From 15e1e23bf22b4a146b87dfdd6a6593a1223a2b4e Mon Sep 17 00:00:00 2001 From: Travis CI Date: Mon, 22 Jan 2018 13:54:44 +0000 Subject: [PATCH] Deploy to GitHub Pages: b156bbc8ad4e4f5bd6302202d9237f9c4b92120a --- develop/doc/api/v2/fluid/evaluator.html | 69 + develop/doc/api/v2/fluid/initializer.html | 69 + develop/doc/api/v2/fluid/io.html | 17 + develop/doc/api/v2/fluid/layers.html | 2912 ++++++++++++++++++ develop/doc/api/v2/fluid/nets.html | 100 + develop/doc/api/v2/fluid/optimizer.html | 81 + develop/doc/api/v2/fluid/profiler.html | 29 + develop/doc/api/v2/fluid/regularizer.html | 28 +- develop/doc/genindex.html | 23 +- develop/doc/objects.inv | Bin 3841 -> 3908 bytes develop/doc/py-modindex.html | 5 + develop/doc/searchindex.js | 2 +- develop/doc_cn/api/v2/fluid/evaluator.html | 69 + develop/doc_cn/api/v2/fluid/initializer.html | 69 + develop/doc_cn/api/v2/fluid/io.html | 17 + develop/doc_cn/api/v2/fluid/layers.html | 2912 ++++++++++++++++++ develop/doc_cn/api/v2/fluid/nets.html | 100 + develop/doc_cn/api/v2/fluid/optimizer.html | 81 + develop/doc_cn/api/v2/fluid/profiler.html | 29 + develop/doc_cn/api/v2/fluid/regularizer.html | 28 +- develop/doc_cn/genindex.html | 23 +- develop/doc_cn/objects.inv | Bin 4487 -> 4557 bytes develop/doc_cn/py-modindex.html | 5 + develop/doc_cn/searchindex.js | 2 +- 24 files changed, 6659 insertions(+), 11 deletions(-) diff --git a/develop/doc/api/v2/fluid/evaluator.html b/develop/doc/api/v2/fluid/evaluator.html index 844b2dff3c1..a44ce3e9592 100644 --- a/develop/doc/api/v2/fluid/evaluator.html +++ b/develop/doc/api/v2/fluid/evaluator.html @@ -220,6 +220,75 @@

Evaluator

Evaluator

+
+
+class paddle.v2.fluid.evaluator.Evaluator(name, **kwargs)
+

Base Class for all evaluators

+ +++ + + + +
Parameters:
    +
  • name (str) – The name of evaluator. such as, “accuracy”. Used for generate +temporary variable name.
    • +
  • main_program (Program, optional) – The evaluator should be added to this +main_program. Default default_main_program()
    • +
  • startup_program (Program, optional) – The parameter should be added to this +startup_program. Default default_startup_program()
    • +
+
+
+
+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:
    +
  • suffix (str) – the state suffix.
    • +
  • dtype (str|core.DataType) – the state data type
    • +
  • shape (tuple|list) – the shape of state
    • +
+
+

Returns: State variable

+
+ +
+
diff --git a/develop/doc/api/v2/fluid/initializer.html b/develop/doc/api/v2/fluid/initializer.html index 4c8471387cc..d248f229403 100644 --- a/develop/doc/api/v2/fluid/initializer.html +++ b/develop/doc/api/v2/fluid/initializer.html @@ -220,21 +220,90 @@

Initializer

Initializer

+
+
+class paddle.v2.fluid.initializer.Initializer
+

Base 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.

+
+

ConstantInitializer

+
+
+class paddle.v2.fluid.initializer.ConstantInitializer(value=0.0)
+

Implements the constant initializer

+
+

UniformInitializer

+
+
+class paddle.v2.fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0)
+

Implements the random uniform distribution initializer

+
+

NormalInitializer

+
+
+class paddle.v2.fluid.initializer.NormalInitializer(loc=0.0, scale=1.0, seed=0)
+

Implements the random Normal(Gaussian) distribution initializer

+
+

XavierInitializer

+
+
+class 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

+
+
[1] Understanding the difficulty of training deep feedforward neural
+
networks. International conference on artificial intelligence and +statistics. +(http://proceedings.mlr.press/v9/glorot10a.html)
+
+
+

MSRAInitializer

+
+
+class 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

+
+
[1] Delving Deep into Rectifiers: Surpassing Human-Level Performance
+
on ImageNet Classification +(https://arxiv.org/abs/1502.01852)
+
+
+
diff --git a/develop/doc/api/v2/fluid/io.html b/develop/doc/api/v2/fluid/io.html index 2df66310412..35c0fe2639b 100644 --- a/develop/doc/api/v2/fluid/io.html +++ b/develop/doc/api/v2/fluid/io.html @@ -220,6 +220,23 @@

IO

is_parameter

+
+
+paddle.v2.fluid.io.is_parameter(var)
+

Check whether the variable is a Parameter.

+

This function checks whether the input variable is a Parameter.

+ +++ + + + + + +
Parameters:var – The input variable.
Returns:boolean result whether the variable is a Parameter.
+
+
diff --git a/develop/doc/api/v2/fluid/layers.html b/develop/doc/api/v2/fluid/layers.html index 118799fa002..57991fe49b8 100644 --- a/develop/doc/api/v2/fluid/layers.html +++ b/develop/doc/api/v2/fluid/layers.html @@ -220,273 +220,3185 @@

Layers

fc

+
+
+paddle.v2.fluid.layers.fc(input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None)
+

Fully Connected Layer

+

The fully connected layer can take multiple tensors as its inputs. It +creates a variable (one for each input tensor) called weights for each input +tensor, which represents a fully connected weight matrix from each input +unit to each output unit. The fully connected layer multiplies each input +tensor with its coresponding weight to produce an output Tensor. If +multiple input tensors are given, the results of multiple multiplications +will be sumed up. If bias_attr is not None, a biases variable will be +created and added to the output. Finally, if activation is not None, +it will be applied to the output as well.

+

This process can be formulated as follows:

+
+\[Out = Act({\sum_{i=0}^{N-1}W_iX_i + b})\]
+

In the above equation:

+
    +
  • \(N\): Number of the input.
    • +
  • \(X_i\): The input tensor.
    • +
  • \(W\): The weights created by this layer.
    • +
  • \(b\): The bias parameter created by this layer (if needed).
    • +
  • \(Act\): The activation funtion.
    • +
  • \(Out\): The output tensor.
    • +
+ +++ + + + + + + + + + +
Parameters:
    +
  • input (Variable|list) – The input tensor(s) to the fully connected layer.
    • +
  • size (int) – The number of output units in the fully connected layer.
    • +
  • num_flatten_dims (int) – The fc layer can accept an input tensor with more +than two dimensions. If this happens, the +multidimensional tensor will first be flattened +into a 2-dimensional matrix. The parameter +num_flatten_dims determines how the input tensor +is flattened: the first num_flatten_dims +dimensions will be flatten to form the first +dimension of the final matrix (height of the +matrix), and the rest rank(X) - num_flatten_dims +dimensions are flattened to form the second +dimension of the final matrix (width of the matrix). +For example, suppose X is a 6-dimensional tensor +with a shape [2, 3, 4, 5, 6], and +num_flatten_dims = 3. Then, the flattened matrix +will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. +By default, num_flatten_dims is set to 1.
    • +
  • param_attr (ParamAttr|list) – The parameter attribute for learnable +parameters/weights of the fully connected +layer.
    • +
  • param_initializer (ParamAttr|list) – The initializer used for the +weight/parameter. If set None, +XavierInitializer() will be used.
    • +
  • bias_attr (ParamAttr|list) – The parameter attribute for the bias parameter +for this layer. If set None, no bias will be +added to the output units.
    • +
  • bias_initializer (ParamAttr|list) – The initializer used for the bias. +If set None, then ConstantInitializer() +will be used.
    • +
  • act (str) – Activation to be applied to the output of the fully connected +layer.
    • +
  • name (str) – Name/alias of the fully connected layer.
    • +
+
Returns:

The output tensor variable.

+
Return type:

Variable

+
Raises:

ValueError – If rank of the input tensor is less than 2.

+
+

Examples

+
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+fc = fluid.layers.fc(input=data, size=1000, act="tanh")
+
+
+
+

embedding

+
+
+paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, param_attr=None, dtype='float32')
+

Embedding Layer

+

This layer is used to lookup a vector of IDs, provided by input, in a lookup table. +The result of this lookup is the embedding of each ID in the input.

+

All the input variables are passed in as local variables to the LayerHelper +constructor.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – Input to the function
    • +
  • size (tuple|list|None) – Shape of the look up table parameter
    • +
  • is_sparse (bool) – Boolean flag that specifying whether the input is sparse
    • +
  • param_attr (ParamAttr) – Parameters for this layer
    • +
  • dtype (np.dtype|core.DataType|str) – The type of data : float32, float_16, int etc
    • +
+
Returns:

The tensor variable storing the embeddings of the supplied inputs.

+
Return type:

Variable

+
+

Examples

+
dict_size = len(dataset.ids)
+data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
+fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
+
+
+
+

dynamic_lstm

+
+
+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')
+

Dynamic LSTM Layer

+

The defalut implementation is diagonal/peephole connection +(https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:

+
+\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i)\\f_t & = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f)\\\tilde{c_t} & = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c)\\o_t & = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o)\\c_t & = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}\\h_t & = o_t \odot act_h(c_t)\end{aligned}\end{align} \]
+

where the \(W\) terms denote weight matrices (e.g. \(W_{xi}\) is +the matrix of weights from the input gate to the input), \(W_{ic}, W_{fc}, W_{oc}\) are diagonal weight matrices for peephole connections. In +our implementation, we use vectors to reprenset these diagonal weight +matrices. The \(b\) terms denote bias vectors (\(b_i\) is the input +gate bias vector), \(\sigma\) is the non-line activations, such as +logistic sigmoid function, and \(i, f, o\) and \(c\) are the input +gate, forget gate, output gate, and cell activation vectors, respectively, +all of which have the same size as the cell output activation vector \(h\).

+

The \(\odot\) is the element-wise product of the vectors. \(act_g\) +and \(act_h\) are the cell input and cell output activation functions +and tanh is usually used for them. \(\tilde{c_t}\) is also called +candidate hidden state, which is computed based on the current input and +the previous hidden state.

+

Set use_peepholes to False to disable peephole connection. The formula +is omitted here, please refer to the paper +http://www.bioinf.jku.at/publications/older/2604.pdf for details.

+

Note that these \(W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\) +operations on the input \(x_{t}\) are NOT included in this operator. +Users can choose to use fully-connect layer before LSTM layer.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – The input of dynamic_lstm layer, which supports +variable-time length input sequence. The underlying +tensor in this Variable is a matrix with shape +(T X 4D), where T is the total time steps in this +mini-batch, D is the hidden size.
    • +
  • size (int) – 4 * hidden size.
    • +
  • param_attr (ParamAttr) –

    The parameter attribute for the learnable +hidden-hidden weights.

    +
      +
    • The shape is (D x 4D), where D is the hidden +size.
      • +
    • Weights = {\(W_{ch}, W_{ih}, W_{fh}, W_{oh}\)}
      • +
    +
    • +
  • bias_attr (ParamAttr) –

    The bias attribute for the learnable bias +weights, which contains two parts, input-hidden +bias weights and peephole connections weights if +setting use_peepholes to True.

    +
      +
    1. use_peepholes = False
      2. +
    +
    +
      +
    • The shape is (1 x 4D).
      • +
    • Biases = {\(b_c, b_i, b_f, b_o\)}.
      • +
    +
    +
      +
    1. use_peepholes = True
      2. +
    +
    +
      +
    • The shape is (1 x 7D).
      • +
    • Biases = { \(b_c, b_i, b_f, b_o, W_{ic}, W_{fc}, W_{oc}\)}.
      • +
    +
    +
    • +
  • use_peepholes (bool) – Whether to enable diagonal/peephole connections, +default True.
    • +
  • is_reverse (bool) – Whether to compute reversed LSTM, default False.
    • +
  • gate_activation (str) – The activation for input gate, forget gate and +output gate. Choices = [“sigmoid”, “tanh”, “relu”, +“identity”], default “sigmoid”.
    • +
  • cell_activation (str) – The activation for cell output. Choices = [“sigmoid”, +“tanh”, “relu”, “identity”], default “tanh”.
    • +
  • candidate_activation (str) – The activation for candidate hidden state. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], +default “tanh”.
    • +
  • dtype (str) – Data type. Choices = [“float32”, “float64”], default “float32”.
    • +
+
Returns:

The hidden state, and cell state of LSTM. The shape of both is (T x D), and lod is the same with the input.

+
Return type:

tuple

+
+

Examples

+
hidden_dim = 512
+forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
+                               act=None, bias_attr=None)
+forward, _ = fluid.layers.dynamic_lstm(
+    input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
+
+
+
+

data

+
+
+paddle.v2.fluid.layers.data(name, shape, append_batch_size=True, dtype='float32', lod_level=0, type=VarType.LOD_TENSOR, stop_gradient=True)
+

Data Layer

+

This function takes in the input and based on whether data has +to be returned back as a minibatch, it creates the global variable by using +the helper functions. The global variables can be accessed by all the +following operators in the graph.

+

All the input variables of this function are passed in as local variables +to the LayerHelper constructor.

+ +++ + + + + + + + +
Parameters:
    +
  • name (str) – The name/alias of the function
    • +
  • shape (list) – Tuple declaring the shape.
    • +
  • append_batch_size (bool) – Whether or not to append the data as a batch.
    • +
  • dtype (int|float) – The type of data : float32, float_16, int etc
    • +
  • type (VarType) – The output type. By default it is LOD_TENSOR.
    • +
  • lod_level (int) – The LoD Level. 0 means the input data is not a sequence.
    • +
  • main_program (Program) – Name of the main program that calls this
    • +
  • startup_program (Program) – Name of the startup program
    • +
  • stop_gradient (bool) – A boolean that mentions whether gradient should flow.
    • +
+
Returns:

The global variable that gives access to the data.

+
Return type:

Variable

+
+

Examples

+
data = fluid.layers.data(name='x', shape=[784], dtype='float32')
+
+
+
+

mean

+
+
+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
+
+

mul

+
+
+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:
    +
  • x – (Tensor), The first input tensor of mul op. +Duplicable: False Optional: False
    • +
  • y – (Tensor), The second input tensor of mul op. +Duplicable: False Optional: False
    • +
  • x_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two +dimensions as its inputs. If the input $X$ is a tensor with more +than two dimensions, $X$ will be flattened into a two-dimensional +matrix first. The flattening rule is: the first num_col_dims +will be flattened to form the first dimension of the final matrix +(the height of the matrix), and the rest rank(X) - num_col_dims +dimensions are flattened to form the second dimension of the final +matrix (the width of the matrix). As a result, height of the +flattened matrix is equal to the product of $X$’s first +x_num_col_dims dimensions’ sizes, and width of the flattened +matrix is equal to the product of $X$’s last rank(x) - num_col_dims +dimensions’ size. For example, suppose $X$ is a 6-dimensional +tensor with the shape [2, 3, 4, 5, 6], and x_num_col_dims = 3. +Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = +[24, 30].
    • +
  • y_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two, +dimensions as its inputs. If the input $Y$ is a tensor with more +than two dimensions, $Y$ will be flattened into a two-dimensional +matrix first. The attribute y_num_col_dims determines how $Y$ is +flattened. See comments of x_num_col_dims for more details.
    • +
+
Returns:

(Tensor), The output tensor of mul op.

+
+
+

elementwise_add

+
+
+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 +set to index of the start dimension to broadcast $Y$ onto $X$.

+
+
For 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
+
+
+
+
+

Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

+ +++ + + + + + +
Parameters:
    +
  • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    • +
+
Returns:

The output of elementwise op.

+
+
+

elementwise_sub

+
+
+paddle.v2.fluid.layers.elementwise_sub(**kwargs)
+

Limited Elementwise Sub 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 +set to index of the start dimension to broadcast $Y$ onto $X$.

+
+
For 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
+
+
+
+
+

Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

+ +++ + + + + + +
Parameters:
    +
  • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    • +
+
Returns:

The output of elementwise op.

+
+
+

elementwise_mul

+
+
+paddle.v2.fluid.layers.elementwise_mul(**kwargs)
+

Limited Elementwise Mul Operator.

+

The equation is:

+

$$Out = X odotY$$

+

$X$ 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 +set to index of the start dimension to broadcast $Y$ onto $X$.

+
+
For 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
+
+
+
+
+

Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

+ +++ + + + + + +
Parameters:
    +
  • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    • +
+
Returns:

The output of elementwise op.

+
+
+

elementwise_div

+
+
+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 +set to index of the start dimension to broadcast $Y$ onto $X$.

+
+
For 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
+
+
+
+
+

Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

+ +++ + + + + + +
Parameters:
    +
  • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    • +
+
Returns:

The output of elementwise op.

+
+
+

dropout

+
+
+paddle.v2.fluid.layers.dropout(x, dropout_prob, is_test=False, seed=0, **kwargs)
+
+

reshape

+
+
+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 2-D tensor:

+
+
[[1, 2, 3, 4]]
+

One dimension in the target shape can be set -1, representing that its +size is unknown. In this case, the real dimension will be infered from +the original shape of Input(X) and other dimensions in the target shape.

+ +++ + + + + + +
Parameters:
    +
  • x – The input tensor of reshape operator. +Duplicable: False Optional: False
    • +
  • shape (INTS) – (vector<int>) Target shape of reshape operator.
    • +
+
Returns:

The output tensor of reshape operator.

+
+
+

sigmoid

+
+
+paddle.v2.fluid.layers.sigmoid(**kwargs)
+

Sigmoid Activation Operator

+

$$out = frac{1}{1 + e^{-x}}$$

+ +++ + + + + + +
Parameters:x – Input of Sigmoid operator +Duplicable: False Optional: False
Returns:Output of Sigmoid operator
+
+

scale

+
+
+paddle.v2.fluid.layers.scale(**kwargs)
+

Scale operator

+

$$Out = scale*X$$

+ +++ + + + + + +
Parameters:
    +
  • x – (Tensor) Input tensor of scale operator. +Duplicable: False Optional: False
    • +
  • scale (FLOAT) – (float, default 1.0)The scaling factor of the scale operator.
    • +
+
Returns:

(Tensor) Output tensor of scale operator.

+
+
+

transpose

+
+
+paddle.v2.fluid.layers.transpose(**kwargs)
+

Transpose Operator.

+

The input tensor will be permuted according to the axis values given. +The op functions is similar to how numpy.transpose works in python.

+

For example:

+
+
input = numpy.arange(6).reshape((2,3))
+
+the input is:
+
+array([[0, 1, 2],
+       [3, 4, 5]])
+
+given axis is:
+
+[1, 0]
+
+output = input.transpose(axis)
+
+then the output is:
+
+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:
    +
  • x – (Tensor)The input tensor, tensors with rank at most 6 are supported +Duplicable: False Optional: False
    • +
  • axis (INTS) – (vector<int>)A list of values, and the size of the list should be the same with the input tensor rank, the tensor will permute the axes according the the values given
    • +
+
Returns:

(Tensor)The output tensor

+
+
+

sigmoid_cross_entropy_with_logits

cast

+
+
+paddle.v2.fluid.layers.cast(x, dtype)
+

This function takes in the input with input_dtype +and casts it to the output_dtype as the output.

+
+

concat

+
+
+paddle.v2.fluid.layers.concat(input, axis=0)
+

Concat

+

This function concatenates the input along the axis mentioned +and returns that as the output.

+ +++ + + + + + + + +
Parameters:
    +
  • input (list) – List of tensors to be concatenated
    • +
  • axis (int) – Integer axis along which the tensors will be concatenated
    • +
+
Returns:

Output variable of the concatenation

+
Return type:

Variable

+
+

Examples

+
+

sums

+
+
+paddle.v2.fluid.layers.sums(input, out=None)
+

This function performs the sum operation on the input and returns the +result as the output.

+ +++ + + + + + + + +
Parameters:input (Variable|list) – The input tensor that has the elements +that need to be summed up.
Returns:
+
The tensor type variable that has the sum of input
+
written to it.
+
+
Return type:Variable
+

Examples

+
+

linear_chain_crf

+
+
+paddle.v2.fluid.layers.linear_chain_crf(input, label, param_attr=None)
+
+

assign

+
+
+paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, param_attr=None, dtype='float32')
+

Embedding Layer

+

This layer is used to lookup a vector of IDs, provided by input, in a lookup table. +The result of this lookup is the embedding of each ID in the input.

+

All the input variables are passed in as local variables to the LayerHelper +constructor.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – Input to the function
    • +
  • size (tuple|list|None) – Shape of the look up table parameter
    • +
  • is_sparse (bool) – Boolean flag that specifying whether the input is sparse
    • +
  • param_attr (ParamAttr) – Parameters for this layer
    • +
  • dtype (np.dtype|core.DataType|str) – The type of data : float32, float_16, int etc
    • +
+
Returns:

The tensor variable storing the embeddings of the supplied inputs.

+
Return type:

Variable

+
+

Examples

+
dict_size = len(dataset.ids)
+data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
+fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
+
+
+
+

split_lod_tensor

+
+
+paddle.v2.fluid.layers.split_lod_tensor(input, mask, level=0)
+

split_lod_tensor

+

This function takes in an input that contains the complete lod information, +and takes in a mask which is used to mask certain parts of the input. +The output is the true branch and the false branch with the mask applied to +the input at a certain level in the tensor.

+ +++ + + + + + + + +
Parameters:
    +
  • input (tuple|list|None) – The input tensor that contains complete +lod information needed to construct the output.
    • +
  • mask (list) – A bool column vector which masks the input.
    • +
  • level (int) – The specific lod level to rank.
    • +
+
Returns:

The true branch of tensor as per the mask applied to input. +Variable: The false branch of tensor as per the mask applied to input.

+
Return type:

Variable

+
+

Examples

+
x = layers.data(name='x', shape=[1])
+x.persistable = True
+
+y = layers.data(name='y', shape=[1])
+y.persistable = True
+
+out_true, out_false = layers.split_lod_tensor(
+      input=x, mask=y, level=level)
+
+
+
+

merge_lod_tensor

+
+
+paddle.v2.fluid.layers.merge_lod_tensor(in_true, in_false, x, mask, level=0)
+

merge_lod_tensor

+

This function takes in an input \(x\), the True branch, the False +branch and a binary \(mask\). Using this information, this function +merges the True and False branches of the tensor into a single Output +at a certain lod level indiacted by \(level\).

+ +++ + + + + + + + +
Parameters:
    +
  • in_true (tuple|list|None) – The True branch to be merged.
    • +
  • in_false (tuple|list|None) – The False branch to be merged.
    • +
  • x (tuple|list|None) – The input tensor that contains complete +lod information needed to construct the output.
    • +
  • mask (list) – A bool column vector which masks the input.
    • +
  • level (int) – The specific lod level to rank.
    • +
+
Returns:

The merged output tensor.

+
Return type:

Variable

+
+

Examples

+
x = layers.data(
+            name='x', shape=[1], dtype='float32', stop_gradient=False)
+y = layers.data(
+      name='y', shape=[1], dtype='bool', stop_gradient=False)
+
+level = 0
+
+out_true, out_false = layers.split_lod_tensor(
+      input=x, mask=y, level=level)
+out = layers.merge_lod_tensor(
+      in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
+
+
+
+

cos_sim

+
+
+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.

+
+

cross_entropy

+
+
+paddle.v2.fluid.layers.cross_entropy(input, label, **kwargs)
+

Cross Entropy Layer

+

This layer computes the cross entropy between input and label. It supports +both standard cross-entropy and soft-label cross-entropy loss computation.

+
    +
  1. +
    One-hot cross-entropy:
    +

    soft_label = False, Label[i, 0] indicates the class index for sample i:

    +
    +\[Y[i] = -\log(X[i, Label[i]])\]
    +
    +
    +
    2. +
  2. +
    Soft-label cross-entropy:
    +

    soft_label = True, Label[i, j] indicates the soft label of class j +for sample i:

    +
    +\[Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}\]
    +
    +
    +

    Please make sure that in this case the summation of each row of label +equals one.

    +
    3. +
  3. +
    One-hot cross-entropy with vecterized label:
    +

    As a special case of 2), when each row of ‘label’ has only one +non-zero element which is equal to 1, soft-label cross-entropy degenerates +to a one-hot cross-entropy with one-hot label representation.

    +
    +
    +
    4. +
+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable|list) – a 2-D tensor with shape [N x D], where N is the +batch size and D is the number of classes. This input is a probability +computed by the previous operator, which is almost always the result +of a softmax operator.
    • +
  • label (Variable|list) – the ground truth which is a 2-D tensor. When +soft_label is set to False, label is a tensor<int64> with shape +[N x 1]. When soft_label is set to True, label is a +tensor<float/double> with shape [N x D].
    • +
  • soft_label (bool, via **kwargs) – a flag indicating whether to interpretate +the given labels as soft labels, default False.
    • +
+
Returns:

A 2-D tensor with shape [N x 1], the cross entropy loss.

+
Raises:

ValueError – 1) the 1st dimension of input and label are not equal; 2) when soft_label == True, and the 2nd dimension of input and label are not equal; 3) when soft_label == False, and the 2nd dimension of label is not 1.

+
+

Examples

+
predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
+cost = fluid.layers.cross_entropy(input=predict, label=label)
+
+
+
+

square_error_cost

+
+
+paddle.v2.fluid.layers.square_error_cost(input, label, **kwargs)
+

Square error cost layer

+

This layer accepts input predictions and target label and returns the squared error cost. +For predictions, \(X\), and target labels, \(Y\), the equation is:

+
+\[Out = (X - Y)^2\]
+

In the above equation:

+
+
    +
  • \(X\): Input predictions, a tensor.
    • +
  • \(Y\): Input labels, a tensor.
    • +
  • \(Out\): Output value, same shape with \(X\).
    • +
+
+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – Input tensor, has predictions.
    • +
  • label (Variable) – Label tensor, has target labels.
    • +
+
Returns:

The tensor variable storing the element-wise squared error difference of input and label.

+
Return type:

Variable

+
+

Examples

+
y = layers.data(name='y', shape=[1], dtype='float32')
+y_predict = layers.data(name='y_predict', shape=[1], dtype='float32')
+cost = layers.square_error_cost(input=y_predict, label=y)
+
+
+
+

accuracy

+
+
+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.

+
+

sequence_conv

+
+
+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)
+

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.

+
+

conv2d

+
+
+paddle.v2.fluid.layers.conv2d(input, num_filters, filter_size, stride=None, padding=None, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None)
+

Convlution2D Layer

+

The convolution2D layer calculates the output based on the input, filter +and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output) +are in NCHW format. Where N is batch size, C is the number of channels, H is the height +of the feature, and W is the width of the feature. +The details of convolution layer, please refer UFLDL’s convolution, . +If bias attribution and activation type are provided, bias is added to the output of the convolution, +and the corresponding activation function is applied to the final result. +For each input \(X\), the equation is:

+
+\[Out = \sigma (W \ast X + b)\]
+

In the above equation:

+
+
    +
  • \(X\): Input value, a tensor with NCHW format.
    • +
  • \(W\): Filter value, a tensor with MCHW format.
    • +
  • \(\ast\): Convolution operation.
    • +
  • \(b\): Bias value, a 2-D tensor with shape [M, 1].
    • +
  • \(\sigma\): Activation function.
    • +
  • \(Out\): Output value, the shape of \(Out\) and \(X\) may be different.
    • +
+
+

Example

+
+
Input:
+

Input shape: $(N, C_{in}, H_{in}, W_{in})$

+

Filter shape: $(C_{out}, C_{in}, H_f, W_f)$

+
+
Output:
+
Output shape: $(N, C_{out}, H_{out}, W_{out})$
+
+

Where

+
+\[\begin{split}H_{out}&= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\ +W_{out}&= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1\end{split}\]
+ +++ + + + + + + + + + +
Parameters:
    +
  • input (Variable) – The input image with [N, C, H, W] format.
    • +
  • num_filters (int) – The number of filter. It is as same as the output +image channel.
    • +
  • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, +it must contain two integers, (filter_size_H, filter_size_W). +Otherwise, the filter will be a square.
    • +
  • stride (int|tuple) – The stride size. If stride is a tuple, it must +contain two integers, (stride_H, stride_W). Otherwise, the +stride_H = stride_W = stride. Default: stride = 1.
    • +
  • padding (int|tuple) – The padding size. If padding is a tuple, it must +contain two integers, (padding_H, padding_W). Otherwise, the +padding_H = padding_W = padding. Default: padding = 0.
    • +
  • groups (int) – The groups number of the Conv2d Layer. According to grouped +convolution in Alex Krizhevsky’s Deep CNN paper: when group=2, +the first half of the filters is only connected to the first half +of the input channels, while the second half of the filters is only +connected to the second half of the input channels. Default: groups=1
    • +
  • param_attr (ParamAttr) – The parameters to the Conv2d Layer. Default: None
    • +
  • bias_attr (ParamAttr) – Bias parameter for the Conv2d layer. Default: None
    • +
  • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn +library is installed. Default: True
    • +
  • act (str) – Activation type. Default: None
    • +
+
Returns:

The tensor variable storing the convolution and non-linearity activation result.

+
Return type:

Variable

+
Raises:

ValueError – If the shapes of input, filter_size, stride, padding and groups mismatch.

+
+

Examples

+
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
+conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
+
+
+
+

sequence_pool

+
+
+paddle.v2.fluid.layers.sequence_pool(input, pool_type, **kwargs)
+

This function add the operator for sequence pooling. +It pools features of all time-steps of each instance, and is applied +on top of the input using pool_type mentioned in the parameters.

+

It supports four pool_type:

+
    +
  • average: \(Out[i] = \frac{\sum_i X_i}{N}\)
    • +
  • sum: \(Out[i] = \sum_jX_{ij}\)
    • +
  • sqrt: \(Out[i] = \frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}\)
    • +
  • max: \(Out[i] = max(X_i)\)
    • +
+
x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+
+for different pool_type:
+  average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
+  sum    : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
+  sqrt   : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
+             6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
+  max    : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
+
+
+ +++ + + + + + +
Parameters:
    +
  • input (variable) – The input variable which is a LoDTensor.
    • +
  • pool_type (string) – The pooling type of sequence_pool. +It supports average, sum, sqrt and max.
    • +
+
Returns:

The sequence pooling variable which is a Tensor.

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[7, 1],
+                 dtype='float32', lod_level=1)
+avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
+sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
+sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
+max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
+
+
+
+

sequence_first_step

+
+
+paddle.v2.fluid.layers.sequence_first_step(input, **kwargs)
+

This funciton get the first step of sequence.

+
x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+  out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
+
+
+ +++ + + + + + +
Parameters:input (variable) – The input variable which is a LoDTensor.
Returns:The sequence’s first step variable which is a Tensor.
+

Examples

+
x = fluid.layers.data(name='x', shape=[7, 1],
+                 dtype='float32', lod_level=1)
+x_first_step = fluid.layers.sequence_first_step(input=x)
+
+
+
+

sequence_last_step

+
+
+paddle.v2.fluid.layers.sequence_last_step(input, **kwargs)
+

This funciton get the last step of sequence.

+
x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+  out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
+
+
+ +++ + + + + + +
Parameters:input (variable) – The input variable which is a LoDTensor.
Returns:The sequence’s last step variable which is a Tensor.
+

Examples

+
x = fluid.layers.data(name='x', shape=[7, 1],
+                 dtype='float32', lod_level=1)
+x_last_step = fluid.layers.sequence_last_step(input=x)
+
+
+
+

pool2d

+
+
+paddle.v2.fluid.layers.pool2d(input, pool_size, pool_type, pool_stride=None, pool_padding=None, global_pooling=False, use_cudnn=True, name=None)
+

This function adds the operator for pooling in 2 dimensions, using the +pooling configurations mentioned in input parameters.

+
+

batch_norm

+
+
+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', name=None)
+

This function helps create an operator to implement +the BatchNorm layer using the configurations from the input parameters.

+
+

beam_search_decode

+
+
+paddle.v2.fluid.layers.beam_search_decode(ids, scores, name=None)
+
+

lod_rank_table

+
+
+paddle.v2.fluid.layers.lod_rank_table(x, level=0)
+

LoD Rank Table Operator. Given an input variable x and a level number +of LoD, this layer creates a LodRankTable object. A LoDRankTable object +contains a list of bi-element tuples. Each tuple consists of an index and +a length, both of which are int type. Refering to specified level of LoD, +the index is the sequence index number and the length representes the +sequence length. Please note that the list is ranked in descending order by +the length. The following is an example:

+
+
x is a LoDTensor:
+    x.lod = [[0,                2, 3],
+             [0,             5, 6, 7]]
+    x.data = [a, b, c, d, e, f, g]
+
+1. set level to 0:
+    Create lod rank table:
+        lod_rank_table_obj = lod_rank_table(x, level=0)
+
+    Get:
+        lod_rank_table_obj.items() = [(0, 2), (1, 1)]
+
+2. set level to 1:
+    Create lod rank table:
+        lod_rank_table_obj = lod_rank_table(x, level=1)
+
+    Get:
+        lod_rank_table_obj.items() = [(0, 5), (1, 1), (2, 1)]
+
+
+
+ +++ + + + + + + + +
Parameters:
    +
  • x (Variable) – Input variable, a LoDTensor based which to create the lod +rank table.
    • +
  • level (int) – Specify the LoD level, on which to create the lod rank +table.
    • +
+
Returns:

The created LoDRankTable object.

+
Return type:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[10],
+                dtype='float32', lod_level=1)
+out = layers.lod_rank_table(x=x, level=0)
+
+
+
+

max_sequence_len

+
+
+paddle.v2.fluid.layers.max_sequence_len(rank_table)
+

Max Sequence Len Operator. Given a LoDRankTable object, this layer +returns the max length of a batch of sequences. In fact, a LoDRankTable +object contains a list of tuples(<sequence index, sequence length>) and +the list is already sorted by sequence length in descending order, so the +operator just returns the sequence length of the first tuple element.

+ +++ + + + + + + + +
Parameters:rank_table (Variable) – Input variable which is a LoDRankTable object.
Returns:The max length of sequence.
Return type:Variable
+

Examples

+
x = fluid.layers.data(name='x', shape=[10],
+                dtype='float32', lod_level=1)
+rank_table = layers.lod_rank_table(x=x, level=0)
+max_seq_len = layers.max_sequence_len(rank_table)
+
+
+
+

topk

+
+
+paddle.v2.fluid.layers.topk(input, k)
+

topk

+

This function performs the operation that selects the k entries in the input +vector and outputs their values and indices as vectors. Thus topk_out[j] is +the j-th largest entry in input, and its index is topk_indices[j]

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable|list) – The input tensor that has all the data.
    • +
  • k (int) – The number of top elements that the function will pick.
    • +
+
Returns:

+
The variable of type array that contains the k largest entries
+

from input.

+
+
Variable: The variable of type array that contains the indices of k
+

largest entries from input.

+
+
+

+
Return type:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[10])
+k = 5
+array = fluid.layers.topk(x, k)
+
+
+
+

lod_tensor_to_array

+
+
+paddle.v2.fluid.layers.lod_tensor_to_array(x, table)
+

Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.

+ +++ + + + + + + + +
Parameters:
    +
  • x (Variable|list) – The LOD tensor to be converted to a LOD tensor array.
    • +
  • table (ParamAttr|list) – The variable that stores the level of lod +which is ordered by sequence length in +descending order.
    • +
+
Returns:

+
The variable of type array that has been converted from a
+

tensor.

+
+
+

+
Return type:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[10])
+table = fluid.layers.lod_rank_table(x, level=0)
+array = fluid.layers.lod_tensor_to_array(x, table)
+
+
+
+

array_to_lod_tensor

+
+
+paddle.v2.fluid.layers.array_to_lod_tensor(x, table)
+

Convert a LoD_Tensor_Aarry to an LoDTensor.

+ +++ + + + + + + + +
Parameters:
    +
  • x (Variable|list) – The lod tensor array to be converted to a tensor.
    • +
  • table (ParamAttr|list) – The variable that stores the level of lod +which is ordered by sequence length in +descending order.
    • +
+
Returns:

+
The variable of type tensor that has been converted
+

from an array.

+
+
+

+
Return type:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[10])
+table = fluid.layers.lod_rank_table(x, level=0)
+array = fluid.layers.lod_tensor_to_array(x, table)
+lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
+
+
+
+

fill_constant

+
+
+paddle.v2.fluid.layers.fill_constant(shape, dtype, value, force_cpu=False, out=None)
+

fill_constant

+

This function creates a tensor with specified shape and dtype, and +initializes it with a constant specifed by value.

+

The attribute stop_gradient of the created tensor is set to True.

+ +++ + + + + + + + +
Parameters:
    +
  • shape (tuple|list|None) – Shape of the output tensor.
    • +
  • dtype (np.dtype|core.DataType|str) – Data type of the output tensor.
    • +
  • value (float) – The constant value used to initialize the output tensor.
    • +
  • out (Variable) – The output tensor.
    • +
+
Returns:

The tensor variable storing the output.

+
Return type:

Variable

+
+

Examples

+
data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
+
+
+
+

fill_constant_batch_size_like

+
+
+paddle.v2.fluid.layers.fill_constant_batch_size_like(input, shape, dtype, value, input_dim_idx=0, output_dim_idx=0)
+

fill_constant_batch_size_like

+

This function creates a tensor of specified shape, dtype and batch size, +and initializes this with a constant supplied in value. The batch size is +obtained from the input tensor.

+

It also sets stop_gradient to True.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – Tensor whose dimensions will be used to get batch size
    • +
  • shape (tuple|list|None) – Shape of output tensor
    • +
  • dtype (np.dtype|core.DataType|str) – Data type of output tensor
    • +
  • value (float) – Constant value to initialize the output tensor
    • +
  • input_dim_idx (int) – Index of input’s batch size dimension
    • +
  • output_dim_idx (int) – Index of output’s batch size dimension
    • +
+
Returns:

The tensor variable storing the output

+
Return type:

Variable

+
+

Examples

+
data = fluid.layers.fill_constant_batch_size_like(
+    input=like, shape=[1], value=0, dtype='int64')
+
+
+
+

ones

+
+
+paddle.v2.fluid.layers.ones(shape, dtype)
+

ones

+

This function creates a tensor of specified shape and +dtype, and initializes this with 1.

+

It also sets stop_gradient to True.

+ +++ + + + + + + + +
Parameters:
    +
  • shape (tuple|list|None) – Shape of output tensor
    • +
  • dtype (np.dtype|core.DataType|str) – Data type of output tensor
    • +
+
Returns:

The tensor variable storing the output

+
Return type:

Variable

+
+

Examples

+
data = fluid.layers.ones(shape=[1], dtype='int64')
+
+
+
+

zeros

+
+
+paddle.v2.fluid.layers.zeros(shape, dtype)
+

zeros

+

This function creates a tensor of specified shape and +dtype, and initializes this with 0.

+

It also sets stop_gradient to True.

+ +++ + + + + + + + +
Parameters:
    +
  • shape (tuple|list|None) – Shape of output tensor
    • +
  • dtype (np.dtype|core.DataType|str) – Data type of output tensor
    • +
+
Returns:

The tensor variable storing the output

+
Return type:

Variable

+
+

Examples

+
data = fluid.layers.zeros(shape=[1], dtype='int64')
+
+
+
+

increment

+
+
+paddle.v2.fluid.layers.increment(x, value=1.0, in_place=True)
+

This function performs an operation that increments each value in the +input \(x\) by an amount: \(value\) as mentioned in the input +parameter. This operation is performed in-place by default.

+ +++ + + + + + + + +
Parameters:
    +
  • x (Variable|list) – The tensor that has the input values.
    • +
  • value (float) – The amount by which the values should be incremented.
    • +
  • in_place (bool) – If the increment should be performed in-place.
    • +
+
Returns:

+
The tensor variable storing the transformation of
+

element-wise increment of each value in the input.

+
+
+

+
Return type:

Variable

+
+

Examples

+
data = fluid.layers.data(name='data', shape=[32, 32], dtype='float32')
+data = fluid.layers.increment(x=data, value=3.0, in_place=True)
+
+
+
+

array_write

+
+
+paddle.v2.fluid.layers.array_write(x, i, array=None)
+

This function writes the given input variable to the specified position +indicating by the arrary index to an output LOD_TENSOR_ARRAY. If the +output LOD_TENSOR_ARRAY is not given(None), a new one will be created and +returned.

+ +++ + + + + + + + +
Parameters:
    +
  • x (Variable|list) – The input tensor from which the data will be read.
    • +
  • i (Variable|list) – The index of the output LOD_TENSOR_ARRAY, pointing to +the position to which the input tensor will be +written.
    • +
  • array (Variable|list) – The output LOD_TENSOR_ARRAY to which the input +tensor will be written. If this parameter is +NONE, a new LOD_TENSOR_ARRAY will be created and +returned.
    • +
+
Returns:

The output LOD_TENSOR_ARRAY where the input tensor is written.

+
Return type:

Variable

+
+

Examples

+
+

create_array

+
+
+paddle.v2.fluid.layers.create_array(dtype)
+

This function creates an array of type \(LOD_TENSOR_ARRAY\) using the +LayerHelper.

+ +++ + + + + + + + +
Parameters:dtype (int|float) – The data type of the elements in the array.
Returns:The tensor variable storing the elements of data type.
Return type:Variable
+

Examples

+
data = fluid.layers.create_array(dtype='float32')
+
+
+
+

less_than

+
+
+paddle.v2.fluid.layers.less_than(x, y, cond=None, **ignored)
+

Less than

+

This layer returns the truth value of \(x < y\) elementwise.

+ +++ + + + + + + + +
Parameters:
    +
  • x (Variable) – First operand of less_than
    • +
  • y (Variable) – Second operand of less_than
    • +
  • cond (Variable|None) – Optional output variable to store the result of less_than
    • +
+
Returns:

The tensor variable storing the output of less_than.

+
Return type:

Variable

+
+

Examples

+
less = fluid.layers.less_than(x=label, y=limit)
+
+
+
+

array_read

+
+
+paddle.v2.fluid.layers.array_read(array, i)
+

This function performs the operation to read the data in as an +LOD_TENSOR_ARRAY. +:param array: The input tensor that will be written to an array. +:type array: Variable|list +:param i: The subscript index in tensor array, that points the

+
+
place where data will be written to.
+ +++ + + + + + +
Returns:The tensor type variable that has the data written to it.
Return type:Variable
+

Examples

+
+

shrink_memory

+
+
+paddle.v2.fluid.layers.shrink_memory(x, i, table)
+

This function creates an operator to shrink_rnn_memory using the RankTable +as mentioned in the input parameter.

+
+

array_length

+
+
+paddle.v2.fluid.layers.array_length(array)
+

This function performs the operation to find the length of the input +LOD_TENSOR_ARRAY.

+ +++ + + + + + + + +
Parameters:array (LOD_TENSOR_ARRAY) – The input array that will be used +to compute the length.
Returns:The length of the input LoDTensorArray.
Return type:Variable
+

Examples

+
+

conv2d_transpose

+
+
+paddle.v2.fluid.layers.conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, dilation=None, param_attr=None, use_cudnn=True, name=None)
+

The transpose of conv2d layer.

+

This layer is also known as deconvolution layer.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – The input image with [N, C, H, W] format.
    • +
  • num_filters (int) – The number of filter. It is as same as the output +image channel.
    • +
  • output_size (int|tuple|None) – The output image size. If output size is a +tuple, it must contain two integers, (image_H, image_W). This +parameter only works when filter_size is None.
    • +
  • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, +it must contain two integers, (filter_size_H, filter_size_W). +Otherwise, the filter will be a square. None if use output size to +calculate filter_size
    • +
  • padding (int|tuple) – The padding size. If padding is a tuple, it must +contain two integers, (padding_H, padding_W). Otherwise, the +padding_H = padding_W = padding.
    • +
  • stride (int|tuple) – The stride size. If stride is a tuple, it must +contain two integers, (stride_H, stride_W). Otherwise, the +stride_H = stride_W = stride.
    • +
  • dilation (int|tuple) – The dilation size. If dilation is a tuple, it must +contain two integers, (dilation_H, dilation_W). Otherwise, the +dilation_H = dilation_W = dilation.
    • +
  • param_attr – Parameter Attribute.
    • +
  • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn +library is installed. Default: True
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
Returns:

Output image.

+
Return type:

Variable

+
+
+

sequence_expand

+
+
+paddle.v2.fluid.layers.sequence_expand(x, y, name=None)
+

Sequence Expand Layer. This layer will expand the input variable x +according to LoD information of y. And the following examples will +explain how sequence_expand works:

+
* Case 1
+    x is a LoDTensor:
+        x.lod = [[0,       2, 3],
+                 [0, 1,    3, 4]]
+        x.data = [a, b, c, d]
+        x.dims = [4, 1]
+
+    y is a LoDTensor:
+        y.lod = [[0,    2,    4],
+                 [0, 3, 6, 7, 8]]
+
+    with condition len(y.lod[-1]) - 1 == x.dims[0]
+
+    then output is a 2-level LoDTensor:
+        out.lod = [[0,                2,    4],
+                   [0,       3,       6, 7, 8]]
+        out.data = [a, a, a, b, b, b, c, d]
+        out.dims = [8, 1]
+
+* Case 2
+    x is a Tensor:
+        x.data = [a, b, c]
+        x.dims = [3, 1]
+
+    y is a LoDTensor:
+        y.lod = [[0, 2, 3, 6]]
+
+    with condition len(y.lod[-1]) - 1 == x.dims[0]
+
+    then output is a 1-level LoDTensor:
+        out.lod = [[0,    2, 3,      6]]
+        out.data = [a, a, b, c, c, c]
+        out.dims = [6, 1]
+
+
+ +++ + + + + + + + +
Parameters:
    +
  • x (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • y (Variable) – The input variable which is a LoDTensor.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
Returns:

The expanded variable which is a LoDTensor.

+
Return type:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[10], dtype='float32')
+y = fluid.layers.data(name='y', shape=[10, 20],
+                 dtype='float32', lod_level=1)
+out = layers.sequence_expand(x=x, y=y)
+
+
+
+

gru_unit

+
+
+paddle.v2.fluid.layers.gru_unit(input, hidden, size, weight=None, bias=None, activation='tanh', gate_activation='sigmoid')
+

GRU unit layer. The equation of a gru step is:

+
+
+\[ \begin{align}\begin{aligned}u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)\\r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)\\m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)\\h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})\end{aligned}\end{align} \]
+
+

The inputs of gru unit includes \(z_t\), \(h_{t-1}\). In terms +of the equation above, the \(z_t\) is split into 3 parts - +\(xu_t\), \(xr_t\) and \(xm_t\). This means that in order to +implement a full GRU unit operator for an input, a fully +connected layer has to be applied, such that \(z_t = W_{fc}x_t\).

+

The terms \(u_t\) and \(r_t\) represent the update and reset gates +of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is +an intermediate candidate hidden output, which is denoted by \(m_t\). +This layer has three outputs \(h_t\), \(dot(r_t, h_{t-1})\) +and concatenation of \(u_t\), \(r_t\) and \(m_t\).

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – The fc transformed input value of current step.
    • +
  • hidden (Variable) – The hidden value of lstm unit from previous step.
    • +
  • size (integer) – The input dimension value.
    • +
  • weight (ParamAttr) – The weight parameters for gru unit. Default: None
    • +
  • bias (ParamAttr) – The bias parameters for gru unit. Default: None
    • +
  • activation (string) – The activation type for cell (actNode). Default: ‘tanh’
    • +
  • gate_activation (string) – The activation type for gates (actGate). Default: ‘sigmoid’
    • +
+
Returns:

The hidden value, reset-hidden value and gate values.

+
Return type:

tuple

+
+

Examples

+
# assuming we have x_t_data and prev_hidden of size=10
+x_t = fluid.layers.fc(input=x_t_data, size=30)
+hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
+                                       hidden = prev_hidden)
+
+
+
+

lstm_unit

+
+
+paddle.v2.fluid.layers.lstm_unit(x_t, hidden_t_prev, cell_t_prev, forget_bias=0.0, param_attr=None, bias_attr=None, name=None)
+

Lstm unit layer. The equation of a lstm step is:

+
+
+\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)\\f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)\\o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]
+
+

The inputs of lstm unit include \(x_t\), \(h_{t-1}\) and +\(c_{t-1}\). The 2nd dimensions of \(h_{t-1}\) and \(c_{t-1}\) +should be same. The implementation separates the linear transformation and +non-linear transformation apart. Here, we take \(i_t\) as an example. +The linear transformation is applied by calling a fc layer and the +equation is:

+
+
+\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i\]
+
+

The non-linear transformation is applied by calling lstm_unit_op and the +equation is:

+
+
+\[i_t = \sigma(L_{i_t})\]
+
+

This layer has two outputs including \(h_t\) and \(o_t\).

+ +++ + + + + + + + + + +
Parameters:
    +
  • x_t (Variable) – The input value of current step, a 2-D tensor with shape +M x N, M for batch size and N for input size.
    • +
  • hidden_t_prev (Variable) – The hidden value of lstm unit, a 2-D tensor +with shape M x S, M for batch size and S for size of lstm unit.
    • +
  • cell_t_prev (Variable) – The cell value of lstm unit, a 2-D tensor with +shape M x S, M for batch size and S for size of lstm unit.
    • +
  • forget_bias (float) – The forget bias of lstm unit.
    • +
  • param_attr (ParamAttr) – The attributes of parameter weights, used to set +initializer, name etc.
    • +
  • bias_attr (ParamAttr) – The attributes of bias weights, if not False, +bias weights will be created and be set to default value.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
Returns:

The hidden value and cell value of lstm unit.

+
Return type:

tuple

+
Raises:

ValueError – The ranks of x_t, hidden_t_prev and cell_t_prev not be 2 or the 1st dimensions of x_t, hidden_t_prev and cell_t_prev not be the same or the 2nd dimensions of hidden_t_prev and cell_t_prev not be the same.

+
+

Examples

+
x_t = fluid.layers.fc(input=x_t_data, size=10)
+prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30)
+prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
+hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
+                                       hidden_t_prev=prev_hidden,
+                                       cell_t_prev=prev_cell)
+
+
+
+

sequence_softmax

+
+
+paddle.v2.fluid.layers.sequence_softmax(**kwargs)
+

Sequence Softmax Operator.

+

SequenceSoftmaxOp computes the softmax activation among all time-steps for each +sequence. The dimension of each time-step should be 1. Thus, the shape of +input Tensor can be either [N, 1] or [N], where N is the sum of the length +of all sequences.

+

The algorithm works as follows:

+
+
for i-th sequence in a mini-batch:
+

$$ +Out(X[lod[i]:lod[i+1]], :) = frac{exp(X[lod[i]:lod[i+1], :])} {sum(exp(X[lod[i]:lod[i+1], :]))} +$$

+

For example, for a mini-batch of 3 sequences with variable-length, +each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7], +then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :] +and N turns out to be 7.

+ +++ + + + + + +
Parameters:x – (LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension of length 1. +Duplicable: False Optional: False
Returns:(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension of length 1.
+
+

reduce_sum

+
+
+paddle.v2.fluid.layers.reduce_sum(input, dim=None, keep_dim=False, name=None)
+

Computes the sum of tensor elements over the given dimension.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • dim (int|None) – The dimension along which the sum is performed. If +None, sum all elements of input and return a +Tensor variable with a single element, otherwise must be in the +range \([-rank(input), rank(input))\). If \(dim < 0\), +the dimension to reduce is \(rank + dim\).
    • +
  • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
Returns:

The reduced Tensor variable.

+
Return type:

Variable

+
+

Examples

+
# x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_sum(x)  # [3.5]
+fluid.layers.reduce_sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
+fluid.layers.reduce_sum(x, dim=-1)  # [1.9, 1.6]
+fluid.layers.reduce_sum(x, dim=1, keep_dim=True)  # [[1.9], [1.6]]
+
+
+
+

reduce_mean

+
+
+paddle.v2.fluid.layers.reduce_mean(input, dim=None, keep_dim=False, name=None)
+

Computes the mean of tensor elements over the given dimension.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • dim (int|None) – The dimension along which the mean is computed. If +None, compute the mean over all elements of input +and return a Tensor variable with a single element, otherwise +must be in the range \([-rank(input), rank(input))\). If +\(dim < 0\), the dimension to reduce is \(rank + dim\).
    • +
  • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
Returns:

The reduced Tensor variable.

+
Return type:

Variable

+
+

Examples

+
# x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_mean(x)  # [0.4375]
+fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
+fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
+fluid.layers.reduce_mean(x, dim=1, keep_dim=True)  # [[0.475], [0.4]]
+
+
+
+

reduce_max

+
+
+paddle.v2.fluid.layers.reduce_max(input, dim=None, keep_dim=False, name=None)
+

Computes the maximum of tensor elements over the given dimension.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • dim (int|None) – The dimension along which the maximum is computed. +If None, compute the maximum over all elements of +input and return a Tensor variable with a single element, +otherwise must be in the range \([-rank(input), rank(input))\). +If \(dim < 0\), the dimension to reduce is \(rank + dim\).
    • +
  • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
Returns:

The reduced Tensor variable.

+
Return type:

Variable

+
+

Examples

+
# x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_max(x)  # [0.9]
+fluid.layers.reduce_max(x, dim=0)  # [0.2, 0.3, 0.6, 0.9]
+fluid.layers.reduce_max(x, dim=-1)  # [0.9, 0.7]
+fluid.layers.reduce_max(x, dim=1, keep_dim=True)  # [[0.9], [0.7]]
+
+
+
+

reduce_min

+
+
+paddle.v2.fluid.layers.reduce_min(input, dim=None, keep_dim=False, name=None)
+

Computes the minimum of tensor elements over the given dimension.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • dim (int|None) – The dimension along which the minimum is computed. +If None, compute the minimum over all elements of +input and return a Tensor variable with a single element, +otherwise must be in the range \([-rank(input), rank(input))\). +If \(dim < 0\), the dimension to reduce is \(rank + dim\).
    • +
  • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
Returns:

The reduced Tensor variable.

+
Return type:

Variable

+
+

Examples

+
# x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_min(x)  # [0.1]
+fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
+fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
+fluid.layers.reduce_min(x, dim=1, keep_dim=True)  # [[0.2], [0.1]]
+
+
+
+

split

+
+
+paddle.v2.fluid.layers.split(input, num_or_sections, dim=-1, name=None)
+

Split the input tensor into multiple sub-tensors.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • num_or_sections (int|list) – If num_or_sections is an integer, +then the integer indicates the number of equal sized sub-tensors +that the tensor will be divided into. If num_or_sections +is a list of integers, the length of list indicates the number of +sub-tensors and the integers indicate the sizes of sub-tensors’ +dim dimension orderly.
    • +
  • dim (int) – The dimension along which to split. If \(dim < 0\), the +dimension to split along is \(rank(input) + dim\).
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
Returns:

The list of segmented tensor variables.

+
Return type:

List

+
+

Examples

+
# x is a Tensor variable with shape [3, 9, 5]:
+x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
+x0.shape  # [3, 3, 5]
+x1.shape  # [3, 3, 5]
+x2.shape  # [3, 3, 5]
+x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
+x0.shape  # [3, 2, 5]
+x1.shape  # [3, 3, 5]
+x2.shape  # [3, 4, 5]
+
+
+
+

matmul

+
+
+paddle.v2.fluid.layers.matmul(x, y, transpose_x=False, transpose_y=False, name=None)
+

Applies matrix multiplication to two tensors. Currently, the input +tensors’ rank can be any, but when the rank of anyone inputs is +bigger than 3, this two inputs’ rank should be equal.

+

The actual behavior depends on the shapes of \(x\), \(y\) and the +flag values of transpose_x, transpose_y. Specifically:

+
    +
  • If a transpose flag is specified, the last two dimensions of the tensor +are transposed. If the tensor is rank-1 of shape \([D]\), then for +\(x\) it is treated as \([1, D]\) in nontransposed form and as +\([D, 1]\) in transposed form, whereas for \(y\) it is the +opposite: It is treated as \([D, 1]\) in nontransposed form and as +\([1, D]\) in transposed form.
    • +
  • After transpose, the two tensors are 2-D or n-D and matrix multiplication +performs in the following way.
      +
    • If both are 2-D, they are multiplied like conventional matrices.
      • +
    • If either is n-D, it is treated as a stack of matrices residing in the +last two dimensions and a batched matrix multiply supporting broadcast +applies on the two tensors.
      • +
    +
    • +
+

Also note that if the raw tensor \(x\) or \(y\) is rank-1 and +nontransposed, the prepended or appended dimension \(1\) will be +removed after matrix multiplication.

+ +++ + + + + + + + +
Parameters:
    +
  • x (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • y (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • transpose_x (bool) – Whether to transpose \(x\) before multiplication.
    • +
  • transpose_y (bool) – Whether to transpose \(y\) before multiplication.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
Returns:

The product Tensor variable.

+
Return type:

Variable

+
+

Examples

+
# Examples to clarify shapes of the inputs and output
+# x: [B, ..., M, K], y: [B, ..., K, N]
+fluid.layers.matmul(x, y)  # out: [B, ..., M, N]
+# x: [B, M, K], y: [B, K, N]
+fluid.layers.matmul(x, y)  # out: [B, M, N]
+# x: [B, M, K], y: [K, N]
+fluid.layers.matmul(x, y)  # out: [B, M, N]
+# x: [B, M, K], y: [K]
+fluid.layers.matmul(x, y)  # out: [B, M]
+# x: [M, K], y: [K, N]
+fluid.layers.matmul(x, y)  # out: [M, N]
+# x: [K], y: [K]
+fluid.layers.matmul(x, y)  # out: [1]
+# x: [M], y: [N]
+
+fluid.layers.matmul(x, y, True, True)  # out: [M, N]
+
+
+
+

logsigmoid

+
+
+paddle.v2.fluid.layers.logsigmoid(**kwargs)
+

Logsigmoid Activation Operator

+

$$out = log frac{1}{1 + e^{-x}}$$

+ +++ + + + + + +
Parameters:x – Input of LogSigmoid operator +Duplicable: False Optional: False
Returns:Output of LogSigmoid operator
+
+

exp

+
+
+paddle.v2.fluid.layers.exp(**kwargs)
+

Exp Activation Operator.

+

$out = e^x$

+ +++ + + + + + +
Parameters:x – Input of Exp operator +Duplicable: False Optional: False
Returns:Output of Exp operator
+
+

relu

+
+
+paddle.v2.fluid.layers.relu(**kwargs)
+

Relu Activation Operator.

+

$out = max(x, 0)$

+ +++ + + + + + +
Parameters:x – Input of Relu operator +Duplicable: False Optional: False
Returns:Output of Relu operator
+
+

tanh

+
+
+paddle.v2.fluid.layers.tanh(**kwargs)
+

Tanh Activation Operator.

+

$$out = frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$

+ +++ + + + + + +
Parameters:x – Input of Tanh operator +Duplicable: False Optional: False
Returns:Output of Tanh operator
+
+

tanh_shrink

+
+
+paddle.v2.fluid.layers.tanh_shrink(**kwargs)
+

TanhShrink Activation Operator.

+

$$out = x - frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$

+ +++ + + + + + +
Parameters:x – Input of TanhShrink operator +Duplicable: False Optional: False
Returns:Output of TanhShrink operator
+
+

softshrink

+
+
+paddle.v2.fluid.layers.softshrink(**kwargs)
+

Softshrink Activation Operator.

+

$$ +out = begin{cases}

+
+
x - lambda, text{if } x > lambda \ +x + lambda, text{if } x < -lambda \ +0, text{otherwise} +end{cases}
+

$$

+ +++ + + + + + +
Parameters:
    +
  • x – Input of Softshrink operator +Duplicable: False Optional: False
    • +
  • lambda (FLOAT) – non-negative offset
    • +
+
Returns:

Output of Softshrink operator

+
+
+

sqrt

+
+
+paddle.v2.fluid.layers.sqrt(**kwargs)
+

Sqrt Activation Operator.

+

$out = sqrt{x}$

+ +++ + + + + + +
Parameters:x – Input of Sqrt operator +Duplicable: False Optional: False
Returns:Output of Sqrt operator
+
+

abs

+
+
+paddle.v2.fluid.layers.abs(**kwargs)
+

Abs Activation Operator.

+

$out = |x|$

+ +++ + + + + + +
Parameters:x – Input of Abs operator +Duplicable: False Optional: False
Returns:Output of Abs operator
+
+

ceil

+
+
+paddle.v2.fluid.layers.ceil(**kwargs)
+

Ceil Activation Operator.

+

$out = ceil(x)$

+ +++ + + + + + +
Parameters:x – Input of Ceil operator +Duplicable: False Optional: False
Returns:Output of Ceil operator
+
+

floor

+
+
+paddle.v2.fluid.layers.floor(**kwargs)
+

Floor Activation Operator.

+

$out = floor(x)$

+ +++ + + + + + +
Parameters:x – Input of Floor operator +Duplicable: False Optional: False
Returns:Output of Floor operator
+
+

round

+
+
+paddle.v2.fluid.layers.round(**kwargs)
+

Round Activation Operator.

+

$out = [x]$

+ +++ + + + + + +
Parameters:x – Input of Round operator +Duplicable: False Optional: False
Returns:Output of Round operator
+
+

reciprocal

+
+
+paddle.v2.fluid.layers.reciprocal(**kwargs)
+

Reciprocal Activation Operator.

+

$$out = frac{1}{x}$$

+ +++ + + + + + +
Parameters:x – Input of Reciprocal operator +Duplicable: False Optional: False
Returns:Output of Reciprocal operator
+
+

log

+
+
+paddle.v2.fluid.layers.log(**kwargs)
+

Log Activation Operator.

+

$out = ln(x)$

+

Natural logarithm of x.

+ +++ + + + + + +
Parameters:x – Input of Log operator +Duplicable: False Optional: False
Returns:Output of Log operator
+
+

square

+
+
+paddle.v2.fluid.layers.square(**kwargs)
+

Square Activation Operator.

+

$out = x^2$

+ +++ + + + + + +
Parameters:x – Input of Square operator +Duplicable: False Optional: False
Returns:Output of Square operator
+
+

softplus

+
+
+paddle.v2.fluid.layers.softplus(**kwargs)
+

Softplus Activation Operator.

+

$out = ln(1 + e^{x})$

+ +++ + + + + + +
Parameters:x – Input of Softplus operator +Duplicable: False Optional: False
Returns:Output of Softplus operator
+
+

softsign

+
+
+paddle.v2.fluid.layers.softsign(**kwargs)
+

Softsign Activation Operator.

+

$$out = frac{x}{1 + |x|}$$

+ +++ + + + + + +
Parameters:x – Input of Softsign operator +Duplicable: False Optional: False
Returns:Output of Softsign operator
+
+

brelu

+
+
+paddle.v2.fluid.layers.brelu(**kwargs)
+

BRelu Activation Operator.

+

$out = max(min(x, t_{min}), t_{max})$

+ +++ + + + + + +
Parameters:
    +
  • x – Input of BRelu operator +Duplicable: False Optional: False
    • +
  • t_min (FLOAT) – The min marginal value of BRelu
    • +
  • t_max (FLOAT) – The max marginal value of BRelu
    • +
+
Returns:

Output of BRelu operator

+
+
+

leaky_relu

+
+
+paddle.v2.fluid.layers.leaky_relu(**kwargs)
+

LeakyRelu Activation Operator.

+

$out = max(x, alpha * x)$

+ +++ + + + + + +
Parameters:
    +
  • x – Input of LeakyRelu operator +Duplicable: False Optional: False
    • +
  • alpha (FLOAT) – The small negative slope
    • +
+
Returns:

Output of LeakyRelu operator

+
+
+

soft_relu

+
+
+paddle.v2.fluid.layers.soft_relu(**kwargs)
+

SoftRelu Activation Operator.

+

$out = ln(1 + exp(max(min(x, threshold), threshold))$

+ +++ + + + + + +
Parameters:
    +
  • x – Input of SoftRelu operator +Duplicable: False Optional: False
    • +
  • threshold (FLOAT) – The threshold value of SoftRelu
    • +
+
Returns:

Output of SoftRelu operator

+
+
+

elu

+
+
+paddle.v2.fluid.layers.elu(**kwargs)
+

ELU Activation Operator.

+

Applies the following element-wise computation on the input according to +https://arxiv.org/abs/1511.07289.

+

$out = max(0, x) + min(0, alpha * (e^x - 1))$

+ +++ + + + + + +
Parameters:
    +
  • x – Input of ELU operator +Duplicable: False Optional: False
    • +
  • alpha (FLOAT) – The alpha value of ELU
    • +
+
Returns:

Output of ELU operator

+
+
+

relu6

+
+
+paddle.v2.fluid.layers.relu6(**kwargs)
+

Relu6 Activation Operator.

+

$out = min(max(0, x), 6)$

+ +++ + + + + + +
Parameters:
    +
  • x – Input of Relu6 operator +Duplicable: False Optional: False
    • +
  • threshold (FLOAT) – The threshold value of Relu6
    • +
+
Returns:

Output of Relu6 operator

+
+
+

pow

+
+
+paddle.v2.fluid.layers.pow(**kwargs)
+

Pow Activation Operator.

+

$out = x^{factor}$

+ +++ + + + + + +
Parameters:
    +
  • x – Input of Pow operator +Duplicable: False Optional: False
    • +
  • factor (FLOAT) – The exponential factor of Pow
    • +
+
Returns:

Output of Pow operator

+
+
+

hard_shrink

+
+
+paddle.v2.fluid.layers.hard_shrink(**kwargs)
+

HardShrink Activation Operator.

+

$$ +out = begin{cases}

+
+
x, text{if } x > lambda \ +x, text{if } x < -lambda \ +0, text{otherwise} +end{cases}
+

$$

+ +++ + + + + + +
Parameters:
    +
  • x – Input of HardShrink operator +Duplicable: False Optional: False
    • +
  • threshold (FLOAT) – The value of threshold for HardShrink
    • +
+
Returns:

Output of HardShrink operator

+
+
+

thresholded_relu

+
+
+paddle.v2.fluid.layers.thresholded_relu(**kwargs)
+

ThresholdedRelu Activation Operator.

+

$$ +out = begin{cases}

+
+
x, text{if } x > threshold \ +0, text{otherwise} +end{cases}
+

$$

+ +++ + + + + + +
Parameters:
    +
  • x – Input of ThresholdedRelu operator +Duplicable: False Optional: False
    • +
  • threshold (FLOAT) – The threshold location of activation
    • +
+
Returns:

Output of ThresholdedRelu operator

+
+
+

hard_sigmoid

+
+
+paddle.v2.fluid.layers.hard_sigmoid(**kwargs)
+

HardSigmoid Activation Operator.

+

Segment-wise linear approximation of sigmoid(https://arxiv.org/abs/1603.00391), +which is much faster than sigmoid.

+

$out = max(0, min(1, slope * x + shift))$

+

The slope should be positive. The offset can be either positive or negative. +The default slope and shift are set according to the above reference. +It is recommended to use the defaults for this activation.

+ +++ + + + + + +
Parameters:
    +
  • x – Input of HardSigmoid operator +Duplicable: False Optional: False
    • +
  • slope (FLOAT) – Slope for linear approximation of sigmoid
    • +
  • offset (FLOAT) – Offset for linear approximation of sigmoid
    • +
+
Returns:

Output of HardSigmoid operator

+
+
+

swish

+
+
+paddle.v2.fluid.layers.swish(**kwargs)
+

Swish Activation Operator.

+

$$out = frac{x}{1 + e^{- beta x}}$$

+ +++ + + + + + +
Parameters:
    +
  • x – Input of Swish operator +Duplicable: False Optional: False
    • +
  • beta (FLOAT) – Constant beta of swish operator
    • +
+
Returns:

Output of Swish operator

+
+
+

l2_normalize

+
+
+paddle.v2.fluid.layers.l2_normalize(x, axis, epsilon=1e-12, name=None)
+

L2 normalize Layer

+

The l2 normalize layer normalizes x along dimension axis using an L2 +norm. For a 1-D tensor (dim is fixed to 0), this layer computes

+

output = x / sqrt(max(sum(x**2), epsilon))

+

For x with more dimensions, this layer independently normalizes each 1-D +slice along dimension axis.

+ +++ + + + + + + + +
Parameters:
    +
  • x (Variable|list) – The input tensor to l2_normalize layer.
    • +
  • axis (int) – Dimension along which to normalize the input.
    • +
  • epsilon (float) – A lower bound value for x‘s l2 norm. sqrt(epsilon) will +be used as the divisor if the l2 norm of x is less than +sqrt(epsilon).
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
Returns:

The output tensor variable.

+
Return type:

Variable

+
+

Examples

+
data = fluid.layers.data(name="data",
+                         shape=(3, 17, 13),
+                         dtype="float32")
+fc = fluid.layers.l2_normalize(x=data, axis=1)
+
+
+
+

sequence_reshape

+
+
+paddle.v2.fluid.layers.sequence_reshape(input, new_dim)
+

Sequence Reshape Layer

+

This layer will rearrange the input sequences. The new dimension is set by +user. Length of each sequence is computed according to original length, +original dimension and new dimension. The following example will help to +illustrate the function of this layer:

+
x is a LoDTensor:
+    x.lod  = [[0, 2, 6]]
+    x.data = [[1, 2], [3, 4],
+              [5, 6], [7, 8], [9, 10], [11, 12]]
+    x.dims = [6, 2]
+
+set new_dim = 4
+
+then out is a LoDTensor:
+    out.lod  = [[0, 1, 3]]
+    out.data = [[1, 2, 3, 4],
+                [5, 6, 7, 8], [9, 10, 11, 12]]
+    out.dims = [3, 4]
+
+
+

Currently, only 1-level LoDTensor is supported and please make sure +(original length * original dimension) can be divided by new dimension with +no remainder for each sequence.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor +with shape being [N, M] where M for dimension.
    • +
  • new_dim (int) – New dimension which the input LoDTensor is reshaped to.
    • +
+
Returns:

Reshaped LoDTensor according to new dimension.

+
Return type:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[5, 20],
+                  dtype='float32', lod_level=1)
+x_reshaped = layers.sequence_reshape(input=x, new_dim=10)
+
+
+
+
diff --git a/develop/doc/api/v2/fluid/nets.html b/develop/doc/api/v2/fluid/nets.html index a5e0fae9aa3..f558444c960 100644 --- a/develop/doc/api/v2/fluid/nets.html +++ b/develop/doc/api/v2/fluid/nets.html @@ -220,18 +220,118 @@

Nets

simple_img_conv_pool

+
+
+paddle.v2.fluid.nets.simple_img_conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act, param_attr=None, pool_type='max', use_cudnn=True)
+
+

img_conv_group

+
+
+paddle.v2.fluid.nets.img_conv_group(input, conv_num_filter, pool_size, conv_padding=1, conv_filter_size=3, conv_act=None, param_attr=None, conv_with_batchnorm=False, conv_batchnorm_drop_rate=None, pool_stride=1, pool_type=None, use_cudnn=True)
+

Image Convolution Group, Used for vgg net.

+
+

sequence_conv_pool

+
+
+paddle.v2.fluid.nets.sequence_conv_pool(input, num_filters, filter_size, param_attr=None, act='sigmoid', pool_type='max')
+
+

glu

+
+
+paddle.v2.fluid.nets.glu(input, dim=-1)
+

The gated linear unit composed by split, sigmoid activation and elementwise +multiplication. Specifically, Split the input into two equal sized parts +\(a\) and \(b\) along the given dimension and then compute as +following:

+
+
+\[{GLU}(a, b)= a \otimes \sigma(b)\]
+
+

Refer to Language Modeling with Gated Convolutional Networks.

+ +++ + + + + + + + +
Parameters:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • dim (int) – The dimension along which to split. If \(dim < 0\), the +dimension to split along is \(rank(input) + dim\).
    • +
+
Returns:

The Tensor variable with half the size of input.

+
Return type:

Variable

+
+

Examples

+
# x is a Tensor variable with shape [3, 6, 9]
+fluid.nets.glu(input=x, dim=1)  # shape of output: [3, 3, 9]
+
+
+
+

dot_product_attention

+
+
+paddle.v2.fluid.nets.dot_product_attention(querys, keys, values)
+

The dot-product attention.

+

Attention mechanism can be seen as mapping a query and a set of key-value +pairs to an output. The output is computed as a weighted sum of the values, +where the weight assigned to each value is computed by a compatibility +function (dot-product here) of the query with the corresponding key.

+

The dot-product attention can be implemented through (batch) matrix +multipication as follows:

+
+
+\[Attention(Q, K, V)= softmax(QK^\mathrm{T})V\]
+
+

Refer to Attention Is All You Need.

+

Note that batch data containing sequences with different lengths is not +supported by this because of the (batch) matrix multipication.

+ +++ + + + + + + + +
Parameters:
    +
  • query (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • key (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • value (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
+
Returns:

The Tensor variables representing the output and attention scores.

+
Return type:

tuple

+
+

Examples

+
# Suppose q, k, v are tensor variables with the following shape:
+# q: [3, 5, 9], k: [3, 6, 9], v: [3, 6, 10]
+out, attn_scores = fluid.nets.dot_product_attention(q, k, v)
+out.shape  # [3, 5, 10]
+attn_scores.shape  # [3, 5, 6]
+
+
+
+
diff --git a/develop/doc/api/v2/fluid/optimizer.html b/develop/doc/api/v2/fluid/optimizer.html index c6c7f5267c9..01150150375 100644 --- a/develop/doc/api/v2/fluid/optimizer.html +++ b/develop/doc/api/v2/fluid/optimizer.html @@ -220,24 +220,105 @@

Optimizer

Optimizer

+
+
+class paddle.v2.fluid.optimizer.Optimizer(global_step=None, regularization=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:
    +
  • loss – the target that this optimization is for.
    • +
  • parameters_and_grads – a list of (variable, gradient) pair to update.
    • +
+
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() and +create_optimization_pass() into one.

+
+ +
+

SGDOptimizer

+
+
+class paddle.v2.fluid.optimizer.SGDOptimizer(learning_rate, **kwargs)
+

Simple SGD optimizer without any state.

+
+

MomentumOptimizer

+
+
+class paddle.v2.fluid.optimizer.MomentumOptimizer(learning_rate, momentum, use_nesterov=False, **kwargs)
+

Simple Momentum optimizer with velocity state

+
+

AdagradOptimizer

+
+
+class paddle.v2.fluid.optimizer.AdagradOptimizer(learning_rate, epsilon=1e-06, **kwargs)
+

Simple Adagrad optimizer with moment state

+
+

AdamOptimizer

+
+
+class paddle.v2.fluid.optimizer.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)
+

Implements the Adam Optimizer

+
+

AdamaxOptimizer

+
+
+class paddle.v2.fluid.optimizer.AdamaxOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)
+

Implements the Adamax Optimizer

+
+

DecayedAdagradOptimizer

+
+
+class paddle.v2.fluid.optimizer.DecayedAdagradOptimizer(learning_rate, decay=0.95, epsilon=1e-06, **kwargs)
+

Simple Decayed Adagrad optimizer with moment state

+
+
diff --git a/develop/doc/api/v2/fluid/profiler.html b/develop/doc/api/v2/fluid/profiler.html index d1a2b4af3ed..cda12d3fe0c 100644 --- a/develop/doc/api/v2/fluid/profiler.html +++ b/develop/doc/api/v2/fluid/profiler.html @@ -220,6 +220,35 @@

Profiler

Profiler

+
+
+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:
    +
  • output_file (string) – The output file name, the result will be +written into this file.
    • +
  • output_mode (string) – The output mode has Key-Value pair format and +Comma separated values format. It should be ‘kvp’ or ‘csv’.
    • +
  • config (list of string) – The profiler options and counters can refer +to “Compute Command Line Profiler User Guide”.
    • +
+
+
+
diff --git a/develop/doc/api/v2/fluid/regularizer.html b/develop/doc/api/v2/fluid/regularizer.html index 317ce40883b..b1c507121be 100644 --- a/develop/doc/api/v2/fluid/regularizer.html +++ b/develop/doc/api/v2/fluid/regularizer.html @@ -220,12 +220,36 @@

Regularizer

WeightDecayRegularizer

+
+
+class paddle.v2.fluid.regularizer.WeightDecayRegularizer
+

Base 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

+
+

L2DecayRegularizer

+
+
+class paddle.v2.fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)
+

Implements the L2 Weight Decay Regularization

+
+
-
-

L1DecayRegularizer

+
+

L1DecayRegularizer

+
+
+class paddle.v2.fluid.regularizer.L1DecayRegularizer(regularization_coeff=0.0)
+

Implements the L1 Weight Decay Regularization

+
+
diff --git a/develop/doc/genindex.html b/develop/doc/genindex.html index ebeed842738..56726dcf6df 100644 --- a/develop/doc/genindex.html +++ b/develop/doc/genindex.html @@ -217,6 +217,7 @@ B | C | L + | M | P | R | S @@ -242,12 +243,14 @@

L

+

M

+ + +
+

P

@@ -283,6 +296,10 @@
+
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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 95e9ffe811e..0054570a769 100644 --- a/develop/doc_cn/api/v2/fluid/evaluator.html +++ b/develop/doc_cn/api/v2/fluid/evaluator.html @@ -239,6 +239,75 @@

Evaluator

Evaluator

+
+
+class paddle.v2.fluid.evaluator.Evaluator(name, **kwargs)
+

Base Class for all evaluators

+ +++ + + + +
参数:
    +
  • name (str) – The name of evaluator. such as, “accuracy”. Used for generate +temporary variable name.
    • +
  • main_program (Program, optional) – The evaluator should be added to this +main_program. Default default_main_program()
    • +
  • startup_program (Program, optional) – The parameter should be added to this +startup_program. Default default_startup_program()
    • +
+
+
+
+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:
    +
  • suffix (str) – the state suffix.
    • +
  • dtype (str|core.DataType) – the state data type
    • +
  • shape (tuple|list) – the shape of state
    • +
+
+

Returns: State variable

+
+ +
+
diff --git a/develop/doc_cn/api/v2/fluid/initializer.html b/develop/doc_cn/api/v2/fluid/initializer.html index 661f84bae98..8427e9a108a 100644 --- a/develop/doc_cn/api/v2/fluid/initializer.html +++ b/develop/doc_cn/api/v2/fluid/initializer.html @@ -239,21 +239,90 @@

Initializer

Initializer

+
+
+class paddle.v2.fluid.initializer.Initializer
+

Base 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.

+
+

ConstantInitializer

+
+
+class paddle.v2.fluid.initializer.ConstantInitializer(value=0.0)
+

Implements the constant initializer

+
+

UniformInitializer

+
+
+class paddle.v2.fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0)
+

Implements the random uniform distribution initializer

+
+

NormalInitializer

+
+
+class paddle.v2.fluid.initializer.NormalInitializer(loc=0.0, scale=1.0, seed=0)
+

Implements the random Normal(Gaussian) distribution initializer

+
+

XavierInitializer

+
+
+class 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

+
+
[1] Understanding the difficulty of training deep feedforward neural
+
networks. International conference on artificial intelligence and +statistics. +(http://proceedings.mlr.press/v9/glorot10a.html)
+
+
+

MSRAInitializer

+
+
+class 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

+
+
[1] Delving Deep into Rectifiers: Surpassing Human-Level Performance
+
on ImageNet Classification +(https://arxiv.org/abs/1502.01852)
+
+
+
diff --git a/develop/doc_cn/api/v2/fluid/io.html b/develop/doc_cn/api/v2/fluid/io.html index 5b5eeab67a1..a68b6e94253 100644 --- a/develop/doc_cn/api/v2/fluid/io.html +++ b/develop/doc_cn/api/v2/fluid/io.html @@ -239,6 +239,23 @@

IO

is_parameter

+
+
+paddle.v2.fluid.io.is_parameter(var)
+

Check whether the variable is a Parameter.

+

This function checks whether the input variable is a Parameter.

+ +++ + + + + + +
参数:var – The input variable.
返回:boolean result whether the variable is a Parameter.
+
+
diff --git a/develop/doc_cn/api/v2/fluid/layers.html b/develop/doc_cn/api/v2/fluid/layers.html index 98c29be53c5..022532c3cb1 100644 --- a/develop/doc_cn/api/v2/fluid/layers.html +++ b/develop/doc_cn/api/v2/fluid/layers.html @@ -239,273 +239,3185 @@

Layers

fc

+
+
+paddle.v2.fluid.layers.fc(input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None)
+

Fully Connected Layer

+

The fully connected layer can take multiple tensors as its inputs. It +creates a variable (one for each input tensor) called weights for each input +tensor, which represents a fully connected weight matrix from each input +unit to each output unit. The fully connected layer multiplies each input +tensor with its coresponding weight to produce an output Tensor. If +multiple input tensors are given, the results of multiple multiplications +will be sumed up. If bias_attr is not None, a biases variable will be +created and added to the output. Finally, if activation is not None, +it will be applied to the output as well.

+

This process can be formulated as follows:

+
+\[Out = Act({\sum_{i=0}^{N-1}W_iX_i + b})\]
+

In the above equation:

+
    +
  • \(N\): Number of the input.
    • +
  • \(X_i\): The input tensor.
    • +
  • \(W\): The weights created by this layer.
    • +
  • \(b\): The bias parameter created by this layer (if needed).
    • +
  • \(Act\): The activation funtion.
    • +
  • \(Out\): The output tensor.
    • +
+ +++ + + + + + + + + + +
参数:
    +
  • input (Variable|list) – The input tensor(s) to the fully connected layer.
    • +
  • size (int) – The number of output units in the fully connected layer.
    • +
  • num_flatten_dims (int) – The fc layer can accept an input tensor with more +than two dimensions. If this happens, the +multidimensional tensor will first be flattened +into a 2-dimensional matrix. The parameter +num_flatten_dims determines how the input tensor +is flattened: the first num_flatten_dims +dimensions will be flatten to form the first +dimension of the final matrix (height of the +matrix), and the rest rank(X) - num_flatten_dims +dimensions are flattened to form the second +dimension of the final matrix (width of the matrix). +For example, suppose X is a 6-dimensional tensor +with a shape [2, 3, 4, 5, 6], and +num_flatten_dims = 3. Then, the flattened matrix +will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. +By default, num_flatten_dims is set to 1.
    • +
  • param_attr (ParamAttr|list) – The parameter attribute for learnable +parameters/weights of the fully connected +layer.
    • +
  • param_initializer (ParamAttr|list) – The initializer used for the +weight/parameter. If set None, +XavierInitializer() will be used.
    • +
  • bias_attr (ParamAttr|list) – The parameter attribute for the bias parameter +for this layer. If set None, no bias will be +added to the output units.
    • +
  • bias_initializer (ParamAttr|list) – The initializer used for the bias. +If set None, then ConstantInitializer() +will be used.
    • +
  • act (str) – Activation to be applied to the output of the fully connected +layer.
    • +
  • name (str) – Name/alias of the fully connected layer.
    • +
+
返回:

The output tensor variable.

+
返回类型:

Variable

+
Raises:

ValueError – If rank of the input tensor is less than 2.

+
+

Examples

+
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+fc = fluid.layers.fc(input=data, size=1000, act="tanh")
+
+
+
+

embedding

+
+
+paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, param_attr=None, dtype='float32')
+

Embedding Layer

+

This layer is used to lookup a vector of IDs, provided by input, in a lookup table. +The result of this lookup is the embedding of each ID in the input.

+

All the input variables are passed in as local variables to the LayerHelper +constructor.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – Input to the function
    • +
  • size (tuple|list|None) – Shape of the look up table parameter
    • +
  • is_sparse (bool) – Boolean flag that specifying whether the input is sparse
    • +
  • param_attr (ParamAttr) – Parameters for this layer
    • +
  • dtype (np.dtype|core.DataType|str) – The type of data : float32, float_16, int etc
    • +
+
返回:

The tensor variable storing the embeddings of the supplied inputs.

+
返回类型:

Variable

+
+

Examples

+
dict_size = len(dataset.ids)
+data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
+fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
+
+
+
+

dynamic_lstm

+
+
+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')
+

Dynamic LSTM Layer

+

The defalut implementation is diagonal/peephole connection +(https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:

+
+\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i)\\f_t & = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f)\\\tilde{c_t} & = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c)\\o_t & = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o)\\c_t & = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}\\h_t & = o_t \odot act_h(c_t)\end{aligned}\end{align} \]
+

where the \(W\) terms denote weight matrices (e.g. \(W_{xi}\) is +the matrix of weights from the input gate to the input), \(W_{ic}, W_{fc}, W_{oc}\) are diagonal weight matrices for peephole connections. In +our implementation, we use vectors to reprenset these diagonal weight +matrices. The \(b\) terms denote bias vectors (\(b_i\) is the input +gate bias vector), \(\sigma\) is the non-line activations, such as +logistic sigmoid function, and \(i, f, o\) and \(c\) are the input +gate, forget gate, output gate, and cell activation vectors, respectively, +all of which have the same size as the cell output activation vector \(h\).

+

The \(\odot\) is the element-wise product of the vectors. \(act_g\) +and \(act_h\) are the cell input and cell output activation functions +and tanh is usually used for them. \(\tilde{c_t}\) is also called +candidate hidden state, which is computed based on the current input and +the previous hidden state.

+

Set use_peepholes to False to disable peephole connection. The formula +is omitted here, please refer to the paper +http://www.bioinf.jku.at/publications/older/2604.pdf for details.

+

Note that these \(W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\) +operations on the input \(x_{t}\) are NOT included in this operator. +Users can choose to use fully-connect layer before LSTM layer.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – The input of dynamic_lstm layer, which supports +variable-time length input sequence. The underlying +tensor in this Variable is a matrix with shape +(T X 4D), where T is the total time steps in this +mini-batch, D is the hidden size.
    • +
  • size (int) – 4 * hidden size.
    • +
  • param_attr (ParamAttr) –

    The parameter attribute for the learnable +hidden-hidden weights.

    +
      +
    • The shape is (D x 4D), where D is the hidden +size.
      • +
    • Weights = {\(W_{ch}, W_{ih}, W_{fh}, W_{oh}\)}
      • +
    +
    • +
  • bias_attr (ParamAttr) –

    The bias attribute for the learnable bias +weights, which contains two parts, input-hidden +bias weights and peephole connections weights if +setting use_peepholes to True.

    +
      +
    1. use_peepholes = False
      2. +
    +
    +
      +
    • The shape is (1 x 4D).
      • +
    • Biases = {\(b_c, b_i, b_f, b_o\)}.
      • +
    +
    +
      +
    1. use_peepholes = True
      2. +
    +
    +
      +
    • The shape is (1 x 7D).
      • +
    • Biases = { \(b_c, b_i, b_f, b_o, W_{ic}, W_{fc}, W_{oc}\)}.
      • +
    +
    +
    • +
  • use_peepholes (bool) – Whether to enable diagonal/peephole connections, +default True.
    • +
  • is_reverse (bool) – Whether to compute reversed LSTM, default False.
    • +
  • gate_activation (str) – The activation for input gate, forget gate and +output gate. Choices = [“sigmoid”, “tanh”, “relu”, +“identity”], default “sigmoid”.
    • +
  • cell_activation (str) – The activation for cell output. Choices = [“sigmoid”, +“tanh”, “relu”, “identity”], default “tanh”.
    • +
  • candidate_activation (str) – The activation for candidate hidden state. +Choices = [“sigmoid”, “tanh”, “relu”, “identity”], +default “tanh”.
    • +
  • dtype (str) – Data type. Choices = [“float32”, “float64”], default “float32”.
    • +
+
返回:

The hidden state, and cell state of LSTM. The shape of both is (T x D), and lod is the same with the input.

+
返回类型:

tuple

+
+

Examples

+
hidden_dim = 512
+forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
+                               act=None, bias_attr=None)
+forward, _ = fluid.layers.dynamic_lstm(
+    input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
+
+
+
+

data

+
+
+paddle.v2.fluid.layers.data(name, shape, append_batch_size=True, dtype='float32', lod_level=0, type=VarType.LOD_TENSOR, stop_gradient=True)
+

Data Layer

+

This function takes in the input and based on whether data has +to be returned back as a minibatch, it creates the global variable by using +the helper functions. The global variables can be accessed by all the +following operators in the graph.

+

All the input variables of this function are passed in as local variables +to the LayerHelper constructor.

+ +++ + + + + + + + +
参数:
    +
  • name (str) – The name/alias of the function
    • +
  • shape (list) – Tuple declaring the shape.
    • +
  • append_batch_size (bool) – Whether or not to append the data as a batch.
    • +
  • dtype (int|float) – The type of data : float32, float_16, int etc
    • +
  • type (VarType) – The output type. By default it is LOD_TENSOR.
    • +
  • lod_level (int) – The LoD Level. 0 means the input data is not a sequence.
    • +
  • main_program (Program) – Name of the main program that calls this
    • +
  • startup_program (Program) – Name of the startup program
    • +
  • stop_gradient (bool) – A boolean that mentions whether gradient should flow.
    • +
+
返回:

The global variable that gives access to the data.

+
返回类型:

Variable

+
+

Examples

+
data = fluid.layers.data(name='x', shape=[784], dtype='float32')
+
+
+
+

mean

+
+
+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
+
+

mul

+
+
+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:
    +
  • x – (Tensor), The first input tensor of mul op. +Duplicable: False Optional: False
    • +
  • y – (Tensor), The second input tensor of mul op. +Duplicable: False Optional: False
    • +
  • x_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two +dimensions as its inputs. If the input $X$ is a tensor with more +than two dimensions, $X$ will be flattened into a two-dimensional +matrix first. The flattening rule is: the first num_col_dims +will be flattened to form the first dimension of the final matrix +(the height of the matrix), and the rest rank(X) - num_col_dims +dimensions are flattened to form the second dimension of the final +matrix (the width of the matrix). As a result, height of the +flattened matrix is equal to the product of $X$’s first +x_num_col_dims dimensions’ sizes, and width of the flattened +matrix is equal to the product of $X$’s last rank(x) - num_col_dims +dimensions’ size. For example, suppose $X$ is a 6-dimensional +tensor with the shape [2, 3, 4, 5, 6], and x_num_col_dims = 3. +Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = +[24, 30].
    • +
  • y_num_col_dims (INT) – (int, default 1), The mul_op can take tensors with more than two, +dimensions as its inputs. If the input $Y$ is a tensor with more +than two dimensions, $Y$ will be flattened into a two-dimensional +matrix first. The attribute y_num_col_dims determines how $Y$ is +flattened. See comments of x_num_col_dims for more details.
    • +
+
返回:

(Tensor), The output tensor of mul op.

+
+
+

elementwise_add

+
+
+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 +set to index of the start dimension to broadcast $Y$ onto $X$.

+
+
For 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
+
+
+
+
+

Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

+ +++ + + + + + +
参数:
    +
  • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    • +
+
返回:

The output of elementwise op.

+
+
+

elementwise_sub

+
+
+paddle.v2.fluid.layers.elementwise_sub(**kwargs)
+

Limited Elementwise Sub 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 +set to index of the start dimension to broadcast $Y$ onto $X$.

+
+
For 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
+
+
+
+
+

Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

+ +++ + + + + + +
参数:
    +
  • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    • +
+
返回:

The output of elementwise op.

+
+
+

elementwise_mul

+
+
+paddle.v2.fluid.layers.elementwise_mul(**kwargs)
+

Limited Elementwise Mul Operator.

+

The equation is:

+

$$Out = X odotY$$

+

$X$ 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 +set to index of the start dimension to broadcast $Y$ onto $X$.

+
+
For 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
+
+
+
+
+

Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

+ +++ + + + + + +
参数:
    +
  • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    • +
+
返回:

The output of elementwise op.

+
+
+

elementwise_div

+
+
+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 +set to index of the start dimension to broadcast $Y$ onto $X$.

+
+
For 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
+
+
+
+
+

Either of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details) +information. However, the output only shares the LoD information with input $X$.

+ +++ + + + + + +
参数:
    +
  • x – (Tensor), The first input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • y – (Tensor), The second input tensor of elementwise op. +Duplicable: False Optional: False
    • +
  • axis (INT) – (int, default -1). The start dimension index for broadcasting Y onto X.
    • +
+
返回:

The output of elementwise op.

+
+
+

dropout

+
+
+paddle.v2.fluid.layers.dropout(x, dropout_prob, is_test=False, seed=0, **kwargs)
+
+

reshape

+
+
+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 2-D tensor:

+
+
[[1, 2, 3, 4]]
+

One dimension in the target shape can be set -1, representing that its +size is unknown. In this case, the real dimension will be infered from +the original shape of Input(X) and other dimensions in the target shape.

+ +++ + + + + + +
参数:
    +
  • x – The input tensor of reshape operator. +Duplicable: False Optional: False
    • +
  • shape (INTS) – (vector<int>) Target shape of reshape operator.
    • +
+
返回:

The output tensor of reshape operator.

+
+
+

sigmoid

+
+
+paddle.v2.fluid.layers.sigmoid(**kwargs)
+

Sigmoid Activation Operator

+

$$out = frac{1}{1 + e^{-x}}$$

+ +++ + + + + + +
参数:x – Input of Sigmoid operator +Duplicable: False Optional: False
返回:Output of Sigmoid operator
+
+

scale

+
+
+paddle.v2.fluid.layers.scale(**kwargs)
+

Scale operator

+

$$Out = scale*X$$

+ +++ + + + + + +
参数:
    +
  • x – (Tensor) Input tensor of scale operator. +Duplicable: False Optional: False
    • +
  • scale (FLOAT) – (float, default 1.0)The scaling factor of the scale operator.
    • +
+
返回:

(Tensor) Output tensor of scale operator.

+
+
+

transpose

+
+
+paddle.v2.fluid.layers.transpose(**kwargs)
+

Transpose Operator.

+

The input tensor will be permuted according to the axis values given. +The op functions is similar to how numpy.transpose works in python.

+

For example:

+
+
input = numpy.arange(6).reshape((2,3))
+
+the input is:
+
+array([[0, 1, 2],
+       [3, 4, 5]])
+
+given axis is:
+
+[1, 0]
+
+output = input.transpose(axis)
+
+then the output is:
+
+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:
    +
  • x – (Tensor)The input tensor, tensors with rank at most 6 are supported +Duplicable: False Optional: False
    • +
  • axis (INTS) – (vector<int>)A list of values, and the size of the list should be the same with the input tensor rank, the tensor will permute the axes according the the values given
    • +
+
返回:

(Tensor)The output tensor

+
+
+

sigmoid_cross_entropy_with_logits

cast

+
+
+paddle.v2.fluid.layers.cast(x, dtype)
+

This function takes in the input with input_dtype +and casts it to the output_dtype as the output.

+
+

concat

+
+
+paddle.v2.fluid.layers.concat(input, axis=0)
+

Concat

+

This function concatenates the input along the axis mentioned +and returns that as the output.

+ +++ + + + + + + + +
参数:
    +
  • input (list) – List of tensors to be concatenated
    • +
  • axis (int) – Integer axis along which the tensors will be concatenated
    • +
+
返回:

Output variable of the concatenation

+
返回类型:

Variable

+
+

Examples

+
+

sums

+
+
+paddle.v2.fluid.layers.sums(input, out=None)
+

This function performs the sum operation on the input and returns the +result as the output.

+ +++ + + + + + + + +
参数:input (Variable|list) – The input tensor that has the elements +that need to be summed up.
返回:
+
The tensor type variable that has the sum of input
+
written to it.
+
+
返回类型:Variable
+

Examples

+
+

linear_chain_crf

+
+
+paddle.v2.fluid.layers.linear_chain_crf(input, label, param_attr=None)
+
+

assign

+
+
+paddle.v2.fluid.layers.embedding(input, size, is_sparse=False, param_attr=None, dtype='float32')
+

Embedding Layer

+

This layer is used to lookup a vector of IDs, provided by input, in a lookup table. +The result of this lookup is the embedding of each ID in the input.

+

All the input variables are passed in as local variables to the LayerHelper +constructor.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – Input to the function
    • +
  • size (tuple|list|None) – Shape of the look up table parameter
    • +
  • is_sparse (bool) – Boolean flag that specifying whether the input is sparse
    • +
  • param_attr (ParamAttr) – Parameters for this layer
    • +
  • dtype (np.dtype|core.DataType|str) – The type of data : float32, float_16, int etc
    • +
+
返回:

The tensor variable storing the embeddings of the supplied inputs.

+
返回类型:

Variable

+
+

Examples

+
dict_size = len(dataset.ids)
+data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
+fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
+
+
+
+

split_lod_tensor

+
+
+paddle.v2.fluid.layers.split_lod_tensor(input, mask, level=0)
+

split_lod_tensor

+

This function takes in an input that contains the complete lod information, +and takes in a mask which is used to mask certain parts of the input. +The output is the true branch and the false branch with the mask applied to +the input at a certain level in the tensor.

+ +++ + + + + + + + +
参数:
    +
  • input (tuple|list|None) – The input tensor that contains complete +lod information needed to construct the output.
    • +
  • mask (list) – A bool column vector which masks the input.
    • +
  • level (int) – The specific lod level to rank.
    • +
+
返回:

The true branch of tensor as per the mask applied to input. +Variable: The false branch of tensor as per the mask applied to input.

+
返回类型:

Variable

+
+

Examples

+
x = layers.data(name='x', shape=[1])
+x.persistable = True
+
+y = layers.data(name='y', shape=[1])
+y.persistable = True
+
+out_true, out_false = layers.split_lod_tensor(
+      input=x, mask=y, level=level)
+
+
+
+

merge_lod_tensor

+
+
+paddle.v2.fluid.layers.merge_lod_tensor(in_true, in_false, x, mask, level=0)
+

merge_lod_tensor

+

This function takes in an input \(x\), the True branch, the False +branch and a binary \(mask\). Using this information, this function +merges the True and False branches of the tensor into a single Output +at a certain lod level indiacted by \(level\).

+ +++ + + + + + + + +
参数:
    +
  • in_true (tuple|list|None) – The True branch to be merged.
    • +
  • in_false (tuple|list|None) – The False branch to be merged.
    • +
  • x (tuple|list|None) – The input tensor that contains complete +lod information needed to construct the output.
    • +
  • mask (list) – A bool column vector which masks the input.
    • +
  • level (int) – The specific lod level to rank.
    • +
+
返回:

The merged output tensor.

+
返回类型:

Variable

+
+

Examples

+
x = layers.data(
+            name='x', shape=[1], dtype='float32', stop_gradient=False)
+y = layers.data(
+      name='y', shape=[1], dtype='bool', stop_gradient=False)
+
+level = 0
+
+out_true, out_false = layers.split_lod_tensor(
+      input=x, mask=y, level=level)
+out = layers.merge_lod_tensor(
+      in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
+
+
+
+

cos_sim

+
+
+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.

+
+

cross_entropy

+
+
+paddle.v2.fluid.layers.cross_entropy(input, label, **kwargs)
+

Cross Entropy Layer

+

This layer computes the cross entropy between input and label. It supports +both standard cross-entropy and soft-label cross-entropy loss computation.

+
    +
  1. +
    One-hot cross-entropy:
    +

    soft_label = False, Label[i, 0] indicates the class index for sample i:

    +
    +\[Y[i] = -\log(X[i, Label[i]])\]
    +
    +
    +
    2. +
  2. +
    Soft-label cross-entropy:
    +

    soft_label = True, Label[i, j] indicates the soft label of class j +for sample i:

    +
    +\[Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}\]
    +
    +
    +

    Please make sure that in this case the summation of each row of label +equals one.

    +
    3. +
  3. +
    One-hot cross-entropy with vecterized label:
    +

    As a special case of 2), when each row of ‘label’ has only one +non-zero element which is equal to 1, soft-label cross-entropy degenerates +to a one-hot cross-entropy with one-hot label representation.

    +
    +
    +
    4. +
+ +++ + + + + + + + +
参数:
    +
  • input (Variable|list) – a 2-D tensor with shape [N x D], where N is the +batch size and D is the number of classes. This input is a probability +computed by the previous operator, which is almost always the result +of a softmax operator.
    • +
  • label (Variable|list) – the ground truth which is a 2-D tensor. When +soft_label is set to False, label is a tensor<int64> with shape +[N x 1]. When soft_label is set to True, label is a +tensor<float/double> with shape [N x D].
    • +
  • soft_label (bool, via **kwargs) – a flag indicating whether to interpretate +the given labels as soft labels, default False.
    • +
+
返回:

A 2-D tensor with shape [N x 1], the cross entropy loss.

+
Raises:

ValueError – 1) the 1st dimension of input and label are not equal; 2) when soft_label == True, and the 2nd dimension of input and label are not equal; 3) when soft_label == False, and the 2nd dimension of label is not 1.

+
+

Examples

+
predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
+cost = fluid.layers.cross_entropy(input=predict, label=label)
+
+
+
+

square_error_cost

+
+
+paddle.v2.fluid.layers.square_error_cost(input, label, **kwargs)
+

Square error cost layer

+

This layer accepts input predictions and target label and returns the squared error cost. +For predictions, \(X\), and target labels, \(Y\), the equation is:

+
+\[Out = (X - Y)^2\]
+

In the above equation:

+
+
    +
  • \(X\): Input predictions, a tensor.
    • +
  • \(Y\): Input labels, a tensor.
    • +
  • \(Out\): Output value, same shape with \(X\).
    • +
+
+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – Input tensor, has predictions.
    • +
  • label (Variable) – Label tensor, has target labels.
    • +
+
返回:

The tensor variable storing the element-wise squared error difference of input and label.

+
返回类型:

Variable

+
+

Examples

+
y = layers.data(name='y', shape=[1], dtype='float32')
+y_predict = layers.data(name='y_predict', shape=[1], dtype='float32')
+cost = layers.square_error_cost(input=y_predict, label=y)
+
+
+
+

accuracy

+
+
+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.

+
+

sequence_conv

+
+
+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)
+

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.

+
+

conv2d

+
+
+paddle.v2.fluid.layers.conv2d(input, num_filters, filter_size, stride=None, padding=None, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None)
+

Convlution2D Layer

+

The convolution2D layer calculates the output based on the input, filter +and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output) +are in NCHW format. Where N is batch size, C is the number of channels, H is the height +of the feature, and W is the width of the feature. +The details of convolution layer, please refer UFLDL’s convolution, . +If bias attribution and activation type are provided, bias is added to the output of the convolution, +and the corresponding activation function is applied to the final result. +For each input \(X\), the equation is:

+
+\[Out = \sigma (W \ast X + b)\]
+

In the above equation:

+
+
    +
  • \(X\): Input value, a tensor with NCHW format.
    • +
  • \(W\): Filter value, a tensor with MCHW format.
    • +
  • \(\ast\): Convolution operation.
    • +
  • \(b\): Bias value, a 2-D tensor with shape [M, 1].
    • +
  • \(\sigma\): Activation function.
    • +
  • \(Out\): Output value, the shape of \(Out\) and \(X\) may be different.
    • +
+
+

Example

+
+
Input:
+

Input shape: $(N, C_{in}, H_{in}, W_{in})$

+

Filter shape: $(C_{out}, C_{in}, H_f, W_f)$

+
+
Output:
+
Output shape: $(N, C_{out}, H_{out}, W_{out})$
+
+

Where

+
+\[\begin{split}H_{out}&= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\ +W_{out}&= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1\end{split}\]
+ +++ + + + + + + + + + +
参数:
    +
  • input (Variable) – The input image with [N, C, H, W] format.
    • +
  • num_filters (int) – The number of filter. It is as same as the output +image channel.
    • +
  • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, +it must contain two integers, (filter_size_H, filter_size_W). +Otherwise, the filter will be a square.
    • +
  • stride (int|tuple) – The stride size. If stride is a tuple, it must +contain two integers, (stride_H, stride_W). Otherwise, the +stride_H = stride_W = stride. Default: stride = 1.
    • +
  • padding (int|tuple) – The padding size. If padding is a tuple, it must +contain two integers, (padding_H, padding_W). Otherwise, the +padding_H = padding_W = padding. Default: padding = 0.
    • +
  • groups (int) – The groups number of the Conv2d Layer. According to grouped +convolution in Alex Krizhevsky’s Deep CNN paper: when group=2, +the first half of the filters is only connected to the first half +of the input channels, while the second half of the filters is only +connected to the second half of the input channels. Default: groups=1
    • +
  • param_attr (ParamAttr) – The parameters to the Conv2d Layer. Default: None
    • +
  • bias_attr (ParamAttr) – Bias parameter for the Conv2d layer. Default: None
    • +
  • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn +library is installed. Default: True
    • +
  • act (str) – Activation type. Default: None
    • +
+
返回:

The tensor variable storing the convolution and non-linearity activation result.

+
返回类型:

Variable

+
Raises:

ValueError – If the shapes of input, filter_size, stride, padding and groups mismatch.

+
+

Examples

+
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
+conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
+
+
+
+

sequence_pool

+
+
+paddle.v2.fluid.layers.sequence_pool(input, pool_type, **kwargs)
+

This function add the operator for sequence pooling. +It pools features of all time-steps of each instance, and is applied +on top of the input using pool_type mentioned in the parameters.

+

It supports four pool_type:

+
    +
  • average: \(Out[i] = \frac{\sum_i X_i}{N}\)
    • +
  • sum: \(Out[i] = \sum_jX_{ij}\)
    • +
  • sqrt: \(Out[i] = \frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}\)
    • +
  • max: \(Out[i] = max(X_i)\)
    • +
+
x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+
+for different pool_type:
+  average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
+  sum    : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
+  sqrt   : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
+             6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
+  max    : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
+
+
+ +++ + + + + + +
参数:
    +
  • input (variable) – The input variable which is a LoDTensor.
    • +
  • pool_type (string) – The pooling type of sequence_pool. +It supports average, sum, sqrt and max.
    • +
+
返回:

The sequence pooling variable which is a Tensor.

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[7, 1],
+                 dtype='float32', lod_level=1)
+avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
+sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
+sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
+max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
+
+
+
+

sequence_first_step

+
+
+paddle.v2.fluid.layers.sequence_first_step(input, **kwargs)
+

This funciton get the first step of sequence.

+
x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+  out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
+
+
+ +++ + + + + + +
参数:input (variable) – The input variable which is a LoDTensor.
返回:The sequence’s first step variable which is a Tensor.
+

Examples

+
x = fluid.layers.data(name='x', shape=[7, 1],
+                 dtype='float32', lod_level=1)
+x_first_step = fluid.layers.sequence_first_step(input=x)
+
+
+
+

sequence_last_step

+
+
+paddle.v2.fluid.layers.sequence_last_step(input, **kwargs)
+

This funciton get the last step of sequence.

+
x is a 1-level LoDTensor:
+  x.lod = [[0, 2, 5, 7]]
+  x.data = [1, 3, 2, 4, 6, 5, 1]
+  x.dims = [7, 1]
+
+then output is a Tensor:
+  out.dim = [3, 1]
+  with condition len(x.lod[-1]) - 1 == out.dims[0]
+  out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
+
+
+ +++ + + + + + +
参数:input (variable) – The input variable which is a LoDTensor.
返回:The sequence’s last step variable which is a Tensor.
+

Examples

+
x = fluid.layers.data(name='x', shape=[7, 1],
+                 dtype='float32', lod_level=1)
+x_last_step = fluid.layers.sequence_last_step(input=x)
+
+
+
+

pool2d

+
+
+paddle.v2.fluid.layers.pool2d(input, pool_size, pool_type, pool_stride=None, pool_padding=None, global_pooling=False, use_cudnn=True, name=None)
+

This function adds the operator for pooling in 2 dimensions, using the +pooling configurations mentioned in input parameters.

+
+

batch_norm

+
+
+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', name=None)
+

This function helps create an operator to implement +the BatchNorm layer using the configurations from the input parameters.

+
+

beam_search_decode

+
+
+paddle.v2.fluid.layers.beam_search_decode(ids, scores, name=None)
+
+

lod_rank_table

+
+
+paddle.v2.fluid.layers.lod_rank_table(x, level=0)
+

LoD Rank Table Operator. Given an input variable x and a level number +of LoD, this layer creates a LodRankTable object. A LoDRankTable object +contains a list of bi-element tuples. Each tuple consists of an index and +a length, both of which are int type. Refering to specified level of LoD, +the index is the sequence index number and the length representes the +sequence length. Please note that the list is ranked in descending order by +the length. The following is an example:

+
+
x is a LoDTensor:
+    x.lod = [[0,                2, 3],
+             [0,             5, 6, 7]]
+    x.data = [a, b, c, d, e, f, g]
+
+1. set level to 0:
+    Create lod rank table:
+        lod_rank_table_obj = lod_rank_table(x, level=0)
+
+    Get:
+        lod_rank_table_obj.items() = [(0, 2), (1, 1)]
+
+2. set level to 1:
+    Create lod rank table:
+        lod_rank_table_obj = lod_rank_table(x, level=1)
+
+    Get:
+        lod_rank_table_obj.items() = [(0, 5), (1, 1), (2, 1)]
+
+
+
+ +++ + + + + + + + +
参数:
    +
  • x (Variable) – Input variable, a LoDTensor based which to create the lod +rank table.
    • +
  • level (int) – Specify the LoD level, on which to create the lod rank +table.
    • +
+
返回:

The created LoDRankTable object.

+
返回类型:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[10],
+                dtype='float32', lod_level=1)
+out = layers.lod_rank_table(x=x, level=0)
+
+
+
+

max_sequence_len

+
+
+paddle.v2.fluid.layers.max_sequence_len(rank_table)
+

Max Sequence Len Operator. Given a LoDRankTable object, this layer +returns the max length of a batch of sequences. In fact, a LoDRankTable +object contains a list of tuples(<sequence index, sequence length>) and +the list is already sorted by sequence length in descending order, so the +operator just returns the sequence length of the first tuple element.

+ +++ + + + + + + + +
参数:rank_table (Variable) – Input variable which is a LoDRankTable object.
返回:The max length of sequence.
返回类型:Variable
+

Examples

+
x = fluid.layers.data(name='x', shape=[10],
+                dtype='float32', lod_level=1)
+rank_table = layers.lod_rank_table(x=x, level=0)
+max_seq_len = layers.max_sequence_len(rank_table)
+
+
+
+

topk

+
+
+paddle.v2.fluid.layers.topk(input, k)
+

topk

+

This function performs the operation that selects the k entries in the input +vector and outputs their values and indices as vectors. Thus topk_out[j] is +the j-th largest entry in input, and its index is topk_indices[j]

+ +++ + + + + + + + +
参数:
    +
  • input (Variable|list) – The input tensor that has all the data.
    • +
  • k (int) – The number of top elements that the function will pick.
    • +
+
返回:

+
The variable of type array that contains the k largest entries
+

from input.

+
+
Variable: The variable of type array that contains the indices of k
+

largest entries from input.

+
+
+

+
返回类型:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[10])
+k = 5
+array = fluid.layers.topk(x, k)
+
+
+
+

lod_tensor_to_array

+
+
+paddle.v2.fluid.layers.lod_tensor_to_array(x, table)
+

Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.

+ +++ + + + + + + + +
参数:
    +
  • x (Variable|list) – The LOD tensor to be converted to a LOD tensor array.
    • +
  • table (ParamAttr|list) – The variable that stores the level of lod +which is ordered by sequence length in +descending order.
    • +
+
返回:

+
The variable of type array that has been converted from a
+

tensor.

+
+
+

+
返回类型:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[10])
+table = fluid.layers.lod_rank_table(x, level=0)
+array = fluid.layers.lod_tensor_to_array(x, table)
+
+
+
+

array_to_lod_tensor

+
+
+paddle.v2.fluid.layers.array_to_lod_tensor(x, table)
+

Convert a LoD_Tensor_Aarry to an LoDTensor.

+ +++ + + + + + + + +
参数:
    +
  • x (Variable|list) – The lod tensor array to be converted to a tensor.
    • +
  • table (ParamAttr|list) – The variable that stores the level of lod +which is ordered by sequence length in +descending order.
    • +
+
返回:

+
The variable of type tensor that has been converted
+

from an array.

+
+
+

+
返回类型:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[10])
+table = fluid.layers.lod_rank_table(x, level=0)
+array = fluid.layers.lod_tensor_to_array(x, table)
+lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
+
+
+
+

fill_constant

+
+
+paddle.v2.fluid.layers.fill_constant(shape, dtype, value, force_cpu=False, out=None)
+

fill_constant

+

This function creates a tensor with specified shape and dtype, and +initializes it with a constant specifed by value.

+

The attribute stop_gradient of the created tensor is set to True.

+ +++ + + + + + + + +
参数:
    +
  • shape (tuple|list|None) – Shape of the output tensor.
    • +
  • dtype (np.dtype|core.DataType|str) – Data type of the output tensor.
    • +
  • value (float) – The constant value used to initialize the output tensor.
    • +
  • out (Variable) – The output tensor.
    • +
+
返回:

The tensor variable storing the output.

+
返回类型:

Variable

+
+

Examples

+
data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
+
+
+
+

fill_constant_batch_size_like

+
+
+paddle.v2.fluid.layers.fill_constant_batch_size_like(input, shape, dtype, value, input_dim_idx=0, output_dim_idx=0)
+

fill_constant_batch_size_like

+

This function creates a tensor of specified shape, dtype and batch size, +and initializes this with a constant supplied in value. The batch size is +obtained from the input tensor.

+

It also sets stop_gradient to True.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – Tensor whose dimensions will be used to get batch size
    • +
  • shape (tuple|list|None) – Shape of output tensor
    • +
  • dtype (np.dtype|core.DataType|str) – Data type of output tensor
    • +
  • value (float) – Constant value to initialize the output tensor
    • +
  • input_dim_idx (int) – Index of input’s batch size dimension
    • +
  • output_dim_idx (int) – Index of output’s batch size dimension
    • +
+
返回:

The tensor variable storing the output

+
返回类型:

Variable

+
+

Examples

+
data = fluid.layers.fill_constant_batch_size_like(
+    input=like, shape=[1], value=0, dtype='int64')
+
+
+
+

ones

+
+
+paddle.v2.fluid.layers.ones(shape, dtype)
+

ones

+

This function creates a tensor of specified shape and +dtype, and initializes this with 1.

+

It also sets stop_gradient to True.

+ +++ + + + + + + + +
参数:
    +
  • shape (tuple|list|None) – Shape of output tensor
    • +
  • dtype (np.dtype|core.DataType|str) – Data type of output tensor
    • +
+
返回:

The tensor variable storing the output

+
返回类型:

Variable

+
+

Examples

+
data = fluid.layers.ones(shape=[1], dtype='int64')
+
+
+
+

zeros

+
+
+paddle.v2.fluid.layers.zeros(shape, dtype)
+

zeros

+

This function creates a tensor of specified shape and +dtype, and initializes this with 0.

+

It also sets stop_gradient to True.

+ +++ + + + + + + + +
参数:
    +
  • shape (tuple|list|None) – Shape of output tensor
    • +
  • dtype (np.dtype|core.DataType|str) – Data type of output tensor
    • +
+
返回:

The tensor variable storing the output

+
返回类型:

Variable

+
+

Examples

+
data = fluid.layers.zeros(shape=[1], dtype='int64')
+
+
+
+

increment

+
+
+paddle.v2.fluid.layers.increment(x, value=1.0, in_place=True)
+

This function performs an operation that increments each value in the +input \(x\) by an amount: \(value\) as mentioned in the input +parameter. This operation is performed in-place by default.

+ +++ + + + + + + + +
参数:
    +
  • x (Variable|list) – The tensor that has the input values.
    • +
  • value (float) – The amount by which the values should be incremented.
    • +
  • in_place (bool) – If the increment should be performed in-place.
    • +
+
返回:

+
The tensor variable storing the transformation of
+

element-wise increment of each value in the input.

+
+
+

+
返回类型:

Variable

+
+

Examples

+
data = fluid.layers.data(name='data', shape=[32, 32], dtype='float32')
+data = fluid.layers.increment(x=data, value=3.0, in_place=True)
+
+
+
+

array_write

+
+
+paddle.v2.fluid.layers.array_write(x, i, array=None)
+

This function writes the given input variable to the specified position +indicating by the arrary index to an output LOD_TENSOR_ARRAY. If the +output LOD_TENSOR_ARRAY is not given(None), a new one will be created and +returned.

+ +++ + + + + + + + +
参数:
    +
  • x (Variable|list) – The input tensor from which the data will be read.
    • +
  • i (Variable|list) – The index of the output LOD_TENSOR_ARRAY, pointing to +the position to which the input tensor will be +written.
    • +
  • array (Variable|list) – The output LOD_TENSOR_ARRAY to which the input +tensor will be written. If this parameter is +NONE, a new LOD_TENSOR_ARRAY will be created and +returned.
    • +
+
返回:

The output LOD_TENSOR_ARRAY where the input tensor is written.

+
返回类型:

Variable

+
+

Examples

+
+

create_array

+
+
+paddle.v2.fluid.layers.create_array(dtype)
+

This function creates an array of type \(LOD_TENSOR_ARRAY\) using the +LayerHelper.

+ +++ + + + + + + + +
参数:dtype (int|float) – The data type of the elements in the array.
返回:The tensor variable storing the elements of data type.
返回类型:Variable
+

Examples

+
data = fluid.layers.create_array(dtype='float32')
+
+
+
+

less_than

+
+
+paddle.v2.fluid.layers.less_than(x, y, cond=None, **ignored)
+

Less than

+

This layer returns the truth value of \(x < y\) elementwise.

+ +++ + + + + + + + +
参数:
    +
  • x (Variable) – First operand of less_than
    • +
  • y (Variable) – Second operand of less_than
    • +
  • cond (Variable|None) – Optional output variable to store the result of less_than
    • +
+
返回:

The tensor variable storing the output of less_than.

+
返回类型:

Variable

+
+

Examples

+
less = fluid.layers.less_than(x=label, y=limit)
+
+
+
+

array_read

+
+
+paddle.v2.fluid.layers.array_read(array, i)
+

This function performs the operation to read the data in as an +LOD_TENSOR_ARRAY. +:param array: The input tensor that will be written to an array. +:type array: Variable|list +:param i: The subscript index in tensor array, that points the

+
+
place where data will be written to.
+ +++ + + + + + +
返回:The tensor type variable that has the data written to it.
返回类型:Variable
+

Examples

+
+

shrink_memory

+
+
+paddle.v2.fluid.layers.shrink_memory(x, i, table)
+

This function creates an operator to shrink_rnn_memory using the RankTable +as mentioned in the input parameter.

+
+

array_length

+
+
+paddle.v2.fluid.layers.array_length(array)
+

This function performs the operation to find the length of the input +LOD_TENSOR_ARRAY.

+ +++ + + + + + + + +
参数:array (LOD_TENSOR_ARRAY) – The input array that will be used +to compute the length.
返回:The length of the input LoDTensorArray.
返回类型:Variable
+

Examples

+
+

conv2d_transpose

+
+
+paddle.v2.fluid.layers.conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, dilation=None, param_attr=None, use_cudnn=True, name=None)
+

The transpose of conv2d layer.

+

This layer is also known as deconvolution layer.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – The input image with [N, C, H, W] format.
    • +
  • num_filters (int) – The number of filter. It is as same as the output +image channel.
    • +
  • output_size (int|tuple|None) – The output image size. If output size is a +tuple, it must contain two integers, (image_H, image_W). This +parameter only works when filter_size is None.
    • +
  • filter_size (int|tuple|None) – The filter size. If filter_size is a tuple, +it must contain two integers, (filter_size_H, filter_size_W). +Otherwise, the filter will be a square. None if use output size to +calculate filter_size
    • +
  • padding (int|tuple) – The padding size. If padding is a tuple, it must +contain two integers, (padding_H, padding_W). Otherwise, the +padding_H = padding_W = padding.
    • +
  • stride (int|tuple) – The stride size. If stride is a tuple, it must +contain two integers, (stride_H, stride_W). Otherwise, the +stride_H = stride_W = stride.
    • +
  • dilation (int|tuple) – The dilation size. If dilation is a tuple, it must +contain two integers, (dilation_H, dilation_W). Otherwise, the +dilation_H = dilation_W = dilation.
    • +
  • param_attr – Parameter Attribute.
    • +
  • use_cudnn (bool) – Use cudnn kernel or not, it is valid only when the cudnn +library is installed. Default: True
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
返回:

Output image.

+
返回类型:

Variable

+
+
+

sequence_expand

+
+
+paddle.v2.fluid.layers.sequence_expand(x, y, name=None)
+

Sequence Expand Layer. This layer will expand the input variable x +according to LoD information of y. And the following examples will +explain how sequence_expand works:

+
* Case 1
+    x is a LoDTensor:
+        x.lod = [[0,       2, 3],
+                 [0, 1,    3, 4]]
+        x.data = [a, b, c, d]
+        x.dims = [4, 1]
+
+    y is a LoDTensor:
+        y.lod = [[0,    2,    4],
+                 [0, 3, 6, 7, 8]]
+
+    with condition len(y.lod[-1]) - 1 == x.dims[0]
+
+    then output is a 2-level LoDTensor:
+        out.lod = [[0,                2,    4],
+                   [0,       3,       6, 7, 8]]
+        out.data = [a, a, a, b, b, b, c, d]
+        out.dims = [8, 1]
+
+* Case 2
+    x is a Tensor:
+        x.data = [a, b, c]
+        x.dims = [3, 1]
+
+    y is a LoDTensor:
+        y.lod = [[0, 2, 3, 6]]
+
+    with condition len(y.lod[-1]) - 1 == x.dims[0]
+
+    then output is a 1-level LoDTensor:
+        out.lod = [[0,    2, 3,      6]]
+        out.data = [a, a, b, c, c, c]
+        out.dims = [6, 1]
+
+
+ +++ + + + + + + + +
参数:
    +
  • x (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • y (Variable) – The input variable which is a LoDTensor.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
返回:

The expanded variable which is a LoDTensor.

+
返回类型:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[10], dtype='float32')
+y = fluid.layers.data(name='y', shape=[10, 20],
+                 dtype='float32', lod_level=1)
+out = layers.sequence_expand(x=x, y=y)
+
+
+
+

gru_unit

+
+
+paddle.v2.fluid.layers.gru_unit(input, hidden, size, weight=None, bias=None, activation='tanh', gate_activation='sigmoid')
+

GRU unit layer. The equation of a gru step is:

+
+
+\[ \begin{align}\begin{aligned}u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)\\r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)\\m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)\\h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})\end{aligned}\end{align} \]
+
+

The inputs of gru unit includes \(z_t\), \(h_{t-1}\). In terms +of the equation above, the \(z_t\) is split into 3 parts - +\(xu_t\), \(xr_t\) and \(xm_t\). This means that in order to +implement a full GRU unit operator for an input, a fully +connected layer has to be applied, such that \(z_t = W_{fc}x_t\).

+

The terms \(u_t\) and \(r_t\) represent the update and reset gates +of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is +an intermediate candidate hidden output, which is denoted by \(m_t\). +This layer has three outputs \(h_t\), \(dot(r_t, h_{t-1})\) +and concatenation of \(u_t\), \(r_t\) and \(m_t\).

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – The fc transformed input value of current step.
    • +
  • hidden (Variable) – The hidden value of lstm unit from previous step.
    • +
  • size (integer) – The input dimension value.
    • +
  • weight (ParamAttr) – The weight parameters for gru unit. Default: None
    • +
  • bias (ParamAttr) – The bias parameters for gru unit. Default: None
    • +
  • activation (string) – The activation type for cell (actNode). Default: ‘tanh’
    • +
  • gate_activation (string) – The activation type for gates (actGate). Default: ‘sigmoid’
    • +
+
返回:

The hidden value, reset-hidden value and gate values.

+
返回类型:

tuple

+
+

Examples

+
# assuming we have x_t_data and prev_hidden of size=10
+x_t = fluid.layers.fc(input=x_t_data, size=30)
+hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
+                                       hidden = prev_hidden)
+
+
+
+

lstm_unit

+
+
+paddle.v2.fluid.layers.lstm_unit(x_t, hidden_t_prev, cell_t_prev, forget_bias=0.0, param_attr=None, bias_attr=None, name=None)
+

Lstm unit layer. The equation of a lstm step is:

+
+
+\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)\\f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)\\o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]
+
+

The inputs of lstm unit include \(x_t\), \(h_{t-1}\) and +\(c_{t-1}\). The 2nd dimensions of \(h_{t-1}\) and \(c_{t-1}\) +should be same. The implementation separates the linear transformation and +non-linear transformation apart. Here, we take \(i_t\) as an example. +The linear transformation is applied by calling a fc layer and the +equation is:

+
+
+\[L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i\]
+
+

The non-linear transformation is applied by calling lstm_unit_op and the +equation is:

+
+
+\[i_t = \sigma(L_{i_t})\]
+
+

This layer has two outputs including \(h_t\) and \(o_t\).

+ +++ + + + + + + + + + +
参数:
    +
  • x_t (Variable) – The input value of current step, a 2-D tensor with shape +M x N, M for batch size and N for input size.
    • +
  • hidden_t_prev (Variable) – The hidden value of lstm unit, a 2-D tensor +with shape M x S, M for batch size and S for size of lstm unit.
    • +
  • cell_t_prev (Variable) – The cell value of lstm unit, a 2-D tensor with +shape M x S, M for batch size and S for size of lstm unit.
    • +
  • forget_bias (float) – The forget bias of lstm unit.
    • +
  • param_attr (ParamAttr) – The attributes of parameter weights, used to set +initializer, name etc.
    • +
  • bias_attr (ParamAttr) – The attributes of bias weights, if not False, +bias weights will be created and be set to default value.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
返回:

The hidden value and cell value of lstm unit.

+
返回类型:

tuple

+
Raises:

ValueError – The ranks of x_t, hidden_t_prev and cell_t_prev not be 2 or the 1st dimensions of x_t, hidden_t_prev and cell_t_prev not be the same or the 2nd dimensions of hidden_t_prev and cell_t_prev not be the same.

+
+

Examples

+
x_t = fluid.layers.fc(input=x_t_data, size=10)
+prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30)
+prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
+hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
+                                       hidden_t_prev=prev_hidden,
+                                       cell_t_prev=prev_cell)
+
+
+
+

sequence_softmax

+
+
+paddle.v2.fluid.layers.sequence_softmax(**kwargs)
+

Sequence Softmax Operator.

+

SequenceSoftmaxOp computes the softmax activation among all time-steps for each +sequence. The dimension of each time-step should be 1. Thus, the shape of +input Tensor can be either [N, 1] or [N], where N is the sum of the length +of all sequences.

+

The algorithm works as follows:

+
+
for i-th sequence in a mini-batch:
+

$$ +Out(X[lod[i]:lod[i+1]], :) = frac{exp(X[lod[i]:lod[i+1], :])} {sum(exp(X[lod[i]:lod[i+1], :]))} +$$

+

For example, for a mini-batch of 3 sequences with variable-length, +each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7], +then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :] +and N turns out to be 7.

+ +++ + + + + + +
参数:x – (LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension of length 1. +Duplicable: False Optional: False
返回:(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension of length 1.
+
+

reduce_sum

+
+
+paddle.v2.fluid.layers.reduce_sum(input, dim=None, keep_dim=False, name=None)
+

Computes the sum of tensor elements over the given dimension.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • dim (int|None) – The dimension along which the sum is performed. If +None, sum all elements of input and return a +Tensor variable with a single element, otherwise must be in the +range \([-rank(input), rank(input))\). If \(dim < 0\), +the dimension to reduce is \(rank + dim\).
    • +
  • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
返回:

The reduced Tensor variable.

+
返回类型:

Variable

+
+

Examples

+
# x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_sum(x)  # [3.5]
+fluid.layers.reduce_sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
+fluid.layers.reduce_sum(x, dim=-1)  # [1.9, 1.6]
+fluid.layers.reduce_sum(x, dim=1, keep_dim=True)  # [[1.9], [1.6]]
+
+
+
+

reduce_mean

+
+
+paddle.v2.fluid.layers.reduce_mean(input, dim=None, keep_dim=False, name=None)
+

Computes the mean of tensor elements over the given dimension.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • dim (int|None) – The dimension along which the mean is computed. If +None, compute the mean over all elements of input +and return a Tensor variable with a single element, otherwise +must be in the range \([-rank(input), rank(input))\). If +\(dim < 0\), the dimension to reduce is \(rank + dim\).
    • +
  • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
返回:

The reduced Tensor variable.

+
返回类型:

Variable

+
+

Examples

+
# x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_mean(x)  # [0.4375]
+fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
+fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
+fluid.layers.reduce_mean(x, dim=1, keep_dim=True)  # [[0.475], [0.4]]
+
+
+
+

reduce_max

+
+
+paddle.v2.fluid.layers.reduce_max(input, dim=None, keep_dim=False, name=None)
+

Computes the maximum of tensor elements over the given dimension.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • dim (int|None) – The dimension along which the maximum is computed. +If None, compute the maximum over all elements of +input and return a Tensor variable with a single element, +otherwise must be in the range \([-rank(input), rank(input))\). +If \(dim < 0\), the dimension to reduce is \(rank + dim\).
    • +
  • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
返回:

The reduced Tensor variable.

+
返回类型:

Variable

+
+

Examples

+
# x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_max(x)  # [0.9]
+fluid.layers.reduce_max(x, dim=0)  # [0.2, 0.3, 0.6, 0.9]
+fluid.layers.reduce_max(x, dim=-1)  # [0.9, 0.7]
+fluid.layers.reduce_max(x, dim=1, keep_dim=True)  # [[0.9], [0.7]]
+
+
+
+

reduce_min

+
+
+paddle.v2.fluid.layers.reduce_min(input, dim=None, keep_dim=False, name=None)
+

Computes the minimum of tensor elements over the given dimension.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • dim (int|None) – The dimension along which the minimum is computed. +If None, compute the minimum over all elements of +input and return a Tensor variable with a single element, +otherwise must be in the range \([-rank(input), rank(input))\). +If \(dim < 0\), the dimension to reduce is \(rank + dim\).
    • +
  • keep_dim (bool) – Whether to reserve the reduced dimension in the +output Tensor. The result tensor will have one fewer dimension +than the input unless keep_dim is true.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
返回:

The reduced Tensor variable.

+
返回类型:

Variable

+
+

Examples

+
# x is a Tensor variable with following elements:
+#    [[0.2, 0.3, 0.5, 0.9]
+#     [0.1, 0.2, 0.6, 0.7]]
+# Each example is followed by the correspending output tensor.
+fluid.layers.reduce_min(x)  # [0.1]
+fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
+fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
+fluid.layers.reduce_min(x, dim=1, keep_dim=True)  # [[0.2], [0.1]]
+
+
+
+

split

+
+
+paddle.v2.fluid.layers.split(input, num_or_sections, dim=-1, name=None)
+

Split the input tensor into multiple sub-tensors.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • num_or_sections (int|list) – If num_or_sections is an integer, +then the integer indicates the number of equal sized sub-tensors +that the tensor will be divided into. If num_or_sections +is a list of integers, the length of list indicates the number of +sub-tensors and the integers indicate the sizes of sub-tensors’ +dim dimension orderly.
    • +
  • dim (int) – The dimension along which to split. If \(dim < 0\), the +dimension to split along is \(rank(input) + dim\).
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
返回:

The list of segmented tensor variables.

+
返回类型:

List

+
+

Examples

+
# x is a Tensor variable with shape [3, 9, 5]:
+x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
+x0.shape  # [3, 3, 5]
+x1.shape  # [3, 3, 5]
+x2.shape  # [3, 3, 5]
+x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
+x0.shape  # [3, 2, 5]
+x1.shape  # [3, 3, 5]
+x2.shape  # [3, 4, 5]
+
+
+
+

matmul

+
+
+paddle.v2.fluid.layers.matmul(x, y, transpose_x=False, transpose_y=False, name=None)
+

Applies matrix multiplication to two tensors. Currently, the input +tensors’ rank can be any, but when the rank of anyone inputs is +bigger than 3, this two inputs’ rank should be equal.

+

The actual behavior depends on the shapes of \(x\), \(y\) and the +flag values of transpose_x, transpose_y. Specifically:

+
    +
  • If a transpose flag is specified, the last two dimensions of the tensor +are transposed. If the tensor is rank-1 of shape \([D]\), then for +\(x\) it is treated as \([1, D]\) in nontransposed form and as +\([D, 1]\) in transposed form, whereas for \(y\) it is the +opposite: It is treated as \([D, 1]\) in nontransposed form and as +\([1, D]\) in transposed form.
    • +
  • After transpose, the two tensors are 2-D or n-D and matrix multiplication +performs in the following way.
      +
    • If both are 2-D, they are multiplied like conventional matrices.
      • +
    • If either is n-D, it is treated as a stack of matrices residing in the +last two dimensions and a batched matrix multiply supporting broadcast +applies on the two tensors.
      • +
    +
    • +
+

Also note that if the raw tensor \(x\) or \(y\) is rank-1 and +nontransposed, the prepended or appended dimension \(1\) will be +removed after matrix multiplication.

+ +++ + + + + + + + +
参数:
    +
  • x (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • y (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • transpose_x (bool) – Whether to transpose \(x\) before multiplication.
    • +
  • transpose_y (bool) – Whether to transpose \(y\) before multiplication.
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
返回:

The product Tensor variable.

+
返回类型:

Variable

+
+

Examples

+
# Examples to clarify shapes of the inputs and output
+# x: [B, ..., M, K], y: [B, ..., K, N]
+fluid.layers.matmul(x, y)  # out: [B, ..., M, N]
+# x: [B, M, K], y: [B, K, N]
+fluid.layers.matmul(x, y)  # out: [B, M, N]
+# x: [B, M, K], y: [K, N]
+fluid.layers.matmul(x, y)  # out: [B, M, N]
+# x: [B, M, K], y: [K]
+fluid.layers.matmul(x, y)  # out: [B, M]
+# x: [M, K], y: [K, N]
+fluid.layers.matmul(x, y)  # out: [M, N]
+# x: [K], y: [K]
+fluid.layers.matmul(x, y)  # out: [1]
+# x: [M], y: [N]
+
+fluid.layers.matmul(x, y, True, True)  # out: [M, N]
+
+
+
+

logsigmoid

+
+
+paddle.v2.fluid.layers.logsigmoid(**kwargs)
+

Logsigmoid Activation Operator

+

$$out = log frac{1}{1 + e^{-x}}$$

+ +++ + + + + + +
参数:x – Input of LogSigmoid operator +Duplicable: False Optional: False
返回:Output of LogSigmoid operator
+
+

exp

+
+
+paddle.v2.fluid.layers.exp(**kwargs)
+

Exp Activation Operator.

+

$out = e^x$

+ +++ + + + + + +
参数:x – Input of Exp operator +Duplicable: False Optional: False
返回:Output of Exp operator
+
+

relu

+
+
+paddle.v2.fluid.layers.relu(**kwargs)
+

Relu Activation Operator.

+

$out = max(x, 0)$

+ +++ + + + + + +
参数:x – Input of Relu operator +Duplicable: False Optional: False
返回:Output of Relu operator
+
+

tanh

+
+
+paddle.v2.fluid.layers.tanh(**kwargs)
+

Tanh Activation Operator.

+

$$out = frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$

+ +++ + + + + + +
参数:x – Input of Tanh operator +Duplicable: False Optional: False
返回:Output of Tanh operator
+
+

tanh_shrink

+
+
+paddle.v2.fluid.layers.tanh_shrink(**kwargs)
+

TanhShrink Activation Operator.

+

$$out = x - frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$

+ +++ + + + + + +
参数:x – Input of TanhShrink operator +Duplicable: False Optional: False
返回:Output of TanhShrink operator
+
+

softshrink

+
+
+paddle.v2.fluid.layers.softshrink(**kwargs)
+

Softshrink Activation Operator.

+

$$ +out = begin{cases}

+
+
x - lambda, text{if } x > lambda \ +x + lambda, text{if } x < -lambda \ +0, text{otherwise} +end{cases}
+

$$

+ +++ + + + + + +
参数:
    +
  • x – Input of Softshrink operator +Duplicable: False Optional: False
    • +
  • lambda (FLOAT) – non-negative offset
    • +
+
返回:

Output of Softshrink operator

+
+
+

sqrt

+
+
+paddle.v2.fluid.layers.sqrt(**kwargs)
+

Sqrt Activation Operator.

+

$out = sqrt{x}$

+ +++ + + + + + +
参数:x – Input of Sqrt operator +Duplicable: False Optional: False
返回:Output of Sqrt operator
+
+

abs

+
+
+paddle.v2.fluid.layers.abs(**kwargs)
+

Abs Activation Operator.

+

$out = |x|$

+ +++ + + + + + +
参数:x – Input of Abs operator +Duplicable: False Optional: False
返回:Output of Abs operator
+
+

ceil

+
+
+paddle.v2.fluid.layers.ceil(**kwargs)
+

Ceil Activation Operator.

+

$out = ceil(x)$

+ +++ + + + + + +
参数:x – Input of Ceil operator +Duplicable: False Optional: False
返回:Output of Ceil operator
+
+

floor

+
+
+paddle.v2.fluid.layers.floor(**kwargs)
+

Floor Activation Operator.

+

$out = floor(x)$

+ +++ + + + + + +
参数:x – Input of Floor operator +Duplicable: False Optional: False
返回:Output of Floor operator
+
+

round

+
+
+paddle.v2.fluid.layers.round(**kwargs)
+

Round Activation Operator.

+

$out = [x]$

+ +++ + + + + + +
参数:x – Input of Round operator +Duplicable: False Optional: False
返回:Output of Round operator
+
+

reciprocal

+
+
+paddle.v2.fluid.layers.reciprocal(**kwargs)
+

Reciprocal Activation Operator.

+

$$out = frac{1}{x}$$

+ +++ + + + + + +
参数:x – Input of Reciprocal operator +Duplicable: False Optional: False
返回:Output of Reciprocal operator
+
+

log

+
+
+paddle.v2.fluid.layers.log(**kwargs)
+

Log Activation Operator.

+

$out = ln(x)$

+

Natural logarithm of x.

+ +++ + + + + + +
参数:x – Input of Log operator +Duplicable: False Optional: False
返回:Output of Log operator
+
+

square

+
+
+paddle.v2.fluid.layers.square(**kwargs)
+

Square Activation Operator.

+

$out = x^2$

+ +++ + + + + + +
参数:x – Input of Square operator +Duplicable: False Optional: False
返回:Output of Square operator
+
+

softplus

+
+
+paddle.v2.fluid.layers.softplus(**kwargs)
+

Softplus Activation Operator.

+

$out = ln(1 + e^{x})$

+ +++ + + + + + +
参数:x – Input of Softplus operator +Duplicable: False Optional: False
返回:Output of Softplus operator
+
+

softsign

+
+
+paddle.v2.fluid.layers.softsign(**kwargs)
+

Softsign Activation Operator.

+

$$out = frac{x}{1 + |x|}$$

+ +++ + + + + + +
参数:x – Input of Softsign operator +Duplicable: False Optional: False
返回:Output of Softsign operator
+
+

brelu

+
+
+paddle.v2.fluid.layers.brelu(**kwargs)
+

BRelu Activation Operator.

+

$out = max(min(x, t_{min}), t_{max})$

+ +++ + + + + + +
参数:
    +
  • x – Input of BRelu operator +Duplicable: False Optional: False
    • +
  • t_min (FLOAT) – The min marginal value of BRelu
    • +
  • t_max (FLOAT) – The max marginal value of BRelu
    • +
+
返回:

Output of BRelu operator

+
+
+

leaky_relu

+
+
+paddle.v2.fluid.layers.leaky_relu(**kwargs)
+

LeakyRelu Activation Operator.

+

$out = max(x, alpha * x)$

+ +++ + + + + + +
参数:
    +
  • x – Input of LeakyRelu operator +Duplicable: False Optional: False
    • +
  • alpha (FLOAT) – The small negative slope
    • +
+
返回:

Output of LeakyRelu operator

+
+
+

soft_relu

+
+
+paddle.v2.fluid.layers.soft_relu(**kwargs)
+

SoftRelu Activation Operator.

+

$out = ln(1 + exp(max(min(x, threshold), threshold))$

+ +++ + + + + + +
参数:
    +
  • x – Input of SoftRelu operator +Duplicable: False Optional: False
    • +
  • threshold (FLOAT) – The threshold value of SoftRelu
    • +
+
返回:

Output of SoftRelu operator

+
+
+

elu

+
+
+paddle.v2.fluid.layers.elu(**kwargs)
+

ELU Activation Operator.

+

Applies the following element-wise computation on the input according to +https://arxiv.org/abs/1511.07289.

+

$out = max(0, x) + min(0, alpha * (e^x - 1))$

+ +++ + + + + + +
参数:
    +
  • x – Input of ELU operator +Duplicable: False Optional: False
    • +
  • alpha (FLOAT) – The alpha value of ELU
    • +
+
返回:

Output of ELU operator

+
+
+

relu6

+
+
+paddle.v2.fluid.layers.relu6(**kwargs)
+

Relu6 Activation Operator.

+

$out = min(max(0, x), 6)$

+ +++ + + + + + +
参数:
    +
  • x – Input of Relu6 operator +Duplicable: False Optional: False
    • +
  • threshold (FLOAT) – The threshold value of Relu6
    • +
+
返回:

Output of Relu6 operator

+
+
+

pow

+
+
+paddle.v2.fluid.layers.pow(**kwargs)
+

Pow Activation Operator.

+

$out = x^{factor}$

+ +++ + + + + + +
参数:
    +
  • x – Input of Pow operator +Duplicable: False Optional: False
    • +
  • factor (FLOAT) – The exponential factor of Pow
    • +
+
返回:

Output of Pow operator

+
+
+

hard_shrink

+
+
+paddle.v2.fluid.layers.hard_shrink(**kwargs)
+

HardShrink Activation Operator.

+

$$ +out = begin{cases}

+
+
x, text{if } x > lambda \ +x, text{if } x < -lambda \ +0, text{otherwise} +end{cases}
+

$$

+ +++ + + + + + +
参数:
    +
  • x – Input of HardShrink operator +Duplicable: False Optional: False
    • +
  • threshold (FLOAT) – The value of threshold for HardShrink
    • +
+
返回:

Output of HardShrink operator

+
+
+

thresholded_relu

+
+
+paddle.v2.fluid.layers.thresholded_relu(**kwargs)
+

ThresholdedRelu Activation Operator.

+

$$ +out = begin{cases}

+
+
x, text{if } x > threshold \ +0, text{otherwise} +end{cases}
+

$$

+ +++ + + + + + +
参数:
    +
  • x – Input of ThresholdedRelu operator +Duplicable: False Optional: False
    • +
  • threshold (FLOAT) – The threshold location of activation
    • +
+
返回:

Output of ThresholdedRelu operator

+
+
+

hard_sigmoid

+
+
+paddle.v2.fluid.layers.hard_sigmoid(**kwargs)
+

HardSigmoid Activation Operator.

+

Segment-wise linear approximation of sigmoid(https://arxiv.org/abs/1603.00391), +which is much faster than sigmoid.

+

$out = max(0, min(1, slope * x + shift))$

+

The slope should be positive. The offset can be either positive or negative. +The default slope and shift are set according to the above reference. +It is recommended to use the defaults for this activation.

+ +++ + + + + + +
参数:
    +
  • x – Input of HardSigmoid operator +Duplicable: False Optional: False
    • +
  • slope (FLOAT) – Slope for linear approximation of sigmoid
    • +
  • offset (FLOAT) – Offset for linear approximation of sigmoid
    • +
+
返回:

Output of HardSigmoid operator

+
+
+

swish

+
+
+paddle.v2.fluid.layers.swish(**kwargs)
+

Swish Activation Operator.

+

$$out = frac{x}{1 + e^{- beta x}}$$

+ +++ + + + + + +
参数:
    +
  • x – Input of Swish operator +Duplicable: False Optional: False
    • +
  • beta (FLOAT) – Constant beta of swish operator
    • +
+
返回:

Output of Swish operator

+
+
+

l2_normalize

+
+
+paddle.v2.fluid.layers.l2_normalize(x, axis, epsilon=1e-12, name=None)
+

L2 normalize Layer

+

The l2 normalize layer normalizes x along dimension axis using an L2 +norm. For a 1-D tensor (dim is fixed to 0), this layer computes

+

output = x / sqrt(max(sum(x**2), epsilon))

+

For x with more dimensions, this layer independently normalizes each 1-D +slice along dimension axis.

+ +++ + + + + + + + +
参数:
    +
  • x (Variable|list) – The input tensor to l2_normalize layer.
    • +
  • axis (int) – Dimension along which to normalize the input.
    • +
  • epsilon (float) – A lower bound value for x‘s l2 norm. sqrt(epsilon) will +be used as the divisor if the l2 norm of x is less than +sqrt(epsilon).
    • +
  • name (str|None) – A name for this layer(optional). If set None, the layer +will be named automatically.
    • +
+
返回:

The output tensor variable.

+
返回类型:

Variable

+
+

Examples

+
data = fluid.layers.data(name="data",
+                         shape=(3, 17, 13),
+                         dtype="float32")
+fc = fluid.layers.l2_normalize(x=data, axis=1)
+
+
+
+

sequence_reshape

+
+
+paddle.v2.fluid.layers.sequence_reshape(input, new_dim)
+

Sequence Reshape Layer

+

This layer will rearrange the input sequences. The new dimension is set by +user. Length of each sequence is computed according to original length, +original dimension and new dimension. The following example will help to +illustrate the function of this layer:

+
x is a LoDTensor:
+    x.lod  = [[0, 2, 6]]
+    x.data = [[1, 2], [3, 4],
+              [5, 6], [7, 8], [9, 10], [11, 12]]
+    x.dims = [6, 2]
+
+set new_dim = 4
+
+then out is a LoDTensor:
+    out.lod  = [[0, 1, 3]]
+    out.data = [[1, 2, 3, 4],
+                [5, 6, 7, 8], [9, 10, 11, 12]]
+    out.dims = [3, 4]
+
+
+

Currently, only 1-level LoDTensor is supported and please make sure +(original length * original dimension) can be divided by new dimension with +no remainder for each sequence.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor +with shape being [N, M] where M for dimension.
    • +
  • new_dim (int) – New dimension which the input LoDTensor is reshaped to.
    • +
+
返回:

Reshaped LoDTensor according to new dimension.

+
返回类型:

Variable

+
+

Examples

+
x = fluid.layers.data(name='x', shape=[5, 20],
+                  dtype='float32', lod_level=1)
+x_reshaped = layers.sequence_reshape(input=x, new_dim=10)
+
+
+
+
diff --git a/develop/doc_cn/api/v2/fluid/nets.html b/develop/doc_cn/api/v2/fluid/nets.html index 37bb78c92ae..e03a8acec3f 100644 --- a/develop/doc_cn/api/v2/fluid/nets.html +++ b/develop/doc_cn/api/v2/fluid/nets.html @@ -239,18 +239,118 @@

Nets

simple_img_conv_pool

+
+
+paddle.v2.fluid.nets.simple_img_conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act, param_attr=None, pool_type='max', use_cudnn=True)
+
+

img_conv_group

+
+
+paddle.v2.fluid.nets.img_conv_group(input, conv_num_filter, pool_size, conv_padding=1, conv_filter_size=3, conv_act=None, param_attr=None, conv_with_batchnorm=False, conv_batchnorm_drop_rate=None, pool_stride=1, pool_type=None, use_cudnn=True)
+

Image Convolution Group, Used for vgg net.

+
+

sequence_conv_pool

+
+
+paddle.v2.fluid.nets.sequence_conv_pool(input, num_filters, filter_size, param_attr=None, act='sigmoid', pool_type='max')
+
+

glu

+
+
+paddle.v2.fluid.nets.glu(input, dim=-1)
+

The gated linear unit composed by split, sigmoid activation and elementwise +multiplication. Specifically, Split the input into two equal sized parts +\(a\) and \(b\) along the given dimension and then compute as +following:

+
+
+\[{GLU}(a, b)= a \otimes \sigma(b)\]
+
+

Refer to Language Modeling with Gated Convolutional Networks.

+ +++ + + + + + + + +
参数:
    +
  • input (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • dim (int) – The dimension along which to split. If \(dim < 0\), the +dimension to split along is \(rank(input) + dim\).
    • +
+
返回:

The Tensor variable with half the size of input.

+
返回类型:

Variable

+
+

Examples

+
# x is a Tensor variable with shape [3, 6, 9]
+fluid.nets.glu(input=x, dim=1)  # shape of output: [3, 3, 9]
+
+
+
+

dot_product_attention

+
+
+paddle.v2.fluid.nets.dot_product_attention(querys, keys, values)
+

The dot-product attention.

+

Attention mechanism can be seen as mapping a query and a set of key-value +pairs to an output. The output is computed as a weighted sum of the values, +where the weight assigned to each value is computed by a compatibility +function (dot-product here) of the query with the corresponding key.

+

The dot-product attention can be implemented through (batch) matrix +multipication as follows:

+
+
+\[Attention(Q, K, V)= softmax(QK^\mathrm{T})V\]
+
+

Refer to Attention Is All You Need.

+

Note that batch data containing sequences with different lengths is not +supported by this because of the (batch) matrix multipication.

+ +++ + + + + + + + +
参数:
    +
  • query (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • key (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
  • value (Variable) – The input variable which is a Tensor or LoDTensor.
    • +
+
返回:

The Tensor variables representing the output and attention scores.

+
返回类型:

tuple

+
+

Examples

+
# Suppose q, k, v are tensor variables with the following shape:
+# q: [3, 5, 9], k: [3, 6, 9], v: [3, 6, 10]
+out, attn_scores = fluid.nets.dot_product_attention(q, k, v)
+out.shape  # [3, 5, 10]
+attn_scores.shape  # [3, 5, 6]
+
+
+
+
diff --git a/develop/doc_cn/api/v2/fluid/optimizer.html b/develop/doc_cn/api/v2/fluid/optimizer.html index a54e5d64d5c..61295441576 100644 --- a/develop/doc_cn/api/v2/fluid/optimizer.html +++ b/develop/doc_cn/api/v2/fluid/optimizer.html @@ -239,24 +239,105 @@

Optimizer

Optimizer

+
+
+class paddle.v2.fluid.optimizer.Optimizer(global_step=None, regularization=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:
    +
  • loss – the target that this optimization is for.
    • +
  • parameters_and_grads – a list of (variable, gradient) pair to update.
    • +
+
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() and +create_optimization_pass() into one.

+
+ +
+

SGDOptimizer

+
+
+class paddle.v2.fluid.optimizer.SGDOptimizer(learning_rate, **kwargs)
+

Simple SGD optimizer without any state.

+
+

MomentumOptimizer

+
+
+class paddle.v2.fluid.optimizer.MomentumOptimizer(learning_rate, momentum, use_nesterov=False, **kwargs)
+

Simple Momentum optimizer with velocity state

+
+

AdagradOptimizer

+
+
+class paddle.v2.fluid.optimizer.AdagradOptimizer(learning_rate, epsilon=1e-06, **kwargs)
+

Simple Adagrad optimizer with moment state

+
+

AdamOptimizer

+
+
+class paddle.v2.fluid.optimizer.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)
+

Implements the Adam Optimizer

+
+

AdamaxOptimizer

+
+
+class paddle.v2.fluid.optimizer.AdamaxOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)
+

Implements the Adamax Optimizer

+
+

DecayedAdagradOptimizer

+
+
+class paddle.v2.fluid.optimizer.DecayedAdagradOptimizer(learning_rate, decay=0.95, epsilon=1e-06, **kwargs)
+

Simple Decayed Adagrad optimizer with moment state

+
+
diff --git a/develop/doc_cn/api/v2/fluid/profiler.html b/develop/doc_cn/api/v2/fluid/profiler.html index e8a9c75f2e0..88a35e02d06 100644 --- a/develop/doc_cn/api/v2/fluid/profiler.html +++ b/develop/doc_cn/api/v2/fluid/profiler.html @@ -239,6 +239,35 @@

Profiler

Profiler

+
+
+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:
    +
  • output_file (string) – The output file name, the result will be +written into this file.
    • +
  • output_mode (string) – The output mode has Key-Value pair format and +Comma separated values format. It should be ‘kvp’ or ‘csv’.
    • +
  • config (list of string) – The profiler options and counters can refer +to “Compute Command Line Profiler User Guide”.
    • +
+
+
+
diff --git a/develop/doc_cn/api/v2/fluid/regularizer.html b/develop/doc_cn/api/v2/fluid/regularizer.html index 4780357cf50..e88ceb3a4aa 100644 --- a/develop/doc_cn/api/v2/fluid/regularizer.html +++ b/develop/doc_cn/api/v2/fluid/regularizer.html @@ -239,12 +239,36 @@

Regularizer

WeightDecayRegularizer

+
+
+class paddle.v2.fluid.regularizer.WeightDecayRegularizer
+

Base 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

+
+

L2DecayRegularizer

+
+
+class paddle.v2.fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)
+

Implements the L2 Weight Decay Regularization

+
+
-
-

L1DecayRegularizer

+
+

L1DecayRegularizer

+
+
+class paddle.v2.fluid.regularizer.L1DecayRegularizer(regularization_coeff=0.0)
+

Implements the L1 Weight Decay Regularization

+
+
diff --git a/develop/doc_cn/genindex.html b/develop/doc_cn/genindex.html index 1ad0423105e..60c09dc632e 100644 --- a/develop/doc_cn/genindex.html +++ b/develop/doc_cn/genindex.html @@ -236,6 +236,7 @@ B | C | L + | M | P | R | S @@ -261,12 +262,14 @@

L

+

M

+ + +
+

P

+ + + +
  • paddle.v2.image (模块)
    • @@ -298,6 +313,10 @@ +
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    + paddle.v2.fluid.regularizer +
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