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_col_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 x_num_col_dims = 3. Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. By default, x_num_col_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')

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 using the helper functions. The global variables can be accessed by all the following operations and layers in the graph.

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

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 the starting dimension index for broadcasting Y onto X.

example

shape(X) = (2, 3, 4, 5), shape(Y) = (,) shape(X) = (2, 3, 4, 5), shape(Y) = (5,) shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0

Both the input X and Y can carry the LoD (Level of Details) information, or not. But the output only shares the LoD information with input X.

Parameters:	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 starting 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 the starting dimension index for broadcasting Y onto X.

example

shape(X) = (2, 3, 4, 5), shape(Y) = (,) shape(X) = (2, 3, 4, 5), shape(Y) = (5,) shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5) shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1 shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0

Both the input X and Y can carry the LoD (Level of Details) information, or not. But the output only shares the LoD information with input X.

Parameters:	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 starting dimension index for broadcasting Y onto X
Returns:	The output of elementwise op

dropout¶

paddle.v2.fluid.layers.dropout(**kwargs)

Dropout Operator.

Dropout refers to randomly dropping out units in a nerual network. It is a regularization technique for reducing overfitting by preventing neuron co-adaption during training. The dropout operator randomly set (according to the given dropout probability) the outputs of some units to zero, while others are set equal to their corresponding inputs.

Parameters:	x – The input of dropout op. Duplicable: False Optional: False dropout_prob (FLOAT) – Probability of setting units to zero. is_test (BOOLEAN) – True if in test phase. seed (INT) – Dropout random seed.
Returns:	The output of dropout op.

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 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)

merge_lod_tensor¶

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

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.

One-hot cross-entropy:

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

\[Y[i] = -\log(X[i, Label[i]])\]
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.
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.

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): This functions returns the squared error cost using the input and label. The output is appending the op to do the above.

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, 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 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): 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'): 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)

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. Reffering 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)

This function performs the operation that converts an LOD_Tensor to: an array.

Parameters:

x (Variable|list) – The tensor that needs to be converted to an 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)

This function performs the operations that converts an array to: an LOD_Tensor.

Parameters:

x (Variable|list) – The array that needs 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, out=None)

fill_constant

This function creates a tensor of specified shape and dtype, and initializes this with a constant supplied in value.

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 value (float) – Constant value to initialize the output tensor out (Variable) – Output Variable to initialize
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(shape=[1], value=0, dtype='int64')

ones¶

paddle.v2.fluid.layers.ones(shape, dtype): This function performs the same function as fill_constant() declared above with the constant value being 1.0.

zeros¶

paddle.v2.fluid.layers.zeros(shape, dtype): This function performs the same function as fill_constant() declared above with the constant value being 0.0.

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 performs the operation to write the data out as an LOD_TENSOR_ARRAY.

Parameters:	x (Variable\|list) – The input tensor from which the data will be read. i (Variable\|list) – The subscript index in tensor array, that points the place from which data will be read. array (Variable\|list) – The data can be read into this variable if this is assigned.
Returns:	The tensor type variable that has the data written to it.
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)

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. main_program (Program) – the main program startup_program (Program) – the startup program
Returns:	Output image.
Return type:	Variable

sequence_expand¶

paddle.v2.fluid.layers.sequence_expand(x, y)

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.
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)

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)

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

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

The inputs of lstm unit includes $x_t$, $h_{t-1}$ and $c_{t-1}$. 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:

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

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. hidden_t_prev (Variable) – The hidden value of lstm unit. cell_t_prev (Variable) – The cell value 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.
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.

Examples

x_t = fluid.layers.fc(input=x_t_data, size=10)
prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=20)
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)

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.
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)

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.
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)

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.
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)

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.
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]]