From 2f1c2b51debf5ed9431080894be050303f97d455 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Tue, 30 May 2017 16:34:39 +0000 Subject: [PATCH] Deploy to GitHub Pages: 0ef86cbd2e8e44a98313fc71acdc1e848e6be718 --- develop/doc/api/v2/config/evaluators.html | 84 ++--- develop/doc/api/v2/config/layer.html | 225 ++++++------ develop/doc/api/v2/config/networks.html | 358 +++++++++---------- develop/doc/searchindex.js | 2 +- develop/doc_cn/api/v2/config/evaluators.html | 84 ++--- develop/doc_cn/api/v2/config/layer.html | 225 ++++++------ develop/doc_cn/api/v2/config/networks.html | 358 +++++++++---------- develop/doc_cn/searchindex.js | 2 +- 8 files changed, 644 insertions(+), 694 deletions(-) diff --git a/develop/doc/api/v2/config/evaluators.html b/develop/doc/api/v2/config/evaluators.html index 0035e1f4b2..1403ac262c 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)
 

 

 

 
 

@@ -283,9 +283,9 @@ 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 precision, recall and F1 scores for the chunk detection.

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

@@ -344,7 +344,7 @@ 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)
 

 

 

 
 

@@ -369,9 +369,9 @@ and the tag is I.

 
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.

 
    
@@ -381,7 +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)
 

 

 

 
 

@@ -408,13 +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)
 

 

 

 
 

@@ -441,12 +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)
 

 

 

 
 

@@ -468,12 +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)
 

 

 

 
 

@@ -496,12 +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)
 

 

 

 
 

@@ -522,13 +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)
 

 

 

 
 

@@ -548,14 +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)
 

 

 

 
 

@@ -577,9 +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.
@@ -589,7 +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)
 

 

 

 
 
 

@@ -609,9 +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
@@ -643,7 +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)
@@ -683,13 +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 cc431b0f05..880901bf6e 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
@@ -228,7 +203,7 @@

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,
@@ -274,7 +249,7 @@ 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
@@ -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 .
    
@@ -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.
        
 
        
@@ -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:
        
@@ -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.
        
 
        
@@ -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
@@ -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
      
 
 
 
@@ -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
        • 
 
      
 @@ -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.
      
 
      
@@ -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:
      
@@ -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:
      
@@ -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.
        
@@ -2253,7 +2228,7 @@ default Bias.
 
        addto
        
 
        
 
        
-class paddle.v2.layer.addto(*args, **kwargs)
        
+class paddle.v2.layer.addto
 
        AddtoLayer.
        
 
        
 \[y = f(\sum_{i} x_i + b)\]
        
@@ -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:
          
 
          
@@ -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.
          
@@ -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 69e2b9c5c4..0ba183af51 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.
          
 
      
 

 
 
      
@@ -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:
 
      
-
-
@@ -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
        • 
 
      
 
      
 
 
      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/searchindex.js b/develop/doc/searchindex.js
index f45b1bb9b8..cd3e703049 100644
--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/develop/doc_cn/api/v2/config/evaluators.html b/develop/doc_cn/api/v2/config/evaluators.html
index 40f428f16d..f05ee25e6b 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)
 
      
 
      
 
      
 
 
      
@@ -290,9 +290,9 @@ 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 precision, recall and F1 scores for the chunk detection.
      
 
      To use chunk evaluator, several concepts need to be clarified firstly.
      
@@ -351,7 +351,7 @@ 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)
 
      
 
      
 
      
 
 
      
@@ -376,9 +376,9 @@ and the tag is I.
      
 
      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.
      
 
        
@@ -388,7 +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)
 
      
 
      
 
      
 
 
      
@@ -415,13 +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)
 
      
 
      
 
      
 
 
      
@@ -448,12 +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)
 
      
 
      
 
      
 
 
      
@@ -475,12 +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)
 
      
 
      
 
      
 
 
      
@@ -503,12 +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)
 
      
 
      
 
      
 
 
      
@@ -529,13 +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)
 
      
 
      
 
      
 
 
      
@@ -555,14 +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)
 
      
 
      
 
      
 
 
      
@@ -584,9 +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.
@@ -596,7 +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)
 
      
 
      
 
      
 
 
 
      
@@ -616,9 +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
@@ -650,7 +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)
@@ -690,13 +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 35ce6a4d5a..7871764e06 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
      
 
      
 
 
      
@@ -235,7 +210,7 @@
 
      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,
@@ -281,7 +256,7 @@ 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
@@ -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 .
        
@@ -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.
            
 
            
@@ -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:
            
@@ -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.
            
 
            
@@ -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
@@ -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.
      
 
      
@@ -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:
      
@@ -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:
      
@@ -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.
        
@@ -2260,7 +2235,7 @@ default Bias.
 
        addto
        
 
        
 
        
-class paddle.v2.layer.addto(*args, **kwargs)
        
+class paddle.v2.layer.addto
 
        AddtoLayer.
        
 
        
 \[y = f(\sum_{i} x_i + b)\]
        
@@ -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:
          
 
          
@@ -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.
          
@@ -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 e0b442391a..484ae13f76 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.
          
 
      
 

 
 
      
@@ -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/searchindex.js b/develop/doc_cn/searchindex.js
index 973b139377..ab8a2109a8 100644
--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js
@@ -1 +1 @@
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