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112 lines
4.4 KiB
112 lines
4.4 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# TODO: define loss functions of neural network
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import paddle.fluid as fluid
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__all__ = [
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#'NCELoss',
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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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]
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class L1Loss(fluid.dygraph.Layer):
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"""
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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 predictions and target
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labels as follows.
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If :attr:`reduction` set to ``'none'``, the unreduced loss is:
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.. math::
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Out = |input - label|
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If :attr:`reduction` set to ``'mean'``, the reduced mean loss is:
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.. math::
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Out = MEAN(|input - label|)
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If :attr:`reduction` set to ``'sum'``, the reduced sum loss is:
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.. math::
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Out = SUM(|input - label|)
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The shape of input predictions and target labels are [N, *], where N is batch_size and `*`
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means any number of additional dimensions.
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If :attr:`reduction` is ``'none'``, the shape of output loss is [N, *], the same as input.
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If :attr:`reduction` is ``'mean'`` or ``'sum'``, the shape of output loss is [1], which means the output is a scalar.
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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 :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 reduced sum loss is returned.
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Default is ``'mean'``.
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Returns:
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A callable object of L1Loss.
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Examples:
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.. code-block:: python
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# declarative mode
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import paddle.fluid as fluid
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import numpy as np
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import paddle
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input = fluid.data(name="input", shape=[1])
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label = fluid.data(name="label", shape=[1])
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l1_loss = paddle.nn.loss.L1Loss(reduction='mean')
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output = l1_loss(input,label)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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input_data = np.array([1.5]).astype("float32")
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label_data = np.array([1.7]).astype("float32")
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output_data = exe.run(fluid.default_main_program(),
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feed={"input":input_data, "label":label_data},
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fetch_list=[output],
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return_numpy=True)
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print(output_data) # [array([0.2], dtype=float32)]
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# imperative mode
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import paddle.fluid.dygraph as dg
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with dg.guard(place) as g:
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input = dg.to_variable(input_data)
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label = dg.to_variable(label_data)
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l1_loss = paddle.nn.loss.L1Loss(reduction='mean')
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output = l1_loss(input,label)
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print(output.numpy()) # [0.2]
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"""
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def __init__(self, reduction='mean'):
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if reduction not in ['sum', 'mean', 'none']:
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raise ValueError(
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"The value of 'reduction' in L1Loss should be 'sum', 'mean' or 'none', but "
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"received %s, which is not allowed." % reduction)
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super(L1Loss, self).__init__()
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self.reduction = reduction
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def forward(self, input, label):
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fluid.data_feeder.check_variable_and_dtype(
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input, 'input', ['float32', 'float64', 'int32', 'int64'], 'l1_loss')
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fluid.data_feeder.check_variable_and_dtype(
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label, 'label', ['float32', 'float64', 'int32', 'int64'], 'l1_loss')
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unreduced = fluid.layers.elementwise_sub(input, label, act='abs')
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if self.reduction == 'sum':
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return fluid.layers.reduce_sum(unreduced)
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elif self.reduction == 'mean':
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return fluid.layers.reduce_mean(unreduced)
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else:
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return unreduced
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