paddle.v2.fluid.evaluator.Evaluator(name, **kwargs)Base Class for all evaluators
+| Parameters: |
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+
|---|
states¶list – The list of state variables. states will be reset to zero +when reset is invoked.
+metrics¶list – The list of metrics variables. They will be calculate +every mini-batch
+reset(executor, reset_program=None)reset metric states at the begin of each pass/user specified batch
+eval(executor, eval_program=None)Evaluate the statistics merged by multiple mini-batches.
+create_state(suffix, dtype, shape)Create state variable.
+NOTE: It is not a public API.
+| Parameters: |
|
+
|---|
Returns: State variable
+paddle.v2.fluid.initializer.InitializerBase class for variable initializers
+Defines the common interface of variable initializers. +They add operations to the init program that are used +to initialize variables. Users should not use this class +directly, but need to use one of its implementations.
+paddle.v2.fluid.initializer.ConstantInitializer(value=0.0)Implements the constant initializer
+paddle.v2.fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0)Implements the random uniform distribution initializer
+paddle.v2.fluid.initializer.NormalInitializer(loc=0.0, scale=1.0, seed=0)Implements the random Normal(Gaussian) distribution initializer
+paddle.v2.fluid.initializer.XavierInitializer(uniform=True, fan_in=None, fan_out=None, seed=0)Implements the Xavier initializer
+This class implements the Xavier weight initializer from the paper +Understanding the difficulty of training deep feedforward neural +networks[1] by Xavier Glorot and Yoshua Bengio.
+This initializer is designed to keep the scale of the gradients +approximately same in all the layers. In case of Uniform distribution, +the range is [-x, x], where x = sqrt(6 / (fan_in + fan_out)). +In case of Normal distribution, the mean is 0 and the standard deviation +is sqrt(2/ (fan_in + fan_out)).
+References
+paddle.v2.fluid.initializer.MSRAInitializer(uniform=True, fan_in=None, seed=0)Implements the MSRA initializer a.k.a. Kaiming Initializer
+This class implements the weight initialization from the paper +Delving Deep into Rectifiers: Surpassing Human-Level Performance on +ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren +and Jian Sun. This is a robust initialization method that particularly +considers the rectifier nonlinearities. In case of Uniform distribution, +the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal +distribution, the mean is 0 and the standard deviation +is sqrt(2/ fan_in).
+References
+paddle.v2.fluid.layers.fc(input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None, main_program=None, startup_program=None)Fully Connected Layer.
+| Parameters: |
|
+
|---|
This function can take in multiple inputs and performs the Fully Connected +function (linear transformation) on top of each of them. +So for input x, the output will be : Wx + b. Where W is the parameter, +b the bias and x is the input.
+The function also applies an activation (non-linearity) on top of the +output, if activation is passed in the input.
+All the input variables of this function are passed in as local variables +to the LayerHelper constructor.
+paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, param_attr=None, dtype='float32', main_program=None, startup_program=None)Embedding Layer.
+| Parameters: |
|
+
|---|
This function can take in the input (which is a vector of IDs) and +performs a lookup in the lookup_table using these IDs, to result into +the embedding of each ID in the input.
+All the input variables of this function are passed in as local variables +to the LayerHelper constructor.
+paddle.v2.fluid.layers.dynamic_lstm(input, size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', dtype='float32', main_program=None, startup_program=None)paddle.v2.fluid.layers.data(name, shape, append_batch_size=True, dtype='float32', lod_level=0, type=VarType.LOD_TENSOR, main_program=None, startup_program=None, stop_gradient=True)Data Layer.
+| Parameters: |
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+
|---|
This function takes in input and based on whether data has +to be returned back as a minibatch, it creates the global variable using +the helper functions. The global variables can be accessed by all the +following operations and layers in the graph.
+All the input variables of this function are passed in as local variables +to the LayerHelper constructor.
+paddle.v2.fluid.layers.mean(**kwargs)Mean Operator.
+Out is a scalar which is the mean of all elements in X.
+| Parameters: | x – The input of mean op +Duplicable: False Optional: False | +
|---|---|
| Returns: | The output of mean op | +
paddle.v2.fluid.layers.mul(**kwargs)Mul Operator.
+This operator is used to perform matrix multiplication for input X and Y.
+The equation is:
+++$$Out = X * Y$$
Both the input X and Y can carry the LoD (Level of Details) information, +or not. But the output only shares the LoD information with input X.
+| Parameters: |
|
+
|---|---|
| Returns: | The output of mul op + |
+
paddle.v2.fluid.layers.elementwise_add(**kwargs)Limited Elementwise Add Operator.
+The equation is:
+$Out = X + Y$
+X is a tensor of any dimension and the dimensions of tensor Y must be smaller than +or equal to the dimensions of X.
+There are two cases for this operator: +1. The shape of Y is same with X; +2. The shape of Y is a subset of X.
+For case 2: +Y will be broadcasted to match the shape of X and axis should be +the starting dimension index for broadcasting Y onto X.
+example
+shape(X) = (2, 3, 4, 5), shape(Y) = (,) +shape(X) = (2, 3, 4, 5), shape(Y) = (5,) +shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) +shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 +shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+Both the input X and Y can carry the LoD (Level of Details) information, +or not. But the output only shares the LoD information with input X.
+| Parameters: |
|
+
|---|---|
| Returns: | The output of elementwise op + |
+
paddle.v2.fluid.layers.elementwise_div(**kwargs)Limited Elementwise Div Operator.
+The equation is:
+$Out = X / Y$
+X is a tensor of any dimension and the dimensions of tensor Y must be smaller than +or equal to the dimensions of X.
+There are two cases for this operator: +1. The shape of Y is same with X; +2. The shape of Y is a subset of X.
+For case 2: +Y will be broadcasted to match the shape of X and axis should be +the starting dimension index for broadcasting Y onto X.
+example
+shape(X) = (2, 3, 4, 5), shape(Y) = (,) +shape(X) = (2, 3, 4, 5), shape(Y) = (5,) +shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) +shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 +shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+Both the input X and Y can carry the LoD (Level of Details) information, +or not. But the output only shares the LoD information with input X.
+| Parameters: |
|
+
|---|---|
| Returns: | The output of elementwise op + |
+
paddle.v2.fluid.layers.dropout(**kwargs)Dropout Operator.
+Dropout refers to randomly dropping out units in a nerual network. It is a +regularization technique for reducing overfitting by preventing neuron +co-adaption during training. The dropout operator randomly set (according to +the given dropout probability) the outputs of some units to zero, while others +are set equal to their corresponding inputs.
+| Parameters: |
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+
|---|---|
| Returns: | The output of dropout op. + |
+
paddle.v2.fluid.layers.reshape(**kwargs)Reshape Operator.
+Reshape Input(X) into the shape specified by Attr(shape).
+An example: +Given a 2-D tensor X with 2 rows and 2 columns
+++[[1, 2], [3, 4]]
and target shape = [1, 4], the reshape operator will transform +the tensor X into a 1-D tensor:
+++[1, 2, 3, 4]
| Parameters: |
|
+
|---|---|
| Returns: | The output tensor of reshape operator. + |
+
paddle.v2.fluid.layers.sigmoid(**kwargs)Sigmoid Activation Operator
+$$y = frac{1}{1 + e^{-x}}$$
+| Parameters: | x – Input of Sigmoid operator +Duplicable: False Optional: False | +
|---|---|
| Returns: | Output of Sigmoid operator | +
paddle.v2.fluid.layers.scale(**kwargs)Scale operator
+$$Out = scale*X$$
+| Parameters: |
|
+
|---|---|
| Returns: | (Tensor) Output tensor of scale operator. + |
+
paddle.v2.fluid.layers.reshape(**kwargs)Reshape Operator.
+Reshape Input(X) into the shape specified by Attr(shape).
+An example: +Given a 2-D tensor X with 2 rows and 2 columns
+++[[1, 2], [3, 4]]
and target shape = [1, 4], the reshape operator will transform +the tensor X into a 1-D tensor:
+++[1, 2, 3, 4]
| Parameters: |
|
+
|---|---|
| Returns: | The output tensor of reshape operator. + |
+
paddle.v2.fluid.layers.transpose(**kwargs)Transpose Operator.
+The input tensor will be permuted according to the axis values given. +The op functions similar to how numpy.transpose works in python. +For example:
+++>> input = numpy.arange(6).reshape((2,3)) +>> input +array([[0, 1, 2],
+++[3, 4, 5]])>> axis = [1, 0] +>> output = input.transpose(axis) +>> output +array([[0, 3],
++++
+- [1, 4],
+- [2, 5]])
+
So, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1}, +the output tensor shape will be (N, H, W, C)
+| Parameters: |
|
+
|---|---|
| Returns: | (Tensor)The output tensor + |
+
paddle.v2.fluid.layers.cast(x, dtype, main_program=None)This function takes in the input with input_dtype +and casts it to the output_dtype as the output.
+paddle.v2.fluid.layers.concat(input, axis, main_program=None, startup_program=None)This function concats the input along the axis mentioned +and returns that as the output.
+paddle.v2.fluid.layers.sums(input, out=None, main_program=None, startup_program=None)This function takes in the input and performs the sum operation on it +and returns that as the output.
+paddle.v2.fluid.layers.linear_chain_crf(input, label, param_attr=None, main_program=None, startup_program=None)paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, param_attr=None, dtype='float32', main_program=None, startup_program=None)Embedding Layer.
+| Parameters: |
|
+
|---|
This function can take in the input (which is a vector of IDs) and +performs a lookup in the lookup_table using these IDs, to result into +the embedding of each ID in the input.
+All the input variables of this function are passed in as local variables +to the LayerHelper constructor.
+paddle.v2.fluid.layers.split_lod_tensor(input, mask, level=0, main_program=None, startup_program=None)paddle.v2.fluid.layers.merge_lod_tensor(in_true, in_false, x, mask, level=0, main_program=None, startup_program=None)paddle.v2.fluid.layers.cos_sim(X, Y, **kwargs)This function performs the cosine similarity between two tensors +X and Y and returns that as the output.
+paddle.v2.fluid.layers.cross_entropy(input, label, **kwargs)This function computes cross_entropy using the input and label.
+paddle.v2.fluid.layers.square_error_cost(input, label, **kwargs)This functions returns the squared error cost using the input and label. +The output is appending the op to do the above.
+paddle.v2.fluid.layers.accuracy(input, label, k=1, correct=None, total=None, **kwargs)This function computes the accuracy using the input and label. +The output is the top_k inputs and their indices.
+paddle.v2.fluid.layers.sequence_conv(input, num_filters, filter_size=3, filter_stride=1, padding=None, bias_attr=None, param_attr=None, act=None, main_program=None, startup_program=None)This function creates the op for sequence_conv, using the inputs and +other convolutional configurations for the filters and stride as given +in the input parameters to the function.
+paddle.v2.fluid.layers.conv2d(input, num_filters, filter_size, stride=[1, 1], padding=None, groups=None, param_attr=None, bias_attr=None, act=None, name=None, main_program=None, startup_program=None)This function creates the op for a 2-dimensional Convolution. +This is performed using the parameters of filters(size, dimensionality etc) +, stride and other configurations for a Convolution operation. +This funciton can also append an activation on top of the +conv-2d output, if mentioned in the input parameters.
+paddle.v2.fluid.layers.sequence_pool(input, pool_type, **kwargs)This function add the operator for sequence pooling. +This is applied on top of the input using pool_type mentioned +in the parameters.
+paddle.v2.fluid.layers.pool2d(input, pool_size, pool_type, pool_stride=[1, 1], pool_padding=[0, 0], global_pooling=False, main_program=None, startup_program=None)This function adds the operator for pooling in 2 dimensions, using the +pooling configurations mentioned in input parameters.
+paddle.v2.fluid.layers.batch_norm(input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW', main_program=None, startup_program=None)This function helps create an operator to implement +the BatchNorm layer using the configurations from the input parameters.
+paddle.v2.fluid.layers.beam_search_decode(ids, scores, main_program=None, startup_program=None)paddle.v2.fluid.layers.lstm(x, c_pre_init, hidden_dim, forget_bias=None, main_program=None, startup_program=None)This function helps create an operator for the LSTM (Long Short Term +Memory) cell that can be used inside an RNN.
+paddle.v2.fluid.layers.lod_rank_table(x, level=0, main_program=None)This function creates an operator for creating a LOD_RANK_TABLE +using the input x.
+paddle.v2.fluid.layers.max_sequence_len(rank_table, main_program=None)This function creates an operator to calculate the length of +max seqence through input rank_table(should be a lod_rank_table)
+paddle.v2.fluid.layers.topk(input, k, main_program=None, startup_program=None)paddle.v2.fluid.layers.lod_tensor_to_array(x, table, main_program=None)This function creates an operator to convert an LOD_Tensor to +an array.
+paddle.v2.fluid.layers.array_to_lod_tensor(x, table, main_program=None, startup_program=None)This function creates an operator to convert an array to a +LOD_Tensor.
+paddle.v2.fluid.layers.fill_constant(shape, dtype, value, out=None, main_program=None, startup_program=None)This function creates a tensor , with shape as mentioned in the input and +specified dtype and fills this up with a constant value that +comes in the input. It also sets the stop_gradient to be True.
+paddle.v2.fluid.layers.fill_constant_batch_size_like(input, shape, dtype, value, input_dim_idx=0, output_dim_idx=0, main_program=None, startup_program=None)paddle.v2.fluid.layers.ones(shape, dtype, main_program=None)This function performs the same function as fill_constant() declared above +with the constant value being 1.0.
+paddle.v2.fluid.layers.zeros(shape, dtype, main_program=None)This function performs the same function as fill_constant() declared above +with the constant value being 0.0.
+paddle.v2.fluid.layers.increment(x, value=1.0, in_place=True, main_program=None, startup_program=None)This function creates an operator to increment each value in the input +x by an amount: value as mentioned in the input parameter. This +operation is performed in-place by default.
+paddle.v2.fluid.layers.array_write(x, i, array=None, main_program=None, startup_program=None)This function creates an operator to write the data out as a +LOD_TENSOR_ARRAY.
+paddle.v2.fluid.layers.create_array(dtype, main_program=None)paddle.v2.fluid.layers.less_than(x, y, cond=None, main_program=None, **ignored)paddle.v2.fluid.layers.array_read(array, i, main_program=None, startup_program=None)This function creates an operator to read the data in as a +LOD_TENSOR_ARRAY.
+paddle.v2.fluid.layers.shrink_memory(x, i, table, main_program=None, startup_program=None)This function creates an operator to shrink_rnn_memory using the RankTable +as mentioned in the input parameter.
+paddle.v2.fluid.layers.array_length(array, main_program=None)This function creates an operator to find the length of the +LOD_TENSOR_ARRAY.
+paddle.v2.fluid.layers.conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, param_attr=None, main_program=None, startup_program=None)The transpose of conv2d layer.
+This layer is also known as deconvolution layer.
+| Parameters: |
|
+
|---|---|
| Returns: | Output image. + |
+
| Return type: | Variable + |
+
paddle.v2.fluid.nets.simple_img_conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act, pool_type='max', main_program=None, startup_program=None)paddle.v2.fluid.nets.img_conv_group(input, conv_num_filter, pool_size, conv_padding=1, conv_filter_size=3, conv_act=None, conv_with_batchnorm=False, conv_batchnorm_drop_rate=None, pool_stride=1, pool_type=None, main_program=None, startup_program=None)Image Convolution Group, Used for vgg net.
+paddle.v2.fluid.nets.sequence_conv_pool(input, num_filters, filter_size, act='sigmoid', pool_type='max', main_program=None, startup_program=None)paddle.v2.fluid.optimizer.Optimizer(global_step=None)Optimizer Base class.
+Define the common interface of an optimizer. +User should not use this class directly, +but need to use one of it’s implementation.
+create_optimization_pass(parameters_and_grads, loss, startup_program=None)Add optimization operators to update gradients to variables.
+| Parameters: |
|
+
|---|---|
| Returns: | a list of operators that will complete one step of +optimization. This will include parameter update ops, global step +update ops and any other custom ops required by subclasses to manage +their internal state. +:param startup_program: + |
+
| Return type: | return_op_list + |
+
minimize(loss, startup_program=None, parameter_list=None, no_grad_set=None)Add operations to minimize loss by updating parameter_list.
+This method combines interface append_backward_ops() and +create_optimization_pass() into one.
+paddle.v2.fluid.optimizer.SGDOptimizer(learning_rate, global_step=None)Simple SGD optimizer without any state.
+paddle.v2.fluid.optimizer.MomentumOptimizer(learning_rate, momentum, use_nesterov=False, global_step=None)Simple Momentum optimizer with velocity state
+paddle.v2.fluid.optimizer.AdagradOptimizer(learning_rate, epsilon=1e-06, global_step=None)Simple Adagrad optimizer with moment state
+paddle.v2.fluid.optimizer.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, global_step=None)Implements the Adam Optimizer
+paddle.v2.fluid.optimizer.AdamaxOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, global_step=None)Implements the Adamax Optimizer
+paddle.v2.fluid.optimizer.DecayedAdagradOptimizer(learning_rate, decay=0.95, epsilon=1e-06, global_step=None)Simple Decayed Adagrad optimizer with moment state
+paddle.v2.fluid.profiler.cuda_profiler(*args, **kwds)The CUDA profiler. +This fuctions is used to profile CUDA program by CUDA runtime application +programming interface. The profiling result will be written into +output_file with Key-Value pair format or Comma separated values format. +The user can set the output mode by output_mode argument and set the +counters/options for profiling by config argument. The default config +is [‘gpustarttimestamp’, ‘gpustarttimestamp’, ‘gridsize3d’, +‘threadblocksize’, ‘streamid’, ‘enableonstart 0’, ‘conckerneltrace’].
+| Parameters: |
|
+
|---|
paddle.v2.fluid.regularizer.WeightDecayRegularizerBase class for weight decay regularizers
+Defines the common interface of weight-decay regularizers. +Weight-decay regularizers are added only during the backward +pass for faster regularization. They add operations to the network +that correspond to gradient of the regularization function. +Users should not use this class directly, but need to use one +of its implementations
+paddle.v2.fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)Implements the L2 Weight Decay Regularization
+paddle.v2.fluid.regularizer.L1DecayRegularizer(regularization_coeff=0.0)¶Implements the L1 Weight Decay Regularization
+| + |
| @@ -277,6 +290,10 @@ |
| + |
paddlepaddle.v2.fluid.regularizerIfElse Operator","Design Doc: InferVarType","Design Doc: Model Format","Paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0","C-API \u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863","RNNOp design","Design: Sequence Decoder Generating LoDTensors","Optimizer Design","Averaging Parameter in PaddlePaddle","Design Doc: The C++ Class Parameters","Design Doc: PaddlePaddle Programs","Prune","Design Doc: Python API","Python Data Reader Design Doc","Design Doc: Distributed Training Architecture","Design Doc: Operation Graph Based Parameter Server","Design Doc: Session","Design Doc: Refactorization Overview","Design Doc: Gradient Operators Registration","Regularization in PaddlePaddle","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Design Doc: Selected Rows","Interaction between C++ and Python","Design for TensorArray","Background","Build from Sources","Run in Docker Containers","Install and Build","Install Using pip","GET STARTED","RNN Models","RNN Configuration","Build using Docker","Contribute Code","Write New Layers","How to write a new operator","How to use Eigen in Paddle","Contribute Documentation","HOW TO","Profiling the Python Code","Tune GPU Performance","PaddlePaddle Distributed Training","Argument Outline","Detail Description","Set Command-line Parameters","Use Case","Distributed PaddlePaddle Training on AWS with Kubernetes","Paddle On Kubernetes","<no title>","<no title>","PaddlePaddle Documentation","Build PaddlePaddle for Android","Build PaddlePaddle for Raspberry Pi","MOBILE","Cluster bootstrapping tool survey","<no title>","Chinese Word Embedding Model Tutorial","Generative Adversarial Networks (GAN)","Model Zoo - ImageNet","Quick 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\ No newline at end of file
diff --git a/develop/doc_cn/api/v2/fluid/evaluator.html b/develop/doc_cn/api/v2/fluid/evaluator.html
index 8fbaec5caa0bd6fb25656a1be137af1a44f4dfc1..2bc2290d0706d55832b3a28ad587dd6083302400 100644
--- a/develop/doc_cn/api/v2/fluid/evaluator.html
+++ b/develop/doc_cn/api/v2/fluid/evaluator.html
@@ -208,6 +208,75 @@
paddle.v2.fluid.evaluator.Evaluator(name, **kwargs)Base Class for all evaluators
+| 参数: |
|
+
|---|
states¶list – The list of state variables. states will be reset to zero +when reset is invoked.
+metrics¶list – The list of metrics variables. They will be calculate +every mini-batch
+reset(executor, reset_program=None)reset metric states at the begin of each pass/user specified batch
+eval(executor, eval_program=None)Evaluate the statistics merged by multiple mini-batches.
+create_state(suffix, dtype, shape)Create state variable.
+NOTE: It is not a public API.
+| 参数: |
|
+
|---|
Returns: State variable
+paddle.v2.fluid.initializer.InitializerBase class for variable initializers
+Defines the common interface of variable initializers. +They add operations to the init program that are used +to initialize variables. Users should not use this class +directly, but need to use one of its implementations.
+paddle.v2.fluid.initializer.ConstantInitializer(value=0.0)Implements the constant initializer
+paddle.v2.fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0)Implements the random uniform distribution initializer
+paddle.v2.fluid.initializer.NormalInitializer(loc=0.0, scale=1.0, seed=0)Implements the random Normal(Gaussian) distribution initializer
+paddle.v2.fluid.initializer.XavierInitializer(uniform=True, fan_in=None, fan_out=None, seed=0)Implements the Xavier initializer
+This class implements the Xavier weight initializer from the paper +Understanding the difficulty of training deep feedforward neural +networks[1] by Xavier Glorot and Yoshua Bengio.
+This initializer is designed to keep the scale of the gradients +approximately same in all the layers. In case of Uniform distribution, +the range is [-x, x], where x = sqrt(6 / (fan_in + fan_out)). +In case of Normal distribution, the mean is 0 and the standard deviation +is sqrt(2/ (fan_in + fan_out)).
+References
+paddle.v2.fluid.initializer.MSRAInitializer(uniform=True, fan_in=None, seed=0)Implements the MSRA initializer a.k.a. Kaiming Initializer
+This class implements the weight initialization from the paper +Delving Deep into Rectifiers: Surpassing Human-Level Performance on +ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren +and Jian Sun. This is a robust initialization method that particularly +considers the rectifier nonlinearities. In case of Uniform distribution, +the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal +distribution, the mean is 0 and the standard deviation +is sqrt(2/ fan_in).
+References
+paddle.v2.fluid.layers.fc(input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None, main_program=None, startup_program=None)Fully Connected Layer.
+| 参数: |
|
+
|---|
This function can take in multiple inputs and performs the Fully Connected +function (linear transformation) on top of each of them. +So for input x, the output will be : Wx + b. Where W is the parameter, +b the bias and x is the input.
+The function also applies an activation (non-linearity) on top of the +output, if activation is passed in the input.
+All the input variables of this function are passed in as local variables +to the LayerHelper constructor.
+paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, param_attr=None, dtype='float32', main_program=None, startup_program=None)Embedding Layer.
+| 参数: |
|
+
|---|
This function can take in the input (which is a vector of IDs) and +performs a lookup in the lookup_table using these IDs, to result into +the embedding of each ID in the input.
+All the input variables of this function are passed in as local variables +to the LayerHelper constructor.
+paddle.v2.fluid.layers.dynamic_lstm(input, size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', dtype='float32', main_program=None, startup_program=None)paddle.v2.fluid.layers.data(name, shape, append_batch_size=True, dtype='float32', lod_level=0, type=VarType.LOD_TENSOR, main_program=None, startup_program=None, stop_gradient=True)Data Layer.
+| 参数: |
|
+
|---|
This function takes in input and based on whether data has +to be returned back as a minibatch, it creates the global variable using +the helper functions. The global variables can be accessed by all the +following operations and layers in the graph.
+All the input variables of this function are passed in as local variables +to the LayerHelper constructor.
+paddle.v2.fluid.layers.mean(**kwargs)Mean Operator.
+Out is a scalar which is the mean of all elements in X.
+| 参数: | x – The input of mean op +Duplicable: False Optional: False | +
|---|---|
| 返回: | The output of mean op | +
paddle.v2.fluid.layers.mul(**kwargs)Mul Operator.
+This operator is used to perform matrix multiplication for input X and Y.
+The equation is:
+++$$Out = X * Y$$
Both the input X and Y can carry the LoD (Level of Details) information, +or not. But the output only shares the LoD information with input X.
+| 参数: |
|
+
|---|---|
| 返回: | The output of mul op + |
+
paddle.v2.fluid.layers.elementwise_add(**kwargs)Limited Elementwise Add Operator.
+The equation is:
+$Out = X + Y$
+X is a tensor of any dimension and the dimensions of tensor Y must be smaller than +or equal to the dimensions of X.
+There are two cases for this operator: +1. The shape of Y is same with X; +2. The shape of Y is a subset of X.
+For case 2: +Y will be broadcasted to match the shape of X and axis should be +the starting dimension index for broadcasting Y onto X.
+example
+shape(X) = (2, 3, 4, 5), shape(Y) = (,) +shape(X) = (2, 3, 4, 5), shape(Y) = (5,) +shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) +shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 +shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+Both the input X and Y can carry the LoD (Level of Details) information, +or not. But the output only shares the LoD information with input X.
+| 参数: |
|
+
|---|---|
| 返回: | The output of elementwise op + |
+
paddle.v2.fluid.layers.elementwise_div(**kwargs)Limited Elementwise Div Operator.
+The equation is:
+$Out = X / Y$
+X is a tensor of any dimension and the dimensions of tensor Y must be smaller than +or equal to the dimensions of X.
+There are two cases for this operator: +1. The shape of Y is same with X; +2. The shape of Y is a subset of X.
+For case 2: +Y will be broadcasted to match the shape of X and axis should be +the starting dimension index for broadcasting Y onto X.
+example
+shape(X) = (2, 3, 4, 5), shape(Y) = (,) +shape(X) = (2, 3, 4, 5), shape(Y) = (5,) +shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) +shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 +shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+Both the input X and Y can carry the LoD (Level of Details) information, +or not. But the output only shares the LoD information with input X.
+| 参数: |
|
+
|---|---|
| 返回: | The output of elementwise op + |
+
paddle.v2.fluid.layers.dropout(**kwargs)Dropout Operator.
+Dropout refers to randomly dropping out units in a nerual network. It is a +regularization technique for reducing overfitting by preventing neuron +co-adaption during training. The dropout operator randomly set (according to +the given dropout probability) the outputs of some units to zero, while others +are set equal to their corresponding inputs.
+| 参数: |
|
+
|---|---|
| 返回: | The output of dropout op. + |
+
paddle.v2.fluid.layers.reshape(**kwargs)Reshape Operator.
+Reshape Input(X) into the shape specified by Attr(shape).
+An example: +Given a 2-D tensor X with 2 rows and 2 columns
+++[[1, 2], [3, 4]]
and target shape = [1, 4], the reshape operator will transform +the tensor X into a 1-D tensor:
+++[1, 2, 3, 4]
| 参数: |
|
+
|---|---|
| 返回: | The output tensor of reshape operator. + |
+
paddle.v2.fluid.layers.sigmoid(**kwargs)Sigmoid Activation Operator
+$$y = frac{1}{1 + e^{-x}}$$
+| 参数: | x – Input of Sigmoid operator +Duplicable: False Optional: False | +
|---|---|
| 返回: | Output of Sigmoid operator | +
paddle.v2.fluid.layers.scale(**kwargs)Scale operator
+$$Out = scale*X$$
+| 参数: |
|
+
|---|---|
| 返回: | (Tensor) Output tensor of scale operator. + |
+
paddle.v2.fluid.layers.reshape(**kwargs)Reshape Operator.
+Reshape Input(X) into the shape specified by Attr(shape).
+An example: +Given a 2-D tensor X with 2 rows and 2 columns
+++[[1, 2], [3, 4]]
and target shape = [1, 4], the reshape operator will transform +the tensor X into a 1-D tensor:
+++[1, 2, 3, 4]
| 参数: |
|
+
|---|---|
| 返回: | The output tensor of reshape operator. + |
+
paddle.v2.fluid.layers.transpose(**kwargs)Transpose Operator.
+The input tensor will be permuted according to the axis values given. +The op functions similar to how numpy.transpose works in python. +For example:
+++>> input = numpy.arange(6).reshape((2,3)) +>> input +array([[0, 1, 2],
+++[3, 4, 5]])>> axis = [1, 0] +>> output = input.transpose(axis) +>> output +array([[0, 3],
++++
+- [1, 4],
+- [2, 5]])
+
So, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1}, +the output tensor shape will be (N, H, W, C)
+| 参数: |
|
+
|---|---|
| 返回: | (Tensor)The output tensor + |
+
paddle.v2.fluid.layers.cast(x, dtype, main_program=None)This function takes in the input with input_dtype +and casts it to the output_dtype as the output.
+paddle.v2.fluid.layers.concat(input, axis, main_program=None, startup_program=None)This function concats the input along the axis mentioned +and returns that as the output.
+paddle.v2.fluid.layers.sums(input, out=None, main_program=None, startup_program=None)This function takes in the input and performs the sum operation on it +and returns that as the output.
+paddle.v2.fluid.layers.linear_chain_crf(input, label, param_attr=None, main_program=None, startup_program=None)paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, param_attr=None, dtype='float32', main_program=None, startup_program=None)Embedding Layer.
+| 参数: |
|
+
|---|
This function can take in the input (which is a vector of IDs) and +performs a lookup in the lookup_table using these IDs, to result into +the embedding of each ID in the input.
+All the input variables of this function are passed in as local variables +to the LayerHelper constructor.
+paddle.v2.fluid.layers.split_lod_tensor(input, mask, level=0, main_program=None, startup_program=None)paddle.v2.fluid.layers.merge_lod_tensor(in_true, in_false, x, mask, level=0, main_program=None, startup_program=None)paddle.v2.fluid.layers.cos_sim(X, Y, **kwargs)This function performs the cosine similarity between two tensors +X and Y and returns that as the output.
+paddle.v2.fluid.layers.cross_entropy(input, label, **kwargs)This function computes cross_entropy using the input and label.
+paddle.v2.fluid.layers.square_error_cost(input, label, **kwargs)This functions returns the squared error cost using the input and label. +The output is appending the op to do the above.
+paddle.v2.fluid.layers.accuracy(input, label, k=1, correct=None, total=None, **kwargs)This function computes the accuracy using the input and label. +The output is the top_k inputs and their indices.
+paddle.v2.fluid.layers.sequence_conv(input, num_filters, filter_size=3, filter_stride=1, padding=None, bias_attr=None, param_attr=None, act=None, main_program=None, startup_program=None)This function creates the op for sequence_conv, using the inputs and +other convolutional configurations for the filters and stride as given +in the input parameters to the function.
+paddle.v2.fluid.layers.conv2d(input, num_filters, filter_size, stride=[1, 1], padding=None, groups=None, param_attr=None, bias_attr=None, act=None, name=None, main_program=None, startup_program=None)This function creates the op for a 2-dimensional Convolution. +This is performed using the parameters of filters(size, dimensionality etc) +, stride and other configurations for a Convolution operation. +This funciton can also append an activation on top of the +conv-2d output, if mentioned in the input parameters.
+paddle.v2.fluid.layers.sequence_pool(input, pool_type, **kwargs)This function add the operator for sequence pooling. +This is applied on top of the input using pool_type mentioned +in the parameters.
+paddle.v2.fluid.layers.pool2d(input, pool_size, pool_type, pool_stride=[1, 1], pool_padding=[0, 0], global_pooling=False, main_program=None, startup_program=None)This function adds the operator for pooling in 2 dimensions, using the +pooling configurations mentioned in input parameters.
+paddle.v2.fluid.layers.batch_norm(input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW', main_program=None, startup_program=None)This function helps create an operator to implement +the BatchNorm layer using the configurations from the input parameters.
+paddle.v2.fluid.layers.beam_search_decode(ids, scores, main_program=None, startup_program=None)paddle.v2.fluid.layers.lstm(x, c_pre_init, hidden_dim, forget_bias=None, main_program=None, startup_program=None)This function helps create an operator for the LSTM (Long Short Term +Memory) cell that can be used inside an RNN.
+paddle.v2.fluid.layers.lod_rank_table(x, level=0, main_program=None)This function creates an operator for creating a LOD_RANK_TABLE +using the input x.
+paddle.v2.fluid.layers.max_sequence_len(rank_table, main_program=None)This function creates an operator to calculate the length of +max seqence through input rank_table(should be a lod_rank_table)
+paddle.v2.fluid.layers.topk(input, k, main_program=None, startup_program=None)paddle.v2.fluid.layers.lod_tensor_to_array(x, table, main_program=None)This function creates an operator to convert an LOD_Tensor to +an array.
+paddle.v2.fluid.layers.array_to_lod_tensor(x, table, main_program=None, startup_program=None)This function creates an operator to convert an array to a +LOD_Tensor.
+paddle.v2.fluid.layers.fill_constant(shape, dtype, value, out=None, main_program=None, startup_program=None)This function creates a tensor , with shape as mentioned in the input and +specified dtype and fills this up with a constant value that +comes in the input. It also sets the stop_gradient to be True.
+paddle.v2.fluid.layers.fill_constant_batch_size_like(input, shape, dtype, value, input_dim_idx=0, output_dim_idx=0, main_program=None, startup_program=None)paddle.v2.fluid.layers.ones(shape, dtype, main_program=None)This function performs the same function as fill_constant() declared above +with the constant value being 1.0.
+paddle.v2.fluid.layers.zeros(shape, dtype, main_program=None)This function performs the same function as fill_constant() declared above +with the constant value being 0.0.
+paddle.v2.fluid.layers.increment(x, value=1.0, in_place=True, main_program=None, startup_program=None)This function creates an operator to increment each value in the input +x by an amount: value as mentioned in the input parameter. This +operation is performed in-place by default.
+paddle.v2.fluid.layers.array_write(x, i, array=None, main_program=None, startup_program=None)This function creates an operator to write the data out as a +LOD_TENSOR_ARRAY.
+paddle.v2.fluid.layers.create_array(dtype, main_program=None)paddle.v2.fluid.layers.less_than(x, y, cond=None, main_program=None, **ignored)paddle.v2.fluid.layers.array_read(array, i, main_program=None, startup_program=None)This function creates an operator to read the data in as a +LOD_TENSOR_ARRAY.
+paddle.v2.fluid.layers.shrink_memory(x, i, table, main_program=None, startup_program=None)This function creates an operator to shrink_rnn_memory using the RankTable +as mentioned in the input parameter.
+paddle.v2.fluid.layers.array_length(array, main_program=None)This function creates an operator to find the length of the +LOD_TENSOR_ARRAY.
+paddle.v2.fluid.layers.conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, param_attr=None, main_program=None, startup_program=None)The transpose of conv2d layer.
+This layer is also known as deconvolution layer.
+| 参数: |
|
+
|---|---|
| 返回: | Output image. + |
+
| 返回类型: | Variable + |
+
paddle.v2.fluid.nets.simple_img_conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act, pool_type='max', main_program=None, startup_program=None)paddle.v2.fluid.nets.img_conv_group(input, conv_num_filter, pool_size, conv_padding=1, conv_filter_size=3, conv_act=None, conv_with_batchnorm=False, conv_batchnorm_drop_rate=None, pool_stride=1, pool_type=None, main_program=None, startup_program=None)Image Convolution Group, Used for vgg net.
+paddle.v2.fluid.nets.sequence_conv_pool(input, num_filters, filter_size, act='sigmoid', pool_type='max', main_program=None, startup_program=None)paddle.v2.fluid.optimizer.Optimizer(global_step=None)Optimizer Base class.
+Define the common interface of an optimizer. +User should not use this class directly, +but need to use one of it’s implementation.
+create_optimization_pass(parameters_and_grads, loss, startup_program=None)Add optimization operators to update gradients to variables.
+| 参数: |
|
+
|---|---|
| 返回: | a list of operators that will complete one step of +optimization. This will include parameter update ops, global step +update ops and any other custom ops required by subclasses to manage +their internal state. +:param startup_program: + |
+
| 返回类型: | return_op_list + |
+
minimize(loss, startup_program=None, parameter_list=None, no_grad_set=None)Add operations to minimize loss by updating parameter_list.
+This method combines interface append_backward_ops() and +create_optimization_pass() into one.
+paddle.v2.fluid.optimizer.SGDOptimizer(learning_rate, global_step=None)Simple SGD optimizer without any state.
+paddle.v2.fluid.optimizer.MomentumOptimizer(learning_rate, momentum, use_nesterov=False, global_step=None)Simple Momentum optimizer with velocity state
+paddle.v2.fluid.optimizer.AdagradOptimizer(learning_rate, epsilon=1e-06, global_step=None)Simple Adagrad optimizer with moment state
+paddle.v2.fluid.optimizer.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, global_step=None)Implements the Adam Optimizer
+paddle.v2.fluid.optimizer.AdamaxOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, global_step=None)Implements the Adamax Optimizer
+paddle.v2.fluid.optimizer.DecayedAdagradOptimizer(learning_rate, decay=0.95, epsilon=1e-06, global_step=None)Simple Decayed Adagrad optimizer with moment state
+paddle.v2.fluid.profiler.cuda_profiler(*args, **kwds)The CUDA profiler. +This fuctions is used to profile CUDA program by CUDA runtime application +programming interface. The profiling result will be written into +output_file with Key-Value pair format or Comma separated values format. +The user can set the output mode by output_mode argument and set the +counters/options for profiling by config argument. The default config +is [‘gpustarttimestamp’, ‘gpustarttimestamp’, ‘gridsize3d’, +‘threadblocksize’, ‘streamid’, ‘enableonstart 0’, ‘conckerneltrace’].
+| 参数: |
|
+
|---|
paddle.v2.fluid.regularizer.WeightDecayRegularizerBase class for weight decay regularizers
+Defines the common interface of weight-decay regularizers. +Weight-decay regularizers are added only during the backward +pass for faster regularization. They add operations to the network +that correspond to gradient of the regularization function. +Users should not use this class directly, but need to use one +of its implementations
+paddle.v2.fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)Implements the L2 Weight Decay Regularization
+paddle.v2.fluid.regularizer.L1DecayRegularizer(regularization_coeff=0.0)¶Implements the L1 Weight Decay Regularization
+| + |
| paddle
| | |||
| + |
+ paddle.v2.fluid.regularizer | + | |||
diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js
index 46b56efe359b301fd57f613cf1b4be33fb5f450c..12f9cd069ead288cbaf8e91b46be2e9127c4a47b 100644
--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js
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