paddle.v2.fluid.evaluator.Evaluator(name, **kwargs)Base Class for all evaluators
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states¶list – The list of state variables. states will be reset to zero -when reset is invoked.
-metrics¶list – The list of metrics variables. They will be calculate -every mini-batch
-reset(executor, reset_program=None)reset metric states at the begin of each pass/user specified batch
-eval(executor, eval_program=None)Evaluate the statistics merged by multiple mini-batches.
-create_state(suffix, dtype, shape)Create state variable.
-NOTE: It is not a public API.
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Returns: State variable
-paddle.v2.fluid.initializer.InitializerBase class for variable initializers
-Defines the common interface of variable initializers. -They add operations to the init program that are used -to initialize variables. Users should not use this class -directly, but need to use one of its implementations.
-paddle.v2.fluid.initializer.ConstantInitializer(value=0.0)Implements the constant initializer
-paddle.v2.fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0)Implements the random uniform distribution initializer
-paddle.v2.fluid.initializer.NormalInitializer(loc=0.0, scale=1.0, seed=0)Implements the random Normal(Gaussian) distribution initializer
-paddle.v2.fluid.initializer.XavierInitializer(uniform=True, fan_in=None, fan_out=None, seed=0)Implements the Xavier initializer
-This class implements the Xavier weight initializer from the paper -Understanding the difficulty of training deep feedforward neural -networks[1] by Xavier Glorot and Yoshua Bengio.
-This initializer is designed to keep the scale of the gradients -approximately same in all the layers. In case of Uniform distribution, -the range is [-x, x], where x = sqrt(6 / (fan_in + fan_out)). -In case of Normal distribution, the mean is 0 and the standard deviation -is sqrt(2/ (fan_in + fan_out)).
-References
-paddle.v2.fluid.initializer.MSRAInitializer(uniform=True, fan_in=None, seed=0)Implements the MSRA initializer a.k.a. Kaiming Initializer
-This class implements the weight initialization from the paper -Delving Deep into Rectifiers: Surpassing Human-Level Performance on -ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren -and Jian Sun. This is a robust initialization method that particularly -considers the rectifier nonlinearities. In case of Uniform distribution, -the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal -distribution, the mean is 0 and the standard deviation -is sqrt(2/ fan_in).
-References
-paddle.v2.fluid.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. | -
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| Returns: | boolean result whether the variable is a Parameter. | -
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:
-In the above equation:
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| Returns: | The output tensor variable. - |
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| Return type: | Variable - |
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Examples
-data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
-fc = fluid.layers.fc(input=data, size=1000, act="tanh")
-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.
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| Returns: | The tensor variable storing the embeddings of the supplied inputs. - |
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| Return type: | Variable - |
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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])
-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:
-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.
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| 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. - |
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| Return type: | tuple - |
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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)
-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.
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| Returns: | The global variable that gives access to the data. - |
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| Return type: | Variable - |
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Examples
-data = fluid.layers.data(name='x', shape=[784], dtype='float32')
-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 | -
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| Returns: | The output of mean op | -
paddle.v2.fluid.layers.mul(**kwargs)Mul Operator.
-This operator is used to perform matrix multiplication for input $X$ and $Y$.
-The equation is:
-$$Out = X * Y$$
-Both the input $X$ and $Y$ can carry the LoD (Level of Details) information, -or not. But the output only shares the LoD information with input $X$.
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| Returns: | (Tensor), The output tensor of mul op. - |
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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 $X$.
-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 $X$.
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| Returns: | The output of elementwise op. - |
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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 $X$.
-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 $X$.
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| Returns: | The output of elementwise op. - |
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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 $X$.
-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: |
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| Returns: | The output of elementwise op. - |
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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 $X$.
-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: |
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| Returns: | The output of elementwise op. - |
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paddle.v2.fluid.layers.dropout(x, dropout_prob, is_test=False, seed=0, **kwargs)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.
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| Returns: | The output tensor of reshape operator. - |
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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 | -
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| Returns: | Output of Sigmoid operator | -
paddle.v2.fluid.layers.scale(**kwargs)Scale operator
-$$Out = scale*X$$
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| Returns: | (Tensor) Output tensor of scale operator. - |
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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)
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| Returns: | (Tensor)The output tensor - |
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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.
-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: |
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| Returns: | Output variable of the concatenation - |
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| Return type: | Variable - |
-
Examples
-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. | -
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| Returns: |
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| Return type: | Variable | -
Examples
-paddle.v2.fluid.layers.linear_chain_crf(input, label, param_attr=None)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: |
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| Returns: | The tensor variable storing the embeddings of the supplied inputs. - |
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| Return type: | Variable - |
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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])
-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.
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| 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. - |
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| Return type: | Variable - |
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Examples
-x = layers.data(name='x', shape=[1])
-x.persistable = True
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-y = layers.data(name='y', shape=[1])
-y.persistable = True
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-out_true, out_false = layers.split_lod_tensor(
- input=x, mask=y, level=level)
-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: |
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| Returns: | The merged output tensor. - |
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| Return type: | Variable - |
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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)
-paddle.v2.fluid.layers.cos_sim(X, Y, **kwargs)This function performs the cosine similarity between two tensors -X and Y and returns that as the output.
-paddle.v2.fluid.layers.cross_entropy(input, label, **kwargs)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.
-soft_label = False, Label[i, 0] indicates the class index for sample i:
-soft_label = True, Label[i, j] indicates the soft label of class j -for sample i:
-Please make sure that in this case the summation of each row of label -equals one.
-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: |
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| Returns: | A 2-D tensor with shape [N x 1], the cross entropy loss. - |
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| 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. - |
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Examples
-predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
-cost = fluid.layers.cross_entropy(input=predict, label=label)
-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:
-In the above equation:
----
-- \(X\): Input predictions, a tensor.
-- \(Y\): Input labels, a tensor.
-- \(Out\): Output value, same shape with \(X\).
-
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| Returns: | The tensor variable storing the element-wise squared error difference of input and label. - |
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| Return type: | Variable - |
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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)
-paddle.v2.fluid.layers.accuracy(input, label, k=1, correct=None, total=None, **kwargs)This function computes the accuracy using the input and label. -The output is the top_k inputs and their indices.
-paddle.v2.fluid.layers.sequence_conv(input, num_filters, filter_size=3, filter_stride=1, padding=None, bias_attr=None, param_attr=None, act=None)This function creates the op for sequence_conv, using the inputs and -other convolutional configurations for the filters and stride as given -in the input parameters to the function.
-paddle.v2.fluid.layers.conv2d(input, num_filters, filter_size, stride=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:
-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 shape: $(N, C_{in}, H_{in}, W_{in})$
-Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
-Where
-| Parameters: |
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| Returns: | The tensor variable storing the convolution and non-linearity activation result. - |
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| Return type: | Variable - |
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| Raises: |
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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")
-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:
-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: |
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| 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')
-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)
-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)
-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.
-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.
-paddle.v2.fluid.layers.beam_search_decode(ids, scores, name=None)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: |
|
-
|---|---|
| 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)
-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)
-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: |
|
-
|---|---|
| Returns: |
|
-
| Return type: | Variable - |
-
Examples
-x = fluid.layers.data(name='x', shape=[10])
-k = 5
-array = fluid.layers.topk(x, k)
-paddle.v2.fluid.layers.lod_tensor_to_array(x, table)Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.
-| Parameters: |
|
-
|---|---|
| Returns: |
|
-
| 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)
-paddle.v2.fluid.layers.array_to_lod_tensor(x, table)Convert a LoD_Tensor_Aarry to an LoDTensor.
-| Parameters: |
|
-
|---|---|
| Returns: |
|
-
| 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)
-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: |
|
-
|---|---|
| Returns: | The tensor variable storing the output. - |
-
| Return type: | Variable - |
-
Examples
-data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
-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: |
|
-
|---|---|
| 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')
-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: |
|
-
|---|---|
| Returns: | The tensor variable storing the output - |
-
| Return type: | Variable - |
-
Examples
-data = fluid.layers.ones(shape=[1], dtype='int64')
-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: |
|
-
|---|---|
| Returns: | The tensor variable storing the output - |
-
| Return type: | Variable - |
-
Examples
-data = fluid.layers.zeros(shape=[1], dtype='int64')
-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: |
|
-
|---|---|
| Returns: |
|
-
| 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)
-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: |
|
-
|---|---|
| Returns: | The output LOD_TENSOR_ARRAY where the input tensor is written. - |
-
| Return type: | Variable - |
-
Examples
-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')
-paddle.v2.fluid.layers.less_than(x, y, cond=None, **ignored)Less than
-This layer returns the truth value of \(x < y\) elementwise.
-| Parameters: |
|
-
|---|---|
| Returns: | The tensor variable storing the output of less_than. - |
-
| Return type: | Variable - |
-
Examples
-less = fluid.layers.less_than(x=label, y=limit)
-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
-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.
-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
-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: |
|
-
|---|---|
| Returns: | Output image. - |
-
| Return type: | Variable - |
-
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: |
|
-
|---|---|
| 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)
-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: |
|
-
|---|---|
| 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)
-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: |
|
-
|---|---|
| Returns: | The hidden value and cell value of lstm unit. - |
-
| Return type: | tuple - |
-
| Raises: |
|
-
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)
-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. | -
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: |
|
-
|---|---|
| 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]]
-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: |
|
-
|---|---|
| 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]]
-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: |
|
-
|---|---|
| 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]]
-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: |
|
-
|---|---|
| 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]]
-paddle.v2.fluid.layers.split(input, num_or_sections, dim=-1, name=None)Split the input tensor into multiple sub-tensors.
-| Parameters: |
|
-
|---|---|
| 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]
-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:
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: |
|
-
|---|---|
| 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]
-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 | -
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 | -
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 | -
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 | -
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 | -
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: |
|
-
|---|---|
| Returns: | Output of Softshrink operator - |
-
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 | -
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 | -
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 | -
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 | -
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 | -
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 | -
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 | -
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 | -
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 | -
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 | -
paddle.v2.fluid.layers.brelu(**kwargs)BRelu Activation Operator.
-$out = max(min(x, t_{min}), t_{max})$
-| Parameters: |
|
-
|---|---|
| Returns: | Output of BRelu operator - |
-
paddle.v2.fluid.layers.leaky_relu(**kwargs)LeakyRelu Activation Operator.
-$out = max(x, alpha * x)$
-| Parameters: |
|
-
|---|---|
| Returns: | Output of LeakyRelu operator - |
-
paddle.v2.fluid.layers.soft_relu(**kwargs)SoftRelu Activation Operator.
-$out = ln(1 + exp(max(min(x, threshold), threshold))$
-| Parameters: |
|
-
|---|---|
| Returns: | Output of SoftRelu operator - |
-
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: |
|
-
|---|---|
| Returns: | Output of ELU operator - |
-
paddle.v2.fluid.layers.relu6(**kwargs)Relu6 Activation Operator.
-$out = min(max(0, x), 6)$
-| Parameters: |
|
-
|---|---|
| Returns: | Output of Relu6 operator - |
-
paddle.v2.fluid.layers.pow(**kwargs)Pow Activation Operator.
-$out = x^{factor}$
-| Parameters: |
|
-
|---|---|
| Returns: | Output of Pow operator - |
-
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: |
|
-
|---|---|
| Returns: | Output of HardShrink operator - |
-
paddle.v2.fluid.layers.thresholded_relu(**kwargs)ThresholdedRelu Activation Operator.
-$$ -out = begin{cases}
---x, text{if } x > threshold \ -0, text{otherwise} -end{cases}
$$
-| Parameters: |
|
-
|---|---|
| Returns: | Output of ThresholdedRelu operator - |
-
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: |
|
-
|---|---|
| Returns: | Output of HardSigmoid operator - |
-
paddle.v2.fluid.layers.swish(**kwargs)Swish Activation Operator.
-$$out = frac{x}{1 + e^{- beta x}}$$
-| Parameters: |
|
-
|---|---|
| Returns: | Output of Swish operator - |
-
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: |
|
-
|---|---|
| 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)
-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: |
|
-
|---|---|
| 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)
-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)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.
-paddle.v2.fluid.nets.sequence_conv_pool(input, num_filters, filter_size, param_attr=None, act='sigmoid', pool_type='max')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: |
|
-
|---|---|
| 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]
-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: |
|
-
|---|---|
| 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]
-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: |
|
-
|---|---|
| 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.
-paddle.v2.fluid.optimizer.SGDOptimizer(learning_rate, **kwargs)Simple SGD optimizer without any state.
-paddle.v2.fluid.optimizer.MomentumOptimizer(learning_rate, momentum, use_nesterov=False, **kwargs)Simple Momentum optimizer with velocity state
-paddle.v2.fluid.optimizer.AdagradOptimizer(learning_rate, epsilon=1e-06, **kwargs)Simple Adagrad optimizer with moment state
-paddle.v2.fluid.optimizer.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)Implements the Adam Optimizer
-paddle.v2.fluid.optimizer.AdamaxOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)Implements the Adamax Optimizer
-paddle.v2.fluid.optimizer.DecayedAdagradOptimizer(learning_rate, decay=0.95, epsilon=1e-06, **kwargs)Simple Decayed Adagrad optimizer with moment state
-paddle.v2.fluid.profiler.cuda_profiler(*args, **kwds)The CUDA profiler. -This fuctions is used to profile CUDA program by CUDA runtime application -programming interface. The profiling result will be written into -output_file with Key-Value pair format or Comma separated values format. -The user can set the output mode by output_mode argument and set the -counters/options for profiling by config argument. The default config -is [‘gpustarttimestamp’, ‘gpustarttimestamp’, ‘gridsize3d’, -‘threadblocksize’, ‘streamid’, ‘enableonstart 0’, ‘conckerneltrace’].
-| Parameters: |
|
-
|---|
paddle.v2.fluid.regularizer.WeightDecayRegularizerBase class for weight decay regularizers
-Defines the common interface of weight-decay regularizers. -Weight-decay regularizers are added only during the backward -pass for faster regularization. They add operations to the network -that correspond to gradient of the regularization function. -Users should not use this class directly, but need to use one -of its implementations
-paddle.v2.fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)Implements the L2 Weight Decay Regularization
-paddle.v2.fluid.regularizer.L1DecayRegularizer(regularization_coeff=0.0)¶Implements the L1 Weight Decay Regularization
-| - | + |
|
| - |
| @@ -296,10 +283,6 @@ |
| - |
paddlepaddle.v2.fluid.regularizerIfElse Operator","Design Doc: InferVarType","Problem","Memory Optimization","Intel\u00ae MKL Packed on PaddlePaddle: Design Doc","Intel\u00ae MKL-DNN on PaddlePaddle: Design Doc","Design Doc: Add MKLDNN Kernel in Fluid Operator","Design Doc: Model Format","Paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0","C-API \u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863","Design Doc: The Keys of Operator Kernel Type","RNNOp design","Design: Sequence Decoder Generating LoDTensors","Optimizer Design","Design Doc: NCCL support in Paddle Fluid","Averaging Parameter in PaddlePaddle","Design Doc: The C++ Class Parameters","Introduction","Design Doc: PaddlePaddle Programs","Prune","Design Doc: Python API","Python Data Reader Design Doc","Design Doc: Refactorization Overview","Design Doc: Gradient Operators Registration","Regularization in PaddlePaddle","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Design Doc: Selected Rows","Interaction between C++ and Python","Design Doc: Supporting new Device/Library","Background","Design for TensorArray","Background","Build from Sources","Run in Docker Containers","Install and Build","Install Using pip","GET STARTED","RNN Models","RNN Configuration","Build using Docker","Contribute Code","Write New Layers","How to write a new operator","Add Kernels for a New Device","How to use Eigen in Paddle","Contribute Documentation","HOW TO","Profiling the Python Code","Tune GPU Performance","PaddlePaddle Fluid Source Code Overview","Distributed Training","Cluster Training Using Fabric","Fluid Distributed Training","Distributed PaddlePaddle Training on AWS with Kubernetes","PaddlePaddle On Kubernetes","Cluster Training Using OpenMPI","<no title>","<no title>","Argument Outline","Detail Description","Set Command-line Parameters","Use Case","PaddlePaddle Documentation","Build PaddlePaddle for Android","Build PaddlePaddle for iOS","Build PaddlePaddle for Raspberry Pi","MOBILE","Cluster bootstrapping tool 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paddle.v2.fluid.evaluator.Evaluator(name, **kwargs)Base Class for all evaluators
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states¶list – The list of state variables. states will be reset to zero -when reset is invoked.
-metrics¶list – The list of metrics variables. They will be calculate -every mini-batch
-reset(executor, reset_program=None)reset metric states at the begin of each pass/user specified batch
-eval(executor, eval_program=None)Evaluate the statistics merged by multiple mini-batches.
-create_state(suffix, dtype, shape)Create state variable.
-NOTE: It is not a public API.
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Returns: State variable
-paddle.v2.fluid.initializer.InitializerBase class for variable initializers
-Defines the common interface of variable initializers. -They add operations to the init program that are used -to initialize variables. Users should not use this class -directly, but need to use one of its implementations.
-paddle.v2.fluid.initializer.ConstantInitializer(value=0.0)Implements the constant initializer
-paddle.v2.fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0)Implements the random uniform distribution initializer
-paddle.v2.fluid.initializer.NormalInitializer(loc=0.0, scale=1.0, seed=0)Implements the random Normal(Gaussian) distribution initializer
-paddle.v2.fluid.initializer.XavierInitializer(uniform=True, fan_in=None, fan_out=None, seed=0)Implements the Xavier initializer
-This class implements the Xavier weight initializer from the paper -Understanding the difficulty of training deep feedforward neural -networks[1] by Xavier Glorot and Yoshua Bengio.
-This initializer is designed to keep the scale of the gradients -approximately same in all the layers. In case of Uniform distribution, -the range is [-x, x], where x = sqrt(6 / (fan_in + fan_out)). -In case of Normal distribution, the mean is 0 and the standard deviation -is sqrt(2/ (fan_in + fan_out)).
-References
-paddle.v2.fluid.initializer.MSRAInitializer(uniform=True, fan_in=None, seed=0)Implements the MSRA initializer a.k.a. Kaiming Initializer
-This class implements the weight initialization from the paper -Delving Deep into Rectifiers: Surpassing Human-Level Performance on -ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren -and Jian Sun. This is a robust initialization method that particularly -considers the rectifier nonlinearities. In case of Uniform distribution, -the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal -distribution, the mean is 0 and the standard deviation -is sqrt(2/ fan_in).
-References
-paddle.v2.fluid.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. | -
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| 返回: | boolean result whether the variable is a Parameter. | -
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:
-In the above equation:
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| 返回: | The output tensor variable. - |
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| 返回类型: | Variable - |
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Examples
-data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
-fc = fluid.layers.fc(input=data, size=1000, act="tanh")
-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.
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| 返回: | The tensor variable storing the embeddings of the supplied inputs. - |
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| 返回类型: | Variable - |
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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])
-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:
-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.
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| 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. - |
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| 返回类型: | tuple - |
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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)
-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.
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| 返回: | The global variable that gives access to the data. - |
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| 返回类型: | Variable - |
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Examples
-data = fluid.layers.data(name='x', shape=[784], dtype='float32')
-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 | -
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| 返回: | The output of mean op | -
paddle.v2.fluid.layers.mul(**kwargs)Mul Operator.
-This operator is used to perform matrix multiplication for input $X$ and $Y$.
-The equation is:
-$$Out = X * Y$$
-Both the input $X$ and $Y$ can carry the LoD (Level of Details) information, -or not. But the output only shares the LoD information with input $X$.
-| 参数: |
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| 返回: | (Tensor), The output tensor of mul op. - |
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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 $X$.
-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 $X$.
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| 返回: | The output of elementwise op. - |
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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 $X$.
-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 $X$.
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| 返回: | The output of elementwise op. - |
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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 $X$.
-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 $X$.
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| 返回: | The output of elementwise op. - |
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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 $X$.
-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 $X$.
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| 返回: | The output of elementwise op. - |
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paddle.v2.fluid.layers.dropout(x, dropout_prob, is_test=False, seed=0, **kwargs)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.
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| 返回: | The output tensor of reshape operator. - |
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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 | -
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| 返回: | Output of Sigmoid operator | -
paddle.v2.fluid.layers.scale(**kwargs)Scale operator
-$$Out = scale*X$$
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| 返回: | (Tensor) Output tensor of scale operator. - |
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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)
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| 返回: | (Tensor)The output tensor - |
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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.
-paddle.v2.fluid.layers.concat(input, axis=0)Concat
-This function concatenates the input along the axis mentioned -and returns that as the output.
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| 返回: | Output variable of the concatenation - |
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| 返回类型: | Variable - |
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Examples
-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. | -
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| 返回类型: | Variable | -
Examples
-paddle.v2.fluid.layers.linear_chain_crf(input, label, param_attr=None)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: |
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| 返回: | The tensor variable storing the embeddings of the supplied inputs. - |
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| 返回类型: | Variable - |
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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])
-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.
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| 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. - |
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| 返回类型: | Variable - |
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Examples
-x = layers.data(name='x', shape=[1])
-x.persistable = True
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-y = layers.data(name='y', shape=[1])
-y.persistable = True
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-out_true, out_false = layers.split_lod_tensor(
- input=x, mask=y, level=level)
-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 \(level\).
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| 返回: | The merged output tensor. - |
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| 返回类型: | Variable - |
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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)
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-level = 0
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-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)
-paddle.v2.fluid.layers.cos_sim(X, Y, **kwargs)This function performs the cosine similarity between two tensors -X and Y and returns that as the output.
-paddle.v2.fluid.layers.cross_entropy(input, label, **kwargs)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.
-soft_label = False, Label[i, 0] indicates the class index for sample i:
-soft_label = True, Label[i, j] indicates the soft label of class j -for sample i:
-Please make sure that in this case the summation of each row of label -equals one.
-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.
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| 返回: | A 2-D tensor with shape [N x 1], the cross entropy loss. - |
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| 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. - |
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Examples
-predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
-cost = fluid.layers.cross_entropy(input=predict, label=label)
-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:
-In the above equation:
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-- \(X\): Input predictions, a tensor.
-- \(Y\): Input labels, a tensor.
-- \(Out\): Output value, same shape with \(X\).
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| 返回: | The tensor variable storing the element-wise squared error difference of input and label. - |
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| 返回类型: | Variable - |
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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)
-paddle.v2.fluid.layers.accuracy(input, label, k=1, correct=None, total=None, **kwargs)This function computes the accuracy using the input and label. -The output is the top_k inputs and their indices.
-paddle.v2.fluid.layers.sequence_conv(input, num_filters, filter_size=3, filter_stride=1, padding=None, bias_attr=None, param_attr=None, act=None)This function creates the op for sequence_conv, using the inputs and -other convolutional configurations for the filters and stride as given -in the input parameters to the function.
-paddle.v2.fluid.layers.conv2d(input, num_filters, filter_size, stride=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:
-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.
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Example
-Input shape: $(N, C_{in}, H_{in}, W_{in})$
-Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
-Where
-| 参数: |
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| 返回: | The tensor variable storing the convolution and non-linearity activation result. - |
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| 返回类型: | Variable - |
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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")
-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:
-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]
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-then output is a Tensor:
- out.dim = [3, 1]
- with condition len(x.lod[-1]) - 1 == out.dims[0]
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-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: |
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| 返回: | The sequence pooling variable which is a Tensor. - |
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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')
-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]
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-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. | -
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| 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)
-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)
-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.
-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.
-paddle.v2.fluid.layers.beam_search_decode(ids, scores, name=None)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: |
|
-
|---|---|
| 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)
-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)
-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: |
|
-
|---|---|
| 返回: |
|
-
| 返回类型: | Variable - |
-
Examples
-x = fluid.layers.data(name='x', shape=[10])
-k = 5
-array = fluid.layers.topk(x, k)
-paddle.v2.fluid.layers.lod_tensor_to_array(x, table)Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.
-| 参数: |
|
-
|---|---|
| 返回: |
|
-
| 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)
-paddle.v2.fluid.layers.array_to_lod_tensor(x, table)Convert a LoD_Tensor_Aarry to an LoDTensor.
-| 参数: |
|
-
|---|---|
| 返回: |
|
-
| 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)
-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: |
|
-
|---|---|
| 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')
-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: |
|
-
|---|---|
| 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')
-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: |
|
-
|---|---|
| 返回: | The tensor variable storing the output - |
-
| 返回类型: | Variable - |
-
Examples
-data = fluid.layers.ones(shape=[1], dtype='int64')
-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: |
|
-
|---|---|
| 返回: | The tensor variable storing the output - |
-
| 返回类型: | Variable - |
-
Examples
-data = fluid.layers.zeros(shape=[1], dtype='int64')
-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: |
|
-
|---|---|
| 返回: |
|
-
| 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)
-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: |
|
-
|---|---|
| 返回: | The output LOD_TENSOR_ARRAY where the input tensor is written. - |
-
| 返回类型: | Variable - |
-
Examples
-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')
-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: |
|
-
|---|---|
| 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)
-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
-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.
-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
-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: |
|
-
|---|---|
| 返回: | Output image. - |
-
| 返回类型: | Variable - |
-
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: |
|
-
|---|---|
| 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)
-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: |
|
-
|---|---|
| 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)
-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: |
|
-
|---|---|
| 返回: | The hidden value and cell value of lstm unit. - |
-
| 返回类型: | tuple - |
-
| Raises: |
|
-
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)
-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. | -
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: |
|
-
|---|---|
| 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]]
-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: |
|
-
|---|---|
| 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]]
-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: |
|
-
|---|---|
| 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]]
-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: |
|
-
|---|---|
| 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]]
-paddle.v2.fluid.layers.split(input, num_or_sections, dim=-1, name=None)Split the input tensor into multiple sub-tensors.
-| 参数: |
|
-
|---|---|
| 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]
-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:
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: |
|
-
|---|---|
| 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]
-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 | -
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 | -
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 | -
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 | -
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 | -
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: |
|
-
|---|---|
| 返回: | Output of Softshrink operator - |
-
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 | -
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 | -
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 | -
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 | -
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 | -
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 | -
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 | -
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 | -
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 | -
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 | -
paddle.v2.fluid.layers.brelu(**kwargs)BRelu Activation Operator.
-$out = max(min(x, t_{min}), t_{max})$
-| 参数: |
|
-
|---|---|
| 返回: | Output of BRelu operator - |
-
paddle.v2.fluid.layers.leaky_relu(**kwargs)LeakyRelu Activation Operator.
-$out = max(x, alpha * x)$
-| 参数: |
|
-
|---|---|
| 返回: | Output of LeakyRelu operator - |
-
paddle.v2.fluid.layers.soft_relu(**kwargs)SoftRelu Activation Operator.
-$out = ln(1 + exp(max(min(x, threshold), threshold))$
-| 参数: |
|
-
|---|---|
| 返回: | Output of SoftRelu operator - |
-
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: |
|
-
|---|---|
| 返回: | Output of ELU operator - |
-
paddle.v2.fluid.layers.relu6(**kwargs)Relu6 Activation Operator.
-$out = min(max(0, x), 6)$
-| 参数: |
|
-
|---|---|
| 返回: | Output of Relu6 operator - |
-
paddle.v2.fluid.layers.pow(**kwargs)Pow Activation Operator.
-$out = x^{factor}$
-| 参数: |
|
-
|---|---|
| 返回: | Output of Pow operator - |
-
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: |
|
-
|---|---|
| 返回: | Output of HardShrink operator - |
-
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: |
|
-
|---|---|
| 返回: | Output of ThresholdedRelu operator - |
-
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: |
|
-
|---|---|
| 返回: | Output of HardSigmoid operator - |
-
paddle.v2.fluid.layers.swish(**kwargs)Swish Activation Operator.
-$$out = frac{x}{1 + e^{- beta x}}$$
-| 参数: |
|
-
|---|---|
| 返回: | Output of Swish operator - |
-
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: |
|
-
|---|---|
| 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)
-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: |
|
-
|---|---|
| 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)
-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)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.
-paddle.v2.fluid.nets.sequence_conv_pool(input, num_filters, filter_size, param_attr=None, act='sigmoid', pool_type='max')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: |
|
-
|---|---|
| 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]
-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: |
|
-
|---|---|
| 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]
-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: |
|
-
|---|---|
| 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.
-paddle.v2.fluid.optimizer.SGDOptimizer(learning_rate, **kwargs)Simple SGD optimizer without any state.
-paddle.v2.fluid.optimizer.MomentumOptimizer(learning_rate, momentum, use_nesterov=False, **kwargs)Simple Momentum optimizer with velocity state
-paddle.v2.fluid.optimizer.AdagradOptimizer(learning_rate, epsilon=1e-06, **kwargs)Simple Adagrad optimizer with moment state
-paddle.v2.fluid.optimizer.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)Implements the Adam Optimizer
-paddle.v2.fluid.optimizer.AdamaxOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, **kwargs)Implements the Adamax Optimizer
-paddle.v2.fluid.optimizer.DecayedAdagradOptimizer(learning_rate, decay=0.95, epsilon=1e-06, **kwargs)Simple Decayed Adagrad optimizer with moment state
-paddle.v2.fluid.profiler.cuda_profiler(*args, **kwds)The CUDA profiler. -This fuctions is used to profile CUDA program by CUDA runtime application -programming interface. The profiling result will be written into -output_file with Key-Value pair format or Comma separated values format. -The user can set the output mode by output_mode argument and set the -counters/options for profiling by config argument. The default config -is [‘gpustarttimestamp’, ‘gpustarttimestamp’, ‘gridsize3d’, -‘threadblocksize’, ‘streamid’, ‘enableonstart 0’, ‘conckerneltrace’].
-| 参数: |
|
-
|---|
paddle.v2.fluid.regularizer.WeightDecayRegularizerBase class for weight decay regularizers
-Defines the common interface of weight-decay regularizers. -Weight-decay regularizers are added only during the backward -pass for faster regularization. They add operations to the network -that correspond to gradient of the regularization function. -Users should not use this class directly, but need to use one -of its implementations
-paddle.v2.fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)Implements the L2 Weight Decay Regularization
-paddle.v2.fluid.regularizer.L1DecayRegularizer(regularization_coeff=0.0)¶Implements the L1 Weight Decay Regularization
-| - | + |
|
| - |
| paddle
| | |||
| - |
- paddle.v2.fluid.regularizer | - | |||
diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js
index 7bbe4bc4c8408d08e63decd4d29741bcbff76cdd..b72680536dec12cd2a2ad950a7860f7f85336005 100644
--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js
@@ -1 +1 @@
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