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109 lines
4.5 KiB
109 lines
4.5 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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from .optimizer import Optimizer
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from ..fluid import core
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from ..fluid import framework
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from ..fluid.framework import Variable, name_scope
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from ..fluid.dygraph import no_grad
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__all__ = ["SGD"]
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class SGD(Optimizer):
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"""
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Optimizer of the stochastic gradient descent algorithm.
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.. math::
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param\_out = param - learning\_rate * grad
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Parameters:
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learning_rate (float|Tensor|LearningRateDecay, optional): The learning rate used to update ``Parameter``.
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It can be a float value, a ``Tensor`` with a float type or a LearningRateDecay. The default value is 0.001.
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parameters (list, optional): List of ``Tensor`` to update to minimize ``loss``. \
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This parameter is required in dygraph mode. \
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The default value is None in static mode, at this time all parameters will be updated.
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weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \
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It canbe a float value as coeff of L2 regularization or \
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:ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`.
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If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \
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the regularization setting here in optimizer will be ignored for this parameter. \
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Otherwise, the regularization setting here in optimizer will take effect. \
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Default None, meaning there is no regularization.
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grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
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some derived class of ``GradientClipBase`` . There are three cliping strategies
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( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
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:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
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name (str, optional): The default value is None. Normally there is no need for user
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to set this property. For more information, please refer to
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:ref:`api_guide_Name` .
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Examples:
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.. code-block:: python
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import paddle
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import numpy as np
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paddle.disable_static()
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inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
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linear = paddle.nn.Linear(10, 10)
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inp = paddle.to_tensor(inp)
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out = linear(inp)
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loss = paddle.mean(out)
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beta1 = paddle.to_tensor([0.9], dtype="float32")
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beta2 = paddle.to_tensor([0.99], dtype="float32")
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sgd = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters(), weight_decay=0.01)
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back = out.backward()
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sgd.step()
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sgd.clear_grad()
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"""
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def __init__(self,
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learning_rate=0.001,
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parameters=None,
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weight_decay=None,
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grad_clip=None,
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name=None):
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if learning_rate is None:
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raise ValueError("learning_rate is not set")
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super(SGD, self).__init__(
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learning_rate=learning_rate,
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parameters=parameters,
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weight_decay=weight_decay,
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grad_clip=grad_clip,
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name=name)
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self.type = "sgd"
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@no_grad
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def _append_optimize_op(self, block, param_and_grad):
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lr = self._create_param_lr(param_and_grad)
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if framework.in_dygraph_mode():
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core.ops.sgd(param_and_grad[0], lr, param_and_grad[1],
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param_and_grad[0])
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return None
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assert isinstance(block, framework.Block)
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# create the optimize op
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sgd_op = block.append_op(
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type=self.type,
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inputs={
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"Param": param_and_grad[0],
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"Grad": param_and_grad[1],
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"LearningRate": lr
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},
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outputs={"ParamOut": param_and_grad[0]},
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stop_gradient=True)
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return sgd_op
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