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regularizer.py
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regularizer.py
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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.
from paddle import _C_ops
from paddle.fluid import framework
from paddle.fluid.framework import in_dygraph_mode
__all__ = ['L1Decay', 'L2Decay']
class WeightDecayRegularizer:
"""Base class for weight decay regularizers
Defines the common interface of weight-decay regularizers.
Weight-decay regularizers are added only during the backward
pass for faster regularization. They add operations to the network
that correspond to gradient of the regularization function.
Users should not use this class directly, but need to use one
of its implementations
"""
def __init__(self):
pass
def __call__(self, param, grad, block):
"""Add corresponding weight decay operations to the network"""
raise NotImplementedError()
def __str__(self):
"""Debug string"""
raise NotImplementedError()
class L1Decay(WeightDecayRegularizer):
r"""
Implement the L1 Weight Decay Regularization, which encourages the weights to be sparse.
It can be set in :ref:`api_paddle_ParamAttr` or ``optimizer`` (such as :ref:`api_paddle_optimizer_Momentum` ).
When set in ``ParamAttr`` , it only takes effect for trainable parameters in this layer. When set in
``optimizer`` , it takes effect for all trainable parameters. When set together, ``ParamAttr`` has
higher priority than ``optimizer`` , which means that for a trainable parameter, if regularizer is defined
in its ParamAttr, then the regularizer in Optimizer will be ignored. Otherwise the regularizer
in Optimizer will be used.
In the implementation, the loss function of L1 Weight Decay Regularization is as follows:
.. math::
loss = coeff * reduce\_sum(abs(x))
Args:
coeff(float, optional): regularization coeff. Default:0.0.
Examples:
.. code-block:: python
# Example1: set Regularizer in optimizer
import paddle
from paddle.regularizer import L1Decay
linear = paddle.nn.Linear(10, 10)
inp = paddle.rand(shape=[10, 10], dtype="float32")
out = linear(inp)
loss = paddle.mean(out)
beta1 = paddle.to_tensor([0.9], dtype="float32")
beta2 = paddle.to_tensor([0.99], dtype="float32")
momentum = paddle.optimizer.Momentum(
learning_rate=0.1,
parameters=linear.parameters(),
weight_decay=L1Decay(0.0001))
back = out.backward()
momentum.step()
momentum.clear_grad()
# Example2: set Regularizer in parameters
# Set L1 regularization in parameters.
# Global regularizer does not take effect on my_conv2d for this case.
from paddle.nn import Conv2D
from paddle import ParamAttr
from paddle.regularizer import L2Decay
my_conv2d = Conv2D(
in_channels=10,
out_channels=10,
kernel_size=1,
stride=1,
padding=0,
weight_attr=ParamAttr(regularizer=L2Decay(coeff=0.01)),
bias_attr=False)
"""
def __init__(self, coeff=0.0):
assert coeff is not None
super().__init__()
self._coeff = coeff
def __call__(self, param, grad, block):
"""Add L1 weight decay ops to network
Adds L1 weight decay ops.
L1WeightDecay = reg_coeff * sign(parameter)
Args:
param: parameter variable for which regularization is applied
block: block in which variable is to be created
Returns:
new variable for weight decay
"""
assert isinstance(param, framework.Variable)
assert isinstance(block, framework.Block)
if in_dygraph_mode():
sign = _C_ops.sign(param)
return _C_ops.scale(sign, self._coeff, 0.0, True)
else:
sign = block.create_var(
dtype=param.dtype, shape=param.shape, lod_level=param.lod_level
)
decay = block.create_var(
dtype=param.dtype, shape=param.shape, lod_level=param.lod_level
)
# Append sign op
block.append_op(
type='sign', inputs={"X": param}, outputs={"Out": sign}
)
# Append scale op to the output of sign op
block.append_op(
type='scale',
inputs={"X": sign},
outputs={"Out": decay},
attrs={"scale": self._coeff},
)
return decay
def __str__(self):
return "L1Decay, coeff=%f" % self._coeff
class L2Decay(WeightDecayRegularizer):
r"""
Implement the L2 Weight Decay Regularization, which helps to prevent the model over-fitting.
It can be set in :ref:`api_paddle_ParamAttr` or ``optimizer`` (such as :ref:`api_paddle_optimizer_Momentum` ).
When set in ``ParamAttr`` , it only takes effect for trainable parameters in this layer. When set in
``optimizer`` , it takes effect for all trainable parameters. When set together, ``ParamAttr`` has
higher priority than ``optimizer`` , which means that for a trainable parameter, if regularizer is defined
in its ParamAttr, then the regularizer in Optimizer will be ignored. Otherwise the regularizer
in Optimizer will be used.
In the implementation, the loss function of L2 Weight Decay Regularization is as follows:
.. math::
loss = 0.5 * coeff * reduce\_sum(square(x))
Args:
coeff(float, optional): regularization coeff. Default:0.0
Examples:
.. code-block:: python
# Example1: set Regularizer in optimizer
import paddle
from paddle.regularizer import L2Decay
linear = paddle.nn.Linear(10, 10)
inp = paddle.rand(shape=[10, 10], dtype="float32")
out = linear(inp)
loss = paddle.mean(out)
beta1 = paddle.to_tensor([0.9], dtype="float32")
beta2 = paddle.to_tensor([0.99], dtype="float32")
momentum = paddle.optimizer.Momentum(
learning_rate=0.1,
parameters=linear.parameters(),
weight_decay=L2Decay(0.0001))
back = out.backward()
momentum.step()
momentum.clear_grad()
# Example2: set Regularizer in parameters
# Set L2 regularization in parameters.
# Global regularizer does not take effect on my_conv2d for this case.
from paddle.nn import Conv2D
from paddle import ParamAttr
from paddle.regularizer import L2Decay
my_conv2d = Conv2D(
in_channels=10,
out_channels=10,
kernel_size=1,
stride=1,
padding=0,
weight_attr=ParamAttr(regularizer=L2Decay(coeff=0.01)),
bias_attr=False)
"""
def __init__(self, coeff=0.0):
assert coeff is not None
super().__init__()
self._coeff = coeff
def __call__(self, param, grad, block):
"""Add L2 weight decay ops to network
Adds L2 weight decay ops.
L2WeightDecay = reg_coeff * parameter
Args:
param: parameter variable for which regularization is applied
block: block in which variable is to be created
Returns:
new variable for weight decay
"""
assert isinstance(param, framework.Variable)
assert isinstance(block, framework.Block)
if in_dygraph_mode():
return _C_ops.scale(param, self._coeff, 0.0, True)
else:
decay = block.create_var(
dtype=param.dtype, shape=param.shape, lod_level=param.lod_level
)
# Append Op to calculate decay
block.append_op(
type='scale',
inputs={"X": param},
outputs={"Out": decay},
attrs={"scale": self._coeff},
)
return decay
def __str__(self):
return "L2Decay, coeff=%f" % self._coeff