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208 lines
8.2 KiB
208 lines
8.2 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
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__all__ = ["RMSProp"]
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class RMSProp(Optimizer):
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"""
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Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning
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rate method. The original slides proposed RMSProp: Slide 29 of
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http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf .
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The original equation is as follows:
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.. math::
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r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
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w & = w - \\frac{\\eta} {\\sqrt{r(w,t) + \\epsilon}} \\nabla Q_{i}(w)
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The first equation calculates moving average of the squared gradient for
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each weight. Then dividing the gradient by :math:`sqrt{v(w,t)}`.
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In some cases, adding a momentum term :math: `\\beta` is beneficial.
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In our implementation, Nesterov momentum is used:
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.. math::
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r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
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v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) +
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\\epsilon}} \\nabla Q_{i}(w)
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w & = w - v(w, t)
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if centered is True:
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.. math::
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r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
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g(w, t) & = \\rho g(w, t-1) + (1 - \\rho)\\nabla Q_{i}(w)
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v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) - (g(w, t))^2 +
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\\epsilon}} \\nabla Q_{i}(w)
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w & = w - v(w, t)
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where, :math:`\\rho` is a hyperparameter and typical values are 0.9, 0.95
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and so on. :math: `beta` is the momentum term. :math: `\\epsilon` is a
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smoothing term to avoid division by zero, usually set somewhere in range
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from 1e-4 to 1e-8.
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Parameters:
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learning_rate (float|LRScheduler): The learning rate used to update ``Parameter``.
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It can be a float value or a LRScheduler.
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rho(float): rho is :math: `\\rho` in equation, default is 0.95.
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epsilon(float): :math: `\\epsilon` in equation is smoothing term to
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avoid division by zero, default is 1e-6.
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momentum(float): :math:`\\beta` in equation is the momentum term,
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default is 0.0.
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centered(bool): If True, gradients are normalized by the estimated variance of
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the gradient; if False, by the uncentered second moment. Setting this to
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True may help with training, but is slightly more expensive in terms of
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computation and memory. Defaults to False.
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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): This parameter is used by developers to print debugging information. \
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For details, please refer to :ref:`api_guide_Name`. Default is None.
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Raises:
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ValueError: If learning_rate, rho, epsilon, momentum are None.
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Examples:
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.. code-block:: python
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import paddle
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inp = paddle.rand([10,10], dtype="float32")
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linear = paddle.nn.Linear(10, 10)
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out = linear(inp)
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loss = paddle.mean(out)
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rmsprop = paddle.optimizer.RMSProp(learning_rate=0.1,
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parameters=linear.parameters(),
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weight_decay=0.01)
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out.backward()
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rmsprop.step()
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rmsprop.clear_grad()
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"""
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_momentum_acc_str = "momentum"
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_mean_square_acc_str = "mean_square"
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_mean_grad_acc_str = "mean_grad"
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def __init__(self,
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learning_rate,
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rho=0.95,
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epsilon=1.0e-6,
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momentum=0.0,
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centered=False,
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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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if rho is None:
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raise ValueError("rho is not set.")
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if epsilon is None:
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raise ValueError("epsilon is not set.")
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if momentum is None:
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raise ValueError("momentum is not set.")
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if not 0.0 <= epsilon:
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raise ValueError("Invalid value of epsilon, expect epsilon >= 0.")
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if not 0.0 <= momentum:
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raise ValueError("Invalid value of momentum, expect momentum >= 0.")
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if not 0.0 <= rho:
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raise ValueError("Invalid value of rho, expect rho >= 0.")
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super(RMSProp, 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 = "rmsprop"
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self._rho = rho
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self._epsilon = epsilon
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self._momentum = momentum
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self._centered = centered
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def _create_accumulators(self, block, parameters):
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if not isinstance(block, framework.Block):
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raise TypeError("block is not instance of framework.Block.")
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for p in parameters:
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self._add_accumulator(self._momentum_acc_str, p)
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self._add_accumulator(self._mean_square_acc_str, p)
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self._add_accumulator(self._mean_grad_acc_str, p)
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def _append_optimize_op(self, block, param_and_grad):
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if not isinstance(block, framework.Block):
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raise TypeError("block is not instance of framework.Block.")
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momentum_acc = self._get_accumulator(self._momentum_acc_str,
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param_and_grad[0])
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mean_square_acc = self._get_accumulator(self._mean_square_acc_str,
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param_and_grad[0])
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mean_grad_acc = self._get_accumulator(self._mean_grad_acc_str,
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param_and_grad[0])
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rmsprop_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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"Moment": momentum_acc,
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"MeanSquare": mean_square_acc,
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"MeanGrad": mean_grad_acc,
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"LearningRate": self._create_param_lr(param_and_grad),
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},
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outputs={
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"ParamOut": param_and_grad[0],
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"MomentOut": momentum_acc,
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"MeanSquareOut": mean_square_acc,
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"MeanGradOut": mean_grad_acc
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},
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attrs={
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"epsilon": self._epsilon,
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"decay": self._rho,
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"momentum": self._momentum,
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"centered": self._centered
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},
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stop_gradient=True)
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return rmsprop_op
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