loss.py 38.1 KB
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#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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# TODO: define loss functions of neural network
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import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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import paddle
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from .. import functional as F
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from paddle.fluid.framework import core, in_dygraph_mode, _varbase_creator
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__all__ = [
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    'BCEWithLogitsLoss',
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    'CrossEntropyLoss',
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    'MSELoss',
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    'L1Loss',
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    'NLLLoss',
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    'BCELoss',
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    'KLDivLoss',
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    'MarginRankingLoss',
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    'CTCLoss',
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    'SmoothL1Loss',
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]


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class BCEWithLogitsLoss(fluid.dygraph.Layer):
    """
    This operator combines the sigmoid layer and the :ref:`api_nn_loss_BCELoss` layer.
    Also, we can see it as the combine of ``sigmoid_cross_entropy_with_logits``
    layer and some reduce operations.

    This measures the element-wise probability error in classification tasks
    in which each class is independent.
    This can be thought of as predicting labels for a data-point, where labels
    are not mutually exclusive. For example, a news article can be about
    politics, technology or sports at the same time or none of these.

    First this operator calculate loss function as follows:

    .. math::
           Out = -Labels * \\log(\\sigma(Logit)) - (1 - Labels) * \\log(1 - \\sigma(Logit))

    We know that :math:`\\sigma(Logit) = \\frac{1}{1 + \\e^{-Logit}}`. By substituting this we get:

    .. math::
           Out = Logit - Logit * Labels + \\log(1 + \\e^{-Logit})

    For stability and to prevent overflow of :math:`\\e^{-Logit}` when Logit < 0,
    we reformulate the loss as follows:

    .. math::
           Out = \\max(Logit, 0) - Logit * Labels + \\log(1 + \\e^{-\|Logit\|})

    Then, if ``weight`` or ``pos_weight`` is not None, this operator multiply the
    weight tensor on the loss `Out`. The ``weight`` tensor will attach different
    weight on every items in the batch. The ``pos_weight`` will attach different
    weight on the positive label of each class.

    Finally, this operator applies reduce operation on the loss.
    If :attr:`reduction` set to ``'none'``, the operator will return the original loss `Out`.
    If :attr:`reduction` set to ``'mean'``, the reduced mean loss is :math:`Out = MEAN(Out)`.
    If :attr:`reduction` set to ``'sum'``, the reduced sum loss is :math:`Out = SUM(Out)`.

    Note that the target labels ``label`` should be numbers between 0 and 1.

    Args:
        weight (Tensor, optional): A manual rescaling weight given to the loss of each
            batch element. If given, it has to be a 1D Tensor whose size is `[N, ]`,
            The data type is float32, float64. Default is ``'None'``.
        reduction (str, optional): Indicate how to average the loss by batch_size,
            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
            If :attr:`reduction` is ``'none'``, the unreduced loss is returned;
            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned;
            If :attr:`reduction` is ``'sum'``, the summed loss is returned.
            Default is ``'mean'``.
        pos_weight (Tensor, optional): A weight of positive examples. Must be a vector
            with length equal to the number of classes. The data type is float32, float64.
            Default is ``'None'``.
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Shapes:
        logit (Tensor): The input predications tensor. 2-D tensor with shape: [N, *],
            N is batch_size, `*` means number of additional dimensions. The ``logit``
            is usually the output of Linear layer. Available dtype is float32, float64.
        label (Tensor): The target labels tensor. 2-D tensor with the same shape as
            ``logit``. The target labels which values should be numbers between 0 and 1.
            Available dtype is float32, float64.
        output (Tensor): If ``reduction`` is ``'none'``, the shape of output is
            same as ``logit`` , else the shape of output is scalar.

    Returns:
        A callable object of BCEWithLogitsLoss.

    Examples:

        .. code-block:: python
            import paddle
            paddle.disable_static()
            logit = paddle.to_tensor([5.0, 1.0, 3.0], dtype="float32")
            label = paddle.to_tensor([1.0, 0.0, 1.0], dtype="float32")
            bce_logit_loss = paddle.nn.BCEWithLogitsLoss()
            output = bce_logit_loss(logit, label)
            print(output.numpy())  # [0.45618808]

    """

    def __init__(self,
                 weight=None,
                 reduction='mean',
                 pos_weight=None,
                 name=None):
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "The value of 'reduction' in BCEWithLogitsLoss should be 'sum', 'mean' or 'none', but "
                "received %s, which is not allowed." % reduction)

        super(BCEWithLogitsLoss, self).__init__()
        self.weight = weight
        self.reduction = reduction
        self.pos_weight = pos_weight
        self.name = name

    def forward(self, logit, label):
        out = paddle.nn.functional.binary_cross_entropy_with_logits(
            logit, label, self.weight, self.reduction, self.pos_weight,
            self.name)
        return out


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class CrossEntropyLoss(fluid.dygraph.Layer):
    """
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	:alias_main: paddle.nn.CrossEntropyLoss
	:alias: paddle.nn.CrossEntropyLoss,paddle.nn.layer.CrossEntropyLoss,paddle.nn.layer.loss.CrossEntropyLoss
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    This operator implements the cross entropy loss function. This OP combines ``LogSoftmax``,
    and ``NLLLoss`` together.
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    It is useful when training a classification problem with ``C`` classes.
    If provided, the optional argument ``weight`` should be a 1D Variable assigning
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    weight to each of the classes.

    For predictions label, and target label, the loss is calculated as follows.
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    .. math::

        loss_j =  -\\text{input[class]} +
        \\log\\left(\\sum_{i=0}^{K}\\exp(\\text{input}_i)\\right), j = 1,..., K

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    If weight is not ``None``:

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    .. math::

        loss_j =  \\text{weight[class]}(-\\text{input[class]} +
        \\log\\left(\\sum_{i=0}^{K}\\exp(\\text{input}_i)\\right)), j = 1,..., K

    Parameters:
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        input (Variable): Input tensor, the data type is float32, float64. Shape is
	    (N, C), where C is number of classes, and if shape is more than 2D, this
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	    is (N, C, D1, D2,..., Dk), k >= 1.
        label (Variable): Label tensor, the data type is int64. Shape is (N), where each
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	    value is 0 <= label[i] <= C-1, and if shape is more than 2D, this is
	    (N, D1, D2,..., Dk), k >= 1.
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        weight (Variable, optional): Weight tensor, a manual rescaling weight given
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            to each class and the shape is (C). It has the same dimensions as class
	    number and the data type is float32, float64. Default is ``'None'``.
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        reduction (str, optional): Indicate how to average the loss by batch_size,
            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned;
            If :attr:`size_average` is ``'sum'``, the reduced sum loss is returned.
            If :attr:`reduction` is ``'none'``, the unreduced loss is returned.
            Default is ``'mean'``.
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        ignore_index (int64, optional): Specifies a target value that is ignored
            and does not contribute to the input gradient. Default is ``-100``.
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    Returns:
        The tensor variable storing the cross_entropy_loss of input and label.
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    Return type: Variable.
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    Examples:
        .. code-block:: python

            # declarative mode
            import paddle
            import paddle.fluid as fluid
            import numpy as np

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            input = fluid.data(name='input', shape=[5, 100], dtype='float64')
            label = fluid.data(name='label', shape=[5], dtype='int64')
            weight = fluid.data(name='weight', shape=[100], dtype='float64')
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            ce_loss = paddle.nn.loss.CrossEntropyLoss(weight=weight, reduction='mean')
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            output = ce_loss(input, label)
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            place = fluid.CPUPlace()
            exe = fluid.Executor(place)
            exe.run(fluid.default_startup_program())
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            input_data = np.random.random([5, 100]).astype("float64")
            label_data = np.random.randint(0, 100, size=(5)).astype(np.int64)
            weight_data = np.random.random([100]).astype("float64")
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            output = exe.run(fluid.default_main_program(),
                        feed={"input": input_data, "label": label_data,"weight": weight_data},
                        fetch_list=[output],
                        return_numpy=True)
            print(output)

            # imperative mode
            import paddle.fluid.dygraph as dg
            with dg.guard(place) as g:
                input = dg.to_variable(input_data)
                label = dg.to_variable(label_data)
                weight = dg.to_variable(weight_data)
                ce_loss = paddle.nn.loss.CrossEntropyLoss(weight=weight, reduction='mean')
                output = ce_loss(input, label)
                print(output.numpy())
    """

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    def __init__(self, weight=None, ignore_index=-100, reduction='mean'):
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        super(CrossEntropyLoss, self).__init__()
        self.weight = weight
        self.reduction = reduction
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        self.ignore_index = ignore_index
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    def forward(self, input, label):
        fluid.data_feeder.check_variable_and_dtype(
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            input, 'input', ['float32', 'float64'], 'cross_entropy_loss')
        fluid.data_feeder.check_variable_and_dtype(label, 'label', ['int64'],
                                                   'cross_entropy_loss')
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        if self.reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
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                "The value of 'reduction' in cross_entropy_loss should be 'sum', 'mean' or"
                " 'none', but received %s, which is not allowed." %
                self.reduction)

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        return paddle.nn.functional.cross_entropy(
            input,
            label,
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            weight=self.weight,
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            ignore_index=self.ignore_index,
            reduction=self.reduction)
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class MSELoss(fluid.dygraph.layers.Layer):
    """
    **Mean Square Error Loss**
    Computes the mean square error (squared L2 norm) of given input and label.

    If :attr:`reduction` is set to ``'none'``, loss is calculated as:

    .. math::
        Out = (input - label)^2

    If :attr:`reduction` is set to ``'mean'``, loss is calculated as:

    .. math::
        Out = \operatorname{mean}((input - label)^2)

    If :attr:`reduction` is set to ``'sum'``, loss is calculated as:

    .. math::
        Out = \operatorname{sum}((input - label)^2)

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    where `input` and `label` are `float32` tensors of same shape.
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    Parameters:
        reduction (string, optional): The reduction method for the output,
            could be 'none' | 'mean' | 'sum'.
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            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned.
            If :attr:`size_average` is ``'sum'``, the reduced sum loss is returned.
            If :attr:`reduction` is ``'none'``, the unreduced loss is returned.
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            Default is ``'mean'``.

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    Shape:
        input (Tensor): Input tensor, the data type is float32 or float64
        label (Tensor): Label tensor, the data type is float32 or float64
        output (Tensor): output tensor storing the MSE loss of input and label, the data type is same as input.
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    Examples:
        .. code-block:: python
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            import numpy as np
            import paddle

            input_data = np.array([1.5]).astype("float32")
            label_data = np.array([1.7]).astype("float32")

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            paddle.disable_static()
            mse_loss = paddle.nn.loss.MSELoss()
            input = paddle.to_tensor(input_data)
            label = paddle.to_tensor(label_data)
            output = mse_loss(input, label)
            print(output.numpy())
            # [0.04000002]
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    """

    def __init__(self, reduction='mean'):
        super(MSELoss, self).__init__()
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "'reduction' in 'MSELoss' should be 'sum', 'mean' or 'none', "
                "but received {}.".format(reduction))
        self.reduction = reduction

    def forward(self, input, label):
        if not fluid.framework.in_dygraph_mode():
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            fluid.data_feeder.check_variable_and_dtype(
                input, 'input', ['float32', 'float64'], 'MSELoss')
            fluid.data_feeder.check_variable_and_dtype(
                label, 'label', ['float32', 'float64'], 'MSELoss')
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        square_out = fluid.layers.square(
            fluid.layers.elementwise_sub(input, label))
        if self.reduction == 'none':
            return square_out

        reduce_op = 'reduce_mean'
        if self.reduction == 'sum':
            reduce_op = 'reduce_sum'

        return getattr(fluid.layers, reduce_op)(square_out)


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class L1Loss(fluid.dygraph.Layer):
    """
    This interface is used to construct a callable object of the ``L1Loss`` class.
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    The L1Loss layer calculates the L1 Loss of ``input`` and ``label`` as follows.
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     If `reduction` set to ``'none'``, the loss is:
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    .. math::
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        Out = \lvert input - label\rvert
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    If `reduction` set to ``'mean'``, the loss is:
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    .. math::
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        Out = MEAN(\lvert input - label\rvert)
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    If `reduction` set to ``'sum'``, the loss is:
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    .. math::
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        Out = SUM(\lvert input - label\rvert)
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    Parameters:
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        reduction (str, optional): Indicate the reduction to apply to the loss,
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            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
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            If `reduction` is ``'none'``, the unreduced loss is returned;
            If `reduction` is ``'mean'``, the reduced mean loss is returned.
            If `reduction` is ``'sum'``, the reduced sum loss is returned.
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            Default is ``'mean'``.
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        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Shape:
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        input (Tensor): The input tensor. The shapes is [N, *], where N is batch size and `*` means any number of additional dimensions. It's data type should be float32, float64, int32, int64.
        label (Tensor): label. The shapes is [N, *], same shape as ``input`` . It's data type should be float32, float64, int32, int64.
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        output (Tensor): The L1 Loss of ``input`` and ``label``.
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            If `reduction` is ``'none'``, the shape of output loss is [N, *], the same as ``input`` .
            If `reduction` is ``'mean'`` or ``'sum'``, the shape of output loss is [1].
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    Examples:
        .. code-block:: python
            import paddle
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            import numpy as np

            paddle.disable_static()
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            input_data = np.array([[1.5, 0.8], [0.2, 1.3]]).astype("float32")
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            label_data = np.array([[1.7, 1], [0.4, 0.5]]).astype("float32")
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            input = paddle.to_variable(input_data)
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            label = paddle.to_variable(label_data)

            l1_loss = paddle.nn.loss.L1Loss()
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            output = l1_loss(input, label)
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            print(output.numpy())
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            # [0.35]

            l1_loss = paddle.nn.loss.L1Loss(reduction='sum')
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            output = l1_loss(input, label)
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            print(output.numpy())
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            # [1.4]

            l1_loss = paddle.nn.loss.L1Loss(reduction='none')
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            output = l1_loss(input, label)
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            print(output.numpy())
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            # [[0.20000005 0.19999999]
            # [0.2        0.79999995]]
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    """

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    def __init__(self, reduction='mean', name=None):
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        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "The value of 'reduction' in L1Loss should be 'sum', 'mean' or 'none', but "
                "received %s, which is not allowed." % reduction)
        super(L1Loss, self).__init__()
        self.reduction = reduction
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        self.name = name
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    def forward(self, input, label):
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        return paddle.nn.functional.l1_loss(
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            input, label, self.reduction, name=self.name)
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class BCELoss(fluid.dygraph.Layer):
    """
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    This interface is used to construct a callable object of the ``BCELoss`` class.
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    The BCELoss layer measures the binary_cross_entropy loss between input predictions ``input``
    and target labels ``label`` . The binary_cross_entropy loss can be described as:
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    If :attr:`weight` is set, the loss is:
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    .. math::
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        Out = -1 * weight * (label * log(input) + (1 - label) * log(1 - input))
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    If :attr:`weight` is None, the loss is:
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    .. math::
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        Out = -1 * (label * log(input) + (1 - label) * log(1 - input))

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    If :attr:`reduction` set to ``'none'``, the interface will return the original loss `Out`.
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    If :attr:`reduction` set to ``'mean'``, the reduced mean loss is:
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    .. math::
        Out = MEAN(Out)
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    If :attr:`reduction` set to ``'sum'``, the reduced sum loss is:
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    .. math::
        Out = SUM(Out)
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    Note that the input predictions ``input`` always be the output of sigmoid, and the target labels ``label``
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    should be numbers between 0 and 1.

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    Parameters:
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        weight (Tensor, optional): A manual rescaling weight given to the loss of each
            batch element. If given, has to be a Tensor of size nbatch and the data type
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            is float32, float64. Default is ``'None'``.
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        reduction (str, optional): Indicate how to average the loss by batch_size,
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            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
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            If :attr:`reduction` is ``'none'``, the unreduced loss is returned;
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            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned;
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            If :attr:`reduction` is ``'sum'``, the summed loss is returned.
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            Default is ``'mean'``.
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        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Shape:
        input (Tensor): 2-D tensor with shape: (N, *), N is batch_size, `*` means
            number of additional dimensions. The input ``input`` should always
            be the output of sigmod.  Available dtype is float32, float64.
        label (Tensor): 2-D tensor with the same shape as ``input``. The target
            labels which values should be numbers between 0 and 1. Available
            dtype is float32, float64.
        output (Tensor): If ``reduction`` is ``'none'``, the shape of output is
            same as ``input`` , else the shape of output is scalar.
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    Returns:
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        A callable object of BCELoss.

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    Examples:
        .. code-block:: python
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            import numpy as np
            import paddle
            input_data = np.array([0.5, 0.6, 0.7]).astype("float32")
            label_data = np.array([1.0, 0.0, 1.0]).astype("float32")
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            paddle.disable_static()
            input = paddle.to_variable(input_data)
            label = paddle.to_variable(label_data)
            bce_loss = paddle.nn.loss.BCELoss()
            output = bce_loss(input, label)
            print(output.numpy())  # [0.65537095]
            paddle.enable_static()

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    """

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    def __init__(self, weight=None, reduction='mean', name=None):
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        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "The value of 'reduction' in bce_loss should be 'sum', 'mean' or 'none', but "
                "received %s, which is not allowed." % reduction)

        super(BCELoss, self).__init__()
        self.weight = weight
        self.reduction = reduction
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        self.name = name
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    def forward(self, input, label):
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        out = paddle.nn.functional.binary_cross_entropy(
            input, label, self.weight, self.reduction, self.name)
        return out
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class NLLLoss(fluid.dygraph.Layer):
    """
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	:alias_main: paddle.nn.NLLLoss
	:alias: paddle.nn.NLLLoss,paddle.nn.layer.NLLLoss,paddle.nn.layer.loss.NLLLoss
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    This class accepts input and target label and returns negative log likelihood
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    cross error. It is useful to train a classification problem with C classes.
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    The input for the loss is epected to contain log-probabilities of
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    each classes. It has to be a Tensor of size either (batch_size, C) or
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    (batch_size, C, d1, d2, ..., dK) with K >= 1 for the K-dimensional case.
    The label for the loss should be a class index in the range [0, C-1]
    where C is the number of classes. If ignore_index is specified, the
    specified target value does not contribute to the input gradient.
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    If the optional argument `weight` is provided, it should be a 1D Tensor
    assigning weight to each of the classed. This is particularly useful
    when you have an unbalanced training set.
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    The loss is calculated as follows.
    The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as:

    .. math::
        \ell(x, y) = L = \{l_1,\dots,l_N\}^\\top, \quad
        l_n = - w_{y_n} x_{n,y_n}, \quad
        w_{c} = \\text{weight}[c] \cdot \mathbb{1}\{c \\not= \\text{ignore\\_index}\},

    where :math:`N` is the batch size. If :attr:`reduction` is not ``'none'``
    (default ``'mean'``), then

    .. math::
        \ell(x, y) = \\begin{cases}
            \\sum_{n=1}^N \\frac{1}{\\sum_{n=1}^N w_{y_n}} l_n, &
            \\text{if reduction} = \\text{'mean';}\\\\
            \\sum_{n=1}^N l_n,  &
            \\text{if reduction} = \\text{'sum'.}
        \\end{cases}

    Parameters:
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        weight (Tensor, optional): Weight tensor, a manual rescaling weight given
            to each class. If given, it has to be a 1D Tensor whose size is `[C, ]`. Otherwise,
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            it treated as if having all ones. the data type is
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            float32, float64, Default is ``'None'``.
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        ignore_index (int64, optional): Specifies a target value that is ignored
            and does not contribute to the input gradient.
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        reduction (str, optional): Indicate how to average the loss,
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            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
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            If `reduction` is ``'mean'``, the reduced mean loss is returned;
            if `reduction` is ``'sum'``, the reduced sum loss is returned;
            if `reduction` is ``'none'``, no reduction will be apllied.
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            Default is ``'mean'``.
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         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
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    Shape:
        input (Tensor): Input tensor, the shape is :math:`[N, C]`, `C` is the number of classes.
            But in K-dimension situation, the shape is :math:`[N, C, d_1, d_2, ..., d_K]`.
            The data type is float32, float64.
        label (Tensor): Label tensor, the shape is :math:`[N,]` or :math:`[N, d_1, d_2, ..., d_K]`.
            The data type is int64.
        output (Tensor): the `negative log likelihood loss` between input `x` and `label`.
            If `reduction` is `'none'`, the shape is `[N, *]`.
            If `reduction` is `'sum'` or `'mean'`, the shape is `[1]`.
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    Examples:
        .. code-block:: python

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                import paddle
                import numpy as np
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                nll_loss = paddle.nn.layer.NLLLoss()
                log_softmax = paddle.nn.LogSoftmax(axis=1)
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                input_np = np.array([[0.88103855, 0.9908683 , 0.6226845 ],
                                 [0.53331435, 0.07999352, 0.8549948 ],
                                 [0.25879037, 0.39530203, 0.698465  ],
                                 [0.73427284, 0.63575995, 0.18827209],
                                 [0.05689114, 0.0862954 , 0.6325046 ]]).astype(np.float32)
                label_np = np.array([0, 2, 1, 1, 0]).astype(np.int64)
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                place = paddle.CPUPlace()
                paddle.disable_static(place)
                input = paddle.to_variable(input_np)
                log_out = log_softmax(input)
                label = paddle.to_variable(label_np)
                result = nll_loss(log_out, label)
                print(result.numpy()) # [1.0720209]
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    """
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    def __init__(self,
                 weight=None,
                 ignore_index=-100,
                 reduction='mean',
                 name=None):
        if reduction not in ['sum', 'mean', 'none']:
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            raise ValueError(
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                "The value of 'reduction' in nll_loss should be 'sum', 'mean' or "
                "'none', but received %s, which is not allowed." % reduction)
        super(NLLLoss, self).__init__()
        self._weight = weight
        self._ignore_index = ignore_index
        self._reduction = reduction
        self._name = name
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    def forward(self, input, label):
        return F.nll_loss(
            input,
            label,
            weight=self._weight,
            ignore_index=self._ignore_index,
            reduction=self._reduction,
            name=self._name)
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class KLDivLoss(fluid.dygraph.Layer):
    """
    This interface calculates the Kullback-Leibler divergence loss
    between Input(X) and Input(Target). Notes that Input(X) is the
    log-probability and Input(Target) is the probability.

    KL divergence loss is calculated as follows:

    $$l(x, y) = y * (\log(y) - x)$$

    Parameters:
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        reduction (str, optional): Indicate how to average the loss,
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            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
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            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned;
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            Default is ``'mean'``.

    Shape:
      - input: (N, *) where * means, any number of additional dimensions.
      - label: (N, *), same shape as input
      - output: tensor with shape: (1) by default.


    Examples:
        .. code-block:: python

            import paddle
            import numpy as np
            import paddle.nn as nn
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            paddle.enable_imperative()

            shape = (5, 20)
            x = np.random.uniform(-10, 10, shape).astype('float32')
            target = np.random.uniform(-10, 10, shape).astype('float32')

            # 'batchmean' reduction, loss shape will be [N]
            kldiv_criterion = nn.KLDivLoss(reduction='batchmean')
            pred_loss = kldiv_criterion(paddle.to_variable(x),
                                        paddle.to_variable(target))
            # shape=[5]
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            # 'mean' reduction, loss shape will be [1]
            kldiv_criterion = nn.KLDivLoss(reduction='mean')
            pred_loss = kldiv_criterion(paddle.to_variable(x),
                                        paddle.to_variable(target))
            # shape=[1]

            # 'sum' reduction, loss shape will be [1]
            kldiv_criterion = nn.KLDivLoss(reduction='sum')
            pred_loss = kldiv_criterion(paddle.to_variable(x),
                                        paddle.to_variable(target))
            # shape=[1]

            # 'none' reduction, loss shape is same with X shape
            kldiv_criterion = nn.KLDivLoss(reduction='none')
            pred_loss = kldiv_criterion(paddle.to_variable(x),
                                        paddle.to_variable(target))
            # shape=[5, 20]
    """

    def __init__(self, reduction='mean'):
        super(KLDivLoss, self).__init__()
        self.reduction = reduction

    def forward(self, input, label):
        out = paddle.nn.functional.kl_div(input, label, self.reduction)
        return out


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class MarginRankingLoss(fluid.dygraph.Layer):
    """

    This interface is used to construct a callable object of the ``MarginRankingLoss`` class.
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    The MarginRankingLoss layer calculates the margin rank loss between the input, other and label
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    , use the math function as follows.

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    .. math::
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        margin\_rank\_loss = max(0, -label * (input - other) + margin)
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    If :attr:`reduction` set to ``'mean'``, the reduced mean loss is:

    .. math::
        Out = MEAN(margin\_rank\_loss)

    If :attr:`reduction` set to ``'sum'``, the reduced sum loss is:

    .. math::
        Out = SUM(margin\_rank\_loss)

    If :attr:`reduction` set to ``'none'``, just return the origin ``margin_rank_loss``.

    Parameters:
        margin (float, optional): The margin value to add, default value is 0;
        reduction (str, optional): Indicate the reduction to apply to the loss, the candicates are ``'none'``, ``'mean'``, ``'sum'``.If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned. If :attr:`reduction` is ``'sum'``, the reduced sum loss is returned. Default is ``'mean'``.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

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    Shape:
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        input: N-D Tensor, the shape is [N, *], N is batch size and `*` means any number of additional dimensions., available dtype is float32, float64.
        other: N-D Tensor, `other` have the same shape and dtype as `input`.
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        label: N-D Tensor, label have the same shape and dtype as `input`.
        output: If :attr:`reduction` is ``'mean'`` or ``'sum'`` , the out shape is :math:`[1]`, otherwise the shape is the same as `input` .The same dtype as input tensor.
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    Returns:
        A callable object of MarginRankingLoss.

    Examples:

        .. code-block:: python

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            import numpy as np
            import paddle

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            paddle.disable_static()
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            input = paddle.to_variable(np.array([[1, 2], [3, 4]]).astype("float32"))
            other = paddle.to_variable(np.array([[2, 1], [2, 4]]).astype("float32"))
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            label = paddle.to_variable(np.array([[1, -1], [-1, -1]]).astype("float32"))
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            margin_rank_loss = paddle.nn.MarginRankingLoss()
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            loss = margin_rank_loss(input, other, label)
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            print(loss.numpy()) # [0.75]
    """

    def __init__(self, margin=0.0, reduction='mean', name=None):
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
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                "The value of 'reduction' in MarginRankingLoss should be 'sum', 'mean' or 'none', but "
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                "received %s, which is not allowed." % reduction)
        super(MarginRankingLoss, self).__init__()
        self.margin = margin
        self.reduction = reduction
        self.name = name

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    def forward(self, input, other, label):
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        out = paddle.nn.functional.margin_ranking_loss(
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            input, other, label, self.margin, self.reduction, self.name)
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        return out
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class CTCLoss(fluid.dygraph.Layer):
    """
	:alias_main: paddle.nn.CTCLoss
	:alias: paddle.nn.CTCLoss, paddle.nn.layer.CTCLoss, paddle.nn.layer.loss.CTCLoss

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    An operator integrating the open source Warp-CTC library (https://github.com/baidu-research/warp-ctc)
    to compute Connectionist Temporal Classification (CTC) loss.
    It can be aliased as softmax with CTC, since a native softmax activation
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    is interated to the Warp-CTC library to normalize values for each row of the input tensor.

    Parameters:
        blank (int, optional): The blank label index of Connectionist Temporal Classification (CTC) loss, which is in the half-opened interval [0, num_classes + 1). The data type must be int32. Default is 0.
        reduction (string, optional): Indicate how to average the loss, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'mean'``, the output loss will be divided by the label_lengths, and then return the mean of quotient; If :attr:`reduction` is ``'sum'``, return the sum of loss; If :attr:`reduction` is ``'none'``, no reduction will be applied. Default is ``'mean'``.

    Shape:
        log_probs (Tensor): The unscaled probability sequence with padding, which is a 3-D Tensor. The tensor shape is [max_logit_length, batch_size, num_classes + 1], where max_logit_length is the longest length of input logit sequence. The data type must be float32.
        labels (Tensor): The ground truth sequence with padding, which must be a 3-D Tensor. The tensor shape is [batch_size, max_label_length], where max_label_length is the longest length of label sequence. The data type must be int32.
        input_lengths (Tensor): The length for each input sequence, it should have shape [batch_size] and dtype int64.
        label_lengths (Tensor): The length for each label sequence, it should have shape [batch_size] and dtype int64.

    Returns:
        Tensor, The Connectionist Temporal Classification (CTC) loss between ``log_probs`` and  ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is [1]. Data type is the same as ``log_probs``.
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    Examples:

        .. code-block:: python

            # declarative mode
            import numpy as np
            import paddle

            # length of the longest logit sequence
            max_seq_length = 4
            #length of the longest label sequence
            max_label_length = 3
            # number of logit sequences
            batch_size = 2
            # class num
            class_num = 3

            np.random.seed(1)
            log_probs = np.array([[[4.17021990e-01, 7.20324516e-01, 1.14374816e-04],
                                    [3.02332580e-01, 1.46755889e-01, 9.23385918e-02]],

                                    [[1.86260208e-01, 3.45560730e-01, 3.96767467e-01],
                                    [5.38816750e-01, 4.19194520e-01, 6.85219526e-01]],

                                    [[2.04452246e-01, 8.78117442e-01, 2.73875929e-02],
                                    [6.70467496e-01, 4.17304814e-01, 5.58689833e-01]],

                                    [[1.40386939e-01, 1.98101491e-01, 8.00744593e-01],
                                    [9.68261600e-01, 3.13424170e-01, 6.92322612e-01]],

                                    [[8.76389146e-01, 8.94606650e-01, 8.50442126e-02],
                                    [3.90547849e-02, 1.69830427e-01, 8.78142476e-01]]]).astype("float32")
            labels = np.array([[1, 2, 2],
                            [1, 2, 2]]).astype("int32")
            input_lengths = np.array([5, 5]).astype("int64")
            label_lengths = np.array([3, 3]).astype("int64")

            paddle.disable_static()
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            log_probs = paddle.to_tensor(log_probs)
            labels = paddle.to_tensor(labels)
            input_lengths = paddle.to_tensor(input_lengths)
            label_lengths = paddle.to_tensor(label_lengths)
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            loss = paddle.nn.CTCLoss(blank=0, reduction='none')(log_probs, labels,
                input_lengths,
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                label_lengths)
            print(loss.numpy())  #[3.9179852 2.9076521]

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            loss = paddle.nn.CTCLoss(blank=0, reduction='mean')(log_probs, labels,
                input_lengths,
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                label_lengths)
            print(loss.numpy())  #[1.1376063]
    """

    def __init__(self, blank=0, reduction='mean'):
        super(CTCLoss, self).__init__()
        self.blank = blank
        self.reduction = reduction

    def forward(self, log_probs, labels, input_lengths, label_lengths):
        return paddle.nn.functional.ctc_loss(log_probs, labels, input_lengths,
                                             label_lengths, self.blank,
                                             self.reduction)


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class SmoothL1Loss(fluid.dygraph.Layer):
    """
    This operator calculates smooth_l1_loss. Creates a criterion that uses a squared
    term if the absolute element-wise error falls below 1 and an L1 term otherwise.
    In some cases it can prevent exploding gradients and it is more robust and less
    sensitivity to outliers. Also known as the Huber loss:

    .. math::

         loss(x,y)=\\frac{1}{n}\\sum_{i}z_i

    where z_i is given by:

    .. math::

         \\mathop{z_i}=\\left\\{\\begin{array}{rcl}
        0.5(x_i - y_i)^2 & & {if |x_i - y_i| < delta} \\\\
        delta * |x_i - y_i| - 0.5 * delta^2 & & {otherwise}
        \\end{array} \\right.

    Parameters:
        reduction (str, optional): Indicate how to average the loss by batch_size,
            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned;
            If :attr:`reduction` is ``'sum'``, the reduced sum loss is returned.
            If :attr:`reduction` is ``'none'``, the unreduced loss is returned.
            Default is ``'mean'``.
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        delta (float, optional): Specifies the hyperparameter delta to be used.
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            The value determines how large the errors need to be to use L1. Errors
            smaller than delta are minimized with L2. Parameter is ignored for
            negative/zero values. Default = 1.0
        name (str, optional): Name for the operation (optional, default is
            None). For more information, please refer to :ref:`api_guide_Name`.

    Call Parameters:
        input (Tensor): Input tensor, the data type is float32 or float64. Shape is
            (N, C), where C is number of classes, and if shape is more than 2D, this
            is (N, C, D1, D2,..., Dk), k >= 1.
        label (Tensor): Label tensor, the data type is float32 or float64. The shape of label
            is the same as the shape of input.

    Returns:
        The tensor variable storing the smooth_l1_loss of input and label.

    Return type: Tensor.

    Examples:
        .. code-block:: python

            import paddle
            import numpy as np
            paddle.disable_static()
            input_data = np.random.rand(3,3).astype("float32")
            label_data = np.random.rand(3,3).astype("float32")
            input = paddle.to_tensor(input_data)
            label = paddle.to_tensor(label_data)
            loss = paddle.nn.SmoothL1Loss()
            output = loss(input, label)
            print(output.numpy())
    """

    def __init__(self, reduction='mean', delta=1.0, name=None):
        super(SmoothL1Loss, self).__init__()
        self.reduction = reduction
        self.delta = delta
        self.name = name

    def forward(self, input, label):
        return F.smooth_l1_loss(
            input,
            label,
            reduction=self.reduction,
            delta=self.delta,
            name=self.name)