5\u770b\u4f5c\u662f\u4e00\u4e2a\u5d4c\u5957\u7684\u53cc\u5c42\u7684\u5e8f\u5217":44,"\u6bcf100\u4e2abatch\u6253\u5370\u4e00\u6b21\u7edf\u8ba1\u4fe1\u606f":70,"\u6bcf100\u4e2abatch\u663e\u793a\u53c2\u6570\u7edf\u8ba1":69,"\u6bcf20\u4e2abatch\u6253\u5370\u4e00\u6b21\u65e5\u5fd7":70,"\u6bcf20\u4e2abatch\u8f93\u51fa\u65e5\u5fd7":69,"\u6bcf\u4e00\u4e2a":30,"\u6bcf\u4e00\u4e2a\u4efb\u52a1\u6d41\u7a0b\u90fd\u53ef\u4ee5\u88ab\u5212\u5206\u4e3a\u5982\u4e0b\u4e94\u4e2a\u6b65\u9aa4":66,"\u6bcf\u4e00\u4e2a\u6587\u4ef6\u662f\u6570\u636e\u96c6\u7684\u4e00\u4e2ashard":20,"\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65":42,"\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u4e4b\u95f4\u7684\u795e\u7ecf\u7f51\u7edc\u5177\u6709\u4e00\u5b9a\u7684\u76f8\u5173\u6027":42,"\u6bcf\u4e00\u4e2a\u6d4b\u8bd5\u5468\u671f\u6d4b\u8bd5\u4e00\u6b21\u6240\u6709\u6570\u636e":68,"\u6bcf\u4e00\u4e2a\u8282\u70b9\u90fd\u6709\u76f8\u540c\u7684\u65e5\u5fd7\u7ed3\u6784":51,"\u6bcf\u4e00\u4e2akey\u7531":68,"\u6bcf\u4e00\u7ec4\u5185\u7684\u6240\u6709\u53e5\u5b50\u548clabel":42,"\u6bcf\u4e00\u884c\u8868\u793a\u4e00\u4e2a\u5b9e\u4f8b":70,"\u6bcf\u4e2a":[45,51,69],"\u6bcf\u4e2a\u503c\u7684\u7c7b\u578b\u53ef\u4ee5\u662f\u6574\u5f62":20,"\u6bcf\u4e2a\u5143\u7d20\u662f\u4e00\u4e2a0\u5c42\u5e8f\u5217":41,"\u6bcf\u4e2a\u5143\u7d20\u662f\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":41,"\u6bcf\u4e2a\u5355\u5c42rnn":44,"\u6bcf\u4e2a\u5355\u8bcd\u7684\u9884\u6d4b\u9519\u8bef\u7387":71,"\u6bcf\u4e2a\u53e5\u5b50\u53c8\u662f\u5355\u8bcd\u7684\u6570\u7ec4":42,"\u6bcf\u4e2a\u53e5\u5b50\u90fd\u4ee5\u5f00\u59cb\u6807\u8bb0\u5f00\u5934":45,"\u6bcf\u4e2a\u53e5\u5b50\u90fd\u4ee5\u7ed3\u675f\u6807\u8bb0\u7ed3\u5c3e":45,"\u6bcf\u4e2a\u5b50\u5e8f\u5217\u957f\u5ea6\u53ef\u4ee5\u4e0d\u4e00\u81f4":42,"\u6bcf\u4e2a\u5b50\u6587\u4ef6\u5939\u4e0b\u5b58\u50a8\u76f8\u5e94\u5206\u7c7b\u7684\u56fe\u7247":63,"\u6bcf\u4e2a\u5b57\u5178\u5305\u542b\u603b\u517130000\u4e2a\u5355\u8bcd":71,"\u6bcf\u4e2a\u5b57\u5178\u90fd\u6709dictsize\u4e2a\u5355\u8bcd":71,"\u6bcf\u4e2a\u5c42\u5728\u5176":47,"\u6bcf\u4e2a\u5c42\u90fd\u6709\u4e00\u4e2a\u6216\u591a\u4e2ainput":66,"\u6bcf\u4e2a\u6279\u6b21\u6570\u636e":53,"\u6bcf\u4e2a\u6574\u6570\u5217\u8868\u88ab\u89c6\u4e3a\u4e00\u4e2a\u6574\u6570\u5e8f\u5217":45,"\u6bcf\u4e2a\u6587\u4ef6\u5939\u90fd\u5305\u542b\u6cd5\u8bed\u5230\u82f1\u8bed\u7684\u5e73\u884c\u8bed\u6599\u5e93":71,"\u6bcf\u4e2a\u6587\u4ef6\u662f\u4e00\u4e2a\u7535\u5f71\u8bc4\u8bba":70,"\u6bcf\u4e2a\u6587\u672c\u6587\u4ef6\u5305\u542b\u4e00\u4e2a\u6216\u8005\u591a\u4e2a\u5b9e\u4f8b":70,"\u6bcf\u4e2a\u65f6\u95f4\u6b65\u4e4b\u5185\u7684\u8fd0\u7b97\u662f\u72ec\u7acb\u7684":44,"\u6bcf\u4e2a\u65f6\u95f4\u6b65\u90fd\u7528\u4e86\u4e0a\u4e00\u4e2a\u65f6\u95f4\u6b65\u7684\u8f93\u51fa\u7ed3\u679c":42,"\u6bcf\u4e2a\u6743\u91cd\u5bf9\u5e94\u4e00\u4e2a\u8f93\u5165":47,"\u6bcf\u4e2a\u6837\u672c\u7531\u4e24\u90e8\u5206\u7ec4\u6210":42,"\u6bcf\u4e2a\u6837\u672c\u95f4\u7528\u7a7a\u884c\u5206\u5f00":42,"\u6bcf\u4e2a\u6d4b\u8bd5\u5468\u671f\u6d4b\u8bd5":68,"\u6bcf\u4e2a\u7279\u5f81\u7684meta\u914d\u7f6e":68,"\u6bcf\u4e2a\u72b6\u6001":44,"\u6bcf\u4e2a\u7c7b\u522b\u4e2d\u968f\u673a\u62bd\u53d6\u4e8610\u5f20\u56fe\u7247":63,"\u6bcf\u4e2a\u7c7b\u5305\u542b6000\u5f20":63,"\u6bcf\u4e2a\u7ebf\u7a0b":53,"\u6bcf\u4e2a\u7ebf\u7a0b\u5206\u914d\u5230128\u4e2a\u6837\u672c\u7528\u4e8e\u8bad\u7ec3":53,"\u6bcf\u4e2a\u8282\u70b9\u6709\u4e24\u4e2a6\u6838cpu":71,"\u6bcf\u4e2a\u8bad\u7ec3\u8282\u70b9\u5fc5\u987b\u6307\u5b9a\u4e00\u4e2a\u552f\u4e00\u7684id\u53f7":53,"\u6bcf\u4e2a\u8bb0\u5fc6\u5355\u5143\u5305\u542b\u56db\u4e2a\u4e3b\u8981\u7684\u5143\u7d20":70,"\u6bcf\u4e2a\u8bc4\u8bba\u7684\u7f51\u5740":70,"\u6bcf\u4e2a\u8f93\u5165\u90fd\u662f\u4e00\u4e2a":47,"\u6bcf\u4e2a\u8f93\u51fa\u8282\u70b9\u90fd\u8fde\u63a5\u5230\u6240\u6709\u7684\u8f93\u5165\u8282\u70b9\u4e0a":47,"\u6bcf\u4e2a\u91cc\u9762\u90fd\u5305\u542b202mb\u7684\u5168\u90e8\u7684\u6a21\u578b\u53c2\u6570":71,"\u6bcf\u4e2acommit\u53ea\u505a\u4e86\u5c11\u91cf\u7684\u4fee\u6539":46,"\u6bcf\u4e2adata":20,"\u6bcf\u4e2aparamet":19,"\u6bcf\u4e2apass\u7684\u7b2c0\u4e2abatch\u5230\u5f53\u524dbatch\u6240\u6709\u6837\u672c\u7684\u5e73\u5747\u5206\u7c7b\u9519\u8bef\u7387":66,"\u6bcf\u4e2apass\u7684\u7b2c0\u4e2abatch\u5230\u5f53\u524dbatch\u6240\u6709\u6837\u672c\u7684\u5e73\u5747cost":66,"\u6bcf\u4e2apod\u5305\u542b\u4e00\u4e2apaddlepaddle\u5bb9\u5668":58,"\u6bcf\u4e2ashard\u5206\u522b\u5b58\u50a8\u5728\u5176\u4e2d\u4e00\u53f0paramet":19,"\u6bcf\u4f4d\u7528\u6237\u81f3\u5c11\u670920\u6761\u8bc4\u5206":67,"\u6bcf\u5c42\u4e0a\u53ea\u80fd\u4fdd\u5b58\u56fa\u5b9a\u6570\u76ee\u4e2a\u6700\u597d\u7684\u72b6\u6001":53,"\u6bcf\u5c42\u4f7f\u7528\u7684gpu\u53f7\u4f9d\u8d56\u4e8e\u53c2\u6570train":55,"\u6bcf\u5f53\u6a21\u578b\u5728\u7ffb\u8bd1\u8fc7\u7a0b\u4e2d\u751f\u6210\u4e86\u4e00\u4e2a\u5355\u8bcd":71,"\u6bcf\u6279\u6b21":53,"\u6bcf\u6b21":34,"\u6bcf\u6b21\u63d0\u4ea4\u4ee3\u7801":46,"\u6bcf\u6b21\u63d0\u4ea4\u65f6":46,"\u6bcf\u6b21\u6d4b\u8bd5\u90fd\u6d4b\u8bd5\u6240\u6709\u6570\u636e":70,"\u6bcf\u6b21\u751f\u62101\u4e2a\u5e8f\u5217":71,"\u6bcf\u6b21\u8bfb\u53d6\u4e00\u6761\u6570\u636e\u540e":66,"\u6bcf\u6b21\u8f93\u51fa\u4e00\u4e2adata":20,"\u6bcf\u6b21\u90fd\u4f1a\u4ecepython\u7aef\u8bfb\u53d6\u6570\u636e":3,"\u6bcf\u884c\u5b58\u50a8\u4e00\u4e2a\u8bcd":62,"\u6bcf\u884c\u5b58\u50a8\u7684\u662f\u4e00\u4e2a\u6837\u672c\u7684\u7279\u5f81":64,"\u6bcf\u884c\u6253\u537032\u4e2a\u53c2\u6570\u4ee5":62,"\u6bcf\u884c\u8868\u793a\u4e00\u4e2a\u6279\u6b21\u4e2d\u7684\u5355\u4e2a\u8f93\u5165":47,"\u6bcf\u884c\u90fd\u662f\u4e00\u4e2a\u6cd5\u8bed\u6216\u8005\u82f1\u8bed\u7684\u53e5\u5b50":71,"\u6bcf\u8f6e\u4f1a\u5c06\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u8bad\u7ec3\u6837\u672c\u4f7f\u7528\u4e00\u6b21":53,"\u6bcf\u8f6e\u7ed3\u675f\u65f6\u5bf9\u6240\u6709\u6d4b\u8bd5\u6570\u636e\u8fdb\u884c\u6d4b\u8bd5":53,"\u6bcf\u8f6e\u90fd\u4f1a\u4fdd\u5b58\u9884\u6d4b\u7ed3\u679c":53,"\u6bcf\u8fd0\u884c\u591a\u5c11\u4e2a\u6279\u6b21\u6267\u884c\u4e00\u6b21\u7a00\u758f\u53c2\u6570\u5206\u5e03\u7684\u68c0\u67e5":53,"\u6bcf\u969410\u5206\u949f":19,"\u6bcf\u9694\u591a\u5c11batch\u6253\u5370\u4e00\u6b21\u65e5\u5fd7":66,"\u6bcfdot":53,"\u6bcflog":53,"\u6bcfsave":53,"\u6bcftest":53,"\u6bd4\u5982":[20,31,34,39,46,66],"\u6bd4\u5982\u4e00\u53e5\u8bdd\u4e2d\u7684\u6bcf\u4e00\u4e2a\u5355\u8bcd":42,"\u6bd4\u5982\u5982\u679c\u8981build\u4e00\u4e2a\u4e0d\u4f9d\u8d56gpu":46,"\u6bd4\u5982\u5e0c\u671b\u6700\u5c0f\u5316\u751f\u6210\u7684\u5e93\u7684\u5927\u5c0f":[39,40],"\u6bd4\u5982\u6bcf\u969410\u5206\u949f\u6700\u65b0\u7684\u5feb\u7167":19,"\u6bd4\u5982\u6d41\u5f0f\u6570\u636e\u5904\u7406":20,"\u6bd4\u5982\u8bbe\u7f6e\u4e00\u4e2a\u5168\u8fde\u63a5\u5c42\u7684\u53c2\u6570\u521d\u59cb\u5316\u65b9\u5f0f\u548cbias\u521d\u59cb\u5316\u65b9\u5f0f":31,"\u6bd4\u5982\u901a\u8fc78080\u7aef\u53e3":56,"\u6bd4\u5982ide\u914d\u7f6e\u91cc":46,"\u6bd4\u5982imagenet\u8fd9\u4e2a\u6570\u636e\u96c6\u53ef\u80fd\u88ab\u5206\u62101000\u4e2ashard":20,"\u6c34\u6e29":42,"\u6c49\u5ead":42,"\u6c60\u5316\u5c42":63,"\u6ca1":42,"\u6ca1\u6709\u4f5c\u7528":3,"\u6ca1\u6709\u4f7f\u7528avx\u6307\u4ee4\u96c6":36,"\u6ca1\u6709\u5b9e\u9645\u610f\u4e49":62,"\u6ca1\u6709\u6d4b\u8bd5\u6570\u636e":3,"\u6ca1\u6709\u8bbe\u7f6e":[39,40],"\u6ca1\u6709\u8fdb\u884c\u6b63\u786e\u6027\u7684\u68c0\u67e5":67,"\u6ca1\u6709\u8fdb\u884c\u7ed3\u6784\u7684\u5fae\u8c03":68,"\u6cd5\u8bed":71,"\u6ce8":19,"\u6ce8\u610f":[3,33,34,37,39,40,45,47,48,58,63],"\u6ce8\u610f\u4e0a\u8ff0\u547d\u4ee4\u4e2d":58,"\u6ce8\u610f\u5230\u6211\u4eec\u5df2\u7ecf\u5047\u8bbe\u673a\u5668\u4e0a\u67094\u4e2agpu":55,"\u6ce8\u610f\u5e94\u8be5\u786e\u4fdd\u9ed8\u8ba4\u6a21\u578b\u8def\u5f84":70,"\u6ce8\u610f\u9884\u6d4b\u6570\u636e\u901a\u5e38\u4e0d\u5305\u542blabel":5,"\u6ce8\u610fnode":58,"\u6ce8\u91ca\u6389":70,"\u6cf3\u6c60":42,"\u6d41":42,"\u6d41\u7a0b\u6765\u63d0\u4ea4\u4ee3\u7801":46,"\u6d44":42,"\u6d4b\u8bd5":46,"\u6d4b\u8bd5\u6570\u636e":51,"\u6d4b\u8bd5\u6570\u636e\u4e5f\u5305\u542b":51,"\u6d4b\u8bd5\u6570\u636e\u548c\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":51,"\u6d4b\u8bd5\u6570\u636e\u548c\u751f\u6210\u6570\u636e":71,"\u6d4b\u8bd5\u6570\u636e\u653e\u7f6e\u5728\u5de5\u4f5c\u7a7a\u95f4\u4e2d\u4e0d\u540c\u76ee\u5f55\u7684\u8981\u6c42":51,"\u6d4b\u8bd5\u6570\u636e\u7684\u6240\u6709\u76f8\u5bf9\u6216\u7edd\u5bf9\u6587\u4ef6\u8def\u5f84":51,"\u6d4b\u8bd5\u6570\u6910\u96c6":70,"\u6d4b\u8bd5\u65f6\u6307\u5b9a\u7684\u5b58\u50a8\u6a21\u578b\u5217\u8868\u7684\u6587\u4ef6":53,"\u6d4b\u8bd5\u65f6\u9ed8\u8ba4\u4e0dshuffl":3,"\u6d4b\u8bd5\u662f":46,"\u6d4b\u8bd5\u6837\u672c":51,"\u6d4b\u8bd5\u6a21\u578b\u662f\u6307\u4f7f\u7528\u8bad\u7ec3\u51fa\u7684\u6a21\u578b\u8bc4\u4f30\u5df2\u6807\u8bb0\u7684\u9a8c\u8bc1\u96c6":70,"\u6d4b\u8bd5\u7684\u6a21\u578b\u5305\u62ec\u4ece\u7b2cm\u8f6e\u5230\u7b2cn":55,"\u6d4b\u8bd5\u811a\u672c\u662f":69,"\u6d4b\u8bd5\u96c6\u548c\u8bad\u7ec3\u96c6\u76ee\u5f55\u5305\u542b\u4e0b\u9762\u7684\u6587\u4ef6":70,"\u6d4b\u8bd5docker\u955c\u50cf":30,"\u6d4b\u8bd5model_list":52,"\u6d4b\u8bd5save_dir":52,"\u6d4f\u89c8\u4ee3\u7801":34,"\u6d6a\u6f2b\u7247":67,"\u6d6e\u70b9\u578b\u6570\u636e":20,"\u6d6e\u70b9\u5f02\u5e38\u901a\u5e38\u7684\u539f\u56e0\u662f\u6d6e\u70b9\u6570\u6ea2\u51fa":31,"\u6d6e\u70b9\u6570\u5360\u7528\u7684\u5b57\u8282\u6570":62,"\u6d6e\u70b9\u7a00\u758f\u6570\u636e":47,"\u6dd8\u5b9d\u7b49":70,"\u6df1\u5ea6\u53cc\u5411lstm\u5c42\u63d0\u53d6softmax\u5c42\u7684\u7279\u5f81":69,"\u6df1\u5ea6\u5b66\u4e60\u5165\u95e8\u8bfe\u7a0b":38,"\u6df7\u5408":69,"\u6df7\u5408\u5f53\u524d\u8bcd\u5411\u91cf\u548cattention\u52a0\u6743\u7f16\u7801\u5411\u91cf":45,"\u6dfb\u52a0\u542f\u52a8\u811a\u672c":58,"\u6e05\u7406\u6389\u8001\u65e7\u7684paddlepaddle\u5b89\u88c5\u5305":31,"\u6e29\u99a8":42,"\u6e90":71,"\u6e90\u4ee3\u7801":[34,66],"\u6e90\u4ee3\u7801\u683c\u5f0f":46,"\u6e90\u5b57\u5178":71,"\u6e90\u5e8f\u5217":45,"\u6e90\u7801":34,"\u6e90\u7801\u4e0edemo":57,"\u6e90\u7801\u7f16\u8bd1\u6587\u6863":48,"\u6e90\u7801\u7f16\u8bd1paddlepaddl":40,"\u6e90\u8bed\u8a00\u5230\u76ee\u6807\u8bed\u8a00\u7684\u5e73\u884c\u8bed\u6599\u5e93\u6587\u4ef6":71,"\u6e90\u8bed\u8a00\u548c\u76ee\u6807\u8bed\u8a00\u5171\u4eab\u76f8\u540c\u7684\u7f16\u7801\u5b57\u5178":62,"\u6e90\u8bed\u8a00\u548c\u76ee\u6807\u8bed\u8a00\u90fd\u662f\u76f8\u540c\u7684\u8bed\u8a00":62,"\u6e90\u8bed\u8a00\u77ed\u8bed\u548c\u76ee\u6807\u8bed\u8a00\u77ed\u8bed\u7684\u5b57\u5178\u5c06\u88ab\u5408\u5e76":62,"\u6ee4\u6ce2\u5668\u6838\u5728\u5782\u76f4\u65b9\u5411\u4e0a\u7684\u5c3a\u5bf8":64,"\u6ee4\u6ce2\u5668\u6838\u5728\u6c34\u5e73\u65b9\u5411\u4e0a\u7684\u5c3a\u5bf8":64,"\u6f14\u793a\u4e2d\u4f7f\u7528\u7684":69,"\u6f14\u793a\u91c7\u7528":69,"\u6fc0\u6d3b":47,"\u6fc0\u6d3b\u51fd\u6570\u7c7b\u578b":66,"\u6fc0\u6d3b\u65b9\u7a0b":47,"\u6fc0\u6d3b\u7684\u7c7b\u578b":47,"\u7075\u6d3b\u6027\u548c\u53ef\u6269\u5c55\u6027":0,"\u70ed\u60c5":42,"\u7136\u540e":[50,51,62,68],"\u7136\u540e\u4ea4\u7ed9\u7528\u6237\u81ea\u5b9a\u4e49\u7684\u51fd\u6570":32,"\u7136\u540e\u4ea4\u7ed9step\u51fd\u6570":44,"\u7136\u540e\u4f60\u53ea\u9700\u8981\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4":71,"\u7136\u540e\u4f7f\u7528\u4e0b\u9762\u7684\u811a\u672c":70,"\u7136\u540e\u518d\u505a\u4e00\u6b21\u6587\u672c\u5377\u79ef\u7f51\u7edc\u64cd\u4f5c":68,"\u7136\u540e\u5229\u7528\u89c2\u6d4b\u6570\u636e\u8c03\u6574":32,"\u7136\u540e\u5355\u51fb":46,"\u7136\u540e\u53ef\u4ee5\u4f7f\u7528\u547d\u4ee4\u884c\u5de5\u5177\u521b\u5efajob":58,"\u7136\u540e\u53ef\u4ee5\u8f6c\u6362\u4e3a\u56fe\u7247":64,"\u7136\u540e\u5728":71,"\u7136\u540e\u5728\u4e0b\u4e00\u4e2a\u65f6\u95f4\u6b65\u8f93\u5165\u7ed9\u53e6\u4e00\u4e2a\u795e\u7ecf\u5143":42,"\u7136\u540e\u5728\u6d4f\u89c8\u5668\u4e2d\u8f93\u5165\u4ee5\u4e0b\u7f51\u5740":34,"\u7136\u540e\u5728\u89e3\u7801\u88ab\u7ffb\u8bd1\u7684\u8bed\u53e5\u65f6":71,"\u7136\u540e\u5728dataprovider\u91cc\u9762\u6839\u636e\u8be5\u5730\u5740\u52a0\u8f7d\u5b57\u5178":31,"\u7136\u540e\u5728etcd\u7684":19,"\u7136\u540e\u5b9a\u4e49":45,"\u7136\u540e\u5c06\u6784\u5efa\u6210\u529f\u7684\u955c\u50cf\u4e0a\u4f20\u5230\u955c\u50cf\u4ed3\u5e93":58,"\u7136\u540e\u5c31\u53ef\u4ee5\u5e76\u53d1\u5199\u5165\u591a\u4e2achunk":24,"\u7136\u540e\u5f97\u5230\u5e73\u5747\u91c7\u6837\u7684\u7ed3\u679c":68,"\u7136\u540e\u6211\u4eec\u5229\u7528\u591a\u8f93\u5165\u7684":68,"\u7136\u540e\u6211\u4eec\u53d1\u73b0pass":71,"\u7136\u540e\u6211\u4eec\u5806\u53e0\u4e00\u5bf9\u5bf9\u7684":69,"\u7136\u540e\u6211\u4eec\u6c42\u8fd9\u4e24\u4e2a\u7279\u5f81\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6":68,"\u7136\u540e\u6240\u6709\u7528":46,"\u7136\u540e\u624d\u80fd\u4f7f\u7528pfsclient":24,"\u7136\u540e\u6267\u884c\u4e0b\u9762\u7684\u547d\u4ee4":64,"\u7136\u540e\u628a\u8fd9\u4e2a\u5305\u542b\u4e86\u8bad\u7ec3\u6570\u636e\u7684container\u4fdd\u5b58\u4e3a\u4e00\u4e2a\u65b0\u7684\u955c\u50cf":57,"\u7136\u540e\u63d0\u4ea4\u65b0\u6dfb\u52a0\u7684":46,"\u7136\u540e\u63d0\u53d6\u9690\u85cflstm\u5c42\u7684\u6240\u6709\u65f6\u95f4\u6b65\u957f\u7684\u6700\u5927\u8bcd\u5411\u91cf\u4f5c\u4e3a\u6574\u4e2a\u5e8f\u5217\u7684\u8868\u793a":70,"\u7136\u540e\u662f\u5bf9\u5e94\u7684\u82f1\u8bed\u5e8f\u5217":71,"\u7136\u540e\u6dfb\u52a0\u4e00\u4e2a\u5168\u8fde\u63a5\u5c42":68,"\u7136\u540e\u70b9\u51fb":46,"\u7136\u540e\u7528\u5bc6\u7801":34,"\u7136\u540e\u7528pickle\u547d\u4ee4\u5c06\u7279\u5f81":68,"\u7136\u540e\u7533\u660e\u4e00\u4e2a\u5b58\u50a8\u5377":58,"\u7136\u540e\u89c2\u5bdf\u5230\u8f93\u51fa\u7684\u53d8\u5316\u4e3a":47,"\u7136\u540e\u89e3\u538b":71,"\u7136\u540e\u89e3\u7801\u5668\u901a\u8fc7\u8fd9\u4e2a\u5411\u91cf\u751f\u6210\u4e00\u4e2a\u76ee\u6807\u8bed\u53e5":71,"\u7136\u540e\u8f93\u51fa\u9884\u6d4b\u5206\u6570":68,"\u7136\u540e\u8fd4\u56de\u7ed9paddlepaddle\u8fdb\u7a0b":3,"\u7136\u540e\u8fdb\u884c\u968f\u673a\u6253\u4e71":68,"\u7136\u540e\u901a\u8fc7\u51fd\u6570":58,"\u7136\u540e\u901a\u8fc7\u81ea\u8eab\u7684ip\u5730\u5740\u5728":58,"\u7136\u540e\u91cd\u65b0cmake\u5373\u53ef":31,"\u7136\u800c":[45,53],"\u7136\u800c\u6709\u4e9b\u8bc4\u8bba\u4e0a\u4e0b\u6587\u975e\u5e38\u957f":70,"\u7248\u672c":36,"\u7248\u672c\u5206\u652f":30,"\u7248\u672c\u53f7":30,"\u7248\u672c\u53f7rc":30,"\u7248\u672c\u5728":46,"\u7248\u672cfork\u51fa\u81ea\u5df1\u7684\u529f\u80fd\u5206\u652f":30,"\u7279\u522b\u611f\u8c22paddlepaddle\u7684":0,"\u7279\u522b\u662f\u5728lstm\u7b49rnn\u4e2d":31,"\u7279\u522b\u662f\u5f53\u76f8\u540c\u7684\u8bcd\u5728\u53e5\u5b50\u4e2d\u51fa\u73b0\u591a\u4e8e\u4e00\u6b21\u65f6":69,"\u7279\u5f81":68,"\u7279\u5f81\u56fe\u5747\u503c":64,"\u7279\u5f81\u56fe\u65b9\u5dee":64,"\u7279\u5f81\u5c06\u4f1a\u5b58\u5230":64,"\u7279\u5f81\u6587\u4ef6":69,"\u7279\u5f81\u7684\u7c7b\u578b\u548c\u7ef4\u5ea6":68,"\u72af\u7f6a\u7247":67,"\u72ec\u7acb\u5de5\u5177\u94fe":39,"\u72ec\u7acb\u5de5\u5177\u94fe\u6240\u5728\u7684\u7edd\u5bf9\u8def\u5f84":39,"\u73af\u5883\u53d8\u91cf":58,"\u73af\u5883\u53d8\u91cf\u6765\u6307\u5b9a\u7279\u5b9a\u7684gpu":31,"\u73b0\u5728\u6211\u4eec\u53ef\u4ee5\u5f00\u59cbpaddle\u8bad\u7ec3\u4e86":68,"\u73b0\u9636\u6bb5paddle\u6709\u4e00\u4e2a\u95ee\u9898\u662f":26,"\u751a\u81f3\u4e0d\u540c\u7ade\u4e89\u5bf9\u624b\u4ea7\u54c1\u7684\u504f\u597d":70,"\u751a\u81f3\u80fd\u89e3\u91ca\u4e3a\u4ec0\u4e48\u67d0\u4e2a\u64cd\u4f5c\u82b1\u4e86\u5f88\u957f\u65f6\u95f4":50,"\u751f\u4ea7\u73af\u5883\u4e2d\u7684\u8bad\u7ec3\u6570\u636e\u96c6\u901a\u5e38\u4f53\u79ef\u5f88\u5927":20,"\u751f\u4ea7\u73af\u5883\u7684\u65e5\u5fd7\u6570\u636e\u4f1a\u901a\u8fc7\u5b9e\u65f6\u6d41\u7684\u65b9\u5f0f":20,"\u751f\u4ea7\u955c\u50cf":[34,46],"\u751f\u4ea7\u955c\u50cf\u7684\u751f\u6210\u5206\u4e3a\u4e24\u6b65":34,"\u751f\u6210":58,"\u751f\u6210\u5404\u79cd\u8bed\u8a00\u7684\u7ed1\u5b9a\u4ee3\u7801":26,"\u751f\u6210\u540e\u7684\u6587\u6863\u5206\u522b\u5b58\u50a8\u5728\u7f16\u8bd1\u76ee\u5f55\u7684":48,"\u751f\u6210\u5e8f\u5217\u7684\u6700\u5927\u957f\u5ea6":45,"\u751f\u6210\u5f53\u524d\u5c42\u7684\u6240\u6709\u540e\u7ee7\u72b6\u6001":71,"\u751f\u6210\u6570\u636e\u7684\u76ee\u5f55":71,"\u751f\u6210\u6587\u6863":26,"\u751f\u6210\u751f\u4ea7\u955c\u50cf\u53ea\u9700\u8981\u8fd0\u884c":34,"\u751f\u6210\u7684":20,"\u751f\u6210\u7684\u6570\u636e\u5c06\u4f1a\u5b58\u50a8\u5728\u8fd9\u4e2avolume\u4e0b":58,"\u751f\u6210\u7684\u6570\u636e\u7f13\u5b58\u5728\u5185\u5b58\u91cc":31,"\u751f\u6210\u7684\u7ed3\u679c\u6587\u4ef6":71,"\u751f\u6210\u7684html\u7248\u672c\u7684c":34,"\u751f\u6210\u7684meta\u914d\u7f6e\u6587\u4ef6\u5982\u4e0b\u6240\u793a":68,"\u751f\u6210\u7ed3\u679c\u6587\u4ef6\u7684\u8def\u5f84":45,"\u751f\u6210\u7ed9\u5b9a":20,"\u751f\u6210\u7f51\u7edc\u5c42\u914d\u7f6e":47,"\u751f\u6210\u81ea\u5df1\u76ee\u5f55\u4e0b\u7684\u4ed3\u5e93":46,"\u751f\u6210\u8bad\u7ec3\u9700\u8981\u7684\u6837\u672c":68,"\u751f\u6210\u8fd0\u884c\u7a0b\u5e8f":34,"\u751f\u6210api\u6587\u6863":26,"\u751f\u6210docker\u955c\u50cf\u7684\u65b9\u5f0f\u6709\u4e24\u4e2a":34,"\u751f\u6210pfsclient\u548cpfsserver\u7684\u6846\u67b6\u90e8\u5206":24,"\u7528":[24,67,68,69],"\u75280\u548c1\u8868\u793a":3,"\u7528\u4e86\u4e24\u4e2a\u6708\u4e4b\u540e\u8fd9\u4e2a\u663e\u793a\u5668\u5c4f\u5e55\u788e\u4e86":66,"\u7528\u4e8e":51,"\u7528\u4e8e\u5207\u5206\u5355\u5355\u8bcd\u548c\u6807\u70b9\u7b26\u53f7":70,"\u7528\u4e8e\u521d\u59cb\u5316\u53c2\u6570\u548c\u8bbe\u7f6e":47,"\u7528\u4e8e\u5c06\u4e0b\u4e00\u884c\u7684\u6570\u636e\u8f93\u5165\u51fd\u6570\u6807\u8bb0\u6210\u4e00\u4e2apydataprovider2":3,"\u7528\u4e8e\u5c06\u53c2\u6570\u4f20\u9012\u7ed9\u7f51\u7edc\u914d\u7f6e":55,"\u7528\u4e8e\u5c06\u8bcdid\u8f6c\u6362\u4e3a\u8bcd\u7684\u5b57\u5178\u6587\u4ef6":45,"\u7528\u4e8e\u6307\u5b9a\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6":53,"\u7528\u4e8e\u653e\u7f6e":51,"\u7528\u4e8e\u6784\u6210\u65b0\u7684\u8bcd\u8868":62,"\u7528\u4e8e\u6807\u8bc6\u751f\u6210\u7684\u6587\u4ef6\u4e2d\u7684\u76f8\u5e94\u8f93\u51fa":45,"\u7528\u4e8e\u7a00\u758f\u8bad\u7ec3\u4e2d":53,"\u7528\u4e8e\u7edf\u8ba1\u8bcd\u9891\u7684bow\u6a21\u578b\u7279\u5f81":70,"\u7528\u4e8e\u81ea\u5b9a\u4e49\u6bcf\u6761\u6570\u636e\u7684batch":3,"\u7528\u4e8e\u8ba1\u7b97\u7f16\u7801\u5411\u91cf\u7684\u52a0\u6743\u548c":45,"\u7528\u4e8e\u8bbe\u7f6e\u8bad\u7ec3\u7b97\u6cd5":63,"\u7528\u4e8e\u8bfb\u53d6\u8bad\u7ec3":51,"\u7528\u4e8e\u96c6\u7fa4\u901a\u4fe1\u901a\u9053\u7684\u7aef\u53e3\u6570":51,"\u7528\u53cc\u5411\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7f16\u7801":45,"\u7528\u591a\u5bf9\u6548\u679c\u5b8c\u5168\u76f8\u540c\u7684":42,"\u7528\u6237":51,"\u7528\u62371\u7684\u7279\u5f81":68,"\u7528\u6237\u4e0a\u4f20\u6570\u636e\u540e":20,"\u7528\u6237\u4e5f\u53ef\u4ee5\u4e0a\u4f20label":20,"\u7528\u6237\u4e5f\u53ef\u4ee5\u5728\u81ea\u5df1\u719f\u6089\u7684\u5f00\u53d1\u5e73\u53f0\u4e0a":40,"\u7528\u6237\u4e5f\u53ef\u4ee5\u5728c":2,"\u7528\u6237\u53ea\u9700\u5b9a\u4e49rnn\u5728\u4e00\u4e2a\u65f6\u95f4\u6b65\u5185\u5b8c\u6210\u7684\u8ba1\u7b97":44,"\u7528\u6237\u53ea\u9700\u6267\u884c":69,"\u7528\u6237\u53ea\u9700\u6267\u884c\u4ee5\u4e0b\u547d\u4ee4\u5c31\u53ef\u4ee5\u4e0b\u8f7d\u5e76\u5904\u7406\u539f\u59cb\u6570\u636e":69,"\u7528\u6237\u53ef\u4ee5\u4f7f\u7528\u5f00\u53d1\u955c\u50cf\u4ee3\u66ff\u914d\u7f6e\u672c\u5730\u73af\u5883":34,"\u7528\u6237\u53ef\u4ee5\u4f7f\u7528ssh\u767b\u5f55\u5230\u8fd9\u53f0\u670d\u52a1\u5668\u4e0a\u5e76\u6267\u884c":34,"\u7528\u6237\u53ef\u4ee5\u5206\u522b\u67e5\u770b\u6700\u65b0\u7684":48,"\u7528\u6237\u53ef\u4ee5\u53c2\u8003sphinx\u6559\u7a0b\u8fdb\u884c\u4e66\u5199":48,"\u7528\u6237\u53ef\u4ee5\u5728\u8f93\u51fa\u7684\u6587\u672c\u6a21\u578b\u4e2d\u770b\u5230":62,"\u7528\u6237\u53ef\u4ee5\u5b89\u5168\u7684\u91ca\u653e\u67d0\u4e2ac":27,"\u7528\u6237\u53ef\u4ee5\u628a\u81ea\u5df1\u7684\u6570\u636e\u5206\u4eab\u7ed9\u522b\u4eba":20,"\u7528\u6237\u53ef\u4ee5\u6839\u636e\u8bad\u7ec3\u65e5\u5fd7":66,"\u7528\u6237\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u8fd9\u4e2a\u52a8\u6001\u5e93\u6765\u5f15\u5165paddl":27,"\u7528\u6237\u53ef\u4ee5\u81ea\u5b9a\u4e49beam":53,"\u7528\u6237\u53ef\u4ee5\u8bbe\u7f6e":55,"\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u4f7f\u7528python\u63a5\u53e3":2,"\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u7f51\u9875\u6d4f\u89c8\u6587\u6863":34,"\u7528\u6237\u53ef\u5728\u8c03\u7528cmake\u7684\u65f6\u5019\u8bbe\u7f6e\u5b83\u4eec":33,"\u7528\u6237\u53ef\u5728cmake\u7684\u547d\u4ee4\u884c\u4e2d":33,"\u7528\u6237\u53ef\u5c06":39,"\u7528\u6237\u53ef\u6839\u636e\u81ea\u5df1\u7684\u7f16\u8bd1\u76ee\u6807\u67b6\u6784":39,"\u7528\u6237\u53ef\u81ea\u884c\u524d\u5f80":40,"\u7528\u6237\u53ef\u81ea\u884c\u524d\u5f80\u4e0b\u8f7d\u9884\u7f16\u8bd1\u597d\u7684\u7248\u672c":39,"\u7528\u6237\u53ef\u901a\u8fc7\u4ea4\u53c9\u7f16\u8bd1\u7684\u65b9\u5f0f":39,"\u7528\u6237\u53ef\u901a\u8fc7ssh\u7b49\u65b9\u5f0f\u767b\u5f55\u5230raspberri":40,"\u7528\u6237\u5728\u4f7f\u7528paddlepaddl":31,"\u7528\u6237\u5728\u672c\u5730\u8f6c\u6362\u597d\u518d\u4e0a\u4f20":20,"\u7528\u6237\u5728\u9996\u6b21\u7f16\u8bd1\u5b89\u88c5paddlepaddle\u65f6":48,"\u7528\u6237\u5b9a\u4e49\u7684\u53c2\u6570":3,"\u7528\u6237\u5c06\u914d\u7f6e\u4e0e\u8bad\u7ec3\u6570\u636e\u5207\u5206\u597d\u653e\u5728\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf\u9884\u5148\u5206\u914d\u597d\u7684\u76ee\u5f55\u4e2d":58,"\u7528\u6237\u5e94\u8be5\u63d0\u4f9b\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6":69,"\u7528\u6237\u5f3a\u5236\u6307\u5b9a\u7279\u5b9a\u7684python\u7248\u672c":31,"\u7528\u6237\u6307\u5b9a\u65b0\u7684\u5b57\u5178\u7684\u8def\u5f84":62,"\u7528\u6237\u6587\u4ef6\u4e2d\u6709\u56db\u79cd\u7c7b\u578b\u7684\u5b57\u6bb5":68,"\u7528\u6237\u6587\u4ef6\u53ef\u80fd\u662f\u6bd4\u8f83\u5927\u7684":24,"\u7528\u6237\u7279\u5f81":68,"\u7528\u6237\u8fd8\u53ef\u4ee5\u6839\u636e\u6982\u7387\u5206\u5e03\u77e9\u9635\u5b9e\u73b0\u67f1\u641c\u7d22\u6216\u7ef4\u7279\u6bd4\u89e3\u7801":69,"\u7528\u6237\u8fd8\u53ef\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u8bbe\u7f6e\u5176\u4ed6\u7f16\u8bd1\u53c2\u6570":[39,40],"\u7528\u6237\u901a\u8fc7c":27,"\u7528\u6237\u9700\u8981\u5728\u7f51\u7edc\u914d\u7f6e\u4e2d\u6307\u5b9a":55,"\u7528\u6237\u9700\u8981\u5728cmake\u65f6\u624b\u52a8\u8bbe\u7f6e\u8fd9\u4e9b\u503c":[39,40],"\u7528\u6237\u9700\u8981\u6307\u5b9a\u672c\u673a\u4e0apython\u7684\u8def\u5f84":31,"\u7528\u6237\u9700\u8981\u63d0\u524d\u51c6\u5907\u597d\u4ea4\u53c9\u7f16\u8bd1\u73af\u5883":[39,40],"\u7528\u6237\u9700\u8981\u9996\u5148\u786e\u8ba4py_paddle\u5305\u5df2\u7ecf\u5b89\u88c5":48,"\u7528\u6237\u9884\u6d4b\u7684\u547d\u4ee4\u884c\u754c\u9762\u5982\u4e0b":68,"\u7528\u6237id":67,"\u7528\u6237id\u8303\u56f4\u4ece1\u52306040":67,"\u7528\u6765\u4ece\u53c2\u6570\u670d\u52a1\u5668\u9884\u53d6\u53c2\u6570\u77e9\u9635\u76f8\u5e94\u7684\u884c":47,"\u7528\u6765\u4f30\u8ba1\u7ebf\u6027\u51fd\u6570\u7684\u53c2\u6570w":32,"\u7528\u6765\u5177\u4f53\u63cf\u8ff0":68,"\u7528\u6765\u5177\u4f53\u8bf4\u660e\u6570\u636e\u96c6\u7684\u5b57\u6bb5\u548c\u6587\u4ef6\u683c\u5f0f":68,"\u7528\u6765\u8ba1\u7b97\u6a21\u578b\u7684\u8bef\u5dee":32,"\u7528\u8fd9\u4e2a\u955c\u50cf\u521b\u5efa\u7684\u5bb9\u5668\u9700\u8981\u6709\u4ee5\u4e0b\u4e24\u4e2a\u529f\u80fd":58,"\u7531":44,"\u7531\u4e8e\u4e0a\u4e00\u6b65cmake\u914d\u7f6e\u4e2d":40,"\u7531\u4e8e\u4e0d\u540c\u7684paddle\u7684\u7248\u672c\u53ef\u80fd\u9700\u8981\u4e0d\u540c\u7684\u4f9d\u8d56\u548c\u5de5\u5177":34,"\u7531\u4e8e\u5b83\u5185\u90e8\u5305\u542b\u4e86\u6bcf\u7ec4\u6570\u636e\u4e2d\u7684\u6240\u6709\u53e5\u5b50":42,"\u7531\u4e8e\u5bb9\u5668\u4e4b\u95f4\u5171\u4eabnet":56,"\u7531\u4e8e\u5bf9parameters\u7684\u66f4\u65b0\u9700\u8981\u83b7\u53d6parameters\u5185\u5b58\u7684":19,"\u7531\u4e8e\u5df2\u7ecf\u77e5\u9053\u4e86\u771f\u5b9e\u7b54\u6848":32,"\u7531\u4e8e\u610f\u5916\u7684\u526f\u672c\u8bb0\u5f55\u548c\u6d4b\u8bd5\u8bb0\u5f55":67,"\u7531\u4e8e\u6211\u4eec\u60f3\u8981\u7684\u53d8\u6362\u662f\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217":42,"\u7531\u4e8e\u6211\u4eec\u652f\u6301\u8bad\u7ec3\u6570\u636e\u6709\u4e0d\u540c\u7684\u6279\u6b21\u5927\u5c0f":47,"\u7531\u4e8e\u6570\u636e\u8bb8\u53ef\u7684\u539f\u56e0":69,"\u7531\u4e8e\u6807\u51c6\u7684\u7ffb\u8bd1\u7ed3\u679c\u5df2\u7ecf\u4e0b\u8f7d\u5230\u8fd9\u91cc":71,"\u7531\u4e8e\u6bcf\u4e2a\u5377\u79ef\u5c42\u540e\u9762\u8fde\u63a5\u7684\u662fbatch":64,"\u7531\u4e8e\u8fd9\u4e2a\u5730\u5740\u4f1a\u88abdataprovider\u4f7f\u7528":2,"\u7531\u4e8e\u8fd9\u6837\u505a\u53ef\u4ee5\u907f\u514d\u5f88\u591a\u6b7b\u9501\u95ee\u9898":3,"\u7531\u4e8e\u96c6\u7fa4\u4e2d\u540c\u65f6\u5b58\u5728\u4e24\u53f0\u673a\u5668\u6545\u969c\u7684\u6982\u7387\u6781\u4f4e":19,"\u7531\u4e8e\u987a\u5e8f\u8c03\u7528\u8fd9\u4e9bgenerator\u4e0d\u4f1a\u51fa\u73b0\u4e0a\u8ff0\u95ee\u9898":3,"\u7531\u4e8ec":26,"\u7531\u4e8echunk\u6bd4\u8f83\u5c0f":24,"\u7531\u4e8epaddlepaddle\u5df2\u7ecf\u5b9e\u73b0\u4e86\u4e30\u5bcc\u7684\u7f51\u7edc\u5c42":32,"\u7531\u4e8estep":44,"\u7531\u4e8etest_data\u5305\u542b\u4e24\u6761\u9884\u6d4b\u6570\u636e":5,"\u7531\u8bcd\u8bed\u6784\u6210\u7684\u53e5\u5b50":41,"\u7531grouplen":67,"\u7533\u8bf7\u7528\u6237\u7a7a\u95f4":24,"\u7535\u5f711\u7684\u7279\u5f81":68,"\u7535\u5f71\u4fe1\u606f\u4ee5\u53ca\u7535\u5f71\u8bc4\u5206":67,"\u7535\u5f71\u540d\u5b57\u6bb5":68,"\u7535\u5f71\u540d\u79f0":67,"\u7535\u5f71\u548c\u7528\u6237":68,"\u7535\u5f71\u548c\u7528\u6237\u6709\u8bb8\u591a\u7684\u7279\u5f81":68,"\u7535\u5f71\u5927\u90e8\u5206\u662f\u624b\u5de5\u8f93\u5165\u6570\u636e":67,"\u7535\u5f71\u7279\u5f81":68,"\u7535\u5f71\u7c7b\u578b":67,"\u7535\u5f71\u7c7b\u578b\u5982\u7b26\u5408\u591a\u79cd\u7528\u7ba1\u9053\u7b26\u53f7":67,"\u7535\u5f71id":67,"\u7535\u5f71id\u8303\u56f4\u4ece1\u52303952":67,"\u7535\u8111":42,"\u767e\u4e07\u6570\u636e\u96c6":67,"\u7684":[42,46,51,57,58,66,70],"\u768410\u7ef4\u6574\u6570\u6807\u7b7e":3,"\u7684\u4e00\u4e2a\u7b80\u5355\u8c03\u7528\u5982\u4e0b":44,"\u7684\u4e00\u4e2a\u7ebf\u6027\u51fd\u6570":32,"\u7684\u4e00\u79cd":71,"\u7684\u4e3a0":53,"\u7684\u4e3b\u8981\u90e8\u5206":69,"\u7684\u4efb\u4e00\u4e00\u79cd":31,"\u7684\u4efb\u52a1":71,"\u7684\u4f7f\u7528\u793a\u4f8b\u5982\u4e0b":41,"\u7684\u503c":[39,40],"\u7684\u503c\u81ea\u52a8\u63a8\u5bfc\u5f97\u5230":39,"\u7684\u504f\u7f6e\u5411\u91cf":47,"\u7684\u5185\u5b58":31,"\u7684\u5185\u5bb9\u5982\u4e0b\u6240\u793a":71,"\u7684\u5185\u5bb9\u6765\u5b9a\u5236imag":58,"\u7684\u5185\u6838block\u4f7f\u7528\u60c5\u51b5":50,"\u7684\u5206\u7c7b\u4efb\u52a1\u4e2d\u8d62\u5f97\u4e86\u7b2c\u4e00\u540d":64,"\u7684\u522b\u540d":[6,7,9],"\u7684\u5355\u8bcd\u7ea7\u522b\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc":68,"\u7684\u53cd\u5411\u4f20\u64ad\u5c06\u4f1a\u6253\u5370\u65e5\u5fd7\u4fe1\u606f":53,"\u7684\u53d8\u6362\u77e9\u9635":47,"\u7684\u53e5\u5b50\u7684\u60c5\u611f":70,"\u7684\u540d\u5b57":3,"\u7684\u540d\u79f0\u76f8\u540c":45,"\u7684\u540e\u7f00":67,"\u7684\u5411\u91cf":47,"\u7684\u542f\u52a8\u53c2\u6570":58,"\u7684\u542f\u52a8\u53c2\u6570\u5e76\u6267\u884c\u8fdb\u7a0b":58,"\u7684\u547d\u540d\u98ce\u683c\u5e76\u4e0d\u80fd\u9002\u5e94\u5176\u4ed6\u7b2c\u4e09\u65b9\u8bed\u8a00":26,"\u7684\u5730\u5740":56,"\u7684\u5730\u65b9":46,"\u7684\u5747\u5300\u5206\u5e03":31,"\u7684\u5934\u6587\u4ef6":26,"\u7684\u5dee\u8ddd\u4e0d\u65ad\u51cf\u5c0f":32,"\u7684\u5e73\u5747\u503c":41,"\u7684\u5e8f\u5217\u5f62\u72b6\u4e00\u81f4":42,"\u7684\u5f00\u53d1\u5de5\u4f5c\u90fd\u5e94\u8be5\u5728\u4e00\u4e2a\u65b0\u7684\u5206\u652f\u4e0a\u5b8c\u6210":46,"\u7684\u603b":51,"\u7684\u63a5\u53e3\u6837\u5f0f":26,"\u7684\u63cf\u8ff0\u8bf4\u660e\u4e2d":46,"\u7684\u6570\u636e\u6d41\u56fe":20,"\u7684\u6570\u636e\u8bfb\u53d6\u811a\u672c\u548c\u7c7b\u4f3c\u4e8e":66,"\u7684\u6570\u76ee\u4e00\u81f4":41,"\u7684\u6587\u4ef6\u4e5f\u5e26\u5230\u65b0\u5206\u652f\u4e0a":46,"\u7684\u65b9\u6cd5\u5df2\u88ab\u8bc1\u660e\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u6a21\u578b":71,"\u7684\u65b9\u7a0b":47,"\u7684\u65f6\u5019\u5982\u679c\u62a5\u4e00\u4e9b\u4f9d\u8d56\u672a\u627e\u5230\u7684\u9519\u8bef\u662f\u6b63\u5e38\u7684":36,"\u7684\u65f6\u95f4\u6b65\u4fe1\u606f\u6210\u6b63\u6bd4":31,"\u7684\u66f4\u8be6\u7ec6\u51c6\u786e\u7684\u5b9a\u4e49":42,"\u7684\u6700\u5c0f\u503c":53,"\u7684\u6700\u65b0\u4ee3\u7801\u5e76\u66f4\u65b0\u5f53\u524d\u5206\u652f":46,"\u7684\u67b6\u6784\u7684\u793a\u4f8b":45,"\u7684\u6837\u5f0f":46,"\u7684\u6838\u5fc3\u662f\u8bbe\u8ba1step\u51fd\u6570\u7684\u8ba1\u7b97\u903b\u8f91":44,"\u7684\u6bb5\u843d\u5b9a\u4e49\u4e3a\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":44,"\u7684\u6d4b\u8bd5\u6570\u636e\u96c6":69,"\u7684\u6e90\u7801\u4ee5\u53ca\u751f\u6210\u6587\u6863\u9700\u8981\u591a\u79cd\u5f00\u53d1\u5de5\u5177":46,"\u7684\u6e90\u7801\u91cc\u4f7f\u7528\u4e86":26,"\u7684\u7248\u672c\u53f7":62,"\u7684\u7279\u5f81":64,"\u7684\u72b6\u6001":44,"\u7684\u7528\u6237\u53c2\u8003":51,"\u7684\u76ee\u5f55\u7ed3\u6784\u4e3a":68,"\u7684\u771f\u5b9e\u5173\u7cfb\u4e3a":32,"\u7684\u77e9\u9635":47,"\u7684\u795e\u7ecf\u7f51\u7edc\u673a\u5668\u7ffb\u8bd1":71,"\u7684\u7a20\u5bc6\u5411\u91cf\u4f5c\u4e3a\u8f93\u5165":47,"\u7684\u7aef\u5230\u7aef\u7cfb\u7edf\u6765\u89e3\u51b3srl\u4efb\u52a1":69,"\u7684\u7b2ci\u4e2a\u503c":47,"\u7684\u7b2cj\u4e2a\u503c":47,"\u7684\u7d22\u5f15\u6587\u4ef6\u5f15\u7528\u8bad\u7ec3":51,"\u7684\u7ed3\u6784\u5982\u4e0b":68,"\u7684\u7ef4\u5ea6":62,"\u7684\u7f29\u5199":24,"\u7684\u884c\u6570\u5e94\u8be5\u4e00\u81f4":71,"\u7684\u89c4\u8303":26,"\u7684\u89d2\u5ea6":20,"\u7684\u8bad\u7ec3\u6a21\u578b\u811a\u672c":66,"\u7684\u8bdd":31,"\u7684\u8def\u5f84\u4e2d":70,"\u7684\u8f93\u5165":44,"\u7684\u8f93\u51fa":50,"\u7684\u8f93\u51fa\u4fe1\u606f\u5165\u624b\u662f\u4e2a\u4e0d\u9519\u7684\u9009\u62e9":50,"\u7684\u8f93\u51fa\u51fd\u6570\u8fd4\u56de\u7684\u662f\u4e0b\u4e00\u4e2a\u65f6\u523b\u8f93\u51fa\u8bcd\u7684":45,"\u7684\u8f93\u51fa\u683c\u5f0f":42,"\u7684\u8f93\u51fa\u88ab\u7528\u4f5c":45,"\u7684\u8fd4\u56de\u503c\u4e00\u81f4":67,"\u7684\u8fdc\u7a0b\u4ed3\u5e93\u7684\u540d\u5b57":46,"\u7684\u90e8\u5206":51,"\u7684\u914d\u7f6e":62,"\u7684\u914d\u7f6e\u5199\u5230\u914d\u7f6e\u6587\u4ef6\u4e2d":20,"\u7684\u9875\u9762\u5220\u9664\u8fdc\u7a0b\u4ed3\u5e93\u7684\u5206\u652f":46,"\u7684docker\u955c\u50cf":34,"\u76ee\u524d":[44,69],"\u76ee\u524d\u4f7f\u7528":46,"\u76ee\u524d\u53ea\u652f\u6301":39,"\u76ee\u524d\u53ea\u8003\u8651\u52a8\u6001\u6269\u5bb9trainer\u6570\u91cf":19,"\u76ee\u524d\u5d4c\u5165python\u89e3\u91ca\u5668":26,"\u76ee\u524d\u5df2\u88ab\u767e\u5ea6\u5185\u90e8\u591a\u4e2a\u4ea7\u54c1\u7ebf\u5e7f\u6cdb\u4f7f\u7528":0,"\u76ee\u524d\u5fc5\u987b\u8bbe\u7f6e\u6210":[39,40],"\u76ee\u524d\u6211\u4eec\u7528cephfs\u6765\u642d\u5efa":24,"\u76ee\u524d\u652f\u6301\u4e24\u79cd":41,"\u76ee\u524d\u652f\u6301fail":53,"\u76ee\u524d\u8be5\u53c2\u6570\u4ec5\u7528\u4e8eaucvalidationlayer\u548cpnpairvalidationlayer\u5c42":53,"\u76ee\u524d\u8fd8\u672a\u652f\u6301":44,"\u76ee\u524dpaddle\u7684\u8fdb\u7a0b\u6a21\u578b\u662fc":26,"\u76ee\u524dpaddlepaddle\u7684develop\u5206\u652f\u7684\u6587\u6863\u662f\u81ea\u52a8\u89e6\u53d1\u66f4\u65b0\u7684":48,"\u76ee\u5f55":[39,40,51,57,58,70],"\u76ee\u5f55\u4e0b":[27,47,66,71],"\u76ee\u5f55\u4e0b\u627e\u5230":66,"\u76ee\u5f55\u4e0b\u7684\u751f\u6210\u6587\u4ef6\u7528\u4e8e\u6df1\u5ea6\u5b66\u4e60\u76f8\u5173android":39,"\u76ee\u5f55\u4e0b\u7684demo\u8bad\u7ec3\u51fa\u6765":5,"\u76ee\u5f55\u4e0b\u7684python\u5305":31,"\u76ee\u5f55\u4e2d":[51,68],"\u76ee\u5f55\u4e2d\u4f1a\u5305\u542b":[39,40],"\u76ee\u5f55\u4e2d\u7684":[50,51],"\u76ee\u5f55\u4e2dpaddl":58,"\u76ee\u5f55\u4f1a\u51fa\u73b0\u5982\u4e0b\u51e0\u4e2a\u65b0\u7684\u6587\u4ef6":69,"\u76ee\u5f55\u548c":[39,40],"\u76ee\u5f55\u5c31\u6210\u4e3a\u4e86\u5171\u4eab\u5b58\u50a8":58,"\u76ee\u5f55\u7ed3\u6784\u5982\u4e0b":71,"\u76ee\u5f55\u91cc\u63d0\u4f9b\u4e86\u8be5\u6570\u636e\u7684\u4e0b\u8f7d\u811a\u672c\u548c\u9884\u5904\u7406\u811a\u672c":66,"\u76ee\u6807":71,"\u76ee\u6807\u51fd\u6570\u662f\u6807\u7b7e\u7684\u4ea4\u53c9\u71b5":69,"\u76ee\u6807\u5411\u91cf":45,"\u76ee\u6807\u5b57\u5178":71,"\u76ee\u6807\u673a\u7248protobuf\u5e93":[39,40],"\u76ee\u6807\u67b6\u6784abi":39,"\u76f4\u5230\u8bad\u7ec3\u6536\u655b\u4e3a\u6b62":31,"\u76f4\u5230\u903c\u8fd1\u771f\u5b9e\u89e3":32,"\u76f4\u63a5\u4f7f\u7528c\u8bed\u8a00\u7684":26,"\u76f4\u63a5\u5220\u9664\u8fd9\u4e2a\u53c2\u6570\u5373\u53ef":27,"\u76f4\u63a5\u5bfc\u51fa\u5230c\u7684\u63a5\u53e3\u6bd4\u8f83\u56f0\u96be":26,"\u76f4\u63a5\u7f16\u8bd1raspberri":40,"\u76f4\u63a5\u8fd0\u884c":34,"\u76f4\u63a5\u8fd4\u56de\u8ba1\u7b97\u7ed3\u679c":5,"\u76f4\u63a5\u8fdb\u5165\u8bad\u7ec3\u6a21\u578b\u7ae0\u8282":66,"\u76f8\u5173\u6587\u6863":40,"\u76f8\u5173\u6587\u6863\u6240\u8ff0":40,"\u76f8\u5173\u6982\u5ff5\u662f":3,"\u76f8\u5173\u7684\u9e1f\u7c7b\u6570\u636e\u96c6\u53ef\u4ee5\u4ece\u5982\u4e0b\u5730\u5740\u4e0b\u8f7d":63,"\u76f8\u5173\u8bba\u6587":69,"\u76f8\u53cd":71,"\u76f8\u540c\u540d\u5b57\u7684\u53c2\u6570":31,"\u76f8\u5bf9":42,"\u76f8\u5bf9\u4e8epaddlepaddle\u7a0b\u5e8f\u8fd0\u884c\u65f6\u7684\u8def\u5f84":2,"\u76f8\u5bf9mnist\u800c\u8a00":3,"\u76f8\u5e94\u7684\u6570\u636e\u8bfb\u53d6\u811a\u672c\u548c\u8bad\u7ec3\u6a21\u578b\u811a\u672c":66,"\u76f8\u5e94\u7684\u6570\u636e\u8fed\u4ee3\u5668\u5982\u4e0b":69,"\u76f8\u5e94\u7684\u6807\u8bb0\u53e5\u5b50\u662f":69,"\u76f8\u5f53":42,"\u77e9\u9635":52,"\u7814\u7a76\u4eba\u5458\u5206\u6790\u4e86\u51e0\u4e2a\u5173\u4e8e\u6d88\u8d39\u8005\u4fe1\u5fc3\u548c\u653f\u6cbb\u89c2\u70b9\u7684\u8c03\u67e5":70,"\u7814\u7a76\u751f":67,"\u786e\u4fdd\u7f16\u8bd1\u5668\u9009\u9879":46,"\u793a":66,"\u793a\u4f8b":[31,64],"\u793a\u4f8b3\u5bf9\u4e8e\u5355\u5c42rnn\u548c\u53cc\u5c42rnn\u6570\u636e\u5b8c\u5168\u76f8\u540c":42,"\u793a\u4f8b3\u7684\u914d\u7f6e\u4f7f\u7528\u4e86\u5355\u5c42rnn\u548c\u53cc\u5c42rnn":42,"\u793a\u4f8b3\u7684\u914d\u7f6e\u5206\u522b\u4e3a":42,"\u793e\u533a\u53c2\u4e0e\u56f0\u96be":26,"\u793e\u533a\u8d21\u732e\u4ee3\u7801\u5b66\u4e60\u6210\u672c\u9ad8":26,"\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u53c2\u6570":19,"\u795e\u7ecf\u7f51\u7edc\u4e5f\u9700\u8981\u4e00\u4e9b\u7279\u5b9a\u7684layer\u4f5c\u4e3a\u8f93\u5165\u63a5\u53e3":37,"\u795e\u7ecf\u7f51\u7edc\u53c2\u6570\u4ee5\u53ca\u8fed\u4ee3\u65b9\u7a0b":37,"\u795e\u7ecf\u7f51\u7edc\u5728\u8bad\u7ec3\u7684\u65f6\u5019":31,"\u795e\u7ecf\u7f51\u7edc\u673a\u5668\u7ffb\u8bd1":71,"\u795e\u7ecf\u7f51\u7edc\u7684\u7f51\u7edc\u7ed3\u6784\u4e2d\u5177\u6709\u6709\u5411\u73af\u7ed3\u6784":42,"\u795e\u7ecf\u7f51\u7edc\u7684\u8bad\u7ec3\u672c\u8eab\u662f\u4e00\u4e2a\u975e\u5e38\u6d88\u8017\u5185\u5b58\u548c\u663e\u5b58\u7684\u5de5\u4f5c":31,"\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e":32,"\u79bb":42,"\u79bb\u7ebf\u6279\u5904\u7406":20,"\u79d1\u5b66\u5bb6":67,"\u79d1\u5e7b\u7247":67,"\u79f0\u4e3a":[45,46],"\u79f0\u4e3a\u5f00\u53d1\u955c\u50cf":34,"\u79f0\u4e3a\u751f\u4ea7\u955c\u50cf":34,"\u79f0\u4e4b\u4e3a\u53cc\u5c42\u5e8f\u5217\u7684\u4e00\u4e2a\u5b50\u5e8f\u5217":41,"\u79f0\u4e4b\u4e3a\u96c6\u675f\u5927\u5c0f":53,"\u7a00\u758f\u6570\u636e\u7684\u683c\u5f0f":47,"\u7a00\u758f\u66f4\u65b0\u7684\u7aef\u53e3\u6570\u91cf":58,"\u7a00\u758f\u768401\u5411\u91cf":[3,37],"\u7a00\u758f\u7684\u5411\u91cf":[3,37],"\u7a00\u758f\u77e9\u9635\u7684\u4e58\u79ef\u5e94\u7528\u4e8e\u524d\u5411\u4f20\u64ad\u8fc7\u7a0b":55,"\u7a0b\u5e8f\u4ece\u6b64\u76ee\u5f55\u62f7\u8d1d\u6587\u4ef6\u5230\u5bb9\u5668\u5185\u8fdb\u884c\u8bad\u7ec3":58,"\u7a0b\u5e8f\u505c\u6b62":53,"\u7a0b\u5e8f\u5458":67,"\u7a0b\u5e8f\u76f4\u63a5\u9000\u51fa":53,"\u7a20\u5bc6\u5411\u91cf":47,"\u7a20\u5bc6\u66f4\u65b0\u7684\u7aef\u53e3\u6570\u91cf":58,"\u7a20\u5bc6\u7684\u6d6e\u70b9\u6570\u5411\u91cf":[3,37],"\u7a97\u6237":42,"\u7acb\u523b\u9000\u51fa":31,"\u7aef\u53e3":51,"\u7aef\u53e3\u6570":51,"\u7aef\u53e3\u9644\u52a0\u5230\u4e3b\u673a\u540d\u4e0a":51,"\u7aef\u81ea\u5b9a\u4e49\u4e00\u4e2a":2,"\u7aef\u8bfb\u53d6\u6570\u636e":31,"\u7b2c":42,"\u7b2c\u4e00\u4e2a":46,"\u7b2c\u4e00\u4e2a\u53c2\u6570\u662fsettings\u5bf9\u8c61":3,"\u7b2c\u4e00\u4e2a\u65b9\u6cd5\u7684\u597d\u5904\u662f\u7b80\u5355\u5feb\u6377":34,"\u7b2c\u4e00\u4e2a\u6837\u672c\u540c\u65f6encode\u4e24\u6761\u6570\u636e\u6210\u4e24\u4e2a\u5411\u91cf":42,"\u7b2c\u4e00\u4e2apass\u4f1a\u4ecepython\u7aef\u8bfb\u53d6\u6570\u636e":3,"\u7b2c\u4e00\u4e2atag\u4e3a":30,"\u7b2c\u4e00\u5929":42,"\u7b2c\u4e00\u6b65\u662f\u8fd0\u884c":34,"\u7b2c\u4e00\u7ae0\u8282":37,"\u7b2c\u4e00\u884c\u4ece":70,"\u7b2c\u4e00\u884c\u5b58\u7684\u662f\u56fe\u50cf":64,"\u7b2c\u4e00\u884c\u662f":62,"\u7b2c\u4e00\u884c\u7684":71,"\u7b2c\u4e00\u90e8\u5206\u5b9a\u4e49\u4e86\u6570\u636e\u8f93\u5165":32,"\u7b2c\u4e00\u90e8\u5206\u662f\u56fe\u7247\u7684\u6807\u7b7e":3,"\u7b2c\u4e09":70,"\u7b2c\u4e09\u5217\u662f\u751f\u6210\u7684\u82f1\u8bed\u5e8f\u5217":71,"\u7b2c\u4e09\u6b65":64,"\u7b2c\u4e09\u6b65\u5b8c\u6210\u540e":30,"\u7b2c\u4e8c":70,"\u7b2c\u4e8c\u4e2a\u4e3a":30,"\u7b2c\u4e8c\u4e2a\u65b9\u6cd5\u7684\u597d\u5904\u662fdockerfile\u53ef\u4ee5\u628a\u6574\u4e2a\u751f\u6210\u6d41\u7a0b\u63cf\u8ff0\u5f88\u6e05\u695a":34,"\u7b2c\u4e8c\u5217\u662f\u96c6\u675f\u641c\u7d22\u7684\u5f97\u5206":71,"\u7b2c\u4e8c\u6b65":[62,64],"\u7b2c\u4e8c\u6b65\u662f\u8fd0\u884c":34,"\u7b2c\u4e8c\u884c\u5b58\u7684\u662f\u56fe\u50cf":64,"\u7b2c\u4e8c\u90e8\u5206\u4e3b\u8981\u662f\u9009\u62e9\u5b66\u4e60\u7b97\u6cd5":32,"\u7b2c\u4e8c\u90e8\u5206\u662f28":3,"\u7b2ci\u884c\u7b2cj\u5217\u7684\u6570\u503c":47,"\u7b49":27,"\u7b49\u5168\u90e8\u9759\u6001\u5e93\u4e2d\u7684\u76ee\u6807\u6587\u4ef6\u5168\u90e8\u6253\u5305\u540e\u4ea7\u751f\u7684\u6587\u4ef6":27,"\u7b49\u5176\u4ed6":71,"\u7b49\u53c2\u6570":58,"\u7b49\u591a\u79cd\u516c\u6709\u4e91\u73af\u5883":56,"\u7b49\u5f85\u8fd9\u4e2a\u7a0b\u5e8f\u6267\u884c\u6210\u529f\u5e76\u8fd4\u56de0\u5219\u6210\u529f\u9000\u51fa":56,"\u7b49\u6587\u4ef6":27,"\u7b49\u7b49":[66,71],"\u7b49\u90fd\u5c5e\u4e8e\u4e00\u4e2a\u547d\u540d\u7a7a\u95f4":56,"\u7b80\u4ecb":49,"\u7b80\u5355\u6765\u8bf4":50,"\u7b80\u5355\u7684\u5168\u8fde\u63a5\u7f51\u7edc":31,"\u7b80\u5355\u7684\u57fa\u4e8e\u5b57\u6bcd\u7684\u8bcd\u5d4c\u5165":68,"\u7b80\u5355\u7684\u6027\u80fd\u5206\u6790":50,"\u7b80\u5355\u7684\u6574\u4e2a\u8bcd\u5d4c\u5165":68,"\u7b80\u5355\u7684pydataprovider2\u6837\u4f8b\u5c31\u8bf4\u660e\u5b8c\u6bd5\u4e86":3,"\u7b80\u5355\u7684yaml\u6587\u4ef6\u5982\u4e0b":57,"\u7b80\u76f4":42,"\u7b97\u6cd5":[31,32,45,70],"\u7b97\u6cd5\u4e2d\u7684beam\u5927\u5c0f":45,"\u7b97\u6cd5\u914d\u7f6e":70,"\u7ba1\u7406\u4eba\u5458":67,"\u7ba1\u7406\u5458":67,"\u7c7b\u4f3c":[27,41],"\u7c7b\u4f3c\u5730":69,"\u7c7b\u4f5c\u4e3a\u53c2\u6570\u7684\u62bd\u8c61":47,"\u7c7b\u522b\u4e2a\u6570":63,"\u7c7b\u522b\u4e2d\u7684\u53c2\u6570\u53ef\u7528\u4e8e\u6240\u6709\u573a\u5408":52,"\u7c7b\u522bid":66,"\u7c7b\u522bid\u548c\u6587\u672c\u4fe1\u606f\u7528":66,"\u7c7b\u540d\u548cc":26,"\u7c7b\u578b":[26,53,68],"\u7c7b\u578b\u53ef\u4ee5\u662fpaddlepaddle\u652f\u6301\u7684\u4efb\u610f\u8f93\u5165\u6570\u636e\u7c7b\u578b":41,"\u7c7b\u578b\u662fsparse_binary_vector":[3,37],"\u7c7b\u578b\u662fsparse_float_vector":[3,37],"\u7c7b\u578b\u6765\u8bbe\u7f6e":3,"\u7c7b\u578b\u7684":42,"\u7c7b\u7684\u6784\u9020\u51fd\u6570\u548c\u6790\u6784\u51fd\u6570":47,"\u7c7b\u9700\u8981\u5b9e\u73b0\u521d\u59cb\u5316":47,"\u7cfb\u7edf\u7f16\u8bd1wheel\u5305\u7684\u65f6\u5019":31,"\u7ea2\u697c\u68a6":62,"\u7eaa\u5f55\u7247":67,"\u7eafcpu\u955c\u50cf\u4ee5\u53cagpu\u955c\u50cf\u90fd\u4f1a\u7528\u5230avx\u6307\u4ee4\u96c6":34,"\u7ebf\u6027\u56de\u5f52\u7684\u8f93\u5165\u662f\u4e00\u6279\u70b9":32,"\u7ebf\u6027\u56de\u5f52\u7684\u8f93\u51fa\u662f\u4ece\u8fd9\u6279\u70b9\u4f30\u8ba1\u51fa\u6765\u7684\u53c2\u6570":32,"\u7ebf\u6027\u8ba1\u7b97\u7f51\u7edc\u5c42":32,"\u7ebf\u7a0bid\u53f7":55,"\u7ec6\u8282\u63cf\u8ff0":54,"\u7ecf\u5e38\u4f1a\u6d88\u8017\u657010gb\u7684\u5185\u5b58\u548c\u6570gb\u7684\u663e\u5b58":31,"\u7ed3\u5408":56,"\u7ed3\u675f\u6807\u8bb0":45,"\u7ed3\u6784\u5982\u4e0b":70,"\u7ed3\u6784\u5982\u4e0b\u56fe":62,"\u7ed3\u679c\u4fdd\u5b58\u5728":69,"\u7ed3\u679c\u53d1\u73b0\u5b83\u4eec\u4e0e\u540c\u65f6\u671f\u7684twitter\u6d88\u606f\u4e2d\u7684\u60c5\u7eea\u8bcd\u9891\u7387\u76f8\u5173":70,"\u7ed3\u8bba":26,"\u7ed9":42,"\u7ed9\u4e2a\u7b80\u5355\u7684":46,"\u7ed9\u51fa\u56fe\u7247\u5c3a\u5bf8":63,"\u7ed9\u51fa\u8f93\u5165\u6570\u636e\u6240\u5728\u8def\u5f84":63,"\u7ed9\u5b9a\u52a8\u8bcd":69,"\u7ed9\u5b9a\u7684\u6587\u672c\u53ef\u4ee5\u662f\u4e00\u4e2a\u6587\u6863":70,"\u7ed9\u5b9aencoder\u8f93\u51fa\u548c\u5f53\u524d\u8bcd":44,"\u7edf\u4e00\u7528":20,"\u7ee7\u7eed\u8bad\u7ec3\u6216\u9884\u6d4b":3,"\u7ef4\u57fa\u767e\u79d1\u4e2d\u6587\u9875\u9762":42,"\u7ef4\u57fa\u767e\u79d1\u9875\u9762":42,"\u7ef4\u5ea6\u4e3aword_dim":66,"\u7ef4\u5ea6\u662f\u7c7b\u522b\u4e2a\u6570":66,"\u7ef4\u5ea6\u662f\u8bcd\u5178\u5927\u5c0f":66,"\u7ef4\u62a4":56,"\u7ef4\u7a7a\u95f4":45,"\u7ef4\u7a7a\u95f4\u5b8c\u6210":45,"\u7f13\u5b58\u6c60\u7684\u51cf\u5c0f":31,"\u7f13\u5b58\u8bad\u7ec3\u6570\u636e\u5230\u5185\u5b58":3,"\u7f16\u5199\u597d\u6570\u636e\u63d0\u4f9b\u811a\u672c\u540e":68,"\u7f16\u5199\u5b8cyaml\u6587\u4ef6\u540e":58,"\u7f16\u5199\u672c\u6b21\u8bad\u7ec3\u7684yaml\u6587\u4ef6":58,"\u7f16\u53f7":68,"\u7f16\u53f7\u4ece0\u5f00\u59cb":31,"\u7f16\u53f7\u5b57\u6bb5":68,"\u7f16\u7801\u5411\u91cf":45,"\u7f16\u7801\u5668\u8f93\u51fa":45,"\u7f16\u7801\u6e90\u5e8f\u5217":45,"\u7f16\u89e3\u7801\u6a21\u578b\u5c06\u4e00\u4e2a\u6e90\u8bed\u53e5\u7f16\u7801\u4e3a\u4e00\u4e2a\u5b9a\u957f\u7684\u5411\u91cf":71,"\u7f16\u8bd1":[34,46],"\u7f16\u8bd1\u548c\u5b89\u88c5paddlepaddl":40,"\u7f16\u8bd1\u548c\u5b89\u88c5paddlepaddle\u9884\u6d4b\u5e93":39,"\u7f16\u8bd1\u5668":[39,40],"\u7f16\u8bd1\u5668\u6ca1\u6709":26,"\u7f16\u8bd1\u578b\u8bed\u8a00":26,"\u7f16\u8bd1\u5b8c\u6210\u4e4b\u540e":48,"\u7f16\u8bd1\u5b8c\u6210\u540e":48,"\u7f16\u8bd1\u5de5\u5177\u94fe\u6240\u5728\u7684\u7edd\u5bf9\u8def\u5f84":40,"\u7f16\u8bd1\u6210\u52a8\u6001\u5e93":53,"\u7f16\u8bd1\u6d41\u7a0b\u4e3b\u8981\u63a8\u8350\u9ad8\u7ea7\u7528\u6237\u67e5\u770b":35,"\u7f16\u8bd1\u751f\u6210":48,"\u7f16\u8bd1\u8fd9\u4e2a\u7248\u672c\u7684docker\u53d1\u884c\u955c\u50cf":30,"\u7f16\u8bd1\u8fd9\u4e2a\u7248\u672c\u7684ubuntu":30,"\u7f16\u8bd1\u9009\u9879":33,"\u7f16\u8bd1c":27,"\u7f16\u8bd1master\u5206\u652f\u7684docker\u53d1\u884c\u955c\u50cf":30,"\u7f16\u8bd1ubuntu\u7684deb\u5305":30,"\u7f16\u8f91":56,"\u7f29\u653e\u53c2\u6570":64,"\u7f51\u7edc":[69,70],"\u7f51\u7edc\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf":56,"\u7f51\u7edc\u540d\u79f0":66,"\u7f51\u7edc\u5c42\u53ef\u4ee5\u6709\u591a\u4e2a\u8f93\u5165":47,"\u7f51\u7edc\u5c42\u7684\u6807\u8bc6\u7b26\u4e3a":47,"\u7f51\u7edc\u5c42\u7684\u7c7b\u578b":47,"\u7f51\u7edc\u5c42\u7684\u7ec6\u8282\u53ef\u4ee5\u901a\u8fc7\u4e0b\u9762\u8fd9\u4e9b\u4ee3\u7801\u7247\u6bb5\u6765\u6307\u5b9a":47,"\u7f51\u7edc\u5c42\u7684\u8f93\u51fa\u662f\u7ecf\u8fc7\u6fc0\u6d3b\u51fd\u6570\u4e4b\u540e\u7684\u503c":53,"\u7f51\u7edc\u5c42\u914d\u7f6e\u5305\u542b\u4ee5\u4e0b\u51e0\u9879":47,"\u7f51\u7edc\u63a5\u53e3\u5361":51,"\u7f51\u7edc\u6a21\u5757":64,"\u7f51\u7edc\u6a21\u578b\u5c06\u8f93\u51fa\u6807\u7b7e\u7684\u6982\u7387\u5206\u5e03":69,"\u7f51\u7edc\u7684\u8bad\u7ec3\u8fc7\u7a0b":70,"\u7f51\u7edc\u7ed3\u6784\u5982\u4e0b\u56fe\u6240\u793a":68,"\u7f51\u7edc\u901a\u4fe1":47,"\u7f51\u7edc\u914d\u7f6e":[51,66,70],"\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6":[64,69],"\u800c":[32,45,57,68],"\u800c\u4e0d\u4f7f\u7528imdb\u6570\u6910\u96c6\u4e2d\u7684imdb":70,"\u800c\u4e0d\u5fc5\u5728\u610fpaddl":27,"\u800c\u4e0d\u652f\u6301pypy\u89e3\u91ca\u5668":26,"\u800c\u4e0d\u662f\u4f7f\u7528\u540c\u6b65":51,"\u800c\u4e0d\u662f\u6e90\u7801\u76ee\u5f55\u91cc":31,"\u800c\u4e0d\u662f\u7279\u5f81\u7684\u96c6\u5408":42,"\u800c\u4e0d\u662f\u7ec4\u5408\u4e0a\u4e0b\u6587\u7ea7\u522b\u4fe1\u606f":70,"\u800c\u4e0d\u66b4\u9732\u6982\u5ff5\u7684\u5b9e\u73b0":27,"\u800c\u4e0d\u7528\u5173\u5fc3\u6570\u636e\u5982\u4f55\u4f20\u8f93":3,"\u800c\u4e14":71,"\u800c\u4e14\u5728\u4f20\u8f93\u7684\u8fc7\u7a0b\u4e2d\u4e5f\u53ef\u80fd\u51fa\u73b0\u7f51\u7edc\u4e0d\u7a33\u5b9a\u7684\u60c5\u51b5":24,"\u800c\u4e4b\u524d\u7684\u53c2\u6570\u5c06\u4f1a\u88ab\u5220\u9664":53,"\u800c\u4ece\u5e94\u7528\u7684\u89d2\u5ea6":50,"\u800c\u4f18\u5316\u6027\u80fd\u7684\u9996\u8981\u4efb\u52a1":50,"\u800c\u5176\u4ed6\u5c42\u4f7f\u7528cpu\u8ba1\u7b97":55,"\u800c\u51fa\u73b0\u9636\u6bb5\u6027\u7684\u8fd0\u884c\u505c\u6ede":19,"\u800c\u53cc\u5c42rnn\u662f\u53ef\u4ee5\u5904\u7406\u8fd9\u79cd\u8f93\u5165\u6570\u636e\u7684\u7f51\u7edc\u7ed3\u6784":42,"\u800c\u53f3\u56fe\u7684\u74f6\u9888\u8fde\u63a5\u6a21\u5757\u7528\u4e8e50\u5c42":64,"\u800c\u5728cpp\u91cc\u9762\u5b9e\u73b0\u8fd9\u4e2ac\u7684\u63a5\u53e3":26,"\u800c\u591a\u8bed\u8a00\u63a5\u53e3\u9700\u8981\u76f4\u63a5\u8bfb\u53d6\u751f\u6210\u7684\u4e8c\u8fdb\u5236":26,"\u800c\u5927\u591a\u6570\u65b9\u6cd5\u53ea\u662f\u5229\u7528n":70,"\u800c\u5bf9\u4e8e\u53cc\u5c42\u5e8f\u5217":42,"\u800c\u5bf9\u4e8e\u6bcf\u4e00\u4e2a\u5185\u5c42\u7279\u5f81\u6570\u636e\u800c\u8a00":42,"\u800c\u5bf9\u4e8egolang":26,"\u800c\u5bf9\u4e8egolang\u9519\u8bef\u5904\u7406\u5e94\u8be5\u4f7f\u7528\u8fd4\u56de\u503c":26,"\u800c\u5c06\u8fd9\u4e2a\u6bb5\u843d\u7684\u6bcf\u4e00\u53e5\u8bdd\u7528lstm\u7f16\u7801\u6210\u4e00\u4e2a\u5411\u91cf":42,"\u800c\u5f53\u524d\u5df2\u7ecf\u67095":50,"\u800c\u662f\u76f4\u63a5\u4ece\u5185\u5b58\u7684\u7f13\u5b58\u91cc\u8bfb\u53d6\u6570\u636e":31,"\u800c\u662f\u76f4\u63a5\u4fee\u6539paddl":27,"\u800c\u662f\u76f4\u63a5\u7528api\u7684\u63a5\u53e3\u8fdc\u7a0b\u8bbf\u95ee":20,"\u800c\u66f4\u6df1\u5165\u7684\u5206\u6790":50,"\u800c\u6709\u4e9b\u53c2\u6570\u9700\u8981\u5728\u96c6\u7fa4\u591a\u673a\u8bad\u7ec3\u4e2d\u4f7f\u7528\u7b49":52,"\u800c\u6ca1\u6709\u77ed\u65f6\u8bb0\u5fc6\u7684\u635f\u5931":70,"\u800c\u6e90\u5e8f\u5217\u7684\u7f16\u7801\u5411\u91cf\u53ef\u4ee5\u88ab\u65e0\u8fb9\u754c\u7684memory\u8bbf\u95ee":45,"\u800c\u795e\u7ecf\u7f51\u7edc\u662f\u6211\u4eec\u8981\u642d\u5efa\u7684\u5b9d\u5854":37,"\u800c\u7a00\u758f\u66f4\u65b0\u5728\u53cd\u5411\u4f20\u64ad\u4e4b\u540e\u7684\u6743\u91cd\u66f4\u65b0\u65f6\u8fdb\u884c":55,"\u800c\u7cfb\u7edf\u4e2d\u7684":31,"\u800c\u8fd9\u4e00\u53e5\u8bdd\u5c31\u53ef\u4ee5\u8868\u793a\u6210\u8fd9\u4e9b\u4f4d\u7f6e\u7684\u6570\u7ec4":42,"\u800c\u8fd9\u4e2acontext\u53ef\u80fd\u4f1a\u975e\u5e38\u5927":3,"\u800c\u8fd9\u6bcf\u4e00\u4e2a\u6570\u7ec4\u5143\u7d20":42,"\u800c\u975e\u76f4\u63a5\u56de\u590d\u7684\u65b9\u5f0f":46,"\u800c\u975e\u9759\u6001\u52a0\u8f7dcuda\u52a8\u6001\u5e93":33,"\u800cpaddlepaddle\u5219\u4f1a\u5e2e\u7528\u6237\u505a\u4ee5\u4e0b\u5de5\u4f5c":3,"\u800crnn\u662f\u6700\u6d41\u884c\u7684\u9009\u62e9":44,"\u800cswig\u53ea\u80fd\u7b80\u5355\u7684\u66b4\u9732c":26,"\u800ctrainer\u9700\u8981\u8bfb\u53d6\u8bad\u7ec3\u6570\u636e\u8fdb\u884c\u8bad\u7ec3":37,"\u800cweight":63,"\u800cy_predict\u662f\u63a5\u6536x\u4f5c\u4e3a\u8f93\u5165":37,"\u804c\u4e1a":67,"\u804c\u4e1a\u4ece\u4e0b\u9762\u6240\u5217\u4e2d\u9009\u62e9":67,"\u80fd\u591f\u5904\u7406\u53cc\u5c42\u5e8f\u5217":44,"\u80fd\u591f\u5bf9\u53cc\u5411\u5e8f\u5217\u8fdb\u884c\u5904\u7406\u7684\u6709":44,"\u80fd\u591f\u627e\u5230\u8fd9\u91cc\u4f7f\u7528\u7684\u6240\u6709\u6570\u636e":66,"\u80fd\u591f\u8bb0\u5f55\u4e0a\u4e00\u4e2asubseq":44,"\u80fd\u83b7\u53d6":51,"\u811a\u672c":[51,63,68],"\u811a\u672c\u4fdd\u5b58\u5728":63,"\u811a\u672c\u5f00\u59cb\u65f6":58,"\u811a\u672c\u63d0\u4f9b\u4e86\u4e00\u4e2a\u9884\u6d4b\u63a5\u53e3":70,"\u811a\u672c\u65f6\u9700\u8981\u52a0\u4e0a":70,"\u811a\u672c\u8fd0\u884c\u5b8c\u6210\u540e":63,"\u81ea\u52a8\u5173\u95ed\u5bf9\u5e94\u7684":46,"\u81ea\u52a8\u5730\u5c06\u8fd9\u4e9b\u9009\u9879\u5e94\u7528\u5230":51,"\u81ea\u52a8\u5b8c\u6210\u8fd9\u4e00\u8fc7\u7a0b":44,"\u81ea\u52a8\u6302\u8f7d\u5206\u5e03\u5f0f\u5b58\u50a8\u76ee\u5f55":19,"\u81ea\u52a8\u751f\u6210":48,"\u81ea\u52a8\u83b7\u53d6\u4e0a\u4e00\u4e2a\u751f\u6210\u7684\u8bcd":45,"\u81ea\u5e95\u5411\u4e0a\u6cd5":66,"\u81ea\u6b64":39,"\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7b49":55,"\u81ea\u7531\u804c\u4e1a\u8005":67,"\u81f3\u4e8e\u4e3a\u4ec0\u4e48\u9700\u8981c":27,"\u81f3\u6b64":[3,42,46],"\u8212\u9002":42,"\u826f\u597d\u7684\u6587\u6863":26,"\u827a\u672f\u5bb6":67,"\u8282\u70b9\u4e2d\u7684":51,"\u82e5":47,"\u82e5\u5e0c\u671b\u6700\u5feb\u7684\u6267\u884c\u901f\u5ea6":[39,40],"\u82e5\u5e72\u4e2a\u53e5\u5b50\u6784\u6210\u4e00\u4e2a\u6bb5\u843d":41,"\u82e5\u6709\u4e0d\u4e00\u81f4\u4e4b\u5904":50,"\u82e5\u6709\u5fc5\u8981":47,"\u82e5\u6ca1\u6709\u663e\u5f0f\u8bbe\u7f6e":39,"\u82e5\u73af\u5883\u53d8\u91cf":[39,40],"\u82e5\u8f93\u51fa\u662f\u5355\u5c42\u5e8f\u5217":41,"\u82e5\u8f93\u51fa\u662f\u53cc\u5c42\u5e8f\u5217":41,"\u82f1\u6587\u6587\u6863":48,"\u82f1\u6587\u6587\u6863\u76ee\u5f55":48,"\u82f1\u8bed":71,"\u8303\u56f4":55,"\u83b7\u53d6":46,"\u83b7\u53d6\u5229\u7528":66,"\u83b7\u53d6\u5b57\u5178\u7ef4\u5ea6":70,"\u83b7\u53d6\u6700\u65b0\u7684\u68c0\u67e5\u70b9\u7684\u6587\u4ef6uuid":19,"\u83b7\u53d6\u8be5\u6761\u6837\u672c\u7c7b\u522bid":66,"\u83b7\u53d6\u901a\u8fc7":70,"\u83b7\u53d6trainer":58,"\u83b7\u5f97\u53c2\u6570\u5c3a\u5bf8":47,"\u867d\u7136":32,"\u867d\u7136\u4e0d\u9f13\u52b1\u8fd9\u6837":27,"\u867d\u7136\u6bcf\u4e2agenerator\u5728\u6ca1\u6709\u8c03\u7528\u7684\u65f6\u5019":3,"\u867d\u7136\u8fd9\u4e9b\u6587\u4ef6\u5e76\u975e\u90fd\u9700\u8981\u96c6\u7fa4\u8bad\u7ec3":51,"\u867d\u7136paddle\u770b\u8d77\u6765\u5305\u542b\u4e86\u4f17\u591a\u53c2\u6570":52,"\u884c":62,"\u884c\u4f18\u5148\u6b21\u5e8f\u5b58\u50a8":64,"\u884c\u5185\u4f7f\u7528":3,"\u884c\u653f\u5de5\u4f5c":67,"\u884c\u955c\u50cf\u5305\u62ec\u7eafcpu\u7248\u672c\u548cgpu\u7248\u672c\u4ee5\u53ca\u5176\u5bf9\u5e94\u7684\u975eavx\u7248\u672c":34,"\u8865\u5145\u4e0a\u6b21\u7684commit":46,"\u8868\u660e\u4e86\u8fd9\u4e9b\u884c\u7684\u6807\u53f7":47,"\u8868\u660e\u8fd9\u4e2a\u5c42\u7684\u4e00\u4e2a\u5b9e\u4f8b\u662f\u5426\u9700\u8981\u504f\u7f6e":47,"\u8868\u793a\u4e00\u4e2akubernetes\u96c6\u7fa4\u4e2d\u7684\u4e00\u4e2a\u5de5\u4f5c\u8282\u70b9":56,"\u8868\u793a\u4e3adeviceid":55,"\u8868\u793a\u5728\u96c6\u7fa4\u4f5c\u4e1a":51,"\u8868\u793a\u5973\u6027":67,"\u8868\u793a\u5c06\u5916\u5c42\u7684outer_mem\u4f5c\u4e3a\u5185\u5c42memory\u7684\u521d\u59cb\u72b6\u6001":42,"\u8868\u793a\u5f53\u524d\u96c6\u7fa4\u4f5c\u4e1a\u7684\u8282\u70b9":51,"\u8868\u793a\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":51,"\u8868\u793a\u751f\u6210\u6570\u636e\u7684\u5e8f\u5217id":71,"\u8868\u793a\u7528\u4e8e\u8bad\u7ec3\u6216\u9884\u6d4b":3,"\u8868\u793a\u7537\u6027":67,"\u8868\u793a\u7684\u6bcf\u4e2a\u5355\u8bcd":66,"\u8868\u793a\u7b2c0\u4e2abatch\u5230\u5f53\u524dbatch\u7684\u5206\u7c7b\u9519\u8bef":70,"\u8868\u793a\u8bad\u7ec3\u4e86xx\u4e2a\u6837\u672c":70,"\u8868\u793a\u8bad\u7ec3\u4e86xx\u4e2abatch":70,"\u8868\u793a\u8bfb\u8005\u6240\u4f7f\u7528\u7684docker\u955c\u50cf\u4ed3\u5e93\u5730\u5740":58,"\u8868\u793a\u8fc7\u4e8620\u4e2abatch":66,"\u8868\u793a\u8fc7\u4e862560\u4e2a\u6837\u672c":66,"\u8868\u793a\u8fd9\u4e2ajob\u7684\u540d\u5b57":58,"\u88ab":46,"\u88ab\u6269\u5c55\u4e3a\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":41,"\u88ab\u653e\u5728":47,"\u88ab\u79f0\u4e3a":45,"\u897f\u90e8\u7247":67,"\u8981\u4e0b\u8f7d\u548c\u89e3\u538b\u6570\u636e\u96c6":68,"\u8981\u4e0b\u8f7d\u89e3\u538b\u8fd9\u4e2a\u6a21\u578b":71,"\u8981\u4f7f\u7528\u547d\u4ee4\u884c\u5206\u6790\u5de5\u5177":50,"\u8981\u5728\u5df2\u6709\u7684kubernetes\u96c6\u7fa4\u4e0a\u8fdb\u884cpaddlepaddle\u7684\u5206\u5e03\u5f0f\u8bad\u7ec3":58,"\u8981\u5728\u6240\u6709\u8282\u70b9\u4e0a\u5b58\u5728":51,"\u8981\u5bf9\u4e00\u4e2a\u56fe\u7247\u7684\u8fdb\u884c\u5206\u7c7b\u9884\u6d4b":63,"\u8981\u5c06\u5b57\u6bb5\u914d\u7f6e\u6587\u4ef6\u8f6c\u5316\u4e3ameta\u914d\u7f6e\u6587\u4ef6":68,"\u8981\u6c42\u5355\u5c42\u5e8f\u5217\u542b\u6709\u5143\u7d20\u7684\u6570\u76ee":41,"\u8981\u751f\u6210\u7684\u76ee\u6807\u5e8f\u5217":44,"\u8981\u8c03\u7528":47,"\u89e3\u51b3\u529e\u6cd5\u662f":31,"\u89e3\u51b3\u65b9\u6848\u662f":31,"\u89e3\u538b":71,"\u89e3\u6790\u5668\u80fd\u901a\u8fc7\u6587\u4ef6\u7684\u6269\u5c55\u540d\u81ea\u52a8\u8bc6\u522b\u6587\u4ef6\u7684\u683c\u5f0f":68,"\u89e3\u6790\u6570\u636e\u96c6\u4e2d\u7684\u6bcf\u4e00\u4e2a\u5b57\u6bb5":68,"\u89e3\u6790\u6a21\u578b\u914d\u7f6e\u6587\u4ef6":5,"\u89e3\u6790\u73af\u5883\u53d8\u91cf\u5f97\u5230":58,"\u89e3\u6790\u8bad\u7ec3\u6a21\u578b\u65f6\u7528\u7684\u914d\u7f6e\u6587\u4ef6":5,"\u89e3\u7801\u5668\u4f7f\u7528":45,"\u89e3\u7801\u5668\u57fa\u4e8e\u7f16\u7801\u6e90\u5e8f\u5217\u548c\u6700\u540e\u751f\u6210\u7684\u76ee\u6807\u8bcd\u9884\u6d4b\u4e0b\u4e00\u76ee\u6807\u8bcd":45,"\u89e3\u7801\u5668\u662f\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":45,"\u89e3\u7801\u5668\u6839\u636e\u4e0a\u4e0b\u6587\u5411\u91cf\u9884\u6d4b\u51fa\u4e00\u4e2a\u76ee\u6807\u5355\u8bcd":71,"\u89e3\u91ca":66,"\u89e3\u91ca\u578b\u8bed\u8a00\u53ea\u80fd\u8c03\u7528\u52a8\u6001\u5e93":26,"\u89e3\u91ca\u6027\u8bed\u8a00\u5b9e\u9645\u8fd0\u884c\u7684\u4e8c\u8fdb\u5236\u662f\u89e3\u91ca\u5668\u672c\u8eab":26,"\u8ba1\u7b97":45,"\u8ba1\u7b97\u504f\u7f6e\u7684\u68af\u5ea6":47,"\u8ba1\u7b97\u5355\u5143\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u6838\u5fc3":32,"\u8ba1\u7b97\u53cd\u5411rnn\u7684\u7b2c\u4e00\u4e2a\u5b9e\u4f8b":45,"\u8ba1\u7b97\u53d8\u6362\u77e9\u9635\u7684\u5927\u5c0f\u548c\u683c\u5f0f":47,"\u8ba1\u7b97\u5f53\u524d\u5c42\u6743\u91cd\u7684\u68af\u5ea6":47,"\u8ba1\u7b97\u6bcf\u4e2a\u8bcd\u7684\u8bcd\u5411\u91cf":45,"\u8ba1\u7b97\u6fc0\u6d3b\u51fd\u6570\u7684\u68af\u5ea6":47,"\u8ba1\u7b97\u7684\u7ec6\u8282\u5c06\u5728\u4e0b\u9762\u7684\u5c0f\u8282\u7ed9\u51fa":47,"\u8ba1\u7b97\u8bef\u5dee\u51fd\u6570":32,"\u8ba1\u7b97\u8f6c\u6362\u77e9\u9635\u548c\u8f93\u5165\u7684\u68af\u5ea6":47,"\u8ba1\u7b97\u8f93\u5165\u548c\u53c2\u6570\u7684\u68af\u5ea6":47,"\u8ba1\u7b97\u8f93\u5165\u5c42\u7684\u504f\u5dee":47,"\u8ba1\u7b97\u8f93\u51fa":47,"\u8ba1\u7b97\u8fd9\u4e2a\u6587\u4ef6\u7684md5":19,"\u8ba9\u6a21\u578b\u80fd\u591f\u5f97\u5230\u8bad\u7ec3\u66f4\u65b0":66,"\u8ba9\u795e\u7ecf\u7f51\u7edc\u53ef\u4ee5\u8fdb\u884c\u8bad\u7ec3\u6216\u9884\u6d4b":2,"\u8ba9\u8fd9\u4e2a\u793a\u4f8b\u53d8\u5f97\u66f4\u597d":68,"\u8ba9paddle\u6838\u5fc3\u4e2d":27,"\u8bad\u7ec3":[52,70],"\u8bad\u7ec3\u4e0e\u5e94\u7528":1,"\u8bad\u7ec3\u4efb\u52a1\u7684\u8fd0\u884c\u53ef\u80fd\u4f1a\u5360\u6ee1trainer\u548cparamet":19,"\u8bad\u7ec3\u4f5c\u4e1a":51,"\u8bad\u7ec3\u53ca\u6d4b\u8bd5\u8bef\u5dee\u66f2\u7ebf\u56fe\u4f1a\u88ab":63,"\u8bad\u7ec3\u53ef\u4ee5\u8bbe\u7f6e\u4e3atrue":69,"\u8bad\u7ec3\u540e":69,"\u8bad\u7ec3\u548c\u7eaf\u4f7f\u7528":30,"\u8bad\u7ec3\u5931\u8d25\u65f6\u53ef\u4ee5\u68c0\u67e5\u9519\u8bef\u65e5\u5fd7":51,"\u8bad\u7ec3\u597d\u4e00\u4e2a\u6df1\u5c42\u795e\u7ecf\u7f51\u7edc\u901a\u5e38\u8981\u8017\u8d39\u975e\u5e38\u957f\u7684\u65f6\u95f4":50,"\u8bad\u7ec3\u5b8c\u6210\u540e":32,"\u8bad\u7ec3\u6570\u636e\u548c\u6d4b\u8bd5\u6570\u636e\u7684\u76ee\u5f55":71,"\u8bad\u7ec3\u6570\u636e\u662f":3,"\u8bad\u7ec3\u6570\u636e\u6709\u95ee\u9898":31,"\u8bad\u7ec3\u6570\u6910\u96c6":70,"\u8bad\u7ec3\u65f6":58,"\u8bad\u7ec3\u65f6\u6240\u9700\u8bbe\u7f6e\u7684\u4e3b\u8981\u53c2\u6570\u5982\u4e0b":66,"\u8bad\u7ec3\u65f6\u9ed8\u8ba4shuffl":3,"\u8bad\u7ec3\u6a21\u578b\u4e4b\u524d":70,"\u8bad\u7ec3\u6a21\u578b\u540e":45,"\u8bad\u7ec3\u6a21\u578b\u6b63\u786e\u6027":30,"\u8bad\u7ec3\u7684\u635f\u5931\u51fd\u6570\u9ed8\u8ba4\u6bcf\u969410\u4e2abatch\u6253\u5370\u4e00\u6b21":71,"\u8bad\u7ec3\u7684\u811a\u672c\u662f":69,"\u8bad\u7ec3\u7ed3\u675f\u540e\u67e5\u770b\u8f93\u51fa\u7ed3\u679c":58,"\u8bad\u7ec3\u811a\u672c":66,"\u8bad\u7ec3\u811a\u672c\u7b49\u7b49":66,"\u8bad\u7ec3\u81f3\u591a":68,"\u8bad\u7ec3\u8282\u70b9\u6570\u91cf":58,"\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u8ddd\u79bb":31,"\u8bad\u7ec3\u8f6e\u6b21":66,"\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u53c2\u6570\u6216\u8005\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u68af\u5ea6\u5c3a\u5ea6\u8fc7\u5927":31,"\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u6d4b\u8bd5test_period":52,"\u8bad\u7ec3\u8fc7\u7a0b\u662f\u5426\u4e3a\u672c\u5730\u6a21\u5f0f":53,"\u8bad\u7ec3\u8fc7\u7a0b\u662f\u5426\u4f7f\u7528gpu":53,"\u8bad\u7ec3\u914d\u7f6e\u4e2d\u7684\u8bbe\u5907\u5c5e\u6027\u5c06\u4f1a\u65e0\u6548":53,"\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u6587\u4ef6\u5217\u8868":70,"\u8bad\u7ec3\u96c6\u5df2\u7ecf\u505a\u4e86\u968f\u673a\u6253\u4e71\u6392\u5e8f\u800c\u6d4b\u8bd5\u96c6\u6ca1\u6709":70,"\u8bad\u7ec3\u96c6\u5df2\u7ecf\u968f\u673a\u6253\u4e71":70,"\u8bad\u7ec3\u96c6\u5e73\u5747\u503c":63,"\u8bad\u7ec3dot_period":52,"\u8bb0\u5f55\u4e0b\u6240\u6709\u5931\u8d25\u7684\u4f8b\u5b50":30,"\u8bb0\u5fc6\u6a21\u5757":45,"\u8bba\u6587":64,"\u8bbe\u4e3a\u5df2\u90e8\u7f72\u7684\u5de5\u4f5c\u7a7a\u95f4\u76ee\u5f55":51,"\u8bbe\u4e3a\u672c\u5730":51,"\u8bbe\u7f6e":27,"\u8bbe\u7f6e\u4e3a":[40,47],"\u8bbe\u7f6e\u4e3atrue\u4f7f\u7528\u672c\u5730\u8bad\u7ec3\u6216\u8005\u4f7f\u7528\u96c6\u7fa4\u4e0a\u7684\u4e00\u4e2a\u8282\u70b9":53,"\u8bbe\u7f6e\u4e3atrue\u4f7f\u7528gpu\u6a21\u5f0f":53,"\u8bbe\u7f6e\u4efb\u52a1\u7684\u6a21\u5f0f\u4e3a\u6d4b\u8bd5":71,"\u8bbe\u7f6e\u4fdd\u5b58\u6a21\u578b\u7684\u8f93\u51fa\u8def\u5f84":71,"\u8bbe\u7f6e\u5168\u5c40\u5b66\u4e60\u7387":70,"\u8bbe\u7f6e\u5185\u5b58\u4e2d\u6682\u5b58\u7684\u6570\u636e\u6761\u6570":3,"\u8bbe\u7f6e\u5185\u5b58\u4e2d\u6700\u5c0f\u6682\u5b58\u7684\u6570\u636e\u6761\u6570":3,"\u8bbe\u7f6e\u53c2\u6570\u7684\u540d\u5b57":31,"\u8bbe\u7f6e\u547d\u4ee4\u884c\u53c2\u6570":[31,49,68],"\u8bbe\u7f6e\u5b57\u5178\u6587\u4ef6":70,"\u8bbe\u7f6e\u5de5\u4f5c\u6a21\u5f0f\u4e3a\u8bad\u7ec3":70,"\u8bbe\u7f6e\u5e73\u5747sgd\u7a97\u53e3":70,"\u8bbe\u7f6e\u6210":31,"\u8bbe\u7f6e\u6210\u4e00\u4e2a\u5c0f\u4e00\u4e9b\u7684\u503c":31,"\u8bbe\u7f6e\u6570\u636e\u904d\u5386\u6b21\u6570":69,"\u8bbe\u7f6e\u6807\u7b7e\u7c7b\u522b\u5b57\u5178":70,"\u8bbe\u7f6e\u6a21\u578b\u8def\u5f84":70,"\u8bbe\u7f6e\u7684\u547d\u4ee4\u884c\u53c2\u6570":70,"\u8bbe\u7f6e\u795e\u7ecf\u7f51\u7edc\u7684\u914d\u7f6e\u6587\u4ef6":71,"\u8bbe\u7f6e\u7c7b\u522b\u6570":70,"\u8bbe\u7f6e\u7ebf\u7a0b\u6570":[69,70],"\u8bbe\u7f6e\u7f51\u7edc\u914d\u7f6e":70,"\u8bbe\u7f6e\u8f93\u51fa\u7684\u5c3a\u5bf8":47,"\u8bbe\u7f6e\u8f93\u51fa\u8def\u5f84\u4ee5\u4fdd\u5b58\u8bad\u7ec3\u5b8c\u6210\u7684\u6a21\u578b":70,"\u8bbe\u7f6e\u8fd9\u4e2apydataprovider2\u8fd4\u56de\u4ec0\u4e48\u6837\u7684\u6570\u636e":3,"\u8bbe\u7f6e\u9ed8\u8ba4\u8bbe\u5907\u53f7\u4e3a0":55,"\u8bbe\u7f6ebatch":70,"\u8bbe\u7f6ecpu\u7ebf\u7a0b\u6570\u6216\u8005gpu\u8bbe\u5907\u6570":71,"\u8bbe\u7f6egpu":53,"\u8bbe\u7f6epass":70,"\u8bbe\u7f6epasses\u7684\u6570\u91cf":71,"\u8bbf\u95eekubernetes\u7684\u63a5\u53e3\u6765\u67e5\u8be2\u6b64job\u5bf9\u5e94\u7684\u6240\u6709pod\u4fe1\u606f":58,"\u8bc4\u4ef7\u9884\u6d4b\u7684\u6548\u679c":32,"\u8bc4\u4f30\u8be5\u4ea7\u54c1\u7684\u8d28\u91cf":66,"\u8bc4\u5206":[67,68],"\u8bc4\u5206\u6587\u4ef6\u7684\u6bcf\u4e00\u884c\u4ec5\u4ec5\u63d0\u4f9b\u7535\u5f71\u6216\u7528\u6237\u7684\u7f16\u53f7\u6765\u4ee3\u8868\u76f8\u5e94\u7684\u7535\u5f71\u6216\u7528\u6237":68,"\u8bc4\u5206\u88ab\u8c03\u6574\u4e3a5\u661f\u7684\u89c4\u6a21":67,"\u8bc4\u5ba1\u4eba\u4e00\u822c\u4e0d\u505a\u8bc4\u5ba1":46,"\u8bc4\u5ba1\u4eba\u7684\u6bcf\u4e2a\u610f\u89c1\u90fd\u5fc5\u987b\u56de\u590d":46,"\u8bc4\u5ba1\u4eba\u9700\u8981\u9010\u4e00\u67e5\u770b\u6bcf\u4e2acommit\u624d\u80fd\u77e5\u9053\u505a\u4e86\u54ea\u4e9b\u4fee\u6539":46,"\u8bc4\u8bba\u6846\u4e2d\u52a0\u4e0a":46,"\u8bc6\u522b\u6570\u5b57":30,"\u8bcd\u5411\u91cf":[30,62],"\u8bcd\u5411\u91cf\u6a21\u578b":65,"\u8bcd\u5411\u91cf\u6a21\u578b\u540d\u79f0":62,"\u8bcd\u672c\u8eab\u548c\u8bcd\u9891":62,"\u8bcd\u9891\u6700\u9ad8\u7684":71,"\u8bd5\u7740\u8ba9\u8f93\u51fa\u7684\u5206\u6790\u6570\u636e\u548c\u7406\u8bba\u503c\u5bf9\u5e94":50,"\u8be5":[51,69],"\u8be5\u51fd\u6570\u5177\u6709\u4e24\u4e2a\u53c2\u6570":3,"\u8be5\u51fd\u6570\u5728\u521d\u59cb\u5316\u7684\u65f6\u5019\u4f1a\u88ab\u8c03\u7528":3,"\u8be5\u51fd\u6570\u7684\u529f\u80fd\u662f":3,"\u8be5\u53c2\u6570\u5728\u7f51\u7edc\u914d\u7f6e\u7684output":53,"\u8be5\u53c2\u6570\u5728\u96c6\u7fa4\u63d0\u4ea4\u73af\u5883\u4e2d\u81ea\u52a8\u8bbe\u7f6e":53,"\u8be5\u53c2\u6570\u5df2\u7ecf\u5728\u96c6\u7fa4\u63d0\u4ea4\u73af\u5883\u4e2d\u5b8c\u6210\u8bbe\u7f6e":53,"\u8be5\u53c2\u6570\u5fc5\u987b\u80fd\u88abflag":53,"\u8be5\u53c2\u6570\u6307\u793a\u662f\u5426\u6253\u5370\u65e5\u5fd7\u622a\u65ad\u4fe1\u606f":53,"\u8be5\u53c2\u6570\u6307\u793a\u662f\u5426\u6253\u5370\u9519\u8bef\u622a\u65ad\u65e5\u5fd7":53,"\u8be5\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u52a8\u6001\u5e93\u8def\u5f84":53,"\u8be5\u53c2\u6570\u7684\u610f\u601d\u662f\u8bad\u7ec3num":53,"\u8be5\u53c2\u6570\u9ed8\u8ba4\u4e3anull":53,"\u8be5\u5bf9\u8c61\u5177\u6709\u4ee5\u4e0b\u4e24\u4e2a\u5c5e\u6027":3,"\u8be5\u5c42\u4ec5\u9700\u8981\u8fd9\u4e9b\u975e\u96f6\u6837\u672c\u4f4d\u7f6e\u6240\u5bf9\u5e94\u7684\u53d8\u6362\u77e9\u9635\u7684\u90a3\u4e9b\u884c":47,"\u8be5\u5c42\u795e\u7ecf\u5143\u4e2a\u6570":66,"\u8be5\u622a\u65ad\u4f1a\u5f71\u54cd":53,"\u8be5\u6279\u6b21\u7684\u8f93\u5165\u4e2d\u4ec5\u6709\u4e00\u4e2a\u5b50\u96c6\u662f\u975e\u96f6\u7684":47,"\u8be5\u63a5\u53e3\u4f7f\u7528\u591a\u7ebf\u7a0b\u8bfb\u53d6\u6570\u636e":3,"\u8be5\u63a5\u53e3\u53ef\u7528\u4e8e\u9884\u6d4b\u548c\u5b9a\u5236\u5316\u8bad\u7ec3":33,"\u8be5\u6570\u636e\u53ca\u6709\u5f88\u591a\u4e0d\u540c\u7684\u7248\u672c":67,"\u8be5\u6570\u636e\u96c6":62,"\u8be5\u6570\u636e\u96c6\u4e8e2003\u5e742\u6708\u53d1\u5e03":67,"\u8be5\u6570\u636e\u96c6\u5305\u542b\u4e00\u4e9b\u7528\u6237\u4fe1\u606f":67,"\u8be5\u6570\u76ee\u662f\u63d0\u524d\u5b9a\u4e49\u597d\u7684":53,"\u8be5\u6587\u4ef6\u53ef\u4ee5\u4ece\u5b57\u6bb5\u914d\u7f6e\u6587\u4ef6\u751f\u6210":68,"\u8be5\u6587\u4ef6\u662f\u7531cpickle\u4ea7\u751f\u7684":64,"\u8be5\u6587\u4ef6\u662fpython\u7684pickle\u5bf9\u8c61":68,"\u8be5\u6587\u4ef6\u8d1f\u8d23\u4ea7\u751f\u56fe\u7247\u6570\u636e\u5e76\u4f20\u9012\u7ed9paddle\u7cfb\u7edf":63,"\u8be5\u6a21\u578b\u4f9d\u7136\u4f7f\u7528\u903b\u8f91\u56de\u5f52\u5206\u7c7b\u7f51\u7edc\u7684\u6846\u67b6":66,"\u8be5\u6a21\u578b\u5728\u957f\u8bed\u53e5\u7ffb\u8bd1\u7684\u573a\u666f\u4e0b\u6548\u679c\u63d0\u5347\u66f4\u52a0\u660e\u663e":71,"\u8be5\u6a21\u578b\u7684\u7f51\u7edc\u914d\u7f6e\u5982\u4e0b":32,"\u8be5\u6a21\u578b\u7684\u8bf4\u660e\u5982\u4e0b\u56fe\u6240\u793a":45,"\u8be5\u6a21\u578b\u7f51\u7edc\u53ea\u662f\u7528\u4e8e\u8fdb\u884cdemo\u5c55\u793apaddle\u5982\u4f55\u5de5\u4f5c":68,"\u8be5\u793a\u4f8b\u5c06\u5c55\u793apaddle\u5982\u4f55\u8fdb\u884c\u8bcd\u5411\u91cf\u5d4c\u5165":68,"\u8be5\u793a\u4f8b\u7684\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e\u6587\u4ef6":68,"\u8be5\u7b97\u6cd5\u6bcf\u6279\u91cf":32,"\u8be5\u7c7b\u7684\u5b9e\u73b0\u7ec6\u8282\u5728":47,"\u8be5\u7f16\u8bd1\u5de5\u5177\u94fe\u9700\u8981\u7cfb\u7edfglibc\u652f\u63012":40,"\u8be5\u811a\u672c\u4ec5\u4ec5\u662f\u5f00\u59cb\u4e00\u4e2apaddle\u8bad\u7ec3\u8fc7\u7a0b":68,"\u8be5\u811a\u672c\u4f1a\u751f\u6210\u4e00\u4e2adot\u6587\u4ef6":64,"\u8be5\u811a\u672c\u5c06\u8f93\u51fa\u9884\u6d4b\u5206\u7c7b\u7684\u6807\u7b7e":63,"\u8be5\u8bed\u53e5\u4f1a\u4e3a\u6bcf\u4e2a\u5c42\u521d\u59cb\u5316\u5176\u6240\u9700\u8981\u7684\u53d8\u91cf\u548c\u8fde\u63a5":47,"\u8be5github\u4ed3\u5e93\u4e2d\u5305\u542b\u82e5\u5e72\u4e2a\u9884\u7f16\u8bd1\u597d\u7684":40,"\u8be6\u7ec6\u4ecb\u7ecd\u53ef\u4ee5\u53c2\u8003":42,"\u8be6\u7ec6\u4fe1\u606f\u8bf7\u68c0\u67e5":51,"\u8be6\u7ec6\u5185\u5bb9\u8bf7\u53c2\u89c1":66,"\u8be6\u7ec6\u53ef\u53c2\u8003":46,"\u8be6\u7ec6\u5730\u5c55\u793a\u4e86\u6574\u4e2a\u7279\u5f81\u63d0\u53d6\u7684\u8fc7\u7a0b":64,"\u8be6\u7ec6\u6587\u6863\u53c2\u8003":31,"\u8be6\u7ec6\u7684\u53c2\u6570\u89e3\u91ca":66,"\u8be6\u7ec6\u7684cmake\u4f7f\u7528\u65b9\u6cd5\u53ef\u4ee5\u53c2\u8003":33,"\u8be6\u7ec6\u89c1":41,"\u8be6\u7ec6\u8bbe\u8ba1":24,"\u8bed\u4e49\u89d2\u8272\u6807\u6ce8":[65,69],"\u8bed\u610f\u89d2\u8272\u6807\u6ce8":30,"\u8bed\u8a00\u6a21\u578b":62,"\u8bf4\u660e":[19,33,58],"\u8bf4\u660e\u63d0\u4ea4\u7684\u4ee3\u7801\u5b58\u5728\u95ee\u9898":46,"\u8bf4\u660e\u6bcf\u4e2a\u7279\u5f81\u6587\u4ef6\u5177\u4f53\u5b57\u6bb5\u662f":68,"\u8bf4\u660e\u8fd9\u4e2a\u5c42\u7684\u8f93\u5165":47,"\u8bf7\u4fdd\u8bc1travi":46,"\u8bf7\u5148\u4f7f\u7528":[39,40],"\u8bf7\u518d\u6b21\u786e\u8ba4py_paddle\u5305\u5df2\u7ecf\u5b89\u88c5":48,"\u8bf7\u53c2\u7167\u7f51\u7edc\u914d\u7f6e\u7684\u6587\u6863\u4e86\u89e3\u66f4\u8be6\u7ec6\u7684\u4fe1\u606f":55,"\u8bf7\u53c2\u8003":[3,27,31,37,42,45,47,48,66],"\u8bf7\u53c2\u8003\u5982\u4e0b\u8868\u683c":66,"\u8bf7\u53c2\u8003\u9875\u9762":68,"\u8bf7\u53c2\u8003layer\u6587\u6863":63,"\u8bf7\u53c2\u89c1":46,"\u8bf7\u53c2\u9605":45,"\u8bf7\u53c2\u9605\u60c5\u611f\u5206\u6790\u7684\u6f14\u793a\u4ee5\u4e86\u89e3\u6709\u5173\u957f\u671f\u77ed\u671f\u8bb0\u5fc6\u5355\u5143\u7684\u66f4\u591a\u4fe1\u606f":69,"\u8bf7\u5728\u8be5pull":46,"\u8bf7\u5b89\u88c5cuda":36,"\u8bf7\u5c06with_doc\u9009\u9879\u5173\u95ed":48,"\u8bf7\u60a8\u6bcf\u6b21\u63d0\u4ea4\u4ee3\u7801\u65f6":46,"\u8bf7\u60a8\u9075\u5b88\u4ee5\u4e0b\u7ea6\u5b9a":46,"\u8bf7\u628a\u6587\u6863\u91cc\u547d\u4ee4\u4e2d\u7684paddlepaddl":34,"\u8bf7\u6307\u5b9a\u8be5\u76ee\u5f55":53,"\u8bf7\u67e5\u770b":62,"\u8bf7\u6ce8\u610f":[34,45,57,62],"\u8bf7\u6ce8\u610f\u6bcf\u4e2acommit\u7684\u540d\u79f0":46,"\u8bf7\u6ce8\u610fcommit\u7684\u6570\u91cf":46,"\u8bf7\u770b\u4e0b\u9762\u7684\u4f8b\u5b50":55,"\u8bf7\u786e\u4fdd":46,"\u8bf7\u7ed9\u51fa\u603b\u4f53\u7684\u4fee\u6539\u60c5\u51b5":46,"\u8bf7\u7ed9\u51fa\u60a8\u81ea\u5df1\u7684\u53cd\u9a73\u7406\u7531":46,"\u8bf7\u8bb0\u4f4f":51,"\u8bf7\u8fd0\u884c":34,"\u8bf7\u9009\u62e9\u5408\u9002\u7684\u8bcd\u6c47":46,"\u8bf7\u9009\u62e9\u6b63\u786e\u7684\u7248\u672c":31,"\u8bf7\u9075\u5b88":46,"\u8bf7\u91c7\u7528":46,"\u8bf8\u5982\u56fe\u50cf\u5206\u7c7b":55,"\u8bfb\u53d612\u4e2a\u91c7\u6837\u6570\u636e\u8fdb\u884c\u968f\u673a\u68af\u5ea6\u8ba1\u7b97\u6765\u66f4\u65b0\u66f4\u65b0":32,"\u8bfb\u53d6\u6570\u636e":3,"\u8bfb\u53d6\u6bcf\u4e00\u884c":3,"\u8bfb\u53d6volume\u4e2d\u7684\u6570\u636e\u8fdb\u884c\u8fd9\u6b21\u5206\u5e03\u5f0f\u8bad\u7ec3":58,"\u8bfb\u8005\u53ef\u4ee5\u67e5\u770b":58,"\u8bfb\u8005\u9700\u8981\u66ff\u6362\u6210\u81ea\u5df1\u4f7f\u7528\u7684\u4ed3\u5e93\u5730\u5740":58,"\u8c03\u7528":[47,63],"\u8c03\u7528\u4e00\u6b21":3,"\u8c03\u7528\u4e0a\u9762\u7684process\u51fd\u6570\u83b7\u5f97\u89c2\u6d4b\u6570\u636e":32,"\u8c03\u7528\u65b9\u6cd5\u89c1c":39,"\u8c03\u7528\u7684pydataprovider2\u662f":3,"\u8c03\u7528\u7b2c\u4e8c\u6b21\u7684\u65f6\u5019":3,"\u8c03\u7528\u8be5\u51fd\u6570\u540e":47,"\u8c03\u7528\u8fd9\u4e2apydataprovider2\u7684\u65b9\u6cd5":3,"\u8c13\u8bcd\u4e0a\u4e0b\u6587":69,"\u8d1f\u6837\u672c":66,"\u8d1f\u9762\u7684\u8bc4\u8bba\u7684\u5f97\u5206\u5c0f\u4e8e\u7b49\u4e8e4":70,"\u8d1f\u9762\u8bc4\u4ef7\u6837\u672c":70,"\u8d21\u732e\u6587\u6863":48,"\u8d44\u6e90\u5bf9\u8c61\u7684\u540d\u5b57\u662f\u552f\u4e00\u7684":56,"\u8d77":42,"\u8def\u5f84\u4e0b":[32,64],"\u8df3\u8f6c\u5230":46,"\u8f6c\u4e3ajpeg\u6587\u4ef6\u5e76\u5b58\u5165\u7279\u5b9a\u7684\u76ee\u5f55":63,"\u8f6c\u6362\u751f\u6210\u7684\u6587\u4ef6\u540d\u4f1a\u662f\u4ee5\u4e0b\u683c\u5f0f":20,"\u8f6c\u6362\u8fc7\u6765\u7684":64,"\u8f6e":68,"\u8f83":42,"\u8f93\u5165":[41,45],"\u8f93\u5165\u548c\u8f93\u51fa\u90fd\u662f\u5355\u5c42\u5e8f\u5217":44,"\u8f93\u5165\u548c\u8f93\u51fa\u90fd\u662f\u53cc\u5c42\u5e8f\u5217":44,"\u8f93\u5165\u56fe\u7247\u7684\u9ad8\u5ea6\u53ca\u5bbd\u5ea6":63,"\u8f93\u5165\u5c42\u5c3a\u5bf8":64,"\u8f93\u5165\u6570\u636e\u4e3a\u4e00\u4e2a\u5b8c\u6574\u7684\u65f6\u95f4\u5e8f\u5217":42,"\u8f93\u5165\u6570\u636e\u4e3a\u5728\u5355\u5c42rnn\u6570\u636e\u91cc\u9762":42,"\u8f93\u5165\u6570\u636e\u6574\u4f53\u4e0a\u662f\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217":42,"\u8f93\u5165\u6570\u636e\u7684\u5b57\u5178\u7ef4\u6570\u662f1\u767e\u4e07":55,"\u8f93\u5165\u6570\u6910\u6240\u5728\u76ee\u5f55":70,"\u8f93\u5165\u6587\u672c":62,"\u8f93\u5165\u6587\u672c\u4e2d\u6ca1\u6709\u5934\u90e8":62,"\u8f93\u5165\u662f\u5426\u662f\u8f6c\u7f6e\u7684":47,"\u8f93\u5165\u662f\u7531\u4e00\u4e2alist\u4e2d\u7684\u7f51\u7edc\u5c42\u5b9e\u4f8b\u7684\u540d\u5b57\u7ec4\u6210\u7684":47,"\u8f93\u5165\u7279\u5f81\u56fe\u7684\u901a\u9053\u6570\u76ee":64,"\u8f93\u5165\u7684":62,"\u8f93\u5165\u7684\u539f\u59cb\u6570\u636e\u96c6\u8def\u5f84":71,"\u8f93\u5165\u7684\u540d\u5b57":47,"\u8f93\u5165\u7684\u5927\u5c0f":47,"\u8f93\u5165\u7684\u6587\u672c\u683c\u5f0f\u5982\u4e0b":62,"\u8f93\u5165\u7684\u6587\u672c\u8bcd\u5411\u91cf\u6a21\u578b\u540d\u79f0":62,"\u8f93\u5165\u7684\u7c7b\u578b":47,"\u8f93\u5165\u95e8":70,"\u8f93\u5165\u9884\u6d4b\u6837\u672c":70,"\u8f93\u5165n\u4e2a\u5355\u8bcd":66,"\u8f93\u51fa":[41,45],"\u8f93\u51fa\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":44,"\u8f93\u51fa\u4e00\u4e2a\u53cc\u5c42\u5e8f\u5217":44,"\u8f93\u51fa\u4e3an\u4e2aword_dim\u7ef4\u5ea6\u5411\u91cf":66,"\u8f93\u51fa\u51fd\u6570":45,"\u8f93\u51fa\u5e8f\u5217\u7684\u7c7b\u578b":41,"\u8f93\u51fa\u5e8f\u5217\u7684\u8bcd\u8bed\u6570\u548c\u8f93\u5165\u5e8f\u5217\u4e00\u81f4":44,"\u8f93\u51fa\u5e94\u8be5\u7c7b\u4f3c\u5982\u4e0b":68,"\u8f93\u51fa\u6587\u4ef6\u7684\u683c\u5f0f\u8bf4\u660e":62,"\u8f93\u51fa\u65e5\u5fd7\u4fdd\u5b58\u5728\u8def\u5f84":70,"\u8f93\u51fa\u65e5\u5fd7\u8bf4\u660e\u5982\u4e0b":70,"\u8f93\u51fa\u67092\u5217":62,"\u8f93\u51fa\u7279\u5f81\u56fe\u7684\u901a\u9053\u6570\u76ee":64,"\u8f93\u51fa\u7684\u4e8c\u8fdb\u5236\u8bcd\u5411\u91cf\u6a21\u578b\u540d\u79f0":62,"\u8f93\u51fa\u7684\u6587\u672c\u6a21\u578b\u540d\u79f0":62,"\u8f93\u51fa\u7684\u68af\u5ea6":53,"\u8f93\u51fa\u76ee\u5f55":64,"\u8f93\u51fa\u7ed3\u679c\u53ef\u80fd\u4f1a\u968f\u7740\u5bb9\u5668\u7684\u6d88\u8017\u800c\u88ab\u5220\u9664":57,"\u8fbe\u5230\u5bb9\u707e\u7684\u76ee\u7684":19,"\u8fc7\u4e86\u4e00\u4e2a\u5f88\u7b80\u5355\u7684recurrent_group":42,"\u8fc7\u5b8c\u6240\u6709\u8bad\u7ec3\u6570\u636e\u5373\u4e3a\u4e00\u4e2apass":31,"\u8fd0":34,"\u8fd0\u884c":36,"\u8fd0\u884c\u4e0b\u9762\u547d\u4ee4\u5373\u53ef":68,"\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\u6765\u4e0b\u8f7d\u8fd9\u4e2a\u811a\u672c":71,"\u8fd0\u884c\u4ee5\u4e0b\u6307\u4ee4":34,"\u8fd0\u884c\u4ee5\u4e0b\u7684\u547d\u4ee4\u4e0b\u8f7d\u548c\u83b7\u53d6\u6211\u4eec\u7684\u5b57\u5178\u548c\u9884\u8bad\u7ec3\u6a21\u578b":62,"\u8fd0\u884c\u4ee5\u4e0b\u7684\u547d\u4ee4\u4e0b\u8f7d\u6570\u636e\u96c6":62,"\u8fd0\u884c\u4ee5\u4e0b\u8bad\u7ec3\u547d\u4ee4":32,"\u8fd0\u884c\u4ee5\u53ca\u53d1\u5e03\u60a8\u7684ai\u7a0b\u5e8f":34,"\u8fd0\u884c\u5206\u5e03\u5f0f\u4f5c\u4e1a":51,"\u8fd0\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3":[31,49,66],"\u8fd0\u884c\u5355\u5143\u6d4b\u8bd5":34,"\u8fd0\u884c\u5931\u8d25":55,"\u8fd0\u884c\u5b8c\u4ee5\u4e0a\u547d\u4ee4":62,"\u8fd0\u884c\u5b8c\u6210\u540e":51,"\u8fd0\u884c\u5b8c\u811a\u672c":70,"\u8fd0\u884c\u6210\u529f\u4ee5\u540e":62,"\u8fd0\u884c\u6210\u529f\u540e\u76ee\u5f55":70,"\u8fd0\u884c\u65e5\u5fd7":51,"\u8fd0\u884c\u7684\u4e00\u4e9b\u53c2\u6570\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\u4f20\u9012\u5230\u5bb9\u5668\u5185":58,"\u8fd1":42,"\u8fd1\u671f\u63d0\u51fa\u7684nmt\u6a21\u578b\u901a\u5e38\u90fd\u5c5e\u4e8e\u7f16\u89e3\u7801\u6a21\u578b":71,"\u8fd4\u56de":[8,9,10,13,15],"\u8fd4\u56de0":3,"\u8fd4\u56de8\u4e2a\u7279\u5f81list\u548c1\u4e2a\u6807\u7b7elist":69,"\u8fd4\u56de\u4e00\u6761\u5b8c\u6574\u7684\u6837\u672c":3,"\u8fd4\u56de\u6570\u636e\u7684\u6bcf\u4e00\u6761\u6837\u672c\u7ed9":68,"\u8fd4\u56de\u65f6":3,"\u8fd4\u56de\u7684\u662f":[3,37],"\u8fd4\u56de\u7684\u987a\u5e8f\u9700\u8981\u548cinput_types\u4e2d\u5b9a\u4e49\u7684\u987a\u5e8f\u4e00\u81f4":3,"\u8fd4\u56de\u7b2c\u4e8c\u6b65":30,"\u8fd4\u56de\u7b2ci\u4e2a\u8f93\u5165\u77e9\u9635":47,"\u8fd4\u56de\u7c7b\u578b":[8,9,10,13,15],"\u8fd8\u4f1a":42,"\u8fd8\u4f1a\u5f3a\u5236\u8bbe\u7f6e\u4e00\u4e9bpaddlepaddle\u53c2\u6570\u7684\u503c":39,"\u8fd8\u4f1a\u8f93\u51fa\u4e00\u4e2a":46,"\u8fd8\u662f":42,"\u8fd8\u662f\u4ece":20,"\u8fd8\u6709":42,"\u8fd8\u80fd\u5904\u7406\u5176\u4ed6\u7528\u6237\u81ea\u5b9a\u4e49\u7684\u6570\u636e":70,"\u8fd8\u91c7\u7528\u4e86\u4e24\u4e2a\u5176\u4ed6\u7279\u5f81":69,"\u8fd8\u9700\u8981\u8fdb\u884c\u9884\u5904\u7406":63,"\u8fd9":[31,42,66],"\u8fd9\u4e00\u5757\u7684\u8017\u65f6\u6bd4\u4f8b\u771f\u7684\u592a\u9ad8":50,"\u8fd9\u4e00\u5c42\u8fdb\u884c\u5c01\u88c5":27,"\u8fd9\u4e00\u6982\u5ff5\u4e0d\u518d\u7410\u788e":27,"\u8fd9\u4e00\u8fc7\u7a0b\u5bf9\u7528\u6237\u662f\u5b8c\u5168\u900f\u660e\u7684":44,"\u8fd9\u4e09\u4e2a\u5206\u652f":30,"\u8fd9\u4e09\u4e2a\u6b65\u9aa4\u53ef\u914d\u7f6e\u4e3a":66,"\u8fd9\u4e0e\u672c\u5730\u8bad\u7ec3\u76f8\u540c":51,"\u8fd9\u4e24\u4e2a\u6587\u4ef6\u5939\u4e0b\u5404\u81ea\u670910\u4e2a\u5b50\u6587\u4ef6\u5939":63,"\u8fd9\u4e24\u4e2a\u6807\u51c6":69,"\u8fd9\u4e2a":[42,56],"\u8fd9\u4e2a\u4efb\u52a1\u7684\u914d\u7f6e\u4e3a":31,"\u8fd9\u4e2a\u4efb\u52a1\u7684dataprovider\u4e3a":31,"\u8fd9\u4e2a\u51fd\u6570\u7684":45,"\u8fd9\u4e2a\u51fd\u6570\u8fdb\u884c\u53d8\u6362":42,"\u8fd9\u4e2a\u51fd\u6570\u9700\u8981\u8bbe\u7f6e":45,"\u8fd9\u4e2a\u52a8\u6001\u5e93\u7684\u8fde\u63a5\u53c2\u6570\u4e0epaddle\u7684\u5176\u4ed6\u4e8c\u8fdb\u5236":27,"\u8fd9\u4e2a\u53c2\u6570\u4e5f\u4e0d\u4f1a\u4e00\u5e76\u5220\u9664":27,"\u8fd9\u4e2a\u5411\u91cf\u4e0e\u6e90\u4e2d\u641c\u7d22\u51fa\u7684\u4f4d\u7f6e\u548c\u6240\u6709\u4e4b\u524d\u751f\u6210\u7684\u76ee\u6807\u5355\u8bcd\u6709\u5173":71,"\u8fd9\u4e2a\u5730\u5740\u5219\u4e3a\u5b83\u7684\u7edd\u5bf9\u8def\u5f84\u6216\u76f8\u5bf9\u8def\u5f84":2,"\u8fd9\u4e2a\u5730\u5740\u6765\u8868\u793a\u6b64\u6b65\u9aa4\u6240\u6784\u5efa\u51fa\u7684\u955c\u50cf":58,"\u8fd9\u4e2a\u57fa\u7c7b":47,"\u8fd9\u4e2a\u5934\u6587\u4ef6\u4e0d\u5047\u8bbe\u5176\u4ed6\u6587\u4ef6\u7684\u5f15\u7528\u987a\u5e8f":27,"\u8fd9\u4e2a\u5b57\u5178\u662f\u6574\u6570\u6807\u7b7e\u548c\u5b57\u7b26\u4e32\u6807\u7b7e\u7684\u4e00\u4e2a\u5bf9\u5e94":70,"\u8fd9\u4e2a\u5e8f\u5217\u7684\u6bcf\u4e2a\u5143\u7d20\u53c8\u662f\u4e00\u4e2a\u5e8f\u5217":44,"\u8fd9\u4e2a\u63a5\u53e3\u9700\u8981\u505a\u5230":26,"\u8fd9\u4e2a\u6570\u636e\u4e5f\u88ab\u5355\u5c42rnn\u7f51\u7edc\u76f4\u63a5\u4f7f\u7528":42,"\u8fd9\u4e2a\u6570\u91cf\u79f0\u4e3abeam":71,"\u8fd9\u4e2a\u6587\u4ef6\u5177\u6709\u72ec\u7279\u7684\u8bed\u6cd5":26,"\u8fd9\u4e2a\u663e\u793a\u5668\u5f88\u68d2":66,"\u8fd9\u4e2a\u6a21\u578b\u5bf9\u4e8e\u7f16\u89e3\u7801\u6a21\u578b\u6765\u8bf4":71,"\u8fd9\u4e2a\u76ee\u5f55\u4e2d\u9664\u4e86":27,"\u8fd9\u4e2a\u795e\u7ecf\u7f51\u7edc\u5355\u5143\u5c31\u53ebmemori":42,"\u8fd9\u4e2a\u7a0b\u5e8f\u662f\u60a8\u5728\u5f00\u53d1\u673a\u4e0a\u4f7f\u7528\u5f00\u53d1\u955c\u50cf\u5b8c\u6210\u5f00\u53d1":34,"\u8fd9\u4e2a\u7c7b\u7684\u53c2\u6570\u5305\u62ec":47,"\u8fd9\u4e2a\u7c7b\u9700\u8981\u7ee7\u627f":47,"\u8fd9\u4e2a\u7cfb\u7edf\u5c06srl\u4efb\u52a1\u89c6\u4e3a\u5e8f\u5217\u6807\u6ce8\u95ee\u9898":69,"\u8fd9\u4e2a\u7ed3\u6784\u4f53\u4e2d\u7684\u53e6\u4e00\u4e2a\u9879\u76ee\u662f":27,"\u8fd9\u4e2a\u7ed3\u6784\u4f53\u5305\u542b\u4e24\u4e2a\u9879\u76ee":27,"\u8fd9\u4e2a\u8282\u70b9\u53ef\u4ee5\u662f\u7269\u7406\u673a\u6216\u8005\u865a\u62df\u673a":56,"\u8fd9\u4e2a\u8868\u683c":56,"\u8fd9\u4e2a\u8fc7\u7a0b\u5bf9\u7528\u6237\u4e5f\u662f\u900f\u660e\u7684":44,"\u8fd9\u4e2a\u8fc7\u7a0b\u5c31\u662f\u6a21\u578b\u7684\u8bad\u7ec3\u8fc7\u7a0b":32,"\u8fd9\u4e2a\u8fc7\u7a0b\u9664\u4e86\u7f16\u8bd1paddlepaddle\u4e3a":46,"\u8fd9\u4e2a\u914d\u7f6e\u4e0e":62,"\u8fd9\u4e2a\u914d\u7f6e\u6587\u4ef6":56,"\u8fd9\u4e2a\u914d\u7f6e\u662f\u5426\u7528\u6765\u751f\u6210":71,"\u8fd9\u4e2a\u955c\u50cf\u5305\u542b\u4e86paddle\u76f8\u5173\u7684\u5f00\u53d1\u5de5\u5177\u4ee5\u53ca\u7f16\u8bd1\u548c\u8fd0\u884c\u73af\u5883":34,"\u8fd9\u4e2a\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u751f\u6210\u4e00\u7cfb\u5217\u6743\u91cd":45,"\u8fd9\u4e2a\u95ee\u9898\u662fpydataprovider\u8bfb\u6570\u636e\u65f6\u5019\u7684\u903b\u8f91\u95ee\u9898":3,"\u8fd9\u4e2a\u9759\u6001\u5e93\u5305\u542b\u4e86paddle\u7684\u5168\u90e8\u7b26\u53f7":27,"\u8fd9\u4e2adataprovider\u8f83\u590d\u6742":3,"\u8fd9\u4e2ainstance\u53ef\u4ee5\u662f\u5355\u4e2a\u503c":20,"\u8fd9\u4e2ajob\u624d\u7b97\u6210\u529f\u7ed3\u675f":58,"\u8fd9\u4e2alayer\u7684\u8f93\u51fa\u4f1a\u4f5c\u4e3a\u6574\u4e2a":44,"\u8fd9\u4e5f\u4f1a\u6781\u5927\u51cf\u5c11\u6570\u636e\u8bfb\u5165\u7684\u8017\u65f6":31,"\u8fd9\u4e9b":51,"\u8fd9\u4e9b\u5206\u5e03\u5f0f\u5b58\u50a8\u670d\u52a1\u901a\u5e38\u4f1a\u628a\u6570\u636e\u5207\u5272\u6210\u591a\u4e2a\u5206\u7247\u5206\u5e03\u5f0f\u7684\u5b58\u50a8\u5728\u591a\u4e2a\u8282\u70b9\u4e4b\u4e0a":20,"\u8fd9\u4e9b\u53c2\u6570\u7684\u5177\u4f53\u63cf\u8ff0":58,"\u8fd9\u4e9b\u53c2\u6570\u7684\u7b80\u77ed\u4ecb\u7ecd\u5982\u4e0b":68,"\u8fd9\u4e9b\u540d\u5b57\u5fc5\u987b\u8981\u5199\u5bf9":47,"\u8fd9\u4e9b\u6570\u636e\u4f1a\u88ab\u7528\u6765\u66f4\u65b0\u53c2\u6570":31,"\u8fd9\u4e9b\u6570\u636e\u4f7f\u7528\u7684\u5185\u5b58\u4e3b\u8981\u548c\u4e24\u4e2a\u53c2\u6570\u6709\u5173\u7cfb":31,"\u8fd9\u4e9b\u6587\u4ef6\u5c06\u4f1a\u88ab\u4fdd\u5b58\u5728":64,"\u8fd9\u4e9b\u6a21\u578b\u90fd\u662f\u7531\u539f\u4f5c\u8005\u63d0\u4f9b\u7684\u6a21\u578b":64,"\u8fd9\u4e9b\u7279\u5f81\u503c\u4e0e\u4e0a\u8ff0\u4f7f\u7528c":64,"\u8fd9\u4e9b\u7279\u5f81\u548c\u6807\u7b7e\u5b58\u50a8\u5728":69,"\u8fd9\u4e9b\u7279\u5f81\u6570\u636e\u4e4b\u95f4\u7684\u987a\u5e8f\u662f\u6709\u610f\u4e49\u7684":42,"\u8fd9\u4efd\u6559\u7a0b\u5c55\u793a\u4e86\u5982\u4f55\u5728paddlepaddle\u4e2d\u5b9e\u73b0\u4e00\u4e2a\u81ea\u5b9a\u4e49\u7684\u7f51\u7edc\u5c42":47,"\u8fd9\u4efd\u7b80\u77ed\u7684\u4ecb\u7ecd\u5c06\u5411\u4f60\u5c55\u793a\u5982\u4f55\u5229\u7528paddlepaddle\u6765\u89e3\u51b3\u4e00\u4e2a\u7ecf\u5178\u7684\u7ebf\u6027\u56de\u5f52\u95ee\u9898":32,"\u8fd9\u4f1a\u63d0\u793a\u5f53\u524d\u76ee\u5f55\u7684\u4e00\u4e9b\u53d8\u5316":46,"\u8fd9\u4f1a\u7ed9\u8bc4\u5ba1\u4eba\u5e26\u6765\u5f88\u5927\u56f0\u6270":46,"\u8fd9\u4f1a\u81ea\u52a8\u8fdb\u884c\u7f51\u7edc\u914d\u7f6e\u4e2d\u58f0\u660e\u7684\u6fc0\u6d3b\u64cd\u4f5c":47,"\u8fd9\u4f7f\u5f97nmt\u6a21\u578b\u5f97\u4ee5\u89e3\u653e\u51fa\u6765":71,"\u8fd9\u4fbf\u662f\u4e00\u79cd\u53cc\u5c42rnn\u7684\u8f93\u5165\u6570\u636e":42,"\u8fd9\u51e0\u4e2a\u7f16\u8bd1\u9009\u9879\u7684\u8bbe\u7f6e":33,"\u8fd9\u53ef\u4ee5\u8ba9\u5176\u4ed6\u4eba\u77e5\u9053\u8fd9\u6b21\u63d0\u4ea4\u505a\u4e86\u54ea\u4e9b\u6539\u53d8":46,"\u8fd9\u53ef\u4ee5\u901a\u8fc7":46,"\u8fd9\u548c\u5355\u5c42rnn\u7684\u914d\u7f6e\u662f\u7b49\u4ef7\u7684":42,"\u8fd9\u56db\u4e2a\u7b80\u5355\u7684\u7279\u5f81\u662f\u6211\u4eec\u7684srl\u7cfb\u7edf\u6240\u9700\u8981\u7684":69,"\u8fd9\u56db\u6761\u6570\u636e\u540c\u65f6\u5904\u7406\u7684\u53e5\u5b50\u6570\u91cf\u4e3a":42,"\u8fd9\u5728\u5f88\u5927\u7a0b\u5ea6\u4e0a\u4f18\u4e8e\u5148\u524d\u7684\u6700\u5148\u8fdb\u7684\u7cfb\u7edf":69,"\u8fd9\u5728\u6784\u9020\u975e\u5e38\u590d\u6742\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u65f6\u662f\u6709\u7528\u7684":45,"\u8fd9\u5bf9\u4e8e\u901a\u5e38\u7684java\u7684\u5f00\u53d1\u8005\u6765\u8bf4":26,"\u8fd9\u5c06\u82b1\u8d39\u6570\u5206\u949f\u7684\u65f6\u95f4":71,"\u8fd9\u5df2\u7ecf\u5728":70,"\u8fd9\u610f\u5473\u7740":45,"\u8fd9\u610f\u5473\u7740\u6a21\u578b\u5728\u8bad\u7ec3\u6570\u636e\u4e0a\u4e0d\u65ad\u7684\u6539\u8fdb":32,"\u8fd9\u610f\u5473\u7740\u9664\u4e86\u6307\u5b9adevic":55,"\u8fd9\u65f6\u8fdb\u884c\u77e9\u9635\u4e58\u6cd5\u8fd0\u7b97\u5c31\u53ef\u80fd\u5bfc\u81f4\u6d6e\u70b9\u6570\u6ea2\u51fa":31,"\u8fd9\u662f\u4e00\u4e2a\u57fa\u4e8e\u7edf\u8ba1\u7684\u673a\u5668\u7ffb\u8bd1\u7cfb\u7edf":70,"\u8fd9\u662f\u4e00\u79cd\u975e\u5e38\u7075\u6d3b\u7684\u6570\u636e\u7ec4\u7ec7\u65b9\u5f0f":41,"\u8fd9\u662f\u56e0\u4e3a":26,"\u8fd9\u662f\u56e0\u4e3a\u5b83\u53d1\u6398\u51fa\u4e86\u56fe\u7247\u7684\u4e24\u7c7b\u91cd\u8981\u4fe1\u606f":63,"\u8fd9\u662f\u5f00\u6e90\u793e\u533a\u7684\u57fa\u672c\u793c\u8c8c":46,"\u8fd9\u662f\u666e\u901a\u7684\u5355\u5c42\u65f6\u95f4\u5e8f\u5217\u7684dataprovider\u4ee3\u7801":42,"\u8fd9\u662f\u76ee\u524dcmake\u5bfb\u627epython\u7684\u903b\u8f91\u5b58\u5728\u7f3a\u9677":31,"\u8fd9\u662f\u96c6\u675f\u641c\u7d22\u7684\u7ed3\u679c":71,"\u8fd9\u6765\u81ea\u4e8epaddlepaddle\u7684\u5185\u5b58\u4e2d":71,"\u8fd9\u6837":[27,32,51],"\u8fd9\u6837\u4fdd\u8bc1":30,"\u8fd9\u6837\u505a\u53ef\u4ee5\u6781\u5927\u7684\u51cf\u5c11\u5185\u5b58\u5360\u7528":31,"\u8fd9\u6837\u53ef\u4ee5\u51cf\u5c0fgpu\u5185\u5b58":55,"\u8fd9\u6837\u5bb9\u5668\u7684":58,"\u8fd9\u6837\u5c31\u4f1a\u751f\u6210\u4e24\u4e2a\u6587\u4ef6":68,"\u8fd9\u6837\u5c31\u53ef\u4ee5\u5728\u4e91\u7aef\u6267\u884c\u591a\u79cd\u6570\u636e\u7c7b\u8ba1\u7b97\u4efb\u52a1":20,"\u8fd9\u6837\u5df2\u7ecf\u4f20\u8f93\u6210\u529f\u7684\u90e8\u5206\u5c31\u4e0d\u7528\u91cd\u65b0\u4f20\u8f93\u4e86":24,"\u8fd9\u6837\u5f00\u53d1\u4eba\u5458\u53ef\u4ee5\u4ee5\u4e00\u81f4\u7684\u65b9\u5f0f\u5728\u4e0d\u540c\u7684\u5e73\u53f0\u4e0a\u5de5\u4f5c":34,"\u8fd9\u6837\u5f53\u8be5pull":46,"\u8fd9\u6837\u7684\u88c5\u9970\u5668":47,"\u8fd9\u6837\u7684\u8bdd":57,"\u8fd9\u6837\u7684\u8bdd\u6bcf\u4f4d\u7528\u6237\u5728\u6d4b\u8bd5\u6587\u4ef6\u4e2d\u5c06\u4e0e\u8bad\u7ec3\u6587\u4ef6\u542b\u6709\u540c\u6837\u7684\u4fe1\u606f":68,"\u8fd9\u6b63\u662f\u5b83\u4eec\u901f\u5ea6\u5feb\u7684\u539f\u56e0":50,"\u8fd9\u6bb5\u7b80\u77ed\u7684\u914d\u7f6e\u5c55\u793a\u4e86paddlepaddle\u7684\u57fa\u672c\u7528\u6cd5":32,"\u8fd9\u7528\u4e8e\u5728\u591a\u7ebf\u7a0b\u548c\u591a\u673a\u4e0a\u66f4\u65b0\u53c2\u6570":47,"\u8fd9\u79cd\u521d\u59cb\u5316\u65b9\u5f0f\u5728\u4e00\u822c\u60c5\u51b5\u4e0b\u4e0d\u4f1a\u4ea7\u751f\u5f88\u5dee\u7684\u7ed3\u679c":31,"\u8fd9\u79cd\u60c5\u51b5\u4e0b\u4e0d\u9700\u8981\u91cd\u5199\u8be5\u51fd\u6570":47,"\u8fd9\u79cd\u65b9\u5f0f\u5fc5\u987b\u4f7f\u7528paddle\u5b58\u50a8\u7684\u6a21\u578b\u8def\u5f84\u683c\u5f0f":55,"\u8fd9\u79cd\u751f\u6210\u6280\u672f\u53ea\u7528\u4e8e\u7c7b\u4f3c\u89e3\u7801\u5668\u7684\u751f\u6210\u8fc7\u7a0b":45,"\u8fd9\u79cd\u7c7b\u578b\u7684\u8f93\u5165\u5fc5\u987b\u901a\u8fc7":44,"\u8fd9\u79cd\u96c6\u7fa4\u8282\u70b9\u7ba1\u7406\u65b9\u5f0f\u4f1a\u5728\u5c06\u6765\u4f7f\u7528":58,"\u8fd9\u7bc7\u6587\u6863":46,"\u8fd9\u7bc7\u6587\u6863\u5c06\u4ee5linux":40,"\u8fd9\u7bc7\u6587\u7ae0":71,"\u8fd9\u7ec4\u8bed\u4e49\u76f8\u540c\u7684\u793a\u4f8b\u914d\u7f6e\u5982\u4e0b":42,"\u8fd9\u901a\u8fc7\u83b7\u5f97\u53cd\u5411\u5faa\u73af\u7f51\u7edc\u7684\u7b2c\u4e00\u4e2a\u5b9e\u4f8b":45,"\u8fd9\u90fd\u9700\u8981\u8fd9\u4e2a\u63a5\u53e3\u6309\u7167\u7ea6\u5b9a\u4fd7\u6210\u7684\u89c4\u5219\u6765\u6ce8\u91ca\u5b8c\u5907":26,"\u8fd9\u91cc":[31,34,45,46,56,58,64,69],"\u8fd9\u91cc\u4ecb\u7ecd\u5982\u751f\u6210\u9020\u8fd9\u4e2a\u5f00\u53d1\u955c\u50cf":34,"\u8fd9\u91cc\u4ee5":66,"\u8fd9\u91cc\u4f7f\u7528\u4e00\u4e2a\u57fa\u4e8emomentum\u7684\u968f\u673a\u68af\u5ea6\u4e0b\u964d":32,"\u8fd9\u91cc\u4f7f\u7528\u4e86\u4e09\u79cd\u7f51\u7edc\u5355\u5143":32,"\u8fd9\u91cc\u4f7f\u7528\u4e86paddlepaddle\u7684python\u63a5\u53e3\u6765\u52a0\u8f7d\u6570\u6910":70,"\u8fd9\u91cc\u4f7f\u7528\u4e86paddlepaddle\u9884\u5b9a\u4e49\u597d\u7684rnn\u5904\u7406\u51fd\u6570":42,"\u8fd9\u91cc\u4f7f\u7528\u7b80\u5355\u7684":31,"\u8fd9\u91cc\u5229\u7528\u5b83\u5efa\u6a21\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb":32,"\u8fd9\u91cc\u53ea\u52a0\u8f7d":71,"\u8fd9\u91cc\u53ea\u7b80\u5355\u4ecb\u7ecd\u4e86\u5355\u673a\u8bad\u7ec3":66,"\u8fd9\u91cc\u5c06\u4ecb\u7ecdpaddlepaddle\u7684\u57fa\u672c\u4f7f\u7528\u6982\u5ff5":37,"\u8fd9\u91cc\u5c55\u793a\u5982\u4f55\u4f7f\u7528\u89c2\u6d4b\u6570\u636e\u6765\u62df\u5408\u8fd9\u4e00\u7ebf\u6027\u5173\u7cfb":32,"\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528":68,"\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528\u7684\u662f\u4e00\u4e2a\u5c0f\u7684vgg\u7f51\u7edc":63,"\u8fd9\u91cc\u6211\u4eec\u4f7f\u7528\u7684\u662fgpu\u6a21\u5f0f\u8fdb\u884c\u8bad\u7ec3":63,"\u8fd9\u91cc\u6211\u4eec\u5728movielens\u6570\u636e\u96c6\u63cf\u8ff0\u4e00\u79cd":68,"\u8fd9\u91cc\u6211\u4eec\u5c55\u793a\u4e00\u4efd\u7b80\u5316\u8fc7\u7684\u4ee3\u7801":47,"\u8fd9\u91cc\u6211\u4eec\u7528paddl":34,"\u8fd9\u91cc\u6211\u4eec\u901a\u8fc7\u5728kubernetes\u96c6\u7fa4\u4e0a\u542f\u52a8\u4e00\u4e2ajob\u6765\u4e0b\u8f7d\u5e76\u5207\u5272\u6570\u636e":58,"\u8fd9\u91cc\u6307\u5b9a\u8bcd\u5178":66,"\u8fd9\u91cc\u6570\u636e\u5c42\u6709\u4e24\u4e2a":32,"\u8fd9\u91cc\u662f\u4e00\u4e2a\u4f8b\u5b50":71,"\u8fd9\u91cc\u6709\u4e00\u4e9b\u4e0d\u540c\u7684\u53c2\u6570\u9700\u8981\u6307\u5b9a":71,"\u8fd9\u91cc\u68c0\u9a8c\u8fd0\u884c\u65f6\u95f4\u6a21\u578b\u7684\u6536\u655b":51,"\u8fd9\u91cc\u6bcf\u4e2a5\u4e2abatch\u6253\u5370\u4e00\u4e2a\u70b9":71,"\u8fd9\u91cc\u6bcf\u9694100\u4e2abatch\u663e\u793a\u4e00\u6b21\u53c2\u6570\u7edf\u8ba1\u4fe1\u606f":71,"\u8fd9\u91cc\u6bcf\u969410\u4e2abatch\u6253\u5370\u4e00\u6b21\u65e5\u5fd7":71,"\u8fd9\u91cc\u7684\u5217\u51fa\u7684\u548c":63,"\u8fd9\u91cc\u76f4\u63a5\u901a\u8fc7\u9884\u6d4b\u811a\u672c":66,"\u8fd9\u91cc\u7ed9\u51fa\u96c6\u4e2d\u5e38\u89c1\u7684\u90e8\u7f72\u65b9\u6cd5":56,"\u8fd9\u91cc\u8bbe\u7f6e\u4e3a\u4f7f\u7528cpu":71,"\u8fd9\u91cc\u8bbe\u7f6e\u4e3afals":71,"\u8fd9\u91cc\u8bbe\u7f6e\u4e3atrue":71,"\u8fd9\u91cc\u91c7\u7528adam\u4f18\u5316\u65b9\u6cd5":66,"\u8fd9\u91cc\u9700\u8981\u7528\u6237\u989d\u5916\u6ce8\u610f":19,"\u8fdb\u4e3b\u4ed3\u5e93\u540e":46,"\u8fdb\u5165":70,"\u8fdb\u5165\u5bb9\u5668":57,"\u8fdb\u5165\u5f00\u53d1\u955c\u50cf\u5e76\u5f00\u59cb\u5de5\u4f5c":34,"\u8fdb\u7a0b\u542f\u52a8\u7684\u5fc5\u8981\u53c2\u6570":58,"\u8fdb\u7a0b\u7684":51,"\u8fdb\u7a0b\u7684\u542f\u52a8\u53c2\u6570":58,"\u8fdb\u7a0b\u7684\u8fd0\u884c\u73af\u5883":58,"\u8fdb\u7a0b\u9700\u8981\u7684":58,"\u8fdb\u800c\u8fdb\u884c\u4ee3\u7801\u8bc4\u5ba1":30,"\u8fdb\u884c\u4e86":42,"\u8fdb\u884c\u4f7f\u7528":63,"\u8fdb\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3\u7684\u65b9\u6848":58,"\u8fdb\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3\u7684\u65b9\u6cd5":58,"\u8fdb\u884c\u56de\u590d":46,"\u8fdb\u884c\u5f00\u53d1":46,"\u8fdb\u884c\u62c6\u89e3":42,"\u8fdb\u884c\u6fc0\u6d3b\u64cd\u4f5c":47,"\u8fdb\u884c\u8bfb\u5165\u548c\u9884\u5904\u7406\u4ece\u800c\u5f97\u5230\u771f\u5b9e\u8f93\u5165":32,"\u8fdb\u884c\u9884\u6d4b":66,"\u8fdb\u9636\u6307\u5357":[37,61],"\u8fde\u63a5":44,"\u8fde\u63a5\u4e09\u4e2alstm\u9690\u85cf\u5c42":70,"\u9000\u4f11\u4eba\u5458":67,"\u9000\u51fa\u5bb9\u5668":57,"\u9002\u4e2d":42,"\u9002\u5408\u81ea\u5df1\u5b9e\u9a8c":34,"\u9009":42,"\u9009\u62e9":42,"\u9009\u62e9\u6d4b\u8bd5\u7ed3\u679c\u6700\u597d\u7684\u6a21\u578b\u6765\u9884\u6d4b":66,"\u9009\u62e9\u76ee\u6807\u5206\u652f":46,"\u9009\u62e9\u8def\u5f84\u6765\u52a8\u6001\u52a0\u8f7dnvidia":53,"\u9009\u62e9\u8fc7\u540e\u7684":71,"\u9009\u62e9\u9002\u5408\u60a8\u7684\u573a\u666f\u7684\u5408\u9002\u65b9\u6848":56,"\u9009\u81ea\u4e0b\u5217\u7c7b\u578b":67,"\u9009\u9879":[33,62],"\u900f\u4f20\u7528\u6237\u8eab\u4efd\u7684\u529e\u6cd5":24,"\u9012\u5f52\u795e\u7ecf\u7f51\u7edc":52,"\u901a\u5e38":[27,51,70],"\u901a\u5e38\u4f1a\u4f7f\u7528\u73af\u5883\u53d8\u91cf\u914d\u7f6ejob\u7684\u914d\u7f6e\u4fe1\u606f":58,"\u901a\u5e38\u4f7f\u7528\u7a00\u758f\u8bad\u7ec3\u6765\u52a0\u901f\u8ba1\u7b97\u8fc7\u7a0b":55,"\u901a\u5e38\u505a\u6cd5\u662f\u4ece\u4e00\u4e2a\u6bd4\u8f83\u5927\u7684learning_rate\u5f00\u59cb\u8bd5":31,"\u901a\u5e38\u540d\u5b57\u662f":46,"\u901a\u5e38\u60c5\u51b5\u4e0b":50,"\u901a\u5e38\u6211\u4eec\u4f1a\u5b89\u88c5ceph\u7b49\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf\u6765\u5b58\u50a8\u8bad\u7ec3\u6570\u636e":57,"\u901a\u5e38\u6307\u5c06\u4e00\u4e2a\u6574\u4f53\u62c6\u5206\u6210\u591a\u4efd\u7684\u5176\u4e2d\u7684\u4e00\u4efd":19,"\u901a\u5e38\u6bcf\u4e2ajob\u5305\u62ec\u4e00\u4e2a\u6216\u8005\u591a\u4e2apod":56,"\u901a\u5e38\u7684\u505a\u6cd5\u662f\u4f7f\u7528":45,"\u901a\u5e38\u7684\u505a\u6cd5\u662f\u5c06\u914d\u7f6e\u5b58\u4e8e":47,"\u901a\u5e38\u8981\u6c42\u65f6\u95f4\u6b65\u4e4b\u95f4\u5177\u6709\u4e00\u4e9b\u4f9d\u8d56\u6027":42,"\u901a\u5e38\u90fd\u4f1a\u4f7f\u7528\u4e0b\u9762\u8fd9\u4e9b\u547d\u4ee4\u884c\u53c2\u6570":55,"\u901a\u7528":52,"\u901a\u77e5":42,"\u901a\u8fc7":[31,42,46,47,51,66],"\u901a\u8fc7\u4e24\u4e2a\u5d4c\u5957\u7684":44,"\u901a\u8fc7\u4ea4\u53c9\u7f16\u8bd1\u7684\u65b9\u5f0f\u6765\u7f16\u8bd1":40,"\u901a\u8fc7\u4ea4\u66ff\u4f7f\u7528\u5377\u79ef\u548c\u6c60\u5316\u5904\u7406":63,"\u901a\u8fc7\u4f7f\u7528":33,"\u901a\u8fc7\u51fd\u6570":58,"\u901a\u8fc7\u5377\u79ef\u64cd\u4f5c\u4ece\u56fe\u7247\u6216\u7279\u5f81\u56fe\u4e2d\u63d0\u53d6\u7279\u5f81":63,"\u901a\u8fc7\u547d\u4ee4\u884c\u53c2\u6570":31,"\u901a\u8fc7\u5f15\u7528memory\u5f97\u5230\u8fd9\u4e2alayer\u4e0a\u4e00\u4e2a\u65f6\u523b\u7684\u8f93\u51fa":44,"\u901a\u8fc7\u5f15\u7528memory\u5f97\u5230\u8fd9\u4e2alayer\u4e0a\u4e00\u4e2a\u65f6\u523b\u8f93\u51fa":44,"\u901a\u8fc7\u6240\u6709\u5355\u5143\u6d4b\u8bd5":46,"\u901a\u8fc7\u6240\u6709\u8bad\u7ec3\u96c6\u4e00\u6b21\u79f0\u4e3a\u4e00\u904d":70,"\u901a\u8fc7\u67e5\u770b\u4e70\u5bb6\u5bf9\u67d0\u4e2a\u4ea7\u54c1\u7684\u8bc4\u4ef7\u53cd\u9988":66,"\u901a\u8fc7\u6a21\u578b\u63a8\u65adapi\u7684\u5b9e\u73b0\u4f5c\u4e3a\u4e00\u4e2a\u6837\u4f8b":27,"\u901a\u8fc7\u7ec4\u5408\u4e0d\u540c\u7684layer":37,"\u901a\u8fc7\u7f16\u8bd1\u4f1a\u751f\u6210py_paddle\u8f6f\u4ef6\u5305":5,"\u901a\u8fc7\u7f51\u7edc\u5c42\u7684\u6807\u8bc6\u7b26\u6765\u6307\u5b9a":47,"\u901a\u8fc7\u8c03\u7528":5,"\u901a\u8fc7\u914d\u7f6e\u7c7b\u4f3c\u4e8e":66,"\u901a\u8fc7data":44,"\u901a\u8fc7volum":56,"\u903b\u8f91\u5212\u4e0a\u6587\u4ef6\u5206\u5757\u7684\u5355\u4f4d":24,"\u903b\u8f91\u56de\u5f52":66,"\u9053\u6b49":42,"\u9069":42,"\u9075\u5b88\u4ee5\u4e0b\u7ea6\u5b9a":46,"\u9075\u5faa\u4ee5\u4e0b\u6d41\u7a0b":30,"\u9075\u5faa\u5982\u4e0b\u7684\u683c\u5f0f":67,"\u9075\u5faa\u6587\u7ae0":62,"\u90a3\u4e48":[27,44,47],"\u90a3\u4e480\u5c42\u5e8f\u5217\u5373\u4e3a\u4e00\u4e2a\u8bcd\u8bed":44,"\u90a3\u4e48\u53ef\u4ee5\u8ba4\u4e3a\u8bad\u7ec3\u4e0d\u6536\u655b":31,"\u90a3\u4e48\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4e0d\u4f1a\u6267\u884c\u6d4b\u8bd5\u64cd\u4f5c":2,"\u90a3\u4e48\u5982\u4f55\u5224\u65ad\u8bad\u7ec3\u4e0d\u6536\u655b\u5462":31,"\u90a3\u4e48\u5e38\u6570\u8f93\u51fa\u6240\u80fd\u8fbe\u5230\u7684\u6700\u5c0fcost\u662f":31,"\u90a3\u4e48\u5f53check\u51fa\u6570\u636e\u4e0d\u5408\u6cd5\u65f6":3,"\u90a3\u4e48\u6211\u4eec\u53ef\u4ee5\u5224\u65ad\u4e3a\u8bad\u7ec3\u4e0d\u6536\u655b":31,"\u90a3\u4e48\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u6536\u96c6\u5e02\u573a\u4e0a\u623f\u5b50\u7684\u5927\u5c0f\u548c\u4ef7\u683c":32,"\u90a3\u4e48\u63a8\u8350\u4f7f\u7528":45,"\u90a3\u4e48\u63a8\u8350\u4f7f\u7528\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u65b9\u6cd5":45,"\u90a3\u4e48\u6536\u655b\u53ef\u80fd\u5f88\u6162":31,"\u90a3\u4e48\u6700\u597d\u5c06\u6570\u636e\u6587\u4ef6\u5728\u6bcf\u6b21\u8bfb\u53d6\u4e4b\u524d\u505a\u4e00\u6b21shuffl":31,"\u90a3\u4e48\u7528\u6237\u9700\u8981\u5728\u672c\u5730\u7f16\u8bd1\u5b89\u88c5paddlepaddl":48,"\u90a3\u4e48\u7528\u6237\u9700\u8981\u62c9\u53d6\u6240\u6709\u7684\u8fdc\u7a0b\u5206\u652f\u5230\u672c\u673a":31,"\u90a3\u4e48\u8bad\u7ec3\u6709\u53ef\u80fd\u4e0d\u6536\u655b":31,"\u90a3\u4e48\u8be5\u4f18\u5316\u7b97\u6cd5\u81f3\u5c11\u9700\u8981":31,"\u90a3\u4e48fc1\u548cfc2\u5c42\u5c06\u4f1a\u4f7f\u7528\u7b2c1\u4e2agpu\u6765\u8ba1\u7b97":55,"\u90a3\u4e48paddlepaddle\u4f1a\u6839\u636elayer\u7684\u58f0\u660e\u987a\u5e8f":3,"\u90a3\u4e5f\u5c31\u4e0d\u9700\u8981\u6025\u7740\u4f18\u5316\u6027\u80fd\u5566":50,"\u90a3\u4f30\u8ba1\u8fd9\u91cc\u7684\u6f5c\u529b\u5c31\u6ca1\u5565\u597d\u6316\u7684\u4e86":50,"\u90a3\u51cf\u5c11\u5b66\u4e60\u738710\u500d\u7ee7\u7eed\u8bd5\u9a8c":31,"\u90a3\u6211\u4f1a\u671f\u671b\u5206\u6790\u5de5\u5177\u7edf\u8ba1\u5230\u901f\u5ea6\u662f100gb":50,"\u90a3\u7a0b\u5e8f\u5206\u6790\u5de5\u5177\u662f\u5fc5\u4e0d\u53ef\u5c11\u7684\u5229\u5668":50,"\u90ae\u7f16":67,"\u90e8\u7f72\u548c\u914d\u7f6e\u6bd4\u8f83\u7b80\u5355":56,"\u90fd":42,"\u90fd\u4f1a\u4ea7\u751f\u5f53\u524d\u5c42\u72b6\u6001\u7684\u6240\u6709\u7ee7\u627f\u7ed3\u679c":53,"\u90fd\u4f7f\u7528\u968f\u673a\u503c\u521d\u59cb\u5316":32,"\u90fd\u53ea\u662f\u4ecb\u7ecd\u53cc\u5c42rnn\u7684api\u63a5\u53e3":42,"\u90fd\u662f\u4e94\u4f4d\u7684\u6570\u5b57":20,"\u90fd\u662f\u5bf9layer1\u5143\u7d20\u7684\u62f7\u8d1d":41,"\u90fd\u662f\u5c06\u6bcf\u4e00\u53e5\u5206\u597d\u8bcd\u540e\u7684\u53e5\u5b50":42,"\u90fd\u662fabi\u8c03\u7528\u6807\u51c6\u7684":26,"\u90fd\u7528":46,"\u90fd\u9700\u8981\u5199\u63d0\u4ea4\u8bf4\u660e":46,"\u90fd\u9700\u8981\u8c03\u7528\u4e00\u6b21":47,"\u914d\u7f6e":70,"\u914d\u7f6e\u4e86\u7f51\u7edc":68,"\u914d\u7f6e\u521b\u5efa\u5b8c\u6bd5\u540e":63,"\u914d\u7f6e\u5982\u4e0b":62,"\u914d\u7f6e\u6253\u5f00":50,"\u914d\u7f6e\u6587\u4ef6":66,"\u914d\u7f6e\u6587\u4ef6\u63a5\u53e3\u662ffc_layer":47,"\u914d\u7f6e\u6a21\u578b\u6587\u4ef6":62,"\u914d\u7f6e\u7684\u65b9\u6cd5\u53c2\u8003":24,"\u914d\u7f6e\u7b49\u6587\u4ef6\u7684\u76ee\u5f55\u89c6\u4e3a":51,"\u914d\u7f6e\u7b80\u5355\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u4f8b\u5b50":45,"\u914d\u7f6e\u7f51\u7edc\u5c42\u7684\u8f93\u5165":47,"\u914d\u7f6eapi":41,"\u9152\u5e97":42,"\u91c7\u6837\u5c42":68,"\u91c7\u7528":69,"\u91c7\u7528\u53e6\u4e00\u79cd\u65b9\u6cd5\u6765\u5806\u53e0lstm\u5c42":69,"\u91c7\u7528\u5747\u5300\u5206\u5e03\u6216\u8005\u9ad8\u65af\u5206\u5e03\u521d\u59cb\u5316":53,"\u91c7\u7528multi":31,"\u91ca\u653e\u5bf9paramters\u5185\u5b58\u7684\u9501\u5b9a":19,"\u91cc\u4ecb\u7ecd\u4e86\u7528paddle\u6e90\u7801\u4e2d\u7684\u811a\u672c\u4e0b\u8f7d\u8bad\u7ec3\u6570\u636e\u7684\u8fc7\u7a0b":57,"\u91cc\u4f1a\u7ee7\u7eed\u5b89\u88c5":36,"\u91cc\u6240\u6709\u7684\u7b26\u53f7\u90fd\u5199\u5165\u81ea\u5df1\u7684\u7a0b\u5e8f\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u91cc":26,"\u91cc\u6307\u5b9a\u56fe\u50cf\u6570\u636e\u5217\u8868":64,"\u91cc\u7684\u65e5\u5fd7":51,"\u91cc\u9762\u6db5\u76d6\u4e86":34,"\u91cc\u9762\u6db5\u76d6\u4e86paddlepaddle\u8fd0\u884c\u6240\u9700\u7684\u6240\u6709\u73af\u5883":34,"\u91cd\u547d\u540d\u6210":26,"\u91cd\u65b0\u7f16\u8bd1paddlepaddl":50,"\u9488\u5bf9\u4e0d\u540c\u5e73\u53f0\u7684\u7f16\u8bd1\u5de5\u5177":40,"\u9488\u5bf9\u4efb\u52a1\u8fd0\u884c\u5b8c\u6210\u540e\u5bb9\u5668\u81ea\u52a8\u9000\u51fa\u7684\u573a\u666f":57,"\u9488\u5bf9\u5185\u5b58\u548c\u663e\u5b58":31,"\u9488\u5bf9\u6587\u672c":68,"\u94fe\u63a5\u4f55\u79cdblas\u5e93\u7b49":33,"\u94fe\u63a5\u5230\u81ea\u5df1\u7684\u7a0b\u5e8f\u91cc":26,"\u94fe\u63a5\u5f85\u8865\u5145":66,"\u9500\u552e":67,"\u9519\u8bef\u5904\u7406":26,"\u9519\u8bef\u5904\u7406\u65b9\u5f0f\u662f\u8fd4\u56de\u503c":26,"\u9519\u8bef\u5904\u7406\u7684\u65b9\u5f0f\u4e5f\u4e0d\u5c3d\u76f8\u540c":26,"\u9519\u8bef\u7387":66,"\u9519\u8bef\u7684define_py_data_sources2\u7c7b\u4f3c":31,"\u955c\u50cf\u91cc\u6709":57,"\u957f\u5ea6":31,"\u95e8\u63a7\u5faa\u73af\u5355\u5143\u5355\u6b65\u51fd\u6570\u548c\u8f93\u51fa\u51fd\u6570":45,"\u95e8\u63a7\u5faa\u73af\u5355\u5143\u7684\u8f93\u51fa\u88ab\u7528\u4f5c\u8f93\u51famemori":45,"\u95ee\u9898":32,"\u95f4\u9694":66,"\u9650\u5236\u5957\u63a5\u5b57\u53d1\u9001\u7f13\u51b2\u533a\u7684\u5927\u5c0f":53,"\u9650\u5236\u5957\u63a5\u5b57\u63a5\u6536\u7f13\u51b2\u533a\u7684\u5927\u5c0f":53,"\u9664\u4e86":3,"\u9664\u4e86boot_lay":42,"\u9664\u53bbdata\u5c42":66,"\u9664\u6784\u9020\u67d0\u79cd\u7c7b\u578b\u7684\u51fd\u6570":27,"\u9664\u8bcd\u5411\u91cf\u6a21\u578b\u5916\u7684\u53c2\u6570\u5c06\u4f7f\u7528\u6b63\u6001\u5206\u5e03\u968f\u673a\u521d\u59cb\u5316":62,"\u9664\u96f6\u7b49\u95ee\u9898":31,"\u968f\u540e\u53ef\u4ee5\u7528\u8fd9\u4e2a\u5f00\u53d1\u955c\u50cf\u5f00build":46,"\u968f\u673a\u521d\u59cb\u4e0d\u5b58\u5728\u7684\u53c2\u6570":69,"\u968f\u673a\u6570\u7684\u79cd\u5b50":53,"\u968f\u673a\u6570seed":52,"\u968f\u7740\u8f6e\u6570\u589e\u52a0\u8bef\u5dee\u4ee3\u4ef7\u51fd\u6570\u7684\u8f93\u51fa\u5728\u4e0d\u65ad\u7684\u51cf\u5c0f":32,"\u9694\u5f00":64,"\u96c6":67,"\u96c6\u675f\u641c\u7d22\u4e2d\u7684\u6269\u5c55\u5e7f\u5ea6":71,"\u96c6\u675f\u641c\u7d22\u4f7f\u7528\u5e7f\u5ea6\u4f18\u5148\u641c\u7d22\u6765\u6784\u5efa\u641c\u7d22\u6811":71,"\u96c6\u675f\u641c\u7d22\u4f7f\u7528\u5e7f\u5ea6\u4f18\u5148\u641c\u7d22\u7684\u65b9\u5f0f\u6784\u5efa\u67e5\u627e\u6811":53,"\u96c6\u7fa4\u4e0a\u542f\u52a8\u4e00\u4e2a\u5355\u673a\u4f7f\u7528cpu\u7684paddle\u8bad\u7ec3\u4f5c\u4e1a":57,"\u96c6\u7fa4\u4f5c\u4e1a\u4e2d\u6240\u6709\u8fdb\u7a0b\u7684\u73af\u5883\u8bbe\u7f6e":51,"\u96c6\u7fa4\u4f5c\u4e1a\u5c06\u4f1a\u5728\u51e0\u79d2\u540e\u542f\u52a8":51,"\u96c6\u7fa4\u5de5\u4f5c":51,"\u96c6\u7fa4\u6d4b\u8bd5":52,"\u96c6\u7fa4\u8bad\u7ec3":52,"\u96c6\u7fa4\u8fdb\u7a0b":51,"\u96c6\u7fa4\u901a\u4fe1\u4fe1\u9053\u7684\u7aef\u53e3\u6570":51,"\u96c6\u7fa4\u901a\u4fe1\u901a\u9053\u7684":51,"\u96c6\u7fa4\u901a\u4fe1\u901a\u9053\u7684\u7aef\u53e3\u53f7":51,"\u9700\u5728nvvp\u754c\u9762\u4e2d\u9009\u4e0a\u624d\u80fd\u5f00\u542f":50,"\u9700\u8981":20,"\u9700\u8981\u4f7f\u7528\u5176\u5236\u5b9a\u7684\u65b9\u5f0f\u6302\u8f7d\u540e\u5e76\u5bfc\u5165\u6570\u636e":58,"\u9700\u8981\u4fdd\u6301\u5f53\u524d\u5206\u652f\u76ee\u5f55":46,"\u9700\u8981\u5148\u6302\u8f7d\u5230\u670d\u52a1\u5668node\u4e0a\u518d\u901a\u8fc7kubernet":56,"\u9700\u8981\u53ef\u4ee5\u8de8\u5e73\u53f0\u6267\u884c":24,"\u9700\u8981\u540c\u6b65\u539f\u4ed3\u5e93":46,"\u9700\u8981\u542f\u52a8\u7684\u8282\u70b9\u4e2a\u6570\u4ee5\u53ca":58,"\u9700\u8981\u5728":51,"\u9700\u8981\u5728\u521b\u5efa\u5bb9\u5668\u524d\u6302\u8f7d\u5377\u4ee5\u4fbf\u6211\u4eec\u4fdd\u5b58\u8bad\u7ec3\u7ed3\u679c":57,"\u9700\u8981\u5728\u7cfb\u7edf\u91cc\u5148\u5b89\u88c5\u597ddocker\u5de5\u5177\u5305":48,"\u9700\u8981\u5728cmake\u7684\u65f6\u5019":27,"\u9700\u8981\u5b89\u88c5graphviz\u6765\u8f6c\u6362dot\u6587\u4ef6\u4e3a\u56fe\u7247":64,"\u9700\u8981\u5bf9":56,"\u9700\u8981\u5c06\u5176parameter\u8bbe\u7f6e\u6210":31,"\u9700\u8981\u5c06\u6807\u8bb0\u7684\u8bad\u7ec3\u548c\u6d4b\u8bd5\u6837\u672c\u79fb\u52a8\u5230\u53e6\u4e00\u4e2a\u8def\u5f84":70,"\u9700\u8981\u5c06bugfix\u7684\u5206\u652f\u540c\u65f6merge\u5230":30,"\u9700\u8981\u5f15\u7528":27,"\u9700\u8981\u6307\u5b9a\u4e0e\u67d0\u4e00\u4e2a\u8f93\u5165\u7684\u5e8f\u5217\u4fe1\u606f\u662f\u4e00\u81f4\u7684":42,"\u9700\u8981\u6307\u5b9alayer\u7684\u8f93\u5165\u6765\u6e90":37,"\u9700\u8981\u660e\u786e\u6307\u5b9a":53,"\u9700\u8981\u6709\u4e00\u4e2a\u5916\u90e8\u7684\u5b58\u50a8\u670d\u52a1\u6765\u4fdd\u5b58\u8bad\u7ec3\u6240\u9700\u6570\u636e\u548c\u8bad\u7ec3\u8f93\u51fa":56,"\u9700\u8981\u6709\u7a33\u5b9a\u7684\u5bfc\u51fa\u7b26\u53f7":26,"\u9700\u8981\u6839\u636e\u4e0d\u540c\u7684\u5206\u5e03\u5f0f\u5b58\u50a8\u6765\u7ed1\u5b9a\u4e00\u4e2a":58,"\u9700\u8981\u6ce8\u610f\u7684\u662f":[30,53,68],"\u9700\u8981\u6ce8\u610f\u7684\u662f\u68af\u5ea6\u68c0\u67e5\u4ec5\u4ec5\u9a8c\u8bc1\u4e86\u68af\u5ea6\u7684\u8ba1\u7b97":47,"\u9700\u8981\u6ce8\u610f\u7684\u662fpaddlepaddle\u76ee\u524d\u53ea\u652f\u6301\u5b50\u5e8f\u5217\u6570\u76ee\u4e00\u6837\u7684\u591a\u8f93\u5165\u53cc\u5c42rnn":42,"\u9700\u8981\u7528\u5230\u7684\u7f16\u8bd1\u5de5\u5177\u548c\u7cfb\u7edf\u5e93":39,"\u9700\u8981\u88ab\u66b4\u9732\u5230\u5176\u4ed6\u8bed\u8a00":27,"\u9700\u8981\u9075\u5faa\u4ee5\u4e0b\u7ea6\u5b9a":44,"\u975e\u5e38\u6570":47,"\u975e\u96f6\u6570\u5b57\u7684\u4e2a\u6570":47,"\u9762\u5411\u67b6\u6784\u4e3a32\u4f4darm\u67b6\u6784":39,"\u97f3\u4e50\u5267":67,"\u987a\u5e8f":42,"\u9884\u5904\u7406\u6570\u636e\u4e00\u822c\u7684\u547d\u4ee4\u4e3a":68,"\u9884\u5904\u7406\u811a\u672c":70,"\u9884\u5b9a\u4e49\u7f51\u7edc":70,"\u9884\u5b9a\u4e49\u7f51\u7edc\u5982\u56fe3\u6240\u793a":70,"\u9884\u63d0\u4ea4\u94a9\u5b50":46,"\u9884\u6d4b\u540e":69,"\u9884\u6d4b\u63a5\u53e3\u811a\u672c":70,"\u9884\u6d4b\u6982\u7387\u53d6\u5e73\u5747":64,"\u9884\u6d4b\u7a0b\u5e8f\u5c06\u8bfb\u53d6\u7528\u6237\u7684\u8f93\u5165":68,"\u9884\u6d4b\u7ed3\u679c\u4ee5\u6587\u672c\u7684\u5f62\u5f0f\u4fdd\u5b58\u5728":66,"\u9884\u6d4b\u811a\u672c\u662f":69,"\u9884\u6d4bid":66,"\u9884\u6d4bimdb\u7684\u672a\u6807\u8bb0\u8bc4\u8bba\u7684\u4e00\u4e2a\u5b9e\u4f8b\u5982\u4e0b":70,"\u9884\u8bad\u7ec3\u6a21\u578b\u4f7f\u7528\u7684\u5b57\u5178\u7684\u8def\u5f84":62,"\u9884\u8bad\u7ec3\u8bcd\u5411\u91cf\u5b57\u5178\u6a21\u578b\u7684\u8def\u5f84":62,"\u989c\u8272\u901a\u9053\u987a\u5e8f\u4e3a":64,"\u989d\u5916\u7684\u53c2\u6570":66,"\u9996\u5148":[3,32,42,45,47,62,64,66,69,70],"\u9996\u5148\u4e0b\u8f7dcifar":63,"\u9996\u5148\u5728\u7cfb\u7edf\u8def\u5f84":33,"\u9996\u5148\u5b89\u88c5\u5e76\u5728\u5f53\u524d\u76ee\u5f55\u8fd0\u884c\u5b83":46,"\u9996\u5148\u5b89\u88c5paddlepaddl":70,"\u9996\u5148\u5bf9\u8f93\u5165\u505a\u4e00\u4e2a\u5c0f\u7684\u6270\u52a8":47,"\u9996\u5148\u6211\u4eec\u9700\u8981\u63a8\u5bfc\u8be5\u7f51\u7edc\u5c42\u7684":47,"\u9996\u5148\u662f\u6cd5\u8bed\u5e8f\u5217":71,"\u9996\u5148\u901a\u8fc7":46,"\u9996\u5148\u9700\u8981\u52a0\u8f7d\u76f8\u5e94\u7684python\u5e93":37,"\u9a71\u52a8":48,"\u9ad8\u4e2d\u6bd5\u4e1a\u751f":67,"\u9ad8\u4eae\u90e8\u5206":42,"\u9ad8\u53ef\u7528":56,"\u9ad8\u5ea6\u652f\u6301\u7075\u6d3b\u548c\u9ad8\u6548\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e":45,"\u9ad8\u6548\u6027":0,"\u9ad8\u65af\u5206\u5e03":31,"\u9ed1\u8272\u7535\u5f71":67,"\u9ed8\u8ba4":[3,53,71],"\u9ed8\u8ba4256k":24,"\u9ed8\u8ba4\u4e00\u4e2apass\u4fdd\u5b58\u4e00\u6b21\u6a21\u578b":66,"\u9ed8\u8ba4\u4e0d\u663e\u793a":53,"\u9ed8\u8ba4\u4e0d\u8bbe\u7f6e":44,"\u9ed8\u8ba4\u4e3a0":[53,55],"\u9ed8\u8ba4\u4e3a1":[3,55],"\u9ed8\u8ba4\u4e3a100":55,"\u9ed8\u8ba4\u4e3a4096mb":53,"\u9ed8\u8ba4\u4e3a\u4e0d\u4f7f\u7528":68,"\u9ed8\u8ba4\u4e3a\u7b2c\u4e00\u4e2a\u8f93\u5165":44,"\u9ed8\u8ba4\u4e3anull":53,"\u9ed8\u8ba4\u4f7f\u7528concurrentremoteparameterupdat":53,"\u9ed8\u8ba4\u503c":[33,41,55],"\u9ed8\u8ba4\u503c\u4e3a":[39,40],"\u9ed8\u8ba4\u521d\u59cb\u72b6\u4e3a0":44,"\u9ed8\u8ba4\u60c5\u51b5\u4e0b":[31,51,70],"\u9ed8\u8ba4\u60c5\u51b5\u4e0b\u4f7f\u7528\u6b64\u7f51\u7edc":70,"\u9ed8\u8ba4\u6307\u5b9a\u7b2c\u4e00\u4e2a\u8f93\u5165":42,"\u9ed8\u8ba4\u7528\u6765\u5207\u5206\u5355\u8bb0\u548c\u6807\u70b9\u7b26\u53f7":70,"\u9ed8\u8ba4\u7684":57,"\u9ed8\u8ba4\u8bbe\u7f6e\u4e3a\u771f":55,"\u9ed8\u8ba4\u8bbe\u7f6e\u6210\u73af\u5883\u53d8\u91cf":[39,40],"\u9ed8\u8ba4\u914d\u7f6e\u5982\u4e0b":51,"adamax\u7b49":66,"amazon\u7535\u5b50\u4ea7\u54c1\u8bc4\u8bba\u6570\u636e":66,"android\u5e73\u53f0\u4e0a\u4f7f\u7528\u7684c":39,"android\u5e73\u53f0\u53ef\u9009\u914d\u7f6e\u53c2\u6570":39,"api\u4e2d\u4f7f\u7528":26,"api\u5bf9\u6bd4\u4ecb\u7ecd":43,"api\u5bfc\u51fa\u7684\u52a8\u6001\u5e93":27,"api\u5bfc\u51fa\u7684\u9759\u6001\u5e93":27,"api\u63a5\u53d7\u7684\u7c7b\u578b\u5168\u662f":27,"api\u63a5\u53e3":[24,56],"api\u63a5\u53e3\u7684\u53c2\u6570\u8f6c\u53d1\u7ed9":27,"api\u6587\u6863":39,"api\u6587\u6863\u751f\u6210\u8fc7\u7a0b\u4f9d\u8d56\u4e8epy_paddl":48,"api\u65f6":27,"api\u65f6\u6240\u552f\u4e00\u9700\u8981\u5f15\u5165\u7684\u5934\u6587\u4ef6":27,"api\u662f\u591a\u8bed\u8a00api\u7684\u57fa\u7840\u90e8\u5206":27,"api\u66b4\u9732\u7684\u7c7b\u578b":27,"api\u6765\u9884\u6d4b":39,"api\u751f\u6210\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6\u4f1a\u88ab\u5b89\u88c5\u5230":27,"api\u7684\u5934\u6587\u4ef6":[39,40],"api\u7684\u5b9e\u4f8b":27,"api\u7684\u5b9e\u73b0\u7ec6\u8282":27,"api\u7684\u63a5\u53e3":27,"api\u7684\u65f6\u5019\u63a8\u8350paddle\u4e0d\u5d4c\u5165python\u89e3\u91ca\u5668":27,"api\u7684\u7f16\u8bd1\u9009\u9879\u9ed8\u8ba4\u5173\u95ed":27,"api\u76ee\u5f55\u7ed3\u6784\u5982\u4e0a\u56fe\u8868\u6240\u793a":27,"api\u7ea7\u522b":39,"api\u7ea7\u522b\u4e0d\u5c0f\u4e8e21":39,"api\u7ea7\u522b\u4e3a21":39,"api\u83b7\u5f97\u4e86\u795e\u7ecf\u7f51\u7edc\u7684\u53c2\u6570\u5b9e\u4f8b":27,"app\u4e2d":39,"async_sgd\u8fdb\u884c\u8bad\u7ec3\u65f6":31,"atlas\u7684\u8def\u5f84":33,"avx\u7684\u955c\u50cf":34,"awselasticblockstore\u7b49":56,"batch\u4e2d\u5305\u542b":31,"batches\u4e2a\u6279\u6b21\u4fdd\u5b58\u4e00\u6b21\u53c2\u6570":53,"batches\u6b21":53,"bin\u548c\u8bc4\u5206\u6587\u4ef6":68,"blas\u7684\u8def\u5f84":33,"block\u6784\u6210\u4e00\u4e2amodel":19,"book\u4e00\u5b9a\u662f\u60a8\u6700\u597d\u7684\u9009\u62e9":34,"book\u4e2d\u6240\u6709\u7ae0\u8282\u529f\u80fd\u7684\u6b63\u786e\u6027":30,"book\u662f\u4e3a\u7528\u6237\u548c\u5f00\u53d1\u8005\u5236\u4f5c\u7684\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7684jupyt":34,"book\u7684":37,"book\u7684docker\u955c\u50cf":34,"bool\u578b\u53c2\u6570":3,"boolean":[9,25,26],"bugfix\u5206\u652f\u4e5f\u662f\u5728\u5f00\u53d1\u8005\u81ea\u5df1\u7684fork\u7248\u672c\u5e93\u7ef4\u62a4":30,"bugfix\u5206\u652f\u9700\u8981\u5206\u522b\u7ed9\u4e3b\u7248\u672c\u5e93\u7684":30,"build\u6307\u4ee4\u6309\u7167dockerfile\u751f\u6210\u955c\u50cf":34,"build\u8fd9\u4e2a\u547d\u4ee4\u7684":34,"byte":[24,31],"c99\u662f\u76ee\u524dc\u6700\u5e7f\u6cdb\u7684\u4f7f\u7528\u6807\u51c6":26,"c\u6709\u6807\u51c6\u7684abi":26,"c\u8bed\u8a00\u662f\u6709\u5bfc\u51fa\u7b26\u53f7\u7684\u6807\u51c6\u7684":26,"caoying\u7684pul":71,"case":[9,27,29,50],"char":22,"class":[6,7,9,11,12,13,15,16,26,31,47,70],"cmake\u4e2d\u5c06":50,"cmake\u53c2\u6570\u5982\u4e0b":40,"cmake\u5b98\u65b9\u5bf9android\u5e73\u53f0\u7684\u4ea4\u53c9\u7f16\u8bd1\u63d0\u4f9b\u4e86\u901a\u7528\u7684\u652f\u6301":39,"cmake\u627e\u5230\u7684python\u5e93\u548cpython\u89e3\u91ca\u5668\u7248\u672c\u53ef\u80fd\u6709\u4e0d\u4e00\u81f4\u73b0\u8c61":31,"cmake\u7cfb\u7edf\u5bf9\u4ea4\u53c9\u7f16\u8bd1\u63d0\u4f9b\u4e86\u652f\u6301":[39,40],"cmake\u7f16\u8bd1\u65f6":33,"cmake\u7f16\u8bd1\u7684\u76ee\u6807\u5e73\u53f0":[39,40],"cmake\u914d\u7f6e\u4e2d\u5c06":50,"cmake\u914d\u7f6e\u5b8c\u6210\u540e":[39,40],"conf\u4f5c\u4e3a\u914d\u7f6e":71,"const":[22,28,47],"container\u4e2d":57,"core\u4e2d\u7684\u6a21\u578b\u8fd8\u5728\u4f7f\u7528\u8fd9\u4e2a\u53c2\u6570":27,"core\u4e2d\u8fd9\u4e00\u7c7b\u578b\u63a5\u53e3\u7684\u667a\u80fd\u6307\u9488":27,"core\u662f\u5426\u8fd8\u5728\u4f7f\u7528\u8fd9\u4e2a\u5b9e\u4f8b":27,"core\u6982\u5ff5":27,"cost\u63a5\u6536y_predict\u4e0ey\u4f5c\u4e3a\u8f93\u5165":37,"cost\u8fd8\u5927\u4e8e\u8fd9\u4e2a\u6570":31,"count\u4e2agpu\u4e0a\u4f7f\u7528\u6570\u636e\u5e76\u884c\u6765\u8ba1\u7b97\u67d0\u4e00\u5c42":55,"count\u548cgpu":55,"cpu\u7248\u672c":36,"cuda\u5e73\u53f0":36,"cuda\u5e93":53,"cudnn\u5e93":[33,53],"dat\u4e2d":68,"data\u5230\u5206\u5e03\u5f0f\u5b58\u50a8\u8865\u5145\u8bad\u7ec3\u6570\u636e":20,"data\u76ee\u5f55\u4e2d\u5b58\u653e\u5207\u5206\u597d\u7684\u6570\u636e":58,"dataprovider\u5171\u8fd4\u56de\u4e24\u4e2a\u6570\u636e":42,"dataprovider\u5171\u8fd4\u56de\u4e24\u7ec4\u6570\u636e":42,"dataprovider\u662fpaddlepaddle\u8d1f\u8d23\u63d0\u4f9b\u6570\u636e\u7684\u6a21\u5757":2,"dataprovider\u7684\u4ecb\u7ecd":[4,66],"dataprovider\u7f13\u51b2\u6c60\u5185\u5b58":31,"deb\u5305":30,"deb\u5305\u7f16\u8bd1\u95ee\u9898":30,"deb\u5b89\u88c5\u5305":36,"decay\u5219\u4e3a0":63,"decoder\u5faa\u73af\u5c55\u5f00\u7684\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u4f1a\u5f15\u7528\u5168\u90e8\u7ed3\u679c":44,"decoder\u63a5\u53d7\u4e24\u4e2a\u8f93\u5165":44,"decoder\u6bcf\u6b21\u9884\u6d4b\u4ea7\u751f\u4e0b\u4e00\u4e2a\u6700\u53ef\u80fd\u7684\u8bcd\u8bed":44,"decoer\u67b6\u6784":44,"default":[7,8,9,10,12,13,15,16,17,55,57,58,70],"demo\u9884\u6d4b\u8f93\u51fa\u5982\u4e0b":5,"dictionary\u662f\u4ece\u7f51\u7edc\u914d\u7f6e\u4e2d\u4f20\u5165\u7684dict\u5bf9\u8c61":3,"dictionary\u7531\u89e3\u6790\u81ea\u52a8\u751f\u6210":68,"dir\u4e2d\u670916\u4e2a\u5b50\u76ee\u5f55":71,"docker\u5b58\u5728\u95ee\u9898":34,"docker\u5b89\u88c5\u8bf7\u53c2\u8003":48,"docker\u7684\u5b98\u7f51":48,"docker\u955c\u50cf\u6765\u670d\u52a1html\u4ee3\u7801":34,"dockerfile\u4f4d\u4e8epaddlepaddl":34,"dockers\u8bbe\u7f6e":34,"dropout\u7684\u6bd4\u4f8b":47,"elec\u6d4b\u8bd5\u96c6":66,"embedding\u6a21\u578b\u9700\u8981\u7a0d\u5fae\u6539\u53d8\u63d0\u4f9b\u6570\u636e\u7684python\u811a\u672c":66,"encode\u6210\u7684\u6700\u540e\u4e00\u4e2a\u5411\u91cf":42,"encoder\u548cdecoder\u53ef\u4ee5\u662f\u80fd\u591f\u5904\u7406\u5e8f\u5217\u7684\u4efb\u610f\u795e\u7ecf\u7f51\u7edc\u5355\u5143":44,"encoder\u8f93\u51fa":44,"entropy\u4f5c\u4e3acost":31,"enum":22,"evaluator\u7684\u503c\u4f4e\u4e8e0":71,"export":[31,34,36,63],"f\u4ee3\u8868\u4e00\u4e2a\u6d6e\u70b9\u6570":[3,37],"false\u7684\u60c5\u51b5":3,"fc1\u548cfc2\u5c42\u5728gpu\u4e0a\u8ba1\u7b97":55,"fc3\u5c42\u4f7f\u7528cpu\u8ba1\u7b97":55,"final":[10,68],"float":[3,7,8,9,11,13,32,50,64,68],"float\u7b49":55,"function":[9,10,11,13,16,18,21,22,23,28,29,45,70],"gen\u6587\u4ef6\u5939\u4e2d\u7684\u6587\u4ef6\u5217\u8868":71,"generator\u4fbf\u4f1a\u5b58\u4e0b\u5f53\u524d\u7684\u4e0a\u4e0b\u6587":3,"generator\u81f3\u5c11\u9700\u8981\u8c03\u7528\u4e24\u6b21\u624d\u4f1a\u77e5\u9053\u662f\u5426\u505c\u6b62":3,"git\u6d41\u5206\u652f\u6a21\u578b":46,"github\u9996\u9875":46,"golang\u53ef\u4ee5\u4f7f\u7528":26,"golang\u7684":26,"gpu\u4e8c\u8fdb\u5236\u6587\u4ef6":33,"gpu\u5219\u8fd8\u9700\u8981\u9ad8\u5e76\u884c\u6027":50,"gpu\u53cc\u7f13\u5b58":3,"gpu\u548c\u975eavx\u533a\u5206\u4e86\u5982\u4e0b4\u4e2a\u955c\u50cf":34,"gpu\u6027\u80fd\u5206\u6790\u4e0e\u8c03\u4f18":49,"gpu\u6838\u5728\u8bad\u7ec3\u914d\u7f6e\u4e2d\u6307\u5b9a":53,"gpu\u7248\u672c":36,"gpu\u7248\u672c\u5e76\u60f3\u4f7f\u7528":69,"gpu\u7684docker\u955c\u50cf\u7684\u65f6\u5019":31,"gram\u7ea7\u522b\u7684\u77e5\u8bc6":70,"group\u6559\u7a0b":43,"gru\u6216lstm":45,"gru\u6a21\u578b":66,"gru\u6a21\u578b\u914d\u7f6e":66,"h\u5e76\u4e0d\u56f0\u96be":26,"hot\u7a20\u5bc6\u5411\u91cf":68,"html\u5373\u53ef\u8bbf\u95ee\u672c\u5730\u6587\u6863":48,"i\u4ee3\u8868\u4e00\u4e2a\u6574\u6570":[3,37],"id\u4e3a0\u7684\u6982\u7387":66,"id\u4e3a1\u7684\u6982\u7387":66,"id\u6307\u5b9a\u4f7f\u7528\u54ea\u4e2agpu\u6838":53,"id\u6307\u5b9a\u7684gpu":55,"id\u65e0\u6548":53,"image\u91cc":57,"images\u6570\u636e\u96c6\u4e0a\u4f20\u5230\u4e91\u7aef\u7684":20,"imdb\u6570\u636e\u96c6\u5305\u542b25":70,"imdb\u6709\u4e24\u4e2a\u6807\u7b7e":70,"imdb\u7684\u6570\u6910\u96c6":70,"import":[3,5,8,9,16,31,32,37,48,62,63,64,68,70,71],"include\u4e0b\u9700\u8981\u5305\u542bcbla":33,"include\u4e0b\u9700\u8981\u5305\u542bmkl":33,"ingress\u9700\u8981\u628apfsclient\u7684\u8eab\u4efd\u4fe1\u606f\u4f20\u7ed9pfsserv":24,"init_hook\u53ef\u4ee5\u4f20\u5165\u4e00\u4e2a\u51fd\u6570":3,"instance\u4e0e\u751f\u6210\u6570\u636e\u96c6\u65f6":20,"instance\u5305\u6db5\u4e24\u4e2a\u503c":20,"instance\u662f\u4e00\u6a21\u4e00\u6837\u7684":20,"int":[3,7,8,9,10,13,21,22,23,26,27,29,42,47,55,66,68,69],"interface\u6587\u4ef6\u7684\u5199\u6cd5\u975e\u5e38":26,"issue\u7f16\u53f7":46,"job\u542f\u52a8\u540e\u4f1a\u521b\u5efa\u8fd9\u4e9bpod\u5e76\u5f00\u59cb\u6267\u884c\u4e00\u4e2a\u7a0b\u5e8f":56,"job\u6216\u8005\u5e94\u7528\u7a0b\u5e8f\u5728\u5bb9\u5668\u4e2d\u8fd0\u884c\u65f6\u751f\u6210\u7684\u6570\u636e\u4f1a\u5728\u5bb9\u5668\u9500\u6bc1\u65f6\u6d88\u5931":56,"job\u662f\u672c\u6b21\u8bad\u7ec3\u5bf9\u5e94\u7684job":58,"job\u7684\u540d\u5b57":58,"kubernetes\u4e3a\u8fd9\u6b21\u8bad\u7ec3\u521b\u5efa\u4e863\u4e2apod\u5e76\u4e14\u8c03\u5ea6\u5230\u4e863\u4e2anode\u4e0a\u8fd0\u884c":58,"kubernetes\u5206\u5e03\u5f0f\u8bad\u7ec3":49,"kubernetes\u5355\u673a\u8bad\u7ec3":49,"kubernetes\u53ef\u4ee5\u5728\u7269\u7406\u673a\u6216\u865a\u62df\u673a\u4e0a\u8fd0\u884c":56,"kubernetes\u53ef\u4ee5\u901a\u8fc7yaml\u6587\u4ef6\u6765\u521b\u5efa\u76f8\u5173\u5bf9\u8c61":58,"kubernetes\u5c31\u4f1a\u521b\u5efa3\u4e2apod\u4f5c\u4e3apaddlepaddle\u8282\u70b9\u7136\u540e\u62c9\u53d6\u955c\u50cf":58,"kubernetes\u63d0\u4f9b\u4e86\u591a\u79cd\u96c6\u7fa4\u90e8\u7f72\u7684\u65b9\u6848":56,"kubernetes\u652f\u6301\u591a\u79cdvolum":56,"kubernetes\u6709job\u7c7b\u578b\u7684\u8d44\u6e90\u6765\u652f\u6301":57,"kubernetes\u96c6\u7fa4\u5c31\u662f\u7531node\u8282\u70b9\u4e0emaster\u8282\u70b9\u7ec4\u6210\u7684":56,"label\u662f\u539f\u59cb\u6570\u636e\u4e2d\u5bf9\u4e8e\u6bcf\u4e00\u53e5\u8bdd\u7684\u5206\u7c7b\u6807\u7b7e":42,"labels\u662f\u6bcf\u7ec4\u5185\u6bcf\u4e2a\u53e5\u5b50\u7684\u6807\u7b7e":42,"layer1\u5fc5\u987b\u662f\u4e00\u4e2a0\u5c42\u5e8f\u5217":41,"layer1\u5fc5\u987b\u662f\u4e00\u4e2a\u5355\u5c42\u5e8f\u5217":41,"layer\u62ff\u5230\u7684\u7528\u6237\u8f93\u5165":44,"layer\u662f\u6211\u4eec\u7684\u79ef\u6728":37,"layer\u7c7b\u53ef\u4ee5\u81ea\u52a8\u8ba1\u7b97\u4e0a\u9762\u7684\u5bfc\u6570":47,"lib\u4e0b\u9700\u8981\u5305\u542bcblas\u548catlas\u4e24\u4e2a\u5e93":33,"lib\u4e0b\u9700\u8981\u5305\u542bcblas\u5e93":33,"lib\u4e0b\u9700\u8981\u5305\u542bopenblas\u5e93":33,"lib\u76ee\u5f55\u4e0b\u9700\u8981\u5305\u542bmkl_cor":33,"list\u4e2d\u7684\u6bcf\u4e00\u884c\u90fd\u4f20\u9012\u7ed9process\u51fd\u6570":3,"list\u4f5c\u4e3a\u68c0\u67e5\u5217\u8868":30,"list\u5199\u5165\u90a3\u4e2a\u6587\u672c\u6587\u4ef6\u7684\u5730\u5740":3,"list\u548ctest":2,"list\u5982\u4e0b\u6240\u793a":55,"list\u5b58\u653e\u5728\u672c\u5730":2,"list\u6307\u5b9a\u6d4b\u8bd5\u7684\u6a21\u578b\u5217\u8868":55,"long":[9,10,13],"lstm\u67b6\u6784\u7684\u6700\u5927\u4f18\u70b9\u662f\u5b83\u53ef\u4ee5\u5728\u957f\u65f6\u95f4\u95f4\u9694\u5185\u8bb0\u5fc6\u4fe1\u606f":70,"lstm\u6a21\u578b":66,"lstm\u6a21\u578b\u914d\u7f6e":66,"lstm\u7f51\u7edc\u7c7b\u4f3c\u4e8e\u5177\u6709\u9690\u85cf\u5c42\u7684\u6807\u51c6\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":70,"memory\u4e0d\u80fd\u72ec\u7acb\u5b58\u5728":44,"memory\u4e5f\u53ef\u4ee5\u5177\u6709":45,"memory\u4e5f\u53ef\u4ee5\u662f\u5e8f\u5217":45,"memory\u53ea\u80fd\u5728":44,"memory\u53ef\u4ee5\u7f13\u5b58\u4e0a\u4e00\u4e2a\u65f6\u523b\u67d0\u4e00\u4e2a\u795e\u7ecf\u5143\u7684\u8f93\u51fa":42,"memory\u6307\u5411\u4e00\u4e2alay":44,"memory\u662f\u5728\u5355\u6b65\u51fd\u6570\u4e2d\u5faa\u73af\u4f7f\u7528\u7684\u72b6\u6001":45,"memory\u662fpaddlepaddle\u5b9e\u73b0rnn\u65f6\u5019\u4f7f\u7528\u7684\u4e00\u4e2a\u6982\u5ff5":42,"memory\u7684":45,"memory\u7684\u521d\u59cb\u72b6\u6001":44,"memory\u7684\u65f6\u95f4\u5e8f\u5217\u957f\u5ea6\u4e00\u81f4\u7684\u60c5\u51b5":42,"memory\u7684\u66f4\u591a\u8ba8\u8bba\u8bf7\u53c2\u8003\u8bba\u6587":44,"memory\u7684\u8f93\u51fa\u5b9a\u4e49\u5728":45,"memory\u7684i":44,"memory\u9ed8\u8ba4\u521d\u59cb\u5316\u4e3a0":44,"mkl\u7684\u8def\u5f84":33,"mkl_sequential\u548cmkl_intel_lp64\u4e09\u4e2a\u5e93":33,"mnist\u662f\u4e00\u4e2a\u5305\u542b\u670970":3,"mode\u548cattent":71,"mode\u7684python\u51fd\u6570":71,"model\u505a\u5206\u652f\u7ba1\u7406":30,"model\u53ef\u4ee5\u901a\u8fc7":5,"model\u6765\u5b9e\u73b0\u624b\u5199\u8bc6\u522b\u7684\u9884\u6d4b\u4ee3\u7801":5,"movielens\u6570\u636e\u96c6":68,"name\u662f\u4f53\u88c1":68,"name\u662f\u5e74\u9f84":68,"name\u662f\u6027\u522b":68,"name\u662f\u7535\u5f71\u540d":68,"name\u662f\u804c\u4e1a":68,"name\u7ec4\u5408\u53ef\u4ee5\u627e\u5230\u672c\u6b21\u8bad\u7ec3\u9700\u8981\u7684\u6587\u4ef6\u8def\u5f84":58,"ndarray\u7c7b\u578b\u7684\u503c\u548c\u6574\u578b\u7684\u503c":20,"ndk\u4e2d\u5305\u542b\u4e86\u6240\u6709android":39,"new":[9,13,17,18,21,22,29,46,47],"nfs\u7684\u90e8\u7f72\u65b9\u6cd5\u53ef\u4ee5\u53c2\u8003":56,"nmt\u6a21\u578b\u53d7\u5236\u4e8e\u6e90\u8bed\u53e5\u7684\u7f16\u7801":71,"noavx\u7248\u672c":36,"normalization\u5c42":64,"normalization\u5c42\u7684\u53c2\u6570":64,"note\u7684\u4e66\u5199":30,"notebook\u662f\u4e00\u4e2a\u5f00\u6e90\u7684web\u7a0b\u5e8f":34,"null":[9,47,53,68],"num_gradient_servers\u53c2\u6570":58,"openblas\u7684\u8def\u5f84":33,"out\u4e0b\u5305\u542b":63,"out\u7684\u6587\u4ef6\u5939":63,"outer_mem\u662f\u4e00\u4e2a\u5b50\u53e5\u7684\u6700\u540e\u4e00\u4e2a\u5411\u91cf":42,"output\u6587\u4ef6\u5939\u5b58\u653e\u8bad\u7ec3\u7ed3\u679c\u4e0e\u65e5\u5fd7":58,"packages\u91cc\u9762":31,"packages\u91cc\u9762\u7684python\u5305":31,"paddepaddle\u901a\u8fc7\u7f16\u8bd1\u65f6\u6307\u5b9a\u8def\u5f84\u6765\u5b9e\u73b0\u5f15\u7528\u5404\u79cdbla":33,"paddle\u4e00\u4e2a\u52a8\u6001\u5e93\u53ef\u4ee5\u5728\u4efb\u4f55linux\u7cfb\u7edf\u4e0a\u8fd0\u884c":26,"paddle\u4e2d\u7684\u4e00\u6761pass\u8868\u793a\u8bad\u7ec3\u6570\u636e\u96c6\u4e2d\u6240\u6709\u7684\u6837\u672c\u4e00\u6b21":71,"paddle\u4e2d\u7ecf\u5e38\u4f1a\u5c06\u65f6\u95f4\u5e8f\u5217\u6210\u4e3a":42,"paddle\u4e8c\u8fdb\u5236\u5728\u8fd0\u884c\u65f6\u6355\u83b7\u4e86\u6d6e\u70b9\u6570\u5f02\u5e38":31,"paddle\u4f7f\u7528git":30,"paddle\u5185\u5d4c\u7684python\u89e3\u91ca\u5668\u548c\u5916\u90e8\u4f7f\u7528\u7684python\u5982\u679c\u7248\u672c\u4e0d\u540c":26,"paddle\u5185\u90e8\u7684\u7c7b\u4e3ac":26,"paddle\u5f00\u53d1\u8fc7\u7a0b\u4f7f\u7528":30,"paddle\u66ff\u6362\u6210":34,"paddle\u6bcf\u6b21\u53d1\u65b0\u7684\u7248\u672c":30,"paddle\u6bcf\u6b21\u53d1\u7248\u672c\u9996\u5148\u8981\u4fdd\u8bc1paddl":30,"paddle\u7684\u4e3b\u7248\u672c\u5e93\u9075\u5faa":30,"paddle\u7684\u5404\u7248\u672c\u955c\u50cf\u53ef\u4ee5\u53c2\u8003":57,"paddle\u7684\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0\u5305\u62ec\u4e00\u4e0b\u51e0\u4e2a\u65b9\u9762":26,"paddle\u7684\u7c7b\u578b\u5168\u90e8\u9000\u5316\u6210":27,"paddle\u7684\u94fe\u63a5\u65b9\u5f0f\u6bd4\u8f83\u590d\u6742":26,"paddle\u7684c":27,"paddle\u7684dock":57,"paddle\u7684docker\u5f00\u53d1\u955c\u50cf\u5e26\u6709\u4e00\u4e2a\u901a\u8fc7":34,"paddle\u8bad\u7ec3\u4efb\u52a1":20,"paddle\u8def\u5f84\u4e0b":27,"paddle\u955c\u50cf":57,"paddle\u9700\u8981\u4e00\u4e2a\u591a\u8bed\u8a00\u63a5\u53e3":26,"paddle\u9700\u8981\u66b4\u9732\u7684api\u5f88\u591a":27,"paddle\u9759\u6001\u5e93\u94fe\u63a5\u590d\u6742":26,"paddle_\u7c7b\u578b\u540d":27,"paddle_\u7c7b\u578b\u540d_\u51fd\u6570\u540d":27,"paddlepaddle\u4e2d":[41,44],"paddlepaddle\u4e2d\u7684\u4e00\u4e2apass\u610f\u5473\u7740\u5bf9\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u6837\u672c\u8fdb\u884c\u4e00\u6b21\u8bad\u7ec3":70,"paddlepaddle\u4e2d\u7684\u8bb8\u591alayer\u5e76\u4e0d\u5728\u610f\u8f93\u5165\u662f\u5426\u662f\u65f6\u95f4\u5e8f\u5217":42,"paddlepaddle\u4e2d\u901a\u8fc7reader\u6765\u52a0\u8f7d\u6570\u636e":37,"paddlepaddle\u4e3a\u4ea4\u53c9\u7f16\u8bd1\u63d0\u4f9b\u4e86\u5de5\u5177\u94fe\u914d\u7f6e\u6587\u6863":[39,40],"paddlepaddle\u4e3a\u6df1\u5ea6\u5b66\u4e60\u7814\u7a76\u4eba\u5458\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684api":37,"paddlepaddle\u4f1a\u5728\u8c03\u7528\u8bfb\u53d6\u6570\u636e\u7684python\u811a\u672c\u4e4b\u524d":66,"paddlepaddle\u4f7f\u7528\u5747\u503c0":31,"paddlepaddle\u4f7f\u7528avx":31,"paddlepaddle\u4f7f\u7528swig\u5bf9\u5e38\u7528\u7684\u9884\u6d4b\u63a5\u53e3\u8fdb\u884c\u4e86\u5c01\u88c5":5,"paddlepaddle\u4fdd\u7559\u6dfb\u52a0\u53c2\u6570\u7684\u6743\u529b":3,"paddlepaddle\u5148\u4ece\u4e00\u4e2a\u6587\u4ef6\u5217\u8868\u91cc\u83b7\u5f97\u6570\u636e\u6587\u4ef6\u5730\u5740":32,"paddlepaddle\u53d1\u5e03\u65b0\u7248\u672c\u7684\u65f6\u5019\u90fd\u4f1a\u53d1\u5e03\u5bf9\u5e94\u7248\u672c\u7684\u751f\u4ea7\u955c\u50cf\u4ee5\u53ca\u5f00\u53d1\u955c\u50cf":34,"paddlepaddle\u53ef\u4ee5\u4f7f\u7528cudnn":33,"paddlepaddle\u53ef\u4ee5\u6267\u884c\u7528\u6237\u7684python\u811a\u672c\u7a0b\u5e8f\u6765\u8bfb\u53d6\u5404\u79cd\u683c\u5f0f\u7684\u6570\u636e\u6587\u4ef6":66,"paddlepaddle\u53ef\u4ee5\u6bd4\u8f83\u7b80\u5355\u7684\u5224\u65ad\u54ea\u4e9b\u8f93\u51fa\u662f\u5e94\u8be5\u8de8\u8d8a\u65f6\u95f4\u6b65\u7684":42,"paddlepaddle\u53ef\u4ee5\u901a\u8fc7\u8be5\u673a\u5236\u5224\u65ad\u662f\u5426\u5df2\u7ecf\u6536\u96c6\u9f50\u6240\u6709\u7684\u68af\u5ea6":47,"paddlepaddle\u5728\u5b9e\u73b0rnn\u7684\u65f6\u5019":42,"paddlepaddle\u5b58\u7684\u662f\u6709\u503c\u4f4d\u7f6e\u7684\u7d22\u5f15":[3,37],"paddlepaddle\u5b9a\u4e49\u7684\u53c2\u6570":3,"paddlepaddle\u5c06\u4ee5\u8bbe\u7f6e\u53c2\u6570\u7684\u65b9\u5f0f\u6765\u8bbe\u7f6e":66,"paddlepaddle\u5c06\u5728\u89c2\u6d4b\u6570\u636e\u96c6\u4e0a\u8fed\u4ee3\u8bad\u7ec330\u8f6e":32,"paddlepaddle\u5c06\u6839\u636e":39,"paddlepaddle\u5c06\u6bcf\u4e2a\u6a21\u578b\u53c2\u6570\u4f5c\u4e3a\u4e00\u4e2anumpy\u6570\u7ec4\u5355\u72ec\u5b58\u4e3a\u4e00\u4e2a\u6587\u4ef6":32,"paddlepaddle\u5c06\u81ea\u52a8\u4e0b\u8f7d\u548c\u7f16\u8bd1\u6240\u6709\u7b2c\u4e09\u65b9\u4f9d\u8d56\u5e93":[39,40],"paddlepaddle\u5c06train":3,"paddlepaddle\u5f00\u53d1\u955c\u50cf\u5c31\u88ab\u6784\u5efa\u5b8c\u6bd5\u4e86":34,"paddlepaddle\u63d0\u4f9b\u4e13\u7528\u7684":20,"paddlepaddle\u63d0\u4f9b\u4e86\u5f88\u591a\u4f18\u79c0\u7684\u5b66\u4e60\u7b97\u6cd5":32,"paddlepaddle\u63d0\u4f9b\u4e86ubuntu":36,"paddlepaddle\u63d0\u4f9b\u6570\u4e2a\u9884\u7f16\u8bd1\u7684\u4e8c\u8fdb\u5236\u6765\u8fdb\u884c\u5b89\u88c5":35,"paddlepaddle\u652f\u6301\u4e0d\u540c\u7c7b\u578b\u7684\u8f93\u5165\u6570\u636e":37,"paddlepaddle\u652f\u6301\u4ee5\u4e0b\u4efb\u610f\u4e00\u79cdblas\u5e93":33,"paddlepaddle\u652f\u6301\u5927\u91cf\u7684\u8ba1\u7b97\u5355\u5143\u548c\u4efb\u610f\u6df1\u5ea6\u7684\u7f51\u7edc\u8fde\u63a5":32,"paddlepaddle\u652f\u6301\u975e\u5e38\u591a\u7684\u4f18\u5316\u7b97\u6cd5":31,"paddlepaddle\u652f\u6301sparse\u7684\u8bad\u7ec3":31,"paddlepaddle\u6587\u6863\u4f7f\u7528":48,"paddlepaddle\u6587\u6863\u9700\u8981\u51c6\u5907\u7684\u73af\u5883\u76f8\u5bf9\u8f83\u590d\u6742":48,"paddlepaddle\u662f\u4e00\u4e2a\u6700\u65e9\u7531\u767e\u5ea6\u79d1\u5b66\u5bb6\u548c\u5de5\u7a0b\u5e08\u5171\u540c\u7814\u53d1\u7684\u5e76\u884c\u5206\u5e03\u5f0f\u6df1\u5ea6\u5b66\u4e60\u5e73\u53f0":0,"paddlepaddle\u662f\u6e90\u4e8e\u767e\u5ea6\u7684\u4e00\u4e2a\u6df1\u5ea6\u5b66\u4e60\u5e73\u53f0":[32,37],"paddlepaddle\u6bcf\u6b21\u53d1\u5e03\u65b0\u7248\u672c\u90fd\u4f1a\u53d1\u5e03\u5bf9\u5e94\u7684\u5f00\u53d1\u955c\u50cf\u4f9b\u5f00\u53d1\u8005\u76f4\u63a5\u4f7f\u7528":34,"paddlepaddle\u7684\u5185\u5b58\u5360\u7528\u4e3b\u8981\u5206\u4e3a\u5982\u4e0b\u51e0\u4e2a\u65b9\u9762":31,"paddlepaddle\u7684\u53c2\u6570\u4f7f\u7528\u540d\u5b57":31,"paddlepaddle\u7684\u5df2\u7ecf\u5b89\u88c5\u5b8c\u6210":39,"paddlepaddle\u7684\u6570\u636e\u5305\u62ec\u56db\u79cd\u4e3b\u8981\u7c7b\u578b":3,"paddlepaddle\u7684\u6587\u6863\u5305\u62ec\u82f1\u6587\u6587\u6863":48,"paddlepaddle\u7684\u6587\u6863\u6784\u5efa\u6709\u76f4\u63a5\u6784\u5efa\u548c\u57fa\u4e8edocker\u6784\u5efa\u4e24\u79cd\u65b9\u5f0f":48,"paddlepaddle\u7684\u6e90\u7801":46,"paddlepaddle\u7684\u7f16\u8bd1\u9009\u9879":35,"paddlepaddle\u7684bas":47,"paddlepaddle\u7684cmake\u7cfb\u7edf\u5c06\u6839\u636e\u8be5\u503c\u81ea\u52a8\u63a8\u5bfc\u548c\u8bbe\u7f6e\u9700\u8981\u4f7f\u7528\u7684\u4ea4\u53c9\u7f16\u8bd1\u5668":39,"paddlepaddle\u7684cmake\u7cfb\u7edf\u5c06\u6839\u636e\u8be5\u503c\u81ea\u52a8\u8bbe\u7f6e\u9700\u8981\u4f7f\u7528\u7684\u4ea4\u53c9\u7f16\u8bd1\u5668":40,"paddlepaddle\u7684cmake\u7cfb\u7edf\u624d\u8ba4\u4e3a\u5728\u662f\u5728\u4ea4\u53c9\u7f16\u8bd1raspberri":40,"paddlepaddle\u7684cmake\u7cfb\u7edf\u624d\u8ba4\u4e3a\u662f\u5728\u4ea4\u53c9\u7f16\u8bd1android\u7cfb\u7edf\u7684\u7248\u672c":39,"paddlepaddle\u7684docker\u5bb9\u5668\u4f7f\u7528\u65b9\u5f0f":35,"paddlepaddle\u76ee\u524d\u53ea\u652f\u6301\u5728\u6bcf\u4e2a\u65f6\u95f4\u6b65\u4e2d":42,"paddlepaddle\u76ee\u524d\u552f\u4e00\u5b98\u65b9\u652f\u6301\u7684\u8fd0\u884c\u7684\u65b9\u5f0f\u662fdocker\u5bb9\u5668":34,"paddlepaddle\u76ee\u524d\u5df2\u7ecf\u5f00\u653e\u6e90\u7801":0,"paddlepaddle\u76ee\u524d\u63d0\u4f9b\u4e24\u79cd\u53c2\u6570\u521d\u59cb\u5316\u7684\u65b9\u5f0f":31,"paddlepaddle\u82e5\u68c0\u6d4b\u5230\u7528\u6237\u4f7f\u7528\u7684cmake\u7248\u672c\u4e0d\u4f4e\u4e8e3":39,"paddlepaddle\u8981\u6c42\u4f7f\u7528\u7684\u7f16\u8bd1\u5de5\u5177\u94fe\u6240\u652f\u6301\u7684andoid":39,"paddlepaddle\u8c03\u7528process\u51fd\u6570\u6765\u8bfb\u53d6\u6570\u636e":66,"paddlepaddle\u8d1f\u8d23\u5b8c\u6210\u4fe1\u606f\u548c\u68af\u5ea6\u5728\u65f6\u95f4\u5e8f\u5217\u4e0a\u7684\u4f20\u64ad":44,"paddlepaddle\u8d1f\u8d23\u5b8c\u6210\u4fe1\u606f\u548c\u8bef\u5dee\u5728\u65f6\u95f4\u5e8f\u5217\u4e0a\u7684\u4f20\u64ad":44,"paddlepaddle\u955c\u50cf\u9700\u8981\u63d0\u4f9b":58,"paddlepaddle\u9700\u8981\u7528\u6237\u5728\u7f51\u7edc\u914d\u7f6e":2,"paddlepaddle\u9700\u8981\u7684\u6240\u6709\u7f16\u8bd1\u5de5\u5177":34,"pass\u4e2a\u6a21\u578b\u5230\u7b2c":53,"pass\u5230":71,"pass\u5c06\u4e0d\u8d77\u4f5c\u7528":53,"pass\u8f6e\u5f00\u59cb\u8bad\u7ec3":53,"pass\u8f6e\u7684\u6a21\u578b\u7528\u4e8e\u6d4b\u8bd5":53,"passes\u8f6e":53,"patch\u53f7":30,"patch\u53f7\u52a0\u4e00":30,"path\u6307\u5b9a\u6d4b\u8bd5\u7684\u6a21\u578b":55,"period\u4e2a\u6279\u6b21\u5bf9\u6240\u6709\u6d4b\u8bd5\u6570\u636e\u8fdb\u884c\u6d4b\u8bd5":53,"period\u4e2a\u6279\u6b21\u6253\u5370\u65e5\u5fd7\u8fdb\u5ea6":53,"period\u4e2a\u6279\u6b21\u8f93\u51fa\u53c2\u6570\u7edf\u8ba1":53,"period\u4e2a\u6279\u6b21\u8f93\u51fa\u7b26\u53f7":53,"period\u4e2abatch\u5904\u7406\u7684\u5f53\u524d\u635f\u5931":70,"period\u4e2abatch\u7684\u5206\u7c7b\u9519\u8bef":70,"period\u6574\u9664":53,"period\u8f6e\u4fdd\u5b58\u8bad\u7ec3\u53c2\u6570":53,"pfsclient\u9700\u8981\u548cingress\u4e4b\u95f4\u505a\u53cc\u5411\u9a8c\u8bc1":24,"pfsclient\u9700\u8981\u5728\u4f20\u8f93\u5b8c\u6bd5\u6700\u540e\u4e00\u4e2achunk\u7684\u65f6\u5019\u68c0\u67e5destination\u6587\u4ef6\u7684md5\u503c\u662f\u5426\u548csource\u6587\u4ef6\u4e00\u81f4":24,"pfsserver\u63d0\u4f9brest":24,"pi\u5e73\u53f0\u4e0a\u9002\u7528\u7684paddlepaddle\u5e93":40,"pi\u5e73\u53f0\u4e0a\u9002\u7528\u7684paddlepaddle\u7684\u65b9\u6cd5\u548c\u6b65\u9aa4":40,"pi\u5e73\u53f0\u4f7f\u7528\u7684c":40,"pi\u5e73\u53f0\u53ef\u9009\u914d\u7f6e\u53c2\u6570":40,"pi\u7248\u672c\u7684\u5e93":40,"pi\u7248\u672cpaddlepaddle\u5e93\u65f6":40,"pi\u7cfb\u7edf":40,"pi\u7cfb\u7edf\u4e0a":40,"pi\u7cfb\u7edf\u7684\u7248\u672c":40,"pod\u4e2d\u7684\u5bb9\u5668\u5171\u4eabnet":56,"pod\u662fkubernetes\u7684\u6700\u5c0f\u8c03\u5ea6\u5355\u5143":56,"pooling\u5bf9\u7279\u5f81\u56fe\u4e0b\u91c7\u6837":63,"process\u51fd\u6570\u4f1a\u7528yield\u8bed\u53e5\u8f93\u51fa\u8fd9\u6761\u6570\u636e":66,"prod\u7684\u751f\u4ea7\u955c\u50cf":34,"public":[13,47,57,70],"py_paddle\u91cc\u9762\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5de5\u5177\u7c7b":5,"pydataprovider2\u4f1a\u5c3d\u53ef\u80fd\u591a\u7684\u4f7f\u7528\u5185\u5b58":3,"pydataprovider2\u63d0\u4f9b\u4e86\u4e24\u79cd\u7b80\u5355\u7684cache\u7b56\u7565":3,"pydataprovider2\u662fpaddlepaddle\u4f7f\u7528python\u63d0\u4f9b\u6570\u636e\u7684\u63a8\u8350\u63a5\u53e3":3,"pydataprovider2\u7684\u4f7f\u7528":[2,4,31,45,66,68],"pydataprovider\u4f7f\u7528\u7684\u662f\u5f02\u6b65\u52a0\u8f7d":31,"python\u4ee3\u7801\u5c06\u968f\u673a\u4ea7\u751f2000\u4e2a\u89c2\u6d4b\u70b9":32,"python\u5305":48,"python\u53ef\u4ee5\u89e3\u9664\u6389\u5185\u90e8\u53d8\u91cf\u7684\u5f15\u7528":3,"python\u5c01\u88c5\u7684\u5b9e\u73b0\u4f7f\u5f97\u6211\u4eec\u53ef\u4ee5\u5728\u914d\u7f6e\u6587\u4ef6\u4e2d\u4f7f\u7528\u65b0\u5b9e\u73b0\u7684\u7f51\u7edc\u5c42":47,"query\u6539\u5199":71,"rate\u4e3a0":71,"rate\u4e3a5":71,"rate\u88ab\u8bbe\u7f6e\u4e3a0":63,"reader\u7684\u4f7f\u7528\u65b9\u5f0f\u90fd\u662f\u4e00\u81f4\u7684":20,"reader\u8f93\u51fa\u7684data":20,"recommendation\u6587\u4ef6\u5939\u5185\u5b58\u653e\u8bad\u7ec3\u6587\u4ef6":58,"release\u9875\u9762":30,"repo\u7684\u6839\u76ee\u5f55":34,"request\u524d":46,"request\u7684":46,"request\u88ab\u5408\u5e76\u540e":46,"research\u5b9e\u9a8c\u5ba4\u641c\u96c6\u6574\u7406":67,"resnet\u6a21\u578b":65,"return":[3,9,10,12,13,15,16,20,22,23,32,37,42,45,47,58,64,66,68],"rnn\u5373\u65f6\u95f4\u9012\u5f52\u795e\u7ecf\u7f51\u7edc":42,"rnn\u5bf9\u4e8e\u6bcf\u4e00\u4e2a\u65f6\u95f4\u6b65\u901a\u8fc7\u4e86\u4e00\u4e2alstm\u7f51\u7edc":42,"rnn\u603b\u662f\u5f15\u7528\u4e0a\u4e00\u65f6\u523b\u9884\u6d4b\u51fa\u7684\u8bcd\u7684\u8bcd\u5411\u91cf":44,"rnn\u6a21\u578b":66,"rnn\u76f8\u5173\u6a21\u578b":49,"s3\u4e4b\u7c7b\u7684\u5206\u5e03\u5f0f\u5b58\u50a8\u4e4b\u4e0a":20,"search\u7684\u65b9\u6cd5":53,"sentences\u662f\u53cc\u5c42\u65f6\u95f4\u5e8f\u5217\u7684\u6570\u636e":42,"seq\u53c2\u6570\u5fc5\u987b\u4e3afals":44,"server\u4e2a\u6279\u6b21\u6253\u5370\u65e5\u5fd7\u8fdb\u5ea6":53,"server\u4e4b\u4e0a":19,"server\u4e4b\u95f4\u7684\u7f51\u7edc\u5e26\u5bbd":19,"server\u4f1a\u6682\u505c\u53c2\u6570\u66f4\u65b0\u5e76\u7b49\u5f85":19,"server\u4f1a\u83b7\u53d6parameters\u5185\u5b58\u7684":19,"server\u5185\u5b58\u4e2d\u7684\u6a21\u578b\u6570\u636e\u7684\u5b8c\u6574\u955c\u50cf":19,"server\u540c\u6b65\u7684\u4fdd\u5b58\u4e00\u4e2a\u7279\u5b9a\u65f6\u95f4\u70b9\u7684\u5168\u5c40\u68c0\u67e5\u70b9":19,"server\u5728\u96c6\u7fa4\u4e2d\u542f\u52a8\u540e":19,"server\u6545\u969c\u540e\u88abkubernetes\u91cd\u65b0\u542f\u52a8":19,"server\u6b64\u65f6\u8fd8\u9700\u8981\u901a\u8fc7\u7f51\u7edc\u8bbf\u95ee\u5206\u5e03\u5f0f\u5b58\u50a8\u4ee5\u4fdd\u5b58\u5feb\u7167":19,"server\u751f\u6210\u4e00\u4e2auuid":19,"server\u7684\u5355\u70b9\u6216\u591a\u70b9\u540c\u65f6\u6545\u969c":19,"server\u7684\u6570\u636e\u5feb\u7167":19,"server\u7684\u68c0\u67e5\u70b9\u5404\u81ea\u72ec\u7acb\u4fdd\u5b58":19,"server\u7b2c\u4e00\u6b21\u542f\u52a8\u6216\u4efb\u610f\u65f6\u95f4paramet":19,"sh\u6765\u8bad\u7ec3\u6a21\u578b":63,"sh\u6765\u8fdb\u884c\u6784\u5efa":48,"sh\u8c03\u7528\u4e86":64,"short":[9,10],"simd\u6307\u4ee4\u63d0\u9ad8cpu\u6267\u884c\u6548\u7387":31,"size\u4e3a1":71,"size\u4e3a50":71,"size\u4e3a512":53,"size\u53ef\u80fd\u4f1a\u5bf9\u8bad\u7ec3\u7ed3\u679c\u4ea7\u751f\u5f71\u54cd":31,"size\u5927\u5c0f\u4e3a128":70,"size\u662f3":71,"size\u672c\u8eab\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u8d85\u53c2\u6570":31,"size\u7684\u503c":3,"softmax\u5c42":62,"softmax\u6fc0\u6d3b\u7684\u8f93\u51fa\u7684\u548c\u603b\u662f1":47,"sparse\u8bad\u7ec3\u9700\u8981\u8bad\u7ec3\u7279\u5f81\u662f":31,"srl\u4f5c\u4e3a\u5f88\u591a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u4e2d\u7684\u4e2d\u95f4\u6b65\u9aa4\u662f\u5f88\u6709\u7528\u7684":69,"ssh\u65b9\u5f0f\u7684\u4e00\u4e2a\u4f18\u70b9\u662f\u6211\u4eec\u53ef\u4ee5\u4ece\u591a\u4e2a\u7ec8\u7aef\u8fdb\u5165\u5bb9\u5668":34,"ssh\u8fdb\u5165\u5bb9\u5668":34,"static":[22,27],"step\u51fd\u6570\u4e2d\u7684memori":44,"step\u51fd\u6570\u5185\u90e8\u53ef\u4ee5\u81ea\u7531\u7ec4\u5408paddlepaddle\u652f\u6301\u7684\u5404\u79cdlay":44,"subseq\u7684\u6bcf\u4e2a\u5143\u7d20\u662f\u4e00\u4e2a0\u5c42\u5e8f\u5217":41,"super":47,"swig\u652f\u6301\u7684\u8bed\u8a00\u6216\u8005\u89e3\u91ca\u5668\u6709\u5c40\u9650":26,"swig\u66b4\u9732\u7684\u63a5\u53e3\u4fdd\u7559\u4e86c":26,"swig\u751f\u6210\u7684\u4ee3\u7801\u4e0d\u80fd\u4fdd\u8bc1\u591a\u8bed\u8a00\u4ee3\u7801\u98ce\u683c\u7684\u4e00\u81f4\u6027":26,"swig\u76f4\u63a5\u8bfb\u53d6c":26,"swig\u9700\u8981\u5199\u4e00\u4e2ainterface\u6587\u4ef6":26,"swig_paddle\u4e2d\u7684\u9884\u6d4b\u63a5\u53e3\u7684\u53c2\u6570\u662f\u81ea\u5b9a\u4e49\u7684c":5,"switch":27,"t\u6307\u5b9a\u4e86\u751f\u6210\u7684\u955c\u50cf\u7684\u540d\u5b57":34,"tag\u4e3a":30,"test\u548cgen\u8fd9\u4e09\u4e2a\u6587\u4ef6\u5939\u662f\u56fa\u5b9a\u7684":71,"tests\u7684paddlepaddl":46,"tflops\u4e86":50,"toolchain\u76ee\u5f55\u751f\u6210\u4e00\u5957\u7f16\u8bd1\u5de5\u5177\u94fe":39,"trainer\u63a5\u6536\u4e09\u4e2a\u53c2\u6570":37,"true":[7,8,9,10,12,13,15,16,29,31,42,45,47,55,58,64,68,69,70,71],"true\u8868\u793a\u53cd\u5411\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":45,"try":[11,17,18,21,22,29,31,68],"type\u5b57\u6bb5\u5747\u4e0d\u5c3d\u76f8\u540c":27,"type\u662fon":68,"ubuntu\u5b89\u88c5\u5305\u7684\u529f\u80fd\u6b63\u786e\u6027":30,"ubuntu\u7684deb\u5b89\u88c5\u5305\u7b49":35,"ubuntu\u90e8\u7f72paddlepaddl":35,"update\u53c2\u6570\u65f6\u624d\u6709\u6548":53,"utf8\u7f16\u7801":62,"uts\u7b49linux":56,"v2\u4e4b\u540e\u7684\u4efb\u4f55\u4e00\u4e2a\u7248\u672c\u6765\u7f16\u8bd1\u8fd0\u884c":33,"vocab\u4e2d\u6bcf\u4e2a\u5207\u5206\u5355\u8bcd\u7684\u9884\u671f\u8bc4\u7ea7":70,"vocab\u505a\u4e3a\u5b57\u5178":70,"void":[22,26,27,47],"volume\u6302\u8f7d\u5230\u5bb9\u5668\u4e2d":56,"w0\u548c":64,"wbias\u662f\u9700\u8981\u5b66\u4e60\u7684\u53c2\u6570":64,"while":[7,13,28,29,58,71],"words\u5373\u4e3a\u8fd9\u4e2a\u6570\u636e\u4e2d\u7684\u5355\u5c42\u65f6\u95f4\u5e8f\u5217":42,"words\u662f\u539f\u59cb\u6570\u636e\u4e2d\u7684\u6bcf\u4e00\u53e5\u8bdd":42,"x\u4e0ey\u4e3a\u4e4b\u524d\u63cf\u8ff0\u7684\u8f93\u5165\u5c42":37,"x\u548cwindow":[34,39],"x\u548cwindows\u4e0a\u7684\u786c\u4ef6\u8d44\u6e90":34,"y\u8868\u793a\u8f93\u5165\u6570\u636e\u662f\u4e00\u4e2a\u7ef4\u5ea6\u4e3a1\u7684\u7a20\u5bc6\u5411\u91cf":37,"yaml\u6587\u4ef6\u4e2d\u5404\u4e2a\u5b57\u6bb5\u7684\u5177\u4f53\u542b\u4e49":58,"yaml\u6587\u4ef6\u63cf\u8ff0\u4e86\u8fd9\u6b21\u8bad\u7ec3\u4f7f\u7528\u7684docker\u955c\u50cf":58,"zero\u4e09\u79cd\u64cd\u4f5c":53,AGE:57,AWS:[20,56,59,60],And:[8,9,11,13,23,29],But:[9,10,13,31],EOS:9,For:[5,8,9,11,13,16,21,22,23,28,29,36,50],IDs:13,NFS:56,Not:[16,18],One:[8,10,15],PFS:24,QoS:57,TLS:[16,24],That:[9,13,29],The:[3,6,7,8,9,10,11,13,15,16,17,18,21,23,25,27,29,47,58,66,68,69,71],Their:[9,18],Then:[9,68],There:[8,9,13,15,16,18,22,23,25,28],These:8,Use:[8,16,25,29],Used:10,Using:[18,70],Will:[13,15],With:[9,10],YES:23,Yes:34,___embedding_0__:58,___embedding_1__:58,__file__:23,__init__:47,__list_to_map__:68,__main__:[5,64],__meta__:68,__mse_cost_0__:58,__name__:[5,64],__rnn_step__:45,_binari:17,_librari:17,_link:10,_proj:9,_recurrent_group:45,_res2_1_branch1_bn:64,_source_language_embed:[45,62],_target_language_embed:[45,62],_test:17,aaaaa:20,abc:9,abl:[9,16],about:[9,10,23,25,69,71],abov:[3,8,9,16,17,18,21,50],abs:10,acceler:19,accept:[13,16,29,69],access:[9,10,16,21,23],accord:[8,9,22],accordingli:[8,9],accrod:10,accumul:[18,22],accuraci:8,acl:70,aclimdb:70,across:[9,13],act:[9,10,31,32,37,42,45],act_typ:66,activ:[9,10,14,37,66],activi:10,actual:9,adadelta:[31,66],adagrad:66,adam:[16,22,66,70],adamoptim:[62,66,70,71],adapt:[8,11],add:[9,10,13,17,31,46,68],add_depend:17,add_execut:17,add_input:47,add_test:[17,47],add_to:9,add_unittest_without_exec:47,addbia:47,added:[8,46],addit:[9,10],address:18,addrow:47,addtolay:9,adversari:29,affect:9,afford:21,afi:3,aforement:17,after:[9,13,21,22,25,28,46],again:[16,18],age:[13,58,68],agg_level:[9,41,42],aggregatelevel:[41,42],ago:17,aircraft:71,airplan:63,aistat:9,alex:[9,70],alexnet_pass1:55,alexnet_pass2:55,algo_hrnn_demo:42,algorithm:[9,11,21,62,70,71],align:[9,10,13,71],all:[3,7,8,9,11,16,18,22,23,25,27,44,58,68,69,70],alloc:[7,23],allow:[16,22,66],allow_only_one_model_on_one_gpu:[52,53,55],almost:10,alreadi:[18,31,36],also:[9,10,13,16,17,28,29,50,66],alwai:[9,10,15,29,58],amazon:57,ambigu:29,amd64:56,amend:46,analysi:[69,70],android:39,android_abi:39,android_arm_neon:39,android_native_api_level:39,android_standalone_toolchain:39,androideabi:39,ani:[9,10,13,16,17,18,22,23,29],annot:69,annual:69,anoth:[9,16,23],anroid_arm_mod:39,anyth:[13,29,69],api:[13,15,16,17,22,23,24,28,30,50,58,61,70],api_shar:17,api_test:17,api_trainer_config:68,api_trainer_config_helpers_data_sourc:68,api_trainer_config_helpers_lay:[45,66],api_trainer_config_helpers_layers_context_project:68,api_trainer_config_helpers_layers_cos_sim:68,api_trainer_config_helpers_layers_data_lay:68,api_trainer_config_helpers_layers_embedding_lay:68,api_trainer_config_helpers_layers_fc_lay:68,api_trainer_config_helpers_layers_pooling_lay:68,apiserv:56,apivers:[56,57,58],apo:71,append:[3,15,29,42,45,58,68],append_gradient_machin:15,appleyard:50,appli:[9,10],applic:[50,57],approach:[9,28,66],apt:[36,63],arbitrari:9,arch:39,architectur:71,archiv:[13,26,27],arg:[3,8,10,13,31,32,58,63,64,66,68,69,70],arg_nam:9,argpars:58,args_ext:58,argument:[3,9,13,21,28,58,66,68,69],argumentpars:58,argv:64,arm64:39,arm:[39,40],arm_soft_fp_abi:39,armeabi:39,around:[3,9],arrai:[5,9,13,15,22,29,37,64],articl:46,arxiv:[9,10,70],ask:18,assert:5,assign:[8,9,21],associ:[28,69],assum:[8,9],astyp:29,asyc:18,async:[18,52],async_count:[52,53],async_lagged_grad_discard_ratio:53,async_lagged_ratio_default:[52,53],async_lagged_ratio_min:[52,53],asynchron:18,atla:33,atlas_root:33,attenion:10,attent:[9,10,71],attr1:9,attr2:9,attr:[7,9,10],attribut:[9,10,14],auc:52,author:[24,56],authorized_kei:51,auto:[26,47,50],automat:[9,16,22,71],automaticli:9,automobil:63,avail:[18,22],averag:[8,9,12,21,69],average_test_period:[52,53,69],averagepool:9,avg:[41,50,66],avgcost:[66,68,70,71],avgpool:[9,66],avoid:[18,28,50],avx:34,await:57,awar:[16,18],aws:24,azur:56,b2t:62,b363:57,b8561f5c79193550d64fa47418a9e67ebdd71546186e840f88de5026b8097465:57,back:18,backward:[6,9,10,22,28,47],backward_first:45,backwardactiv:47,bag:66,baidu:[9,57],balasubramanyan:70,bank:69,bardward:10,bare:[56,57],barrierstatset:50,base:[9,12,13,16,21],baseactiv:10,baseev:15,basematrix:47,basenam:8,basepoolingtyp:[9,10],basestr:[7,8,9,10,12,15,68],bash:[34,46,48,57,58],basic:[9,15],batch:[9,10,11,13,15,16,18,20,32,37,51,57,58,63,64,66,68,70,71],batch_0:64,batch_id:[15,37],batch_norm_lay:10,batch_norm_typ:9,batch_read:[20,29],batch_siz:[13,31,32,37,51,62,63,66,68,70,71],batchsiz:[9,47],bazel:17,bbbbb:20,bcd:9,bcebo:13,bcm2708:40,beam:[9,45,71],beam_gen:[9,45],beam_search:[15,44,45],beam_siz:[9,45,52,53,55],beamsiz:71,becaus:[8,9,13,16,17,18,22,28,29,42],been:[21,69],befor:[9,10,18,25,28,29,31,68],begin:[8,9,22,25],beginiter:[15,16],beginn:45,beginpass:[15,16],begintrain:16,being:[18,29],belong:9,below:[9,13,18,22,28,29],benefit:[10,23],bengio:9,bertolami:70,besid:[9,13],best:[9,68],best_model_path:69,besteffort:57,beta1:11,beta2:11,beta:64,better:[9,10,17],between:[8,9,11,17,18,22,27,28,71],bgr:64,bi_gru:10,bi_lstm:10,bia:[9,10,11,47,64],bias:9,bias_attr:[9,10,31,32,42,45],bias_param:31,bias_param_attr:10,biases_:47,biasparameter_:47,biassiz:47,bidi:57,bidirect:[10,69,70],bidirectional_lstm_net:70,bigger:18,bilinear:9,bilinear_interpol:9,bilinearfwdbwd:50,bin:[34,36,51,56,57,58,68],binari:[8,9,13,17,23,66],bird:63,bitext:71,blank:9,bleu:71,block:[9,19,22],block_i:9,block_x:9,bn_bias_attr:10,bn_layer_attr:10,bn_param_attr:10,bollen:70,book:13,bool:[7,8,9,10,12,13,47,53,55,66,68,70],boot:[9,44,45],boot_bia:9,boot_bias_active_typ:9,boot_lay:[42,45],boot_with_const_id:9,bos_id:[9,45],both:[6,7,9,10,16,17,18],bottom:66,boundri:8,bow:66,branch:[9,16,30,46],brendan:70,brief:22,broadcast:18,broken:46,browser:34,bryan:70,buf_siz:13,buffer:[13,29],buffered_read:29,bug:46,build:[13,17,23,34,39,40,46,58,59,60,71],build_dict:13,build_doc:48,built:[17,50],bunk:70,c11:26,c703c041:46,c99:27,cach:[31,66,68,69],cache_pass_in_mem:[3,31,66,68,69],cachetyp:[3,31,66,68,69],calc_batch_s:[3,69],calcul:[8,9,10,11,18,22],call:[8,9,10,16,21,22,23,50,58,66],callabl:[9,13],callback:47,calrnn:42,caltech:63,can:[7,8,9,10,13,16,18,21,23,28,29,50,66],can_over_batch_s:[3,69],candid:9,cannot:31,capi:26,capi_prvi:27,caption:71,card:51,care:[10,23,29],cat:[34,58,63,64,70],categori:[9,13,18,66],categorig:13,categoryfil:57,caus:[18,25],cc_:17,cc_binari:17,cc_test:17,ccb2_pc30:71,cde:9,cdn:13,ceil:9,ceil_mod:9,cell:[9,10],ceph:[20,56],cephf:[20,23,24],certif:[16,24,56],cffi:26,cfg:57,cgo:26,chain:13,challeng:18,chanc:16,chang:[9,13,17,21,23,28,29,46,70],channel:[9,50],char_bas:68,check:[3,13,31,36,46,47,53],check_align:13,check_eq:47,check_fail_continu:3,check_l:47,check_sparse_distribution_batch:[52,53],check_sparse_distribution_in_pserv:[52,53],check_sparse_distribution_ratio:[52,53],check_sparse_distribution_unbalance_degre:[52,53],checkgrad:53,checkgrad_ep:53,checkout:46,checksum:24,chunk:[21,24],chunk_schem:8,chunktyp:8,cifar:63,cifar_vgg_model:63,claimnam:58,clang:[26,34,46],clarifi:8,class1:70,class2:70,class_dim:70,classic:9,classif:[9,66,70,71],classifi:64,classification_cost:[31,42,63,66],classification_error_evalu:[8,66,70,71],classification_evalu:8,clean:[17,28,31,46],clear:[8,17,28],clearer:28,client:56,clip:[7,53,66],clock:9,clone:[34,40],close:[3,29,46],cloud:[18,23,24,25],cluster:[16,18,22,51,56,58],cluster_train:51,cmake:[27,31,33,39,40,46,48,50],cmake_build_typ:[39,40],cmake_c:[39,40],cmake_system_nam:[39,40],cmakelist:[17,47],cmatrix:[26,27],cna:9,cnn:[9,57,66],code:[3,5,13,16,17,28,29,34,47,57,68],coded_stream:31,codedinputstream:31,coeff:9,coeffici:9,collabor:18,collect:[9,13,15],collectbia:47,color:[63,64],colour:13,column:[8,9,29],column_evalu:8,com:[9,10,13,34,36,39,40,46,56,57,64],combin:[8,9,10,13,15,68],command:[17,23,25,47,55,57,58,59,60],commandlin:[50,58],comment:[10,17,42,58,66],commit:57,common:20,common_util:[51,68],commonli:25,commun:[18,22],compil:[17,36,39,40],complet:[9,10,13,15,18,21,22,24,57,58],complex:[10,29],complic:9,compos:[13,16],composenotalign:13,compress:21,comput:[9,10,16,18,69,70],conat:9,concat:71,concat_lay:45,concaten:10,concept:[8,16,28],concern:16,concurr:18,condit:[9,21,57],conf:[5,9,31,42,51,58,62,64,71],conf_paddle_gradient_num:58,conf_paddle_n:58,conf_paddle_port:58,conf_paddle_ports_num:58,conf_paddle_ports_num_spars:58,config:[7,9,10,20,25,32,37,47,51,52,53,55,56,57,58,63,66,68,69,70,71],config_:53,config_arg:[52,53,55,64,66,69,70],config_bas:[8,9,15],config_fil:69,config_gener:[51,68],config_lay:47,config_len:22,config_pars:[5,47],config_proto:22,configprotostr:31,configur:[9,22,23,47,62,64,71],confirm:25,conflict:46,confront:71,conll05st:69,conll:[13,69],connect:[10,23,57,66],connectionist:[9,70],connor:70,consequ:[9,10],consid:[8,9,11],consider:10,consist:[8,9,13,21,29],construct:[8,16,68],construct_featur:68,consum:18,contact:18,contain:[3,8,9,10,12,13,15,16,21,28,57,58,66],content:[22,25],content_len:22,context:[3,9,10,13,45,56],context_attr:10,context_len:[9,10,66,68],context_proj_layer_nam:10,context_proj_param_attr:10,context_project:[10,68],context_start:[9,10,66],continu:[8,18],contrast:9,control:[7,57,71],conv:10,conv_act:10,conv_batchnorm_drop_r:10,conv_bias_attr:10,conv_filter_s:10,conv_layer_attr:10,conv_num_filt:10,conv_op:9,conv_pad:10,conv_param_attr:10,conv_strid:10,conv_with_batchnorm:10,conveni:16,convent:22,convert:[3,5,13,20,29,66,68],convlay:9,convolut:[9,10,13],convoper:9,convtran:9,convtranslay:9,cool:46,coordin:18,copi:[15,16,21,25,68],core:[7,27,28],corpora:71,corpu:[13,69],correct:9,correctli:[8,13],correspoind:16,correspond:16,corss_entropi:16,cos:9,cos_sim:68,cosin:9,cost:[11,15,16,32,37,68,70,71],cost_id:9,could:[9,13,15,16,21,28,29],couldn:36,count:[18,23,29,50,53,55,57,68,69,70,71],counter:[18,21],cours:[8,23],cpickl:68,cpp:[26,27,28,31,42,47,50,58,66,68,71],cpu:[3,7,9,23,28,30,34,36,50,55,57,58,69],cpuinfo:34,cpusparsematrix:27,crash:[18,50],creat:[7,9,13,15,16,18,24,25,28,37,46,47,57,58],create_bias_paramet:47,create_input_paramet:47,createfromconfigproto:5,creator:[13,20],credenti:25,crf:69,critic:70,crlf:46,crop:64,crop_siz:64,cross:[9,31,39,40,66],cross_entropi:[9,16],cross_entropy_with_selfnorm:9,crt:[24,56],csc:47,cslm:71,csr:47,ctc:8,ctc_evalu:8,ctest:[34,46],ctrl:[51,68],ctx:69,ctx_0:69,ctx_0_slot:69,ctx_n1:69,ctx_n1_slot:69,ctx_n2:69,ctx_n2_slot:69,ctx_p1:69,ctx_p1_slot:69,ctx_p2:69,ctx_p2_slot:69,cub:63,cuda:[17,36,50,51,53],cuda_dir:[52,53],cuda_so:[31,34],cuda_visible_devic:31,cudaconfigurecal:50,cudadevicegetattribut:50,cudaeventcr:50,cudaeventcreatewithflag:50,cudafre:50,cudagetdevic:50,cudagetdevicecount:50,cudagetdeviceproperti:50,cudagetlasterror:50,cudahostalloc:50,cudalaunch:50,cudamalloc:50,cudamemcpi:50,cudaprofilerstart:50,cudaprofilerstop:50,cudaprofilestop:50,cudaruntimegetvers:50,cudasetdevic:50,cudasetupargu:50,cudastreamcr:50,cudastreamcreatewithflag:50,cudastreamsynchron:50,cudeviceget:50,cudevicegetattribut:50,cudevicegetcount:50,cudevicegetnam:50,cudevicetotalmem:50,cudnn:[9,12,17],cudnn_batch_norm:9,cudnn_conv:9,cudnn_conv_workspace_limit_in_mb:[52,53],cudnn_convt:9,cudnn_dir:[52,53],cudnnv5:33,cudrivergetvers:50,cuinit:50,cumul:9,curl:56,current:[3,9,18,28,56,66],current_word:45,currentcost:[66,68,70,71],currentev:[66,68,70,71],curv:16,custom:[16,23],custom_batch_read:29,cutoff:13,cxx_compil:[39,40],cxx_flags_minsizerel:[39,40],cycl:18,cyclic:9,cython:26,dalla:3,dan:69,dandroid_abi:39,dandroid_arm_mod:39,dandroid_arm_neon:39,dandroid_standalone_toolchain:39,darwin:56,dat:[13,20,51,68],data:[3,8,10,11,15,16,20,21,24,28,31,36,37,42,51,52,57,58,59,62,63,64,66,68,69,70,71],data_config:5,data_dir:[62,63,70,71],data_feed:13,data_fil:32,data_initialz:66,data_lay:[3,31,32,42,45,63,66,68,69],data_nam:13,data_read:[13,29],data_reader_creator_random_imag:29,data_typ:[13,37],databas:13,datacent:[20,25],datacenter1:20,datacenter2:20,datacenter_1:20,datacenter_2:20,datacenter_nam:20,datadim:9,dataprovid:[2,31,32,45,51,58,66,68,69],dataprovider_:66,dataprovider_bow:66,dataprovider_emb:66,dataproviderconvert:5,datasci:9,dataset:[20,23,29,37,64,66,67,70,71],date:69,db_lstm:69,dcmake_install_prefix:[39,40],dcmake_system_nam:[39,40],dcudnn_root:33,dead:18,deb:[36,46],debug:25,decai:11,decent:21,decid:[16,29],declar:9,decod:[9,10,44,45,71],decoder_boot:45,decoder_group_nam:45,decoder_input:45,decoder_mem:45,decoder_prev:10,decoder_s:45,decoder_st:[10,45],deconv:9,deconvolut:9,decor:[3,13],deep:[9,50,63,64],deer:63,def:[3,5,9,13,16,20,23,29,31,32,37,42,45,47,58,64,66,68,69],defalut:9,default_decor:58,default_devic:55,default_valu:55,defect:28,defin:[3,9,10,13,16,17,18,29,31,37,66,68],define_py_data_sources2:[3,31,32,63,64,66,68],defini:71,definit:[13,18,21,62],degre:9,del:68,delar:66,delet:[18,23,24,46],delimit:[8,68],demand:18,demo:[5,9,13,45,51,57,59,62,63,64,66,68,70,71],dens:[9,13,22,68],dense_arrai:13,dense_vector:[3,5,13,32,37,68],dense_vector_sequ:13,dep:17,depend:[17,18,23],deriv:[6,16],descent:[9,18],describ:[16,17,21,28,57,66],descript:[8,17,58],deseri:[15,28],design:[9,13,26],desir:[18,57],destin:[22,25],detail:[7,8,9,10,11,21,23,25],detect:[8,46],determin:[9,13],dev:[31,34,46,63,68,71],develop:[17,28,30,46,71],deviat:7,devic:[7,28,31,34,55],deviceid:55,devid:9,dez:70,dict:[3,8,13,15,31,42,58,66,68,70,71],dict_dim:[31,42,70],dict_fil:[8,42,45,66,69],dict_siz:13,dictionai:66,dictionari:[3,8,9,13,15,16,31,66,71],dictrionari:66,dictsiz:71,did:28,diff:46,differ:[8,9,17,18,22],digit:9,dim:[13,47,62,70],dimens:[6,9,12,13,31,66],dimes:9,din:68,dir:[39,51,58,64,68,69,70,71],direct:[9,10],directli:[10,23,28],directori:[17,20,24,25,46,50,57],disabl:31,discard:[13,18,21,46,53],discount:9,discov:18,discuss:[16,21,22],dispatch:28,displai:[23,25],disput:71,dist_train:[16,23],distanc:8,distinguish:17,distribut:[9,21,22,53,59,60],distribute_test:[52,53],disucss:16,divid:11,diy_beam_search_prob_so:[52,53],dmkl_root:33,do_forward_backward:29,doc:[5,10,13,34,48,58],doc_cn:48,docker:[30,31,34,46,57,58,59,60],docker_build:16,docker_push:16,dockerfil:[34,46,58],dockerhub:34,document:[10,24],documentari:3,doe:[10,18,21,22,23,28,29],doesn:[7,9,13,16,29],dog:[63,64],don:[10,16,17,29],done:[8,9,10,17,18,21,22,46,50,58],dot:71,dot_period:[53,55,58,63,68,70,71],dotmuloper:9,dotmulproject:9,doubl:53,down:50,download:[13,18,24,57],download_cifar:63,doxygen:46,dpkg:36,dpython_execut:31,dpython_include_dir:31,dpython_librari:31,drop_rat:7,dropout:[7,9],dropout_r:10,drpi_arm_neon:40,drpi_toolchain:40,drwxr:57,dso_handl:36,dst:22,dtoh:50,dtype:[5,32,64],dubai:71,due:[21,68],dummi:21,durat:21,dure:[3,9,18,21,23,66,71],dwith_c_api:[27,39,40],dwith_gpu:[33,40],dwith_profil:50,dwith_python:[27,40],dwith_swig_pi:[27,39,40],dwith_tim:50,dynam:[3,22,27,29],dynamic_cast:47,each:[3,8,9,12,13,15,17,18,21,22,23,28,29,66,68],each_feature_vector:6,each_meta:68,each_pixel_str:3,each_sequ:[9,41,42],each_time_step_output:6,each_timestep:[9,41],each_word:3,eaqual:9,eas:[13,29],easi:29,easier:[16,29],easili:[16,29],ec2:56,echo:[31,34,68,70],edit:8,edu:[13,57,63],efg:9,either:[9,13,15,16,66],electron:57,elem_dim:9,element:[8,9,10,13,15,21,29],element_typ:22,elif:[16,68],els:[9,16,23,34,42,47,64,66,68],emailweixu:17,emb1:42,emb2:42,emb:[31,42,57,66],emb_group:42,emb_sum:31,embed:[16,62,68,70],embedding_lay:[31,42,45,66,68],embedding_nam:[9,45],embedding_s:[9,45],empir:9,emplace_back:47,empti:[8,13,18,32],enabl:[7,17,21,50],enable_grad_shar:[52,53],enable_parallel_vector:53,enc_proj:[10,45],enc_seq:10,enc_vec:45,encapsul:22,encod:[10,21,42,71],encoded_proj:[10,45],encoded_sequ:[10,45],encoded_vector:45,encoder1:42,encoder1_expand:42,encoder1_rep:42,encoder2:42,encoder2_rep:42,encoder_last:9,encoder_proj:45,encoder_s:45,end:[8,9,28,29,45,46,69,70],end_pass:16,enditer:[15,16,37],endpass:[15,16],endpoint:20,endtrain:16,engin:23,english:[9,71],ensembl:10,ensur:18,entir:[9,10,22],entiti:8,entri:[13,21,23],entropi:[9,66],entry_point:23,enumer:[6,31,66,68],enumerate_data_types_of_data_lay:13,env:[31,58],environ:[16,31,50,57],eol:46,eos_id:[9,45],epsilon:11,equal:[9,10,18,42],equat:[8,9,10,11],equival:[9,16],error:[7,8,9,11,16,21,25,31,53,66,68,70,71],error_clipping_threshold:7,especi:10,essenc:16,essenti:[9,16],estim:[9,16],eta:57,etc:[8,13,29,71],etcd:[18,21],eth0:[51,58],eval:[8,66,68,70,71],eval_bleu:71,evalu:[9,14,15,50,51,68,70,71],evaluate_pass:70,even:[16,29],evenli:22,event:[37,57],event_handl:[15,16,37],everi:[8,9,10,13,16,18,21,22],evid:28,exactli:[9,10],exampl:[8,9,10,11,13,15,23,25,28,29,64,66],exc_path:31,exceed:9,except:[13,68],exchang:28,excluded_chunk_typ:8,exconv:9,exconvt:9,exdb:13,exe:56,exec:34,execut:[17,18,21,23],exist:[16,18,25,29,70],exit:[22,25,57],expand:42,expand_a:[9,41,42],expand_lay:42,expand_level:[9,41],expandconvlay:9,expandlevel:41,expect:9,expert:17,expir:18,explain:[8,18],explan:[9,23],explicit:47,explicitli:16,explor:9,exponenti:6,express:16,extend:[8,68],extent:27,extern:[17,26,27],external_librari:17,extra:[7,9,10],extra_lay:15,extraattr:[7,55],extraattribut:9,extraattributenon:9,extract:[8,9,28,62,64,69],extract_fea_c:64,extract_fea_pi:64,extralayerattribut:[7,10],extralayeroutput:10,extrem:9,f1205:31,f120da72:57,fa0wx:57,fabric:51,face:17,facotr:9,factor:[7,9,11],factori:26,fail:[18,21,31,36,53,57],failur:[18,22],fake_imag:29,falloc:24,fals:[7,8,9,10,11,13,29,31,32,37,42,45,47,55,57,62,66,68,69,70,71],false_label:29,false_read:29,faq:61,fast:[9,21,50],faster:[9,10,18],fault:21,fbd1f2bb71f4:57,fc1:[47,55],fc2:55,fc3:55,fc4:55,fc_act:10,fc_attr:10,fc_bias_attr:10,fc_layer:[31,32,42,55,66,68],fc_layer_nam:10,fc_mat:15,fc_param:31,fc_param_attr:10,fclayer:47,fdata:[42,69],fea:64,fea_output:64,feat:70,featur:[3,6,9,13,46,64,66,68,69],feature_a:31,feature_b:31,feature_map:68,feed:[10,13,15,16,37],feeder:13,fernan:70,festiv:3,fetch:[13,18,31,46],few:[17,18,29],fewer:9,fg0:9,field1:15,field2:15,field:[9,15,68],figur:[16,17,62,71],file1:71,file2:71,file:[3,8,9,13,15,16,17,18,20,21,22,23,24,25,27,29,46,64,66,67,68,70,71],file_list:3,file_nam:[32,42,64,66,69],filenam:[3,20,31,68],fileoffset:24,filer:9,filesystem:[23,24],fill:[9,18,21,66],filter:9,filter_s:[9,10],filter_size_i:9,find:[9,11,18,36],fine:[7,21],finish:[18,21,23,57],first:[9,13,16,18,21,23,25,66,68],first_seq:45,firstli:8,firstn:13,firstseen:57,fit:13,fix:[7,26,46],flag:13,flexiabl:29,flexibl:[9,10,16,22],flight:71,float32:[5,13,29,32,64],floor:9,flow:30,fly:66,folder:[20,23,25,71],follow:[8,9,10,11,13,16,17,18,21,23,29,59,60,68],forbid:16,force_load:26,forget:[11,16],form:[10,11],format:[8,21,46,47],former:[16,17],formula:[9,10],formular:9,forward:[6,10,22,28,47],forwardactiv:47,forwardtest:5,found:9,four:8,frame:8,framework:[16,66],free:13,french:71,frequenc:13,frequent:[21,29],frog:63,from:[3,5,8,9,10,13,15,17,18,20,21,22,25,28,29,31,32,34,44,50,57,62,63,66,68,69,70,71],from_sequ:[9,41],from_timestep:[9,41],fromfil:[29,32,64],fulfil:50,full:[9,18],full_matrix_project:[10,42,45],fulli:66,fullmatrixproject:9,fully_matrix_project:10,fullyconnectedlay:47,func:[3,13,21],further:9,fusion:68,gain:9,gamma:64,gan:16,gangliao:17,gate:[9,10],gate_act:[9,10,42],gate_recurr:9,gather:[9,28,68],gauss:7,gcc:[26,34,39,40],gce:56,gcepersistentdisk:56,gdebi:36,gen:[9,71],gen_conf:71,gen_data:71,gen_result:71,gen_trans_fil:45,gender:[13,58,68],gener:[3,8,9,10,13,15,16,17,18,20,21,22,29,50,55,58,62,66,71],generated_word_embed:9,generatedinput:[9,44,45],genr:[58,68],gereat:8,get:[3,8,9,10,13,15,17,21,22,23,24,36,47,57,63,66,68,69,70],get_batch_s:69,get_best_pass:70,get_config_arg:[55,66,68,70],get_data:[57,66,69],get_dict:13,get_embed:13,get_imdb:70,get_input_lay:47,get_model:64,get_movie_title_dict:13,get_output_layer_attr:10,get_sample_from_lin:31,get_shap:15,get_word_dict:13,getbatchs:47,getenv:[16,23,58],gethostbynam:58,gethostnam:58,getidmap:58,getinput:47,getinputgrad:47,getinputvalu:47,getoutputgrad:47,getoutputvalu:47,getparameterptr:47,getpodlist:58,getsiz:47,gettask:21,gettranspos:47,getw:47,getweight:47,getwgrad:47,gildea:69,gist:10,git:[30,31,34,40,46],github:[9,10,34,36,40,46,64],give:[3,18],given:[13,15,22,29,66],global:[7,16,17,18,28,50,68],global_learning_r:7,globalstat:50,globalstatinfo:50,globe:3,glusterf:56,gnueabihf:40,go_librari:17,go_test:17,gob:21,godoc:26,goe:[9,10,18],good:[9,29],goodfellow13:9,googl:[16,31,39],googleapi:56,gpu:[7,9,12,23,28,30,31,34,36,50,55,64,69,70,71],gpu_id:[31,53,55],gpugpu_id:52,grab:18,grad:[22,53],grad_share_block_num:[52,53],gradient:[7,8,9,11,15,18,21,28,53,58,66],gradient_clipping_threshold:[7,66,70],gradient_evalu:8,gradient_machin:[15,27],gradientmachin:[5,15,27,28,58,68,71],gram:62,graph:[9,15,17,18],grave:70,greater:9,grep:[34,70],groudtruth:45,ground:[8,9,71],group:[10,21],group_id:68,group_input:[42,45],grouplen:[13,67],gru:[9,66],gru_bias_attr:10,gru_decod:45,gru_decoder_with_attent:45,gru_encoder_decod:[62,71],gru_layer_attr:10,gru_memori:10,gru_siz:66,gru_step:45,gru_step_lay:[10,45],grumemori:[10,45],gserver:[9,47],gsizex:50,guid:[24,57],gur_group:10,gzip:[21,57],hadoop:16,handi:17,handl:[16,23,28,29],handler:15,handwrit:70,happen:21,harvest:66,has:[8,9,10,11,13,16,18,21,22,50,66,69],has_kei:15,hassubseq:42,have:[9,10,13,16,17,18,21,22,23,28,29],hdf:20,head:70,header:[22,27,32,64,68],height:[9,13,26,29,47],held:18,hello:16,help:[5,25,46],helper:[9,10],here:[7,8,9,10,13,16,17,18,25,29],heurist:[9,71],hidden:[9,10,31,68],hidden_a:31,hidden_b:31,hidden_dim:42,hidden_s:[10,68],hierach:44,hierarch:[9,42],high:7,highest:13,highli:13,him:16,hint:5,hl_dso_load:36,hl_get_sync_flag:47,hold:[16,18,21],home:[20,25,51,57,58],honor:21,hook2:42,hook:[42,68,69],horizont:9,hors:63,horst:70,host:[23,51,57],host_c:[39,40],hostnetwork:58,hostpath:[56,57,58],hot:66,hous:[3,13],how:[7,9,16,18,21,25,28],howardjohnson:42,howev:[10,28,29],howto:58,hpp:26,html:[13,63],htod:50,http:[9,10,13,23,34,36,39,40,46,56,57,63,64,67,71],huber:9,huge:9,huina:70,hyper:9,hyperplan:13,i0601:68,i0706:71,i0719:71,i1116:58,i1117:50,ib0:51,ics:13,icwsm:70,id_input:[8,45],idea:[9,17,29],identityoffsetproject:9,identityproject:9,idmap:58,ids:[8,9,31,66,68],idx:[21,47],ieee:70,ies:25,ignor:[3,9],ijcnlp:70,illustr:[8,18,22],ilsvrc:64,imag:[12,13,16,29,46,57,58,59,60,63,64,71],image_a:29,image_b:29,image_classif:63,image_fil:29,image_lay:29,image_list_provid:64,image_nam:16,image_path:29,image_provid:63,image_reader_cr:29,image_s:64,imagenet:20,imagepullpolici:58,imageri:9,images_reader_cr:29,imdber:70,img:[3,9,63],img_conv_lay:10,img_pool_lay:10,img_siz:63,imgsiz:50,imgsizei:50,imgsizex:50,immutable_paramet:16,implement:[9,10,11,13,21,22,23,26,27,28],impli:17,importerror:68,inarg:5,inc_path:31,includ:[8,9,10,13,16,17,22,23,26,27,39,40,46,50],incorrect:9,increas:[18,21,31],incupd:47,inde:[13,29],independ:[9,22],index:[8,9,12,13,15,18,21,42,68],indexslot:9,indic:[8,9,22],individu:18,industri:18,ineffici:28,infer:[16,18,26],infiniband:51,info:[8,9,13,37,42,47,51,58],inform:[13,23,25],ininst:16,init:[7,37,47,58,68,69],init_hook:[42,66,68,69],init_model_path:[52,53,55,62,66,69],initi:[3,7,9,13,17,21,53,66],initial_max:[7,31],initial_mean:[7,9,31],initial_min:[7,31],initial_std:[7,9,31],initpaddl:5,inlcud:10,inner:[31,42],inner_:42,inner_mem:42,inner_param_attr:10,inner_rnn_output:42,inner_rnn_st:42,inner_rnn_state_:42,inner_step:42,inner_step_impl:42,input1:[9,10],input2:9,input:[3,6,8,9,10,12,13,15,28,29,31,32,37,41,42,44,45,47,55,58,62,63,66,68,69,71],input_data:47,input_data_target:47,input_featur:6,input_fil:[32,69],input_hassub_sequence_data:47,input_id:9,input_imag:[10,63],input_index:47,input_label:47,input_lay:47,input_nam:16,input_sequence_data:47,input_sequence_label:47,input_sparse_float_value_data:47,input_sparse_non_value_data:47,input_t:47,input_typ:[31,32,42,45,66,68],inputdef:47,inputlayers_:47,inputtyp:13,insert:46,insid:[8,9,18,28,29],instal:[23,31,36,39,40,46,51,57,63,68],instanc:[9,18,20],instanti:18,instead:[9,10,12,17,23,29],int32:53,int64:24,integ:[3,8,9,13,21,23,26,37,66],integer_sequ:31,integer_valu:[3,13,31,37,42,66],integer_value_sequ:[3,13,42,45,66,69],integer_value_sub_sequ:42,integr:69,inter:9,intercept:9,interfac:[7,9,10,21,23,25,28,51],intergr:9,intermedi:25,intern:[9,10,13,15],internet:18,interpret:8,introduc:18,invalid:29,invoc:17,invok:[3,9,15,28,50,68],iob:8,ioe:8,ip_str:58,ipc:56,ips:58,ipt:[9,31,42,45],ipython:16,is_gener:[9,62,71],is_kei:68,is_loc:15,is_predict:[66,68,70],is_seq:[9,45,68],is_sequ:68,is_stat:7,is_test:[64,69,70],is_train:3,isinst:[5,37],ispodallrun:58,isspars:47,issue_numb:46,item:[9,13,15,29,58],iter:[3,9,10,11,13,15,16,18,29],its:[3,9,10,16,18,21,28,36,50],itself:[10,18],java:26,jeremi:50,jie:[69,70],jmlr:9,job:[13,51,52,53,55,56,58,64,66,69,70,71],job_dispatch_packag:51,job_id:13,job_mod:62,job_nam:[23,58],job_namespac:58,job_path:58,job_path_output:58,job_workspac:51,jobnam:58,jobpath:58,jobselector:58,jobserv:23,johan:70,join:[18,42],joint:71,jointli:[10,71],journal:[69,70],jpg:64,json:[51,57,68],jth:10,jupyt:[23,34],just:[6,8,9,10,13,21,22,28],jypyt:16,k8s:58,k8s_data:58,k8s_job:16,k8s_token:16,k8s_train:58,k8s_user:16,kafka:20,kaim:9,kaimingh:64,kebilinearinterpbw:50,kebilinearinterpfw:50,keep:[9,18],kei:[3,13,15,18,20,21,24,46,50,56,58,68],kernel:9,key1:53,key2:53,keyword:58,kill:18,kind:[16,18,56,57,58],kingsburi:69,know:[10,16,21],kriz:[13,63],ksimonyan:10,kube:56,kube_cluster_tl:16,kube_ctrl_start_job:16,kube_list_containers_in_job_and_return_current_containers_rank:16,kubeadm:56,kubectl:[56,57,58],kuberent:18,kubernet:[16,18,49,51,58,59,60],kubernetes_service_host:16,kwarg:[3,10,11,13,42,66,68,69],l1_rate:7,l2_rate:7,l2regular:[63,66,70],label:[3,8,9,11,13,15,29,31,32,37,42,57,63,64,66,68,69,70],label_dict:69,label_dim:[9,42,66],label_fil:[29,69],label_lay:29,label_list:69,label_path:29,label_slot:69,labeledbow:70,labelselector:58,lag:53,lake:3,lambdacost:9,lambdarank:9,languag:[9,13,62],larg:[12,13,46,71],larger:[7,8,9],last:[8,9,10,41,42],last_seq:42,last_time_step_output:9,lastseen:57,later:[17,66],latest:[9,18,31,57,58],launcher:16,layer1:[9,10,41],layer2:[9,41],layer3:9,layer:[5,7,8,10,12,13,14,15,29,37,41,44,45,47,64,66,68,69],layer_0:47,layer_attr:[9,45,55],layer_num:[55,64],layer_s:9,layer_typ:9,layerbas:47,layerconfig:47,layergradutil:47,layermap:47,layeroutout:9,layeroutput:[10,68],layers_test:31,lbl:[8,63],ld_library_path:[36,51],learn:[7,8,9,10,11,13,16,22,29,34,50,63,64,69,70,71],learnabl:9,learning_method:[32,62,63,66,68,70,71],learning_r:[7,22,31,32,62,63,66,68,70,71],leas:18,least:[8,9,18],lecun:13,left:9,leman:71,len:[3,9,22,24,42,45,47,58,66,68,69],length:[9,10,13,22,57],less:[9,16],less_than:16,let02:57,let:[8,9,16],level:[7,9,44],lgtest:17,lgtest_main:17,lib64:[31,34,36,51,53],lib:[27,33,36,39,40],lib_path:31,libapi:17,libari:27,libcuda:[31,34],libjpeg:63,libnvidia:[31,34],libpaddl:[26,27,46],libpaddle_capi:27,libpaddle_gserv:27,libpaddle_math:27,libprotobuf:31,librari:[9,17,27,36,51,53],life:18,like:[8,9,13,17,18,23,29,64],limit:[9,13,31,50],linaro:40,line:[3,8,13,17,23,25,31,42,46,55,66,68,69],line_count:31,linear:[9,37],linearactiv:32,linguist:69,link:[9,10,17,24,25,44,70],linux:[24,34,39,40,56],lipeng:62,lipton:70,list:[2,3,8,9,13,15,16,21,23,25,28,32,51,55,63,64,66,68,69,70,71],littl:22,lium:71,live:18,liwicki:70,load:[9,16,18,32,58,64,68,69,70,71],load_data_arg:5,load_featur:64,load_feature_c:64,load_feature_pi:64,load_missing_parameter_strategi:[52,53,55,62,69],loadparamet:5,loadsave_parameters_in_pserv:[52,53],loc:8,local:[7,18,33,36,48,51,52,53,58],localhost:[34,56],localip:58,localpath:25,lock:[18,21,22],log:[21,25,31,47,51,53,57,58,63,68,69,70,71],log_barrier_abstract:[52,53],log_barrier_lowest_nod:[52,53],log_barrier_show_log:[52,53],log_clip:[52,53],log_error_clip:[52,53],log_period:[53,55,57,58,63,66,68,69,70,71],log_period_serv:[52,53],logarithm:6,logger:[3,42],logic:28,logist:66,look:[3,8,23,66],loop:29,loss:[9,66],low:9,lpaddle_capi_shar:27,lpaddle_capi_whol:27,lst:68,lstm:[9,42,45,57,66],lstm_bias_attr:10,lstm_cell_attr:10,lstm_group:[10,42],lstm_group_input:42,lstm_input:42,lstm_last:42,lstm_layer_attr:10,lstm_nest_group:42,lstm_output:42,lstm_size:66,lstm_step:10,lstmemori:[10,42,45],lstmemory_group:[9,42],ltr:9,mac:[27,34,39],machan:10,machin:[9,10,13,15,44,70,71],made:[18,22],mai:[9,29],main:5,maintain:[9,21],majel:17,major:71,make:[3,8,16,17,18,21,22,28,29,36,39,40,46,47,50,70],man:24,manag:[18,22,25],mandarin:9,mani:[9,10,21,28],manufactur:71,mao:70,map:[8,9,13,15,16,21,37,68],map_read:13,mapreduc:16,marcu:70,mark:69,mark_slot:69,market:70,martha:69,mask:[7,9],master:[16,30,56,70],mat:[26,27],mat_param_attr:10,math:[10,26,47,50],matirx:9,matplotlib:63,matrix:[8,9,10,13,15,26,27,47],matrixptr:47,matrixtyp:27,max:[7,9,13,41,50,55,68],max_id:[9,15],max_job_id:13,max_length:[9,45],max_movie_id:13,max_sort_s:9,max_user_id:13,maxframe_evalu:8,maxid:8,maxid_evalu:8,maxim:9,maximum:[8,13,22],maxinum:12,maxpool:9,mayb:[9,10],md5:[13,19],mean:[7,8,9,10,11,12,13,15,17,29,31,53,64,66,68],mean_img_s:63,mean_meta:64,mean_meta_224:64,mean_valu:64,mechan:[9,10],meet:69,mem:[9,23,42],member:[13,16,28],memcpi:[28,50],memori:[10,21,23,45,50,57,66],memory_nam:9,memory_threshold_on_load_data:[52,53],mention:21,mere:10,merg:[22,28,46],mergedict:[62,71],messag:[46,57],meta:[51,63,64,68],meta_config:[51,68],meta_fil:68,meta_gener:[51,68],meta_path:63,meta_to_head:68,metadata:[24,57,58],metal:56,metaplotlib:16,method:[3,9,11,15,68,71],mfs:58,might:[9,17],million:13,min:[7,50,55,68],min_pool_s:[3,31],min_word_freq:13,mini:[9,13,15,18],mini_batch:29,minibatch:9,minibatch_data:13,minikub:56,minim:11,minimum:9,minsizerel:[39,40],minut:18,mip:39,mislead:22,miss:69,mix:10,mixed_bias_attr:10,mixed_lay:[10,42,45,69],mixed_layer_attr:10,mixedlayertyp:9,mkdir:25,mkl:33,mkl_root:33,ml_data:[51,68],mnist:[3,5,20,29],mnist_model:5,mnist_provid:3,mnist_random_image_batch_read:29,mnist_train:[3,29],mnist_train_batch_read:29,mnt:58,mod:69,mode:[9,46,58,70],model:[9,10,13,18,19,55,62,63,66,68,69,70,71],model_config:5,model_list:[53,55,69,70],model_output:70,model_path:55,model_zoo:[62,64],modifi:46,modul:[3,10,13,15,31,32,63,64,66,68],modulo:9,momentum:[7,31,37,66],momentumoptim:[32,63],mon:57,mono:9,month:[17,71],mood:70,moosef:56,more:[8,9,10,13,16,18,21,23,25,29,31,50],morin:9,mose:[70,71],moses_bleu:71,mosesdecod:70,most:[9,13,16,29],mount:23,mountpath:[57,58],move:[9,18,21,25],movi:[3,13,68],movie_categori:13,movie_featur:68,movie_head:68,movie_id:[58,68],movie_info:13,movie_meta:68,movie_nam:68,movie_review:13,movieinfo:13,movielen:67,moving_average_fract:9,mpi:51,mse_cost:[32,37,68],much:[9,18,29],mul:47,multi:[9,28,64,71],multi_binary_label_cross_entropi:9,multi_crop:64,multigradientmachin:28,multinomi:9,multipl:[8,9,10,13,15,16,21,22],multipli:[8,9],must:[6,8,9,10,13,29,36,47],my_standalone_toolchain:39,mypaddl:[57,58],name:[3,7,8,9,10,12,13,15,16,18,20,22,23,27,31,32,34,37,42,45,47,50,55,56,57,58,59,60,63,66,68,71],name_prefix:20,namespac:[26,47,56,57,58],nativ:9,natur:21,nchw:9,ndarrai:[15,20],ndarri:15,ndcg:9,ndcg_num:9,ndk:39,necessari:[9,22,28,66],need:[8,9,10,13,16,17,21,22,23,25,28,31,50,58,66],neg:[3,8,9,66,69,70],neg_distribut:9,ner:8,nest:[9,13],net:[9,10,70],net_conf:70,net_diagram:64,network:[5,7,8,9,11,13,14,15,16,18,29,37,42,51,55,58,62,63,64,68,69,70,71],network_config:55,neural:[9,10,11,13,15,16,18,42,44,62,68,69,70,71],neuralnetwork:28,never:[13,29,57,58],newest:22,next:[3,9,13,18],nfs:58,nfsdir:58,nginx:34,ngram:13,nic:[51,52,53,58],nil:21,nine:13,nlp:9,nltk:13,nmt:71,nnz:47,no_cach:3,no_sequ:[3,9,37,68],noah:70,noavx:34,node0:58,node:[9,17,56,57,58],node_0:58,node_1:58,node_2:58,nodebook:34,nodefil:51,nois:[9,18],non:[9,18],none:[3,5,7,8,9,10,11,12,13,15,16,32,45,64,66],norm_by_tim:9,normal:[9,10,13,57,58,64],notat:9,note:[7,9,10,12,15,16,21,23,29,36,70],notebook:23,noth:[6,15,46],novel:70,now:[9,13,17,18,44],np_arrai:13,ntst1213:71,ntst14:71,nullptr:[36,47],num:[9,51,53,58,69,70,71],num_channel:[9,10,63],num_chunk_typ:8,num_class:[9,10,63],num_filt:[9,10],num_gradient_serv:[52,53],num_group:9,num_neg_sampl:9,num_parameter_serv:16,num_pass:[15,32,37,52,53,55,57,58,66,68,69,70,71],num_repeat:9,num_result:8,num_results_per_sampl:9,num_shard:20,number:[8,9,13,18,20,29,71],numchunktyp:8,numdevices_:55,numlogicaldevices_:55,numofallsampl:8,numofwrongpredict:8,numpi:[13,15,20,29,32,37,64],numsampl:50,numtagtyp:8,numtimeout:21,nv_:17,nv_librari:17,nv_test:17,nvcc:[17,34],nvidia:[31,34],obei:8,obj:[3,31,32,63,64,66,68],object:[3,7,9,10,11,13,15,16,26,50,66,68],observ:11,occup:[58,68],occur:[13,15],oct:57,odd:9,off:[27,33,34,36,39,40,46],offlin:[18,20],offset:[9,68],ograd:47,old:22,omit:[31,66],on_init:3,onc:[9,18,21],one:[3,6,8,9,10,11,12,13,16,18,21,22,23,28,29,66,69,70],one_host_dens:68,one_hot_dens:68,onli:[8,9,10,12,13,15,16,17,21,22,23,25,28,42,44],onlin:[11,18,20,29],open:[3,9,16,20,29,31,32,42,64,66,68,69],openbla:33,openblas_root:33,oper:[9,10,11,13],opinion:70,ops:17,ops_test:17,opt:[16,33,58],optim:[7,14,15,28,31,37],option:[8,9,16,17],order:[9,10,13,15,29,58],org:[8,9,10,13,20,24,34,67],organ:[8,9],origin:[9,13,46],other:[8,9,10,13,18,22,25,66,68],otherchunktyp:8,otherwis:[9,13,16,18,22,29],our:[16,17],out:[9,15,16,17,21,42,44,45,63],out_dir:58,out_left:9,out_mem:45,out_right:9,out_size_i:9,out_size_x:9,outer:42,outer_mem:42,outer_rnn_st:42,outer_rnn_state_:42,outer_step:42,outout_lay:15,outout_layer1:15,outout_layer2:15,output:[6,7,8,9,10,12,13,15,16,20,25,29,31,32,42,45,51,55,57,58,62,63,64,66,68,69,70],output_:9,output_dir:64,output_fil:69,output_id:9,output_lay:[15,64],output_max_index:12,output_mem:[9,45],output_path:20,outputh:9,outputw:9,outsid:[3,9,10],outv:47,over:[9,10,16],overrid:[18,25],overview:[21,22],overwrit:25,own:22,packag:[13,21,23,31],pad_c:9,pad_h:9,pad_w:9,padding_attr:9,padding_i:9,padding_x:9,paddl:[3,5,6,7,8,9,10,11,12,13,15,16,18,20,23,25,26,27,28,31,32,34,36,37,46,47,48,50,51,55,57,58,62,63,66,68,69,70,71],paddle_begin_init_param:22,paddle_element_typ:22,paddle_element_type_float32:22,paddle_element_type_float64:22,paddle_element_type_int32:22,paddle_element_type_int64:22,paddle_element_type_uint32:22,paddle_element_type_uint64:22,paddle_error:[26,27],paddle_exampl:23,paddle_finish_init_param:22,paddle_get_param:22,paddle_gradi:22,paddle_init_param:22,paddle_job:23,paddle_matrix:[26,27],paddle_matrix_cr:27,paddle_matrix_get_shap:26,paddle_matrix_shap:26,paddle_n:[51,58],paddle_new_pserver_cli:22,paddle_on_cloud:23,paddle_output:57,paddle_paramet:22,paddle_port:[51,58],paddle_ports_num:[51,58],paddle_ports_num_for_spars:51,paddle_ports_num_spars:58,paddle_process_by_paddl:58,paddle_pserver2:51,paddle_pserver_cli:22,paddle_pserver_client_releas:22,paddle_root:62,paddle_save_model:22,paddle_send_grad:22,paddle_server_num:58,paddle_source_root:62,paddle_train:[27,30,51,58],paddledev:[31,34,57,58],paddlepaddl:[9,10,13,18,20,22,23,24,25,29,31,34,36,45,46,50,51,59,60,62,69],pair:8,palceholder_just_ignore_the_embed:62,palmer:69,paper:[9,71],para:62,paraconvert:62,parallel:[50,55,57,58,71],parallel_nn:[7,52,53],param:[7,9,13,22,28,68],param_attr:[9,10,31,32,45],param_config_proto:22,paramattr:[7,9,31,32,45],paramet:[8,9,10,11,12,13,14,17,19,25,29,37,53,58,68,69,70,71],parameter_attribut:9,parameter_block_s:[52,53],parameter_block_size_for_spars:[52,53],parameter_learning_r:7,parameter_nam:[15,16],parameter_serv:16,parameterattribut:[7,9,10],parameterclient2:58,parametermap:47,parameters_:47,parameterset:16,parameterupdat:28,parametris:11,paramutil:68,paraphras:[62,71],paraphrase_data:62,paraphrase_model:62,paraphrase_modeldata:62,paraspars:47,pars:[13,17,68],parse_config:5,parse_known_arg:58,parsefromstr:31,parser:58,part:[8,68,70],partial:9,partit:[18,20],paserv:58,pass:[3,9,13,15,18,28,29,31,32,37,46,50,53,55,57,58,63,66,68,69,70,71],pass_id:[15,37],pass_idx:29,passtyp:47,past:16,patch:24,path:[8,13,15,18,21,22,23,29,36,39,40,51,53,56,57,58,69,70],pattern:[13,18,26,68,70],paul:69,paus:18,pave:71,pdf:[9,10],pem:[16,20],pend:[18,21],penn:69,per:[8,9,13,18,22,29],perform:[9,10,22,28,50,52],period:[18,53,69,70,71],perl:[70,71],peroid:9,persistentvolum:56,persistentvolumeclaim:[56,58],person:[8,16],pfs:[20,25],pfsclient:20,pfspath:25,pickl:68,pid:56,piec:[9,10],pillow:[23,63],pip:[31,46,51,63,68],pixel:[3,9,13],pixels_float:3,pixels_str:3,place:[3,18],plain:[8,9,15,23,27],plan:18,platform:39,pleas:[7,9,10,11,16,18,21,22,29,31,36,58],plot:[16,63],plotcurv:63,png:63,pnpairvalid:52,pod:[20,23,56,57,58],podip:58,podlist:58,point:[18,23,50],polar:13,poll:70,pool3:47,pool:[10,14,68],pool_attr:10,pool_bias_attr:10,pool_layer_attr:10,pool_pad:10,pool_siz:[3,9,10],pool_size_i:9,pool_strid:10,pool_typ:[9,10],pooling_lay:[10,31,66],pooling_typ:[9,31,41,66],poolingtyp:12,port:[17,51,52,53,57,58],port_num:52,ports_num:[53,58],ports_num_for_spars:[52,53,55,58],pos:[68,70],pose:18,posit:[3,8,9,13,66],positive_label:8,posix:56,possibl:[16,21],post:[23,24],potenti:50,practic:9,pre:[9,10,13,16,62,70,71],pre_dictandmodel:62,precis:8,precision_evalu:8,pred:[66,69],predetermin:9,predic:[13,69],predicate_dict:69,predicate_dict_fil:69,predicate_slot:69,predict:[3,5,8,9,11,15,31,51,62,63,64,66,68,69,70],predict_fil:[52,53],predict_output_dir:[52,53,66],predict_sampl:5,prediction1:15,prediction2:15,predin:63,prefer:56,prefetch:47,prefix:[8,18,20],pregrad:47,premodel:62,prepar:[23,28,59],preprocess:[13,51,62,63,66,68,70,71],present:[16,46],preserv:25,prev_batch_st:[52,53],prevent:[11,16,18,21],previou:[9,10,18,25],previous:9,price:13,principl:16,print:[5,7,15,16,32,37],printallstatu:50,printer:8,printstatu:50,prite:8,privat:[27,46],prob:[8,15],probabilist:[9,62],probabl:[8,9,15],problem:[9,11,16,17],proc:34,proce:[13,18,29],proceed:[9,69],process2:42,process:[3,7,9,10,16,20,21,31,32,42,45,58,66,68,69],process_predict:66,processdata:[63,64],processor:50,prod:[34,46],produc:[10,13,18,29],product:23,productgraph:57,profil:[25,50],proflier:50,prog:58,program:[13,16,20,22,29,50,58],progress:[18,21],proj:9,project:[9,10,23,27],promis:[9,10],prompt:25,prone:16,prop:69,propag:11,properli:66,proposit:69,protect:47,proto:12,protobuf:[23,31],protocol:8,provid:[9,13,16,22,23,31,32,42,52,66,68,69],prune:9,ps_desir:18,pserver:[23,51,52,53,58],pserver_cpu:23,pserver_id:19,pserver_mem:23,pserver_num_thread:[52,53],pseudo:[16,23],psize:47,ptr:27,pull:30,purpos:[18,50],push:58,push_back:47,put:[18,66],pwd:[34,46],py_paddl:[5,13,48],pydataprovid:31,pydataprovider2:[3,5,32,58,68],pyramid:9,pyramid_height:9,python:[15,16,26,28,30,31,34,46,47,48,51,62,63,64,68,69,70,71],pythonpath:[31,63],pzo:70,queri:[9,71],question:[9,16],quick:57,quick_start:[23,57,58,59,66],quick_start_data:57,quickstart:57,r14b:39,rac:9,rais:13,ramnath:70,ran:50,rand:[50,53,55,69],random:[7,9,13,20,28,29,32],random_imag:20,rang:[9,13,20,29,58,66],rank:[9,16,64,66],rare:3,raspberri:40,raspberrypi:40,raspbian:40,rate:[7,8,11,13,22,51,58,68],rather:23,ratio:53,raw:9,raw_meta:68,rdma_tcp:[52,53],reach:18,read:[3,13,15,16,18,20,29,32,64,66,68],read_from_realistic_imag:16,read_from_rng:16,read_lock:19,read_mnist_imag:16,read_next_from_fil:31,read_ranking_model_data:16,reader:[15,20,37],reader_cr:20,reader_creator_bool:29,reader_creator_random_imag:[13,29],reader_creator_random_image_and_label:[13,29],readi:[18,57],readm:[27,46,68,70],real:29,real_process:3,realist:16,reason:[10,16,18,57],recal:8,receiv:[18,23],recognit:[9,64,70],recommend:[10,16,51,58,68],record:[21,68,69],recordio:[16,20,21],recov:18,recoveri:21,rectangular:9,recurr:[42,43,69,70],recurrent_group:[10,42,44,45],recurrent_lay:10,recurrentgradientmachin:27,recurrentgroup:8,recurs:25,reduc:11,ref:68,refactor:28,refer:[7,9,10,11,18,21,22,33],referenc:[9,21],reference_cblas_root:33,refine_unknown_arg:58,reflect:21,regex:68,register_gpu_profil:50,register_lay:47,register_timer_info:50,registri:[23,57],regular:[7,63,66,70],rel:10,relat:[18,23,68],relationship:13,releas:[30,36,39,40,56,69],reliabl:18,relu:9,rememb:9,remot:[7,46,51,53,55],remov:[13,25,46],renam:25,reorgan:9,replac:[17,21],replicaset:23,report:21,reportdataset:21,repositori:39,repres:[9,13,21,66],represent:[22,66],request:[18,30,57,71],requir:[8,9,16,18,22,23,25,51,68],res5_3_branch2c_bn:64,res5_3_branch2c_conv:64,res:69,research:[9,13,63],reserv:25,reserveoutput:47,reset:[9,18],reshap:29,reshape_s:9,residu:64,resnet_101:64,resnet_152:64,resnet_50:64,resolv:[46,57],respons:[9,28,57],rest:[9,23,24],restart:[18,22,57],restartpolici:[57,58],resu:29,result:[3,6,8,9,15,21,50,66,71],result_fil:[8,45],ret_val:68,return_seq:10,reuqest:30,reus:29,reveal:16,revers:[9,10,44,45],review:[13,46,57,70],reviews_electronics_5:57,rewrit:[17,71],rgb:9,rgen:70,rho:11,right:[9,17,23],rkt:23,rmsprop:66,rmspropoptim:68,rnn:[9,10,44,45,52,70],rnn_bias_attr:45,rnn_layer_attr:45,rnn_out:45,rnn_state:42,rnn_state_:42,rnn_step:9,rnn_use_batch:[52,53],rnnlm:13,robot:63,role:[13,16,21,22,69,70],roman:70,root:[11,12,34,51,57,58,62],root_dir:51,rot:9,routin:68,routledg:70,row:[8,9,13],row_id:9,rpc:21,rpcserver:21,rpi:40,rpi_arm_neon:40,rpi_toolchain:40,rstrip:58,rtype:[9,68],rule:8,run:[16,18,23,31,34,46,50,51,57,58,59,60,68],runinitfunct:[50,58],running_on_cloud:23,runtim:[3,31],s_fusion:68,s_id:68,safe:23,same:[3,8,9,10,16,21,22,42,66],samping_id:9,sampl:[3,8,13,66,68,70,71],sample_id:8,sample_num:8,santiago:70,satifi:8,save:[3,9,13,18,20,21,22,23,57,68,69,70,71],save_dir:[32,53,55,57,58,63,66,68,69,70,71],save_only_on:[52,53],saving_period:[52,53,58],saving_period_by_batch:[52,53,55],saw:3,scalar:9,scale:[6,68],scalingproject:9,scan:21,scatter:9,schedul:[21,23],scheduler_factor:7,scheme:8,schmidhub:70,schwenk:71,scienc:70,score:[8,9,68],script:[13,48],seaplane_s_000978:63,search:[9,18,45,71],seat:71,second:[9,13,16,25,29,66,68],sed:70,see:[9,10,16,18,31,66],seed:[50,53],seem:17,segment:8,sel_fc:9,select:9,selectiv:9,selector:57,self:47,selfnorm:9,semant:[13,16,30,69,70],semantic_role_label:45,semat:16,sen_len:69,send:[18,22],sent:[16,22,57],sent_id:45,sentanc:31,sentenc:[3,9,13,42,45,69],sentence_last_state1:42,sentence_last_state2:42,sentiment:[3,69,70],sentiment_data:70,sentiment_net:70,sentimental_provid:3,sentimental_train:3,separ:[8,66,68],seq:[9,13,42,68],seq_pool:[9,41],seq_text_print:8,seq_to_seq_data:[62,71],seq_typ:[13,68],seqlastin:42,seqtext_evalu:8,seqtext_printer_evalu:45,seqtoseq:[9,31,45,62,71],seqtoseq_net:[9,45,62,71],sequel:3,sequenc:[3,6,8,9,10,12,13,31,37,42,44,66,68,70,71],sequence_conv_pool:66,sequence_group:9,sequence_layer_group:42,sequence_nest_group:9,sequence_nest_layer_group:42,sequencegen:42,sequencestartposit:9,sequencetextprint:8,sequencetyp:[3,9,37],sequenti:[9,69],seri:[10,42,70],serial:[15,21,28],server:[16,17,51,53,56,58],serverless:18,set:[3,7,8,9,10,13,15,16,18,23,31,32,42,45,50,51,57,62,63,64,66,68,69,70,71],set_active_typ:47,set_default_parameter_nam:7,set_drop_r:47,set_input:9,set_siz:47,set_typ:47,settotalbyteslimit:31,setup:[47,66],sever:[8,9,28],sgd:[11,15,16,18,23,28,32,37,51,52,70],shape:[9,13,15,37],shard:[18,19,20,21,22],share:[9,17,27,28,69],shared_bia:10,shared_bias:9,shared_librari:17,shared_ptr:[26,27],ship:63,shold:70,shorten:9,should:[8,9,13,15,16,22,23,28,29,44],should_be_fals:16,should_be_tru:16,should_shuffl:[3,42,69],show:[8,11,13,18,25,69,70,71],show_check_sparse_distribution_log:[52,53],show_layer_stat:[52,53],show_parameter_stats_period:[52,53,55,57,69,70,71],shown:[9,16],shuf:[31,68],shuffl:[13,31],side:[9,15,28],sigint:51,sigmoid:9,sigmoidactiv:[10,42],sign:24,similar:[9,29,68],similarli:[9,13],simpl:[6,8,9,10,13,15,58],simple_attent:45,simple_gru:66,simple_lstm:[9,66],simple_rnn:[9,45],simpler:28,simpli:[9,16,22],simplifi:16,sinc:[9,18,21,29],singl:[8,10,13,18],sinlg:15,size:[3,8,9,10,11,13,18,20,29,31,32,37,42,45,47,63,64,66,68,70,71],size_a:9,size_b:9,size_t:47,skip:[29,32,46,64],sleep:58,slide:[9,11,13,18],slope:9,slot:[68,69],slot_dim:68,slot_nam:68,slottyp:68,small:13,small_messag:[52,53],small_vgg:63,smaller:[9,18],smith:70,smooth:9,snap:57,snapshot:19,sock:23,sock_recv_buf_s:[52,53],sock_send_buf_s:[52,53],socket:58,softmax:[9,10,16,31,45,47,66],softmax_param:31,softmax_param_attr:10,softmax_selfnorm_alpha:9,softmaxactiv:[31,42,45,66],solv:16,some:[7,9,13,15,16,17,21,22,23],some_c_api_funct:27,some_inst:27,some_python_class:26,somecppclass:26,somedata:15,somegotyp:26,someth:9,sometim:[11,29],somewhat:22,soon:18,sort:[9,13,58,68,70],sourc:[9,13,17,25,27,29,71],source_dict_dim:45,source_language_word:45,space:8,space_seperated_tokens_from_dictionary_according_to_seq:8,space_seperated_tokens_from_dictionary_according_to_sub_seq:8,spars:[7,9,11,13,31,47,51,53,55,58,66],sparse_binary_vector:[3,13,31,37,66],sparse_binary_vector_sequ:13,sparse_float_vector:[3,37],sparse_non_value_slot:13,sparse_upd:[7,31],sparse_value_slot:13,sparse_vector:[13,31],sparse_vector_sequ:13,sparseparam:47,sparseprefetchrowcpumatrix:47,spatial:9,spec:[57,58],special:[9,22],specif:25,specifi:[8,9,13,16,21,22,23,25,36,66],speech:9,speed:10,sphinx:[26,34,48],split:[3,9,42,51,66,68,69],split_count:58,squar:[9,11,12],squarerootnpool:9,squash:46,srand:53,src:[17,58,71],src_backward:45,src_dict:[31,45],src_dict_path:31,src_embed:45,src_forward:45,src_id:45,src_root:5,src_word_id:45,srl:[13,69],ssd:9,ssh:[34,51],sshd:34,sstabl:16,stabl:[30,56],stacked_lstm_net:70,stacked_num:70,stackexchang:9,stage:46,stake:71,stale:18,standalon:39,standard:7,stanford:[13,57],start:[9,15,18,21,22,23,28,31,46,53,57,58],start_paddl:58,start_pass:[52,53],start_pserv:[52,53],startpaddl:58,startup:[18,23],stat:[50,53,69,70,71],state:[9,10,18,44,57],state_act:[9,10,42],statfulset:58,staticinput:[9,44,45],statist:9,statset:50,statu:[23,46,50,57,58],status:57,std:[17,26,27,47,53],stderr:51,stdout:51,step:[9,10,12,18,22,28,42,44,45],stepout:42,still:21,stochast:[11,18,21],stock:70,stop:9,storag:[24,56],store:[8,9,13,28,66,68],str:[15,23,55,58],strategi:[12,18,53,69],street:[9,69],strict:29,stride:9,stride_i:9,stride_x:9,string:[3,8,9,21,25,47,53],strip:[31,42,66,68,69],struct:[21,22,24,27],structur:[13,21,66],stub:9,stuff:46,stun:3,style:9,sub:[8,9,13,16,28],sub_sequ:[3,9,37],subcommand:25,subgradi:11,subnet:16,subobjectpath:57,subseq:[41,44],subsequenceinput:[9,42],succeed:[21,57],success:[22,57],successfulcr:57,sudo:[36,63],suffic:29,suffix:23,suggest:[9,17],sum:[9,11,19],sum_:9,sumpool:[9,31],support:[7,8,9,12,13,18,23,28,29,42],suppos:17,suppress:25,sure:[36,70],swagger:24,swap_channel:64,swig:[26,27],swig_paddl:[5,13],symbol:[9,27],symlink:46,sync:18,syncflag:47,synchron:[11,18,21],syntax:29,sys:64,sysroot:39,system:[17,18,22,24,31,70],t2b:62,t_i:9,tab:66,tabl:[8,9],tableproject:9,tag:[8,13,31,34],tagtyp:8,tainer_id:58,take:[3,8,9,10,16,17],tanh:[9,10,47],tanhactiv:[10,42,45],target:[9,13,15,17,71],target_dict_dim:45,target_dictionary_dim:9,target_language_embed:9,target_language_word:45,target_link_librari:17,targetinlink:[9,42],task:[8,9,69],task_queu:21,taskentri:21,taskqueu:21,tbd:42,tconf:70,tcp:53,tear:50,technic:18,tee:[57,63,68,69,70,71],tell:[18,21,22],tellig:70,templat:[57,58],tempor:9,temporari:23,tensor:17,tensor_test:17,term:[9,10,18],termin:57,tesh:69,test100:13,test10:13,test1:20,test:[2,3,9,13,15,16,17,27,29,30,46,47,50,51,53,55,63,64,66,68,69,70,71],test_all_data_in_one_period:[57,63,68,69,70],test_compar:31,test_comparespars:31,test_comparetwonet:31,test_comparetwoopt:31,test_config_pars:31,test_data:[5,71],test_fcgrad:47,test_gpuprofil:50,test_layergrad:47,test_list:[3,31,32,63,66],test_networkcompar:31,test_part_000:70,test_pass:[52,53,55,71],test_period:[52,53,55],test_predict:31,test_pydataprovid:31,test_pydataprovider2:31,test_pydataproviderwrapp:31,test_ratio:68,test_recurrent_machine_gener:31,test_recurrentgradientmachin:[31,42],test_swig_api:31,test_train:31,test_traineronepass:31,test_wait:[52,53],testa:16,testb:16,testbilinearfwdbwd:50,testconfig:47,tester:[68,71],testfcgrad:47,testfclay:47,testlayergrad:47,testq:16,testresult:15,testutil:47,text1:25,text:[3,8,10,13,16,62,66,70,71],text_conv:66,text_conv_pool:68,text_fil:[13,70],tflop:50,tgz:13,than:[7,8,9,10,18,23,31],thei:[16,17,18,22,25,50],them:[10,16,18,23,29,50,66,68],therein:9,thi:[3,7,8,9,10,11,13,15,16,17,18,21,22,23,28,29,50,66,68,70],thing:3,think:[16,17],third:[9,18],third_parti:[39,40],those:[17,18,69],thought:17,thread:50,thread_local_rand_use_global_se:[52,53],threadid:55,threadloc:50,three:[8,9,18,28,29,64],threshold:[7,8,18,21,53],through:[9,18,21],throughput:50,thu:9,tier:57,time:[9,10,12,13,16,17,18,21,29,42,50,53,57,58,70],timelin:9,timeout:[18,21],timer:50,timestamp:[9,19],timestep:9,titl:[13,58,68],tls:24,tmp:3,to_your_paddle_clone_path:48,todo:[8,10,13,18,21],toend:9,togeth:[9,10,13,15],token:[8,9,16,45,70],too:13,tool:[39,40,48,58],toolchain:[39,40],top:[8,64],top_k:8,topolog:[13,16,18,28],topolopi:15,toronto:[13,63],total:[15,18,29,50,57,71],total_pass:29,touch:70,tourist:71,track:[18,21,46],tractabl:9,tradit:9,trail:13,train100:13,train10:13,train:[2,3,7,8,9,11,13,20,21,22,28,31,32,36,37,51,53,55,57,58,59,60,62,63,64,66,68,69,70,71],train_arg:58,train_args_dict:58,train_args_list:58,train_conf:[62,71],train_config_dir:58,train_data:71,train_i:37,train_list:[3,31,32,63,64,66],train_part_000:70,train_read:37,train_x:37,trainabl:9,trainer:[3,5,16,19,20,21,28,32,37,47,53,55,58,68,69,70,71],trainer_config:[2,3,5,32,51,57,58,66,68,70],trainer_config_help:[3,31,32,47,63,68],trainer_count:[31,52,53,55,57,58,68,69,70,71],trainer_cpu:23,trainer_cr:23,trainer_gpu:23,trainer_id:[53,58],trainer_mem:23,trainer_packag:23,trainerconfighelp:31,trainerid:58,trainerintern:[66,68,71],tran:47,transact:[18,21,70],transform:9,transform_param_attr:10,translat:[9,10,62,71],transpos:9,transposedfullmatrixproject:9,travel:3,travi:46,treat:[9,22],treatment:22,tree:[9,58],trg:71,trg_dict:45,trg_dict_path:45,trg_embed:45,trg_id:45,trg_ids_next:45,tricki:26,trigger:28,trn:66,truck:63,true_imag:29,true_label:29,true_read:29,truth:[8,9,71],tst:66,tune:[7,52],tupl:[9,13,15,29],ture:9,turn:[9,29,44],tutori:[57,58,59,60,62,71],tweet:70,twitter:70,two:[8,9,10,16,22,23,25,29,50,66],txt:[3,17,23,25,47,51,56,66,68,70],type:[3,8,9,10,12,13,16,18,21,23,24,25,26,27,29,32,37,42,47,55,57,64,66,68,69],type_nam:68,typedef:[22,26,27],typic:8,ubuntu:30,ubyt:29,uci:13,ufldl:9,uid:57,uint32:24,uint64:26,uint64_t:26,unawar:22,unconstrain:70,under:21,undeterminist:50,uniform:[7,9,13,20,29],uninstal:31,uniqu:[16,18,23],unique_ptr:47,unit:[9,10,17],unittest:[27,31],univ:71,unix:51,unk:[62,71],unk_idx:[66,69],unknown:9,unseg:9,unsign:22,unsup:70,unsupbow:70,until:[18,22,58],untrack:46,unus:68,unzip:39,updat:[7,9,18,21,22,24,28,46,51,55],update_equ:[15,37],updatecallback:47,upgrad:31,upload:[18,23,24],upstream:[31,46],url:[13,70],urls_neg:70,urls_po:70,urls_unsup:70,usag:[8,9,10,13,15,28,58,68],use:[7,8,9,10,12,13,15,16,17,18,28,46,50,58,64,66,68,69,70,71],use_global_stat:9,use_gpu:[5,31,37,52,53,55,57,58,63,64,66,68,69,70,71],use_jpeg:63,use_old_updat:[52,53],use_seq:[32,68],use_seq_or_not:68,used:[3,8,9,10,11,12,13,15,16,18,28,29,50,66,68,70],useful:[9,10],usegpu:47,user:[7,9,10,13,15,16,20,21,23,25,29,56,66,68],user_featur:68,user_head:68,user_id:[58,68],user_info:13,user_meta:68,user_nam:[20,68],usercert:20,userinfo:13,userkei:20,usernam:[20,46],uses:[18,28],using:[7,9,10,13,16,17,18,21,22,23,25,29,36,69],usr:[31,33,34,36,51,53,58],usrdict:62,usrmodel:62,usual:[9,13,15,23,50],utc:67,util:[50,58,63,68],uuid:19,v28:9,v7a:39,valid:29,valu:[3,5,7,8,9,11,12,13,15,18,28,47,55,58,64,66,69],value1:53,value2:53,value_evalu:8,value_rang:13,vanilla:45,variabl:[9,13,16,57],varianc:9,vector:[9,10,13,16,22,66,68],verb:13,verbos:25,veri:[9,12,21,28,63],version:[9,10,23,25,30,34,36,50,52,53],versu:16,vertic:9,vgg:[10,63],vgg_16_cifar:63,via:[18,29,36,46],view:9,vision:63,visipedia:63,visual:9,vocab:70,volum:[34,56,57,58],volumemount:[57,58],vutbr:13,wai:[8,9,10,16,22,71],wait:[11,18,22,58],wall:69,want:[3,9,10,16,23,29],warn:[9,25,31,58],warp:9,watch:18,wbia:64,wei:[69,70],weight:[8,9,10,11,47,64],weight_act:10,weightlist:47,weights_:47,weights_t:47,welcom:17,well:23,were:[8,17],wether:9,wget:39,what:[7,9,10,11,17,46,66],when:[3,7,8,9,13,15,17,18,21,22,23,25,28,50],where:[9,10,11,16,18],wherea:21,whether:[8,9,10,29,70],which:[8,9,10,11,13,16,18,20,21,22,23,28,29,66,68],whoever:22,whole:[3,8,13,26,27],whole_cont:68,whose:[9,13,18],why:[10,27,50],width:[8,9,13,26,29,47,71],wiki:9,wikipedia:[9,13],wilder:3,window:[9,12,13,56],wise:9,with_avx:[33,34,36,39,46],with_c_api:[39,40],with_doc:33,with_dock:48,with_doubl:[33,36,47],with_dso:33,with_gpu:[33,34,36,39,46],with_predict_sdk:36,with_profil:50,with_python:[33,36,39],with_rdma:[33,36,39],with_style_check:[33,46],with_swig_pi:[33,39],with_test:[33,34,46],with_tim:[33,36,50],within:[9,21],without:[8,9,18,22,29],wmt14:71,wmt14_data:71,wmt14_model:71,wmt_shrinked_data:13,woboq:34,won:42,wonder:3,word2vec:[23,31],word:[3,8,9,13,31,42,44,66,69],word_dict:[42,66,69],word_dim:[42,66],word_id:[3,31],word_idx:13,word_slot:69,word_vector:66,word_vector_dim:[9,45,62],words_freq_sort:13,work:[13,16,17,18,29,34,42,46,57,58],workspac:51,would:[15,17,18,28,29,69],wrapper:[10,17,50],write:[13,16,18,29,69],write_lock:19,writelin:32,writer:16,wrong:29,wsj:69,wuyi:56,www:[9,13,63,71],x64:40,x86:[39,40],x86_64:39,xarg:[8,31,34,47],xgbe0:53,xgbe1:53,xiaojun:70,xrang:[29,32,37,47],xxbow:70,xxx:[16,64,71],xxxx:19,y_i:9,y_predict:37,yaml:[57,58],yancey1989:23,yann:13,yapf:46,yeild:15,yield:[3,13,16,20,29,31,32,37,42,45,66,68,69],you:[3,7,9,10,23,36,64,70],your:[9,16,17,23,25,31,39,40],your_param_nam:31,your_repo:58,your_source_root:27,yuyang18:[10,13],zachari:70,zeng:70,zero:[7,9,11,13,18,28,53],zhou:[69,70],zip:[13,39,58,67],zoo:62},titles:["\u5173\u4e8ePaddlePaddle","API","DataProvider\u7684\u4ecb\u7ecd","PyDataProvider2\u7684\u4f7f\u7528","API\u4e2d\u6587\u624b\u518c","\u57fa\u4e8ePython\u7684\u9884\u6d4b","Activation","Parameter Attribute","Evaluators","Layers","Networks","Optimizer","Pooling","Data Reader Interface and DataSets","Model Configuration","Training and Inference","PaddlePaddle Design Doc","Required CMake Function","Design Doc: Distributed Training","\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9\uff08Checkpointing\uff09","\u8bad\u7ec3\u6570\u636e\u7684\u5b58\u50a8\u548c\u5206\u53d1","Design Doc: Master Server","Design Doc: The Client Library of Parameter Server","Submit a Distributed Training Job","FileManager\u8bbe\u8ba1\u6587\u6863","PFSClient","Paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0","C-API \u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863","Design Doc: The C++ Class Parameters","Python Data Reader Design Doc","Paddle\u53d1\u884c\u89c4\u8303","FAQ","\u7ecf\u5178\u7684\u7ebf\u6027\u56de\u5f52\u4efb\u52a1","PaddlePaddle\u7684\u7f16\u8bd1\u9009\u9879","PaddlePaddle\u7684Docker\u5bb9\u5668\u4f7f\u7528\u65b9\u5f0f","\u5b89\u88c5\u4e0e\u7f16\u8bd1","Ubuntu\u90e8\u7f72PaddlePaddle","\u57fa\u672c\u4f7f\u7528\u6982\u5ff5","\u65b0\u624b\u5165\u95e8","\u6784\u5efaAndroid\u5e73\u53f0\u4e0a\u7684PaddlePaddle\u5e93","\u6784\u5efaRaspberry Pi\u5e73\u53f0\u4e0a\u7684PaddlePaddle\u5e93","\u652f\u6301\u53cc\u5c42\u5e8f\u5217\u4f5c\u4e3a\u8f93\u5165\u7684Layer","\u5355\u53cc\u5c42RNN API\u5bf9\u6bd4\u4ecb\u7ecd","RNN\u76f8\u5173\u6a21\u578b","Recurrent Group\u6559\u7a0b","RNN\u914d\u7f6e","\u5982\u4f55\u8d21\u732e\u4ee3\u7801","\u5b9e\u73b0\u65b0\u7684\u7f51\u7edc\u5c42","\u5982\u4f55\u8d21\u732e/\u4fee\u6539\u6587\u6863","\u8fdb\u9636\u6307\u5357","GPU\u6027\u80fd\u5206\u6790\u4e0e\u8c03\u4f18","\u8fd0\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3","\u53c2\u6570\u6982\u8ff0","\u7ec6\u8282\u63cf\u8ff0","\u8bbe\u7f6e\u547d\u4ee4\u884c\u53c2\u6570","\u4f7f\u7528\u6848\u4f8b","Kubernetes \u7b80\u4ecb","Kubernetes\u5355\u673a\u8bad\u7ec3","Kubernetes\u5206\u5e03\u5f0f\u8bad\u7ec3","<no title>","<no title>","PaddlePaddle \u6587\u6863","\u4e2d\u6587\u8bcd\u5411\u91cf\u6a21\u578b\u7684\u4f7f\u7528","\u56fe\u50cf\u5206\u7c7b\u6559\u7a0b","Model Zoo - ImageNet","\u5b8c\u6574\u6559\u7a0b","\u5feb\u901f\u5165\u95e8\u6559\u7a0b","MovieLens\u6570\u636e\u96c6","MovieLens\u6570\u636e\u96c6\u8bc4\u5206\u56de\u5f52\u6a21\u578b","\u8bed\u4e49\u89d2\u8272\u6807\u6ce8\u6559\u7a0b","\u60c5\u611f\u5206\u6790\u6559\u7a0b","\u6587\u672c\u751f\u6210\u6559\u7a0b"],titleterms:{"\u4e00\u4e9b\u7ec6\u8282\u7684\u8865\u5145":58,"\u4e0a\u4f20\u8bad\u7ec3\u6587\u4ef6":20,"\u4e0b\u8f7d\u4e0e\u89e3\u538b\u7f29":71,"\u4e0b\u8f7d\u548c\u6570\u636e\u62bd\u53d6":62,"\u4e0b\u8f7d\u5e76\u89e3\u538b\u6570\u636e\u96c6":68,"\u4e0b\u8f7d\u6570\u636e":57,"\u4e0d\u4f7f\u7528":26,"\u4e0d\u4f7f\u7528swig\u8fd9\u79cd\u4ee3\u7801\u751f\u6210\u5668":26,"\u4e0d\u5bfc\u51fapaddle\u5185\u90e8\u7684\u7ed3\u6784\u4f53":26,"\u4e0d\u5f15\u7528\u5176\u4ed6\u52a8\u6001\u5e93":26,"\u4e2d\u6587\u5b57\u5178":62,"\u4e2d\u6587\u77ed\u8bed\u6539\u5199\u7684\u4f8b\u5b50":62,"\u4e2d\u6587\u8bcd\u5411\u91cf\u6a21\u578b\u7684\u4f7f\u7528":62,"\u4e2d\u6587\u8bcd\u5411\u91cf\u7684\u9884\u8bad\u7ec3\u6a21\u578b":62,"\u4e3a\u4ec0\u4e48\u9700\u8981\u6027\u80fd\u5206\u6790":50,"\u4ec0\u4e48\u662f\u6027\u80fd\u5206\u6790":50,"\u4ec5\u4ec5\u4f7f\u7528void":26,"\u4ecb\u7ecd":[62,64],"\u4ece\u5feb\u7167\u6062\u590d":19,"\u4ee3\u7801\u8981\u6c42":46,"\u4efb\u52a1\u7b80\u4ecb":32,"\u4f18\u5316\u7b97\u6cd5":66,"\u4f7f\u7528":[46,57],"\u4f7f\u7528\u52a8\u6001\u5e93\u6765\u5206\u53d1paddl":26,"\u4f7f\u7528\u6848\u4f8b":55,"\u4f7f\u7528\u6982\u8ff0":66,"\u4f7f\u7528\u6a21\u578b\u521d\u59cb\u5316\u7f51\u7edc":55,"\u4f7f\u7528\u73af\u5883\u53d8\u91cf":58,"\u4f7f\u7528\u7528\u6237\u6307\u5b9a\u7684\u8bcd\u5411\u91cf\u5b57\u5178":62,"\u4f7f\u7528\u8bf4\u660e":49,"\u4f7f\u7528\u8f6c\u6362\u5e93":20,"\u4f7f\u7528docker\u6784\u5efapaddlepaddle\u7684\u6587\u6863":48,"\u4f7f\u7528paddlepaddle\u751f\u6210\u6a21\u578b":71,"\u4f7f\u7528paddlepaddle\u8bad\u7ec3\u6a21\u578b":71,"\u4fdd\u6301\u672c\u5730\u4ed3\u5e93\u6700\u65b0":46,"\u4fee\u6539\u542f\u52a8\u811a\u672c":57,"\u4fee\u6539\u6587\u6863":48,"\u514b\u9686":46,"\u5173\u4e8epaddlepaddl":0,"\u5177\u4f53\u67d0\u79cd\u7c7b\u578b\u7684\u5934\u6587\u4ef6":27,"\u5177\u4f53\u67d0\u79cd\u7c7b\u578b\u7684\u5b9e\u73b0\u6587\u4ef6":27,"\u5185\u5b58\u4e0d\u591f\u7528\u7684\u60c5\u51b5":3,"\u5185\u7f6e\u5b9a\u65f6\u5668":50,"\u5199\u68af\u5ea6\u68c0\u67e5\u5355\u5143\u6d4b\u8bd5":47,"\u51c6\u5907\u4ea4\u53c9\u7f16\u8bd1\u73af\u5883":[39,40],"\u51c6\u5907\u5de5\u4f5c\u7a7a\u95f4":51,"\u51c6\u5907\u5e8f\u5217\u6570\u636e":45,"\u51c6\u5907\u6570\u636e":[32,68],"\u51c6\u5907\u8bad\u7ec3\u6570\u636e":58,"\u51c6\u5907\u96c6\u7fa4\u4f5c\u4e1a\u914d\u7f6e":51,"\u51cf\u5c11\u6570\u636e\u8f7d\u5165\u7684\u8017\u65f6":31,"\u51cf\u5c11dataprovider\u7f13\u51b2\u6c60\u5185\u5b58":31,"\u5206\u5272\u8bad\u7ec3":68,"\u5206\u5757\u6587\u4ef6\u4f20\u8f93":24,"\u5206\u652f\u89c4\u8303":30,"\u521b\u5efa\u672c\u5730\u5206\u652f":46,"\u521b\u5efajob":58,"\u521b\u5efapaddl":57,"\u5220\u9664\u672c\u5730\u5206\u652f":46,"\u5220\u9664\u8fdc\u7a0b\u5206\u652f":46,"\u5229\u7528\u66f4\u591a\u7684\u8ba1\u7b97\u8d44\u6e90":31,"\u5230\u8fdc\u7a0b\u4ed3\u5e93":46,"\u5236\u4f5c\u955c\u50cf":58,"\u5236\u4f5cdocker\u955c\u50cf":57,"\u524d\u63d0\u6761\u4ef6":51,"\u52a0\u8f7dpaddlepaddl":37,"\u52a0\u901f\u6267\u884c":19,"\u52a0\u901f\u8bad\u7ec3\u901f\u5ea6":31,"\u52a8\u6001\u5e93\u4e2d\u4e0d\u5d4c\u5165\u4efb\u4f55\u5176\u4ed6\u8bed\u8a00\u7684\u89e3\u91ca\u5668":26,"\u52a8\u6001\u6269\u5bb9":19,"\u5355\u5143\u6d4b\u8bd5":53,"\u5355\u53cc\u5c42rnn":42,"\u5377\u79ef\u6a21\u578b":66,"\u5377\u79ef\u795e\u7ecf\u7f51\u7edc":63,"\u539f\u56e0":26,"\u539f\u56e0\u5217\u8868":26,"\u53c2\u6570\u4fe1\u606f":64,"\u53c2\u6570\u5185\u5b58":31,"\u53c2\u6570\u670d\u52a1\u5668\u548c\u5206\u5e03\u5f0f\u901a\u4fe1":53,"\u53c2\u6570\u6982\u8ff0":52,"\u53c2\u6570\u8bfb\u53d6":64,"\u53c2\u8003":3,"\u53c2\u8003\u6587\u6863":[24,70],"\u53c2\u8003\u8d44\u6599":50,"\u53cc\u5411lstm":70,"\u53cc\u5c42rnn":42,"\u53cc\u5c42rnn\u4ecb\u7ecd":44,"\u53cc\u5c42rnn\u7684\u4f7f\u7528":44,"\u53ef\u80fd\u7684\u5185\u5b58\u6cc4\u9732\u95ee\u9898":3,"\u53ef\u80fd\u9047\u5230\u7684\u95ee\u9898":36,"\u53ef\u9009\u529f\u80fd":62,"\u540d\u8bcd\u89e3\u91ca":24,"\u5411\u7cfb\u7edf\u4f20\u9001\u6570\u636e":66,"\u5411\u91cf":53,"\u542f\u52a8\u4efb\u52a1":58,"\u542f\u52a8\u96c6\u7fa4\u4f5c\u4e1a":51,"\u547d\u4ee4\u884c\u53c2\u6570":66,"\u548c":41,"\u56fe\u50cf\u5206\u7c7b\u6559\u7a0b":63,"\u5728\u4e0d\u540c\u8bbe\u5907\u4e0a\u6307\u5b9a\u5c42":55,"\u5728paddlepaddle\u5e73\u53f0\u8bad\u7ec3\u6a21\u578b":62,"\u57fa\u4e8epython\u7684\u9884\u6d4b":5,"\u57fa\u672c\u4f7f\u7528\u6982\u5ff5":37,"\u57fa\u672c\u539f\u7406":44,"\u57fa\u672c\u8981\u6c42":26,"\u5982\u4f55\u4e66\u5199paddlepaddle\u7684\u6587\u6863":48,"\u5982\u4f55\u5171\u4eab\u53c2\u6570":31,"\u5982\u4f55\u51cf\u5c11\u5185\u5b58\u5360\u7528":31,"\u5982\u4f55\u521d\u59cb\u5316\u53c2\u6570":31,"\u5982\u4f55\u52a0\u901fpaddlepaddle\u7684\u8bad\u7ec3\u901f\u5ea6":31,"\u5982\u4f55\u6307\u5b9agpu\u8bbe\u5907":31,"\u5982\u4f55\u66f4\u65b0www":48,"\u5982\u4f55\u6784\u5efapaddlepaddle\u7684\u6587\u6863":48,"\u5982\u4f55\u8d21\u732e":48,"\u5982\u4f55\u8d21\u732e\u4ee3\u7801":46,"\u5982\u4f55\u8fdb\u884c\u6027\u80fd\u5206\u6790":50,"\u5982\u4f55\u9009\u62e9sgd\u7b97\u6cd5\u7684\u5b66\u4e60\u7387":31,"\u5b50\u5e8f\u5217\u95f4\u65e0memori":42,"\u5b50\u5e8f\u5217\u95f4\u6709memori":42,"\u5b57\u6bb5\u914d\u7f6e\u6587\u4ef6":68,"\u5b89\u88c5":[36,66],"\u5b89\u88c5\u4e0e\u7f16\u8bd1":35,"\u5b89\u88c5\u6d41\u7a0b":35,"\u5b89\u88c5kubectl":56,"\u5b8c\u6574\u6559\u7a0b":65,"\u5b9e\u73b0":26,"\u5b9e\u73b0\u65b0\u7684\u7f51\u7edc\u5c42":47,"\u5b9e\u73b0\u65b9\u5f0f":27,"\u5b9e\u73b0c":47,"\u5b9e\u73b0python\u5c01\u88c5":47,"\u5bfc\u51fac":26,"\u5c06\u547d\u4ee4\u53c2\u6570\u4f20\u7ed9\u7f51\u7edc\u914d\u7f6e":55,"\u5c0f\u7ed3":3,"\u5de5\u5177":50,"\u5e38\u7528\u6a21\u578b":65,"\u5e76\u5b8c\u6210":46,"\u5efa\u7acb":46,"\u5f00\u53d1\u6807\u51c6":49,"\u5f00\u59cb\u5f00\u53d1":46,"\u5f02\u6b65\u968f\u673a\u68af\u5ea6\u4e0b\u964d":53,"\u5f15\u7528":69,"\u5feb\u7167\u4fdd\u5b58\u7684\u8bbe\u8ba1\u5982\u4e0b":19,"\u5feb\u901f\u5165\u95e8\u6559\u7a0b":66,"\u6027\u80fd\u4f18\u5316":49,"\u6027\u80fd\u5206\u6790\u5c0f\u6280\u5de7":50,"\u6027\u80fd\u5206\u6790\u5de5\u5177\u4ecb\u7ecd":50,"\u6027\u80fd\u8c03\u4f18":53,"\u603b\u4f53\u6548\u679c\u603b\u7ed3":66,"\u60c5\u611f\u5206\u6790\u6559\u7a0b":70,"\u6216\u8005\u662f":31,"\u627e\u5230\u7684pythonlibs\u548cpythoninterp\u7248\u672c\u4e0d\u4e00\u81f4":31,"\u6307\u9488\u4f5c\u4e3a\u7c7b\u578b\u7684\u53e5\u67c4":26,"\u63a5\u53e3":64,"\u63a8\u5bfc\u65b9\u7a0b":47,"\u63a8\u6d4b\u6267\u884c":19,"\u63d0\u4ea4":46,"\u63d0\u4ea4\u4ee3\u7801\u7684\u4e00\u4e9b\u7ea6\u5b9a":46,"\u63d0\u4ea4\u955c\u50cf":57,"\u63d0\u53d6\u7535\u5f71\u6216\u7528\u6237\u7684\u7279\u5f81\u5e76\u751f\u6210python\u5bf9\u8c61":68,"\u642d\u5efa\u795e\u7ecf\u7f51\u7edc":37,"\u652f\u6301\u53cc\u5c42\u5e8f\u5217\u4f5c\u4e3a\u8f93\u5165\u7684layer":41,"\u652f\u6301\u7528\u6237\u81ea\u5b9a\u4e49\u7684\u6570\u636e\u9884\u5904\u7406job":20,"\u6570\u636e\u51c6\u5907":[63,68,71],"\u6570\u636e\u63cf\u8ff0":69,"\u6570\u636e\u63d0\u4f9b":69,"\u6570\u636e\u63d0\u4f9b\u811a\u672c":68,"\u6570\u636e\u652f\u6301":53,"\u6570\u636e\u683c\u5f0f\u51c6\u5907":66,"\u6570\u636e\u7684\u51c6\u5907\u548c\u9884\u5904\u7406":62,"\u6570\u636e\u96c6\u7279\u5f81":67,"\u6570\u636e\u9884\u5904\u7406":71,"\u6570\u6910\u4ecb\u7ecd":70,"\u6570\u6910\u51c6\u5907":70,"\u6574\u4f53\u65b9\u6848":58,"\u6587\u4ef6\u4f20\u8f93\u4f18\u5316":24,"\u6587\u4ef6\u8bbf\u95ee\u65b9\u5f0f":20,"\u6587\u4ef6\u8bbf\u95ee\u7684\u6743\u9650":20,"\u6587\u4ef6\u9884\u5904\u7406":20,"\u6587\u672c\u751f\u6210":71,"\u6587\u672c\u751f\u6210\u6559\u7a0b":71,"\u6587\u6863":[34,61],"\u65b0\u624b\u5165\u95e8":38,"\u65f6\u5e8f\u6a21\u578b":66,"\u65f6\u5e8f\u6a21\u578b\u7684\u4f7f\u7528\u573a\u666f":3,"\u65f6\u95f4\u5e8f\u5217":42,"\u65f6\u95f4\u6b65":42,"\u66b4\u9732\u63a5\u53e3\u539f\u5219":27,"\u672c\u5730\u6d4b\u8bd5":55,"\u672c\u5730\u8bad\u7ec3":55,"\u672f\u8bed":19,"\u6784\u5efa\u548c\u6d4b\u8bd5":46,"\u6784\u5efaandroid\u5e73\u53f0\u4e0a\u7684paddlepaddle\u5e93":39,"\u6784\u5efaraspberri":40,"\u67b6\u6784\u56fe":24,"\u67e5\u770b\u8bad\u7ec3\u7ed3\u679c":57,"\u67e5\u770b\u8f93\u51fa":58,"\u6837\u4f8b\u6570\u636e":3,"\u6846\u67b6\u751f\u6210":24,"\u6848\u4f8b\u4e00":55,"\u6848\u4f8b\u4e8c":55,"\u68c0\u67e5\u6a21\u578b\u8f93\u51fa":51,"\u68c0\u67e5\u96c6\u7fa4\u8bad\u7ec3\u7ed3\u679c":51,"\u6982\u5ff5\u89e3\u91ca":20,"\u6982\u8ff0":[41,44],"\u6a21\u5757":24,"\u6a21\u578b":64,"\u6a21\u578b\u4e0b\u8f7d":64,"\u6a21\u578b\u53c2\u6570\u68c0\u67e5\u70b9":19,"\u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863":27,"\u6a21\u578b\u68c0\u9a8c":32,"\u6a21\u578b\u7f51\u7edc\u7ed3\u6784":66,"\u6a21\u578b\u8bad\u7ec3":[63,71],"\u6a21\u578b\u8bc4\u4f30\u548c\u9884\u6d4b":68,"\u6a21\u578b\u914d\u7f6e":[42,49],"\u6a21\u578b\u914d\u7f6e\u7684\u6a21\u578b\u914d\u7f6e":42,"\u6ce8\u610f\u4e8b\u9879":3,"\u6d41\u7a0b\u4ecb\u7ecd":20,"\u6d4b\u8bd5":[53,69],"\u6d4b\u8bd5\u6587\u4ef6":68,"\u6d4b\u8bd5\u6a21\u578b":70,"\u7279\u5f81":69,"\u7279\u5f81\u63d0\u53d6":64,"\u751f\u6210\u5e8f\u5217":45,"\u751f\u6210\u6a21\u578b\u7684\u547d\u4ee4\u4e0e\u7ed3\u679c":71,"\u751f\u6210\u6d41\u7a0b\u7684\u4f7f\u7528\u65b9\u6cd5":44,"\u751f\u6210sparse\u6587\u4ef6":24,"\u7528\u6237\u4f7f\u7528\u6d41\u7a0b":24,"\u7528\u6237\u6587\u4ef6\u63cf\u8ff0":67,"\u7528\u6237\u81ea\u5b9a\u4e49\u6570\u636e\u96c6":71,"\u7528\u6237\u81ea\u5b9a\u4e49\u6570\u6910\u9884\u5904\u7406":70,"\u7535\u5f71\u6587\u4ef6\u63cf\u8ff0":67,"\u76ee\u5f55\u7ed3\u6784":27,"\u76ee\u6807":24,"\u76f4\u63a5\u6784\u5efapaddlepaddle\u7684\u6587\u6863":48,"\u76f8\u5173\u6982\u5ff5":44,"\u77e9\u9635":53,"\u793a\u4f8b1":42,"\u793a\u4f8b2":42,"\u793a\u4f8b3":42,"\u793a\u4f8b4":42,"\u793a\u4f8b\u7a0b\u5e8f":20,"\u795e\u7ecf\u5143\u6fc0\u6d3b\u5185\u5b58":31,"\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u914d\u7f6e":68,"\u795e\u7ecf\u7f51\u7edc\u914d\u7f6e":69,"\u7a00\u758f\u8bad\u7ec3":55,"\u7b26\u53f7":26,"\u7b80\u4ecb":[56,71],"\u7b80\u5355\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc":45,"\u7c7b":[26,47],"\u7ebf\u6027\u56de\u5f52\u5b8c\u6574\u793a\u4f8b":37,"\u7ec3\u4e60":63,"\u7ec6\u8282\u63a2\u7a76":63,"\u7ec6\u8282\u63cf\u8ff0":53,"\u7ec8\u6b62\u96c6\u7fa4\u4f5c\u4e1a":51,"\u7ecf\u5178\u7684\u7ebf\u6027\u56de\u5f52\u4efb\u52a1":32,"\u7f16\u5199yaml\u6587\u4ef6":57,"\u7f16\u8bd1\u548c\u5b89\u88c5":[39,40],"\u7f16\u8bd1\u6d41\u7a0b":35,"\u7f16\u8bd1\u9009\u9879":27,"\u7f16\u8bd1\u9009\u9879\u7684\u8bbe\u7f6e":33,"\u7f29\u5bb9":19,"\u7f51\u7edc\u53ef\u89c6\u5316":64,"\u7f51\u7edc\u914d\u7f6e\u4e2d\u7684\u8c03\u7528":3,"\u800c\u662f\u624b\u5199\u591a\u8bed\u8a00\u7ed1\u5b9a":26,"\u80cc\u666f":26,"\u81ea\u7136\u8bed\u8a00\u5904\u7406":53,"\u81f4\u8c22":0,"\u8986\u76d6\u4e0d\u4e00\u81f4\u7684\u90e8\u5206":24,"\u89c2\u6d4b\u8bcd\u5411\u91cf":62,"\u8bad\u7ec3":[53,68,69],"\u8bad\u7ec3\u5668\u914d\u7f6e\u6587\u4ef6":68,"\u8bad\u7ec3\u56e0\u6b64\u9000\u51fa\u600e\u4e48\u529e":31,"\u8bad\u7ec3\u6570\u636e\u5b58\u50a8":20,"\u8bad\u7ec3\u6570\u636e\u7684\u5b58\u50a8\u548c\u5206\u53d1":20,"\u8bad\u7ec3\u6a21\u578b":[32,37,66,70],"\u8bad\u7ec3\u6a21\u578b\u7684\u547d\u4ee4\u4e0e\u7ed3\u679c":71,"\u8bad\u7ec3\u6d41\u7a0b\u7684\u4f7f\u7528\u65b9\u6cd5":44,"\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u51fa\u73b0":31,"\u8bbe\u7f6e\u547d\u4ee4\u884c\u53c2\u6570":54,"\u8bc4\u5206\u6587\u4ef6\u63cf\u8ff0":67,"\u8bcd\u5411\u91cf\u6a21\u578b":66,"\u8bcd\u5411\u91cf\u6a21\u578b\u7684\u4fee\u6b63":62,"\u8bcd\u6c47\u8868":42,"\u8be6\u7ec6\u6559\u7a0b":50,"\u8bed\u4e49\u89d2\u8272\u6807\u6ce8\u6559\u7a0b":69,"\u8bfb\u53d6\u53cc\u5c42\u5e8f\u5217\u6570\u636e":42,"\u8f6c\u6362\u5e93":20,"\u8f93\u5165":44,"\u8f93\u5165\u4e0d\u7b49\u957f":42,"\u8f93\u5165\u793a\u4f8b":44,"\u8f93\u51fa":44,"\u8f93\u51fa\u65e5\u5fd7":66,"\u8fd0\u884c\u5206\u5e03\u5f0f\u8bad\u7ec3":51,"\u8fd0\u884c\u5bb9\u5668":57,"\u8fd0\u884cdocker":31,"\u8fd0\u884cpaddlepaddl":34,"\u8fd9\u4e2a\u52a8\u6001\u5e93\u4f7f\u7528c99\u6807\u51c6\u7684\u5934\u6587\u4ef6\u5bfc\u51fa\u4e00\u4e9b\u51fd\u6570":26,"\u8fdb\u884c\u8bad\u7ec3":[20,57],"\u8fdb\u9636\u6307\u5357":49,"\u9009\u62e9\u5b58\u50a8\u65b9\u6848":56,"\u901a\u7528":53,"\u901a\u8fc7docker\u5bb9\u5668\u5f00\u53d1paddlepaddl":34,"\u903b\u8f91\u56de\u5f52\u6a21\u578b":66,"\u9047\u5230":31,"\u90e8\u7f72kubernetes\u96c6\u7fa4":56,"\u914d\u7f6e\u4e2d\u7684\u6570\u636e\u52a0\u8f7d\u5b9a\u4e49":66,"\u914d\u7f6e\u4ea4\u53c9\u7f16\u8bd1\u53c2\u6570":[39,40],"\u914d\u7f6e\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u67b6\u6784":45,"\u914d\u7f6e\u7f51\u7edc":37,"\u914d\u7f6ekubectl":56,"\u914d\u7f6ekubectl\u8bbf\u95ee\u4f60\u7684kubernetes\u96c6\u7fa4":56,"\u94a9\u5b50":46,"\u9644\u5f55":66,"\u968f\u673a\u6570":53,"\u96c6\u7fa4\u8bad\u7ec3":55,"\u975e\u6cd5\u6307\u4ee4":31,"\u9884\u5904\u7406":63,"\u9884\u5904\u7406\u547d\u4ee4\u548c\u7ed3\u679c":71,"\u9884\u5904\u7406\u5de5\u4f5c\u6d41\u7a0b":71,"\u9884\u6d4b":[63,64,66,69,70],"\u9884\u6d4b\u6d41\u7a0b":5,"\u9884\u6d4bdemo":5,"\u9884\u8bad\u7ec3\u7684\u6a21\u578b":71,"api\u4e2d\u6587\u624b\u518c":4,"api\u5bf9\u6bd4\u4ecb\u7ecd":42,"beam_search\u7684\u751f\u6210":42,"blas\u8def\u5f84\u76f8\u5173\u7684\u7f16\u8bd1\u9009\u9879":33,"bleu\u8bc4\u4f30":71,"book\u4e2d\u6240\u6709\u7ae0\u8282":30,"bool\u578b\u7684\u7f16\u8bd1\u9009\u9879":33,"class":28,"cmake\u6e90\u7801\u7f16\u8bd1":31,"cudnn\u7684\u7f16\u8bd1\u9009\u9879":33,"dataprovider\u7684\u4ecb\u7ecd":2,"dataprovider\u7684\u4f7f\u7528":3,"filemanager\u8bbe\u8ba1\u6587\u6863":24,"float":31,"function":17,"gpu\u548ccpu\u6df7\u5408\u4f7f\u7528":55,"gpu\u6027\u80fd\u5206\u6790\u4e0e\u8c03\u4f18":50,"gpu\u955c\u50cf\u51fa\u73b0":31,"group\u6559\u7a0b":44,"kubernetes\u5206\u5e03\u5f0f\u8bad\u7ec3":58,"kubernetes\u5355\u673a\u8bad\u7ec3":57,"meta\u6587\u4ef6":68,"meta\u914d\u7f6e\u6587\u4ef6":68,"mnist\u7684\u4f7f\u7528\u573a\u666f":3,"movielens\u6570\u636e\u96c6":67,"movielens\u6570\u636e\u96c6\u8bc4\u5206\u56de\u5f52\u6a21\u578b":68,"org\u6587\u6863":48,"paddle\u52a8\u6001\u5e93\u4e2d":26,"paddle\u53d1\u884c\u89c4\u8303":30,"paddle\u56de\u5f52\u6d4b\u8bd5\u5217\u8868":30,"paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0":26,"paddle\u7248\u672c\u53f7\u4e3a0":31,"paddlepaddle\u53d1\u5e03\u7684docker\u955c\u50cf\u4f7f\u7528\u8bf4\u660e":34,"paddlepaddle\u7684\u7f16\u8bd1\u9009\u9879":33,"paddlepaddle\u7684docker\u5bb9\u5668\u4f7f\u7528\u65b9\u5f0f":34,"pi\u5e73\u53f0\u4e0a\u7684paddlepaddle\u5e93":40,"pod\u95f4\u901a\u4fe1":58,"pydataprovider2\u7684\u4f7f\u7528":3,"python\u63a5\u53e3":64,"python\u76f8\u5173\u7684\u5355\u5143\u6d4b\u8bd5\u90fd\u8fc7\u4e0d\u4e86":31,"python\u811a\u672c\u8bfb\u53d6\u6570\u636e":66,"return":29,"rnn\u76f8\u5173\u6a21\u578b":43,"rnn\u914d\u7f6e":45,"so\u627e\u4e0d\u5230":36,"ubuntu\u90e8\u7f72paddlepaddl":36,Abs:6,The:[22,28],Using:22,With:23,activ:6,adadelta:11,adagrad:11,adam:11,adamax:11,addto:9,aggreg:9,aggregatelevel:9,algorithm:18,api:[1,4,27],applic:4,argument:[25,29],async:53,attent:45,attribut:7,auc:8,avg:12,base:23,basepool:12,batch:29,batch_norm:9,batch_siz:29,beam_search:9,becaus:31,between:16,bidirectional_gru:10,bidirectional_lstm:10,big:31,bilinear_interp:9,bla:33,block_expand:9,book:34,brelu:6,cach:3,capi:27,capi_priv:27,check:9,checkpoint:[18,19],chunk:8,cifar:13,classif:8,classification_error:8,classification_error_print:8,client:22,clone:46,cmake:17,code:23,column_sum:8,commit:46,compos:29,concat:9,config:4,configur:14,conll05:13,connect:9,content:[3,27,31,41,50],context_project:9,conv:9,conv_oper:9,conv_project:9,conv_shift:9,cos_sim:9,cost:9,cp27mu:31,creat:29,creation:21,creator:29,crf:9,crf_decod:9,cross_channel_norm:9,cross_entropy_cost:9,cross_entropy_with_selfnorm_cost:9,ctc:9,ctc_error:8,cuda:[31,33],cudnn:33,cudnnavg:12,cudnnmax:12,custom:29,dat:67,data:[9,13,18,29],datafeed:13,dataprovid:[4,53],dataset:[13,18,21],datatyp:13,decayedadagrad:11,decor:29,deploi:23,descript:25,design:[16,18,21,22,28,29],dictionari:29,dispatch:[18,21],distribut:[16,18,23],doc:[16,18,21,22,28,29],docker:23,dotmul_oper:9,dotmul_project:9,driver:31,dropout_lay:10,dylib:27,dynam:18,embed:9,entri:29,environ:23,eos:9,evalu:8,event:[15,16],exampl:[16,17,27],except:31,exp:6,expand:[9,41],expandlevel:9,faq:31,fault:18,first_seq:[9,41],fork:46,format:18,from:16,full_matrix_project:9,fulli:9,gate:45,get_output:9,gpu:53,gradient:22,gradient_print:8,group:9,gru:[10,53],gru_group:10,gru_step:9,gru_unit:10,grumemori:9,handler:[16,26],how:29,hsigmoid:9,huber_cost:9,ident:6,identity_project:9,illeg:31,imag:[9,10,23],imagenet:64,imdb:[13,70],img_cmrnorm:9,img_conv:9,img_conv_bn_pool:10,img_conv_group:10,img_pool:9,imikolov:13,implement:[17,29],infer:15,ingredi:16,ingress:24,init_hook:3,initi:22,input_typ:3,instruct:31,insuffici:31,interfac:[13,18,22,29],interpol:9,isn:29,issu:46,job:[18,23,57],join:9,kubernet:[23,56,57],lambda_cost:9,last_seq:[9,41],layer:[9,16],libcudart:36,libcudnn:36,libpaddle_capi_shar:27,libpaddle_capi_whol:27,librari:22,linear:6,linear_comb:9,linux_x86_64:31,list:[19,29],log:6,logic:21,lstm:[10,53,69,70],lstm_step:9,lstmemori:9,lstmemory_group:10,lstmemory_unit:10,map:29,master:[18,21,23],math:9,max:12,maxframe_print:8,maxid:9,maxid_print:8,maxout:9,memori:[9,42,44],messag:31,mini:29,minibatch:13,misc:10,mix:9,mnist:13,model:[4,14,16,22,45,64],momentum:11,movi:67,movielen:13,mse_cost:9,multi_binary_label_cross_entropy_cost:9,multipl:29,nce:9,need:29,network:[10,45],neural:45,nlp:[10,53],norm:9,nvprof:50,nvvp:50,object:18,onli:29,optim:[11,18,22],option:25,order:25,pad:9,paddl:[29,30],paddlejob:23,paddlepaddl:[16,48,61],parallel_nn:55,paramet:[7,15,16,18,22,23,28],partit:22,path:25,perform:53,persist:21,pfsclient:[24,25],pfsserver:24,platform:31,pnpair:8,point:31,pool:[9,12,41],power:9,pre:46,precision_recal:8,prefetch:29,print:8,process:[18,22,23],project:17,protocol:31,provid:[3,29],pull:46,push:46,python:[23,29],queue:[18,21],rank:8,rank_cost:9,rate:67,reader:[13,16,29],recoveri:18,recurr:[9,10,44,45],recurrent_group:9,refer:3,regular:22,reject:31,relu:6,repeat:9,request:46,requir:17,reshap:9,resnet:64,retri:21,rmsprop:11,rnn:[42,53],rotat:9,runtim:23,sampl:9,sampling_id:9,scale:[9,18],scaling_project:9,select:22,selective_fc:9,sentiment:13,seq_concat:9,seq_reshap:9,seqtext_print:8,sequenc:45,sequence_conv_pool:10,sequencesoftmax:6,server:[18,21,22,23],sgd:53,share:16,shuffl:29,sigmoid:6,simple_attent:10,simple_gru2:10,simple_gru:10,simple_img_conv_pool:10,simple_lstm:10,singl:29,slice:9,slope_intercept:9,small_vgg:10,smooth_l1_cost:9,softmax:6,softrelu:6,spars:22,spp:9,squar:6,squarerootn:12,stack:70,stanh:6,start:16,store:18,subcommond:25,submit:23,suffici:29,sum:[8,12],sum_cost:9,sum_to_one_norm:9,summar:16,support:31,synopsi:25,tabl:27,table_project:9,take:29,tanh:6,task:[18,21],tensor:9,text_conv_pool:10,thi:31,todo:[19,20],toler:18,too:31,train:[15,16,18,23,29],trainer:[15,18,22,23],tran:9,trans_full_matrix_project:9,tune:53,uci_h:13,updat:16,usag:29,use:29,user:[18,67],util:8,value_print:8,version:31,vgg_16_network:10,warp_ctc:9,wheel:31,whl:31,why:29,wmt14:13,zoo:64}})
\ No newline at end of file

    •