130 lines
4.3 KiB
Python
130 lines
4.3 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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 __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from paddle import optimizer as optim
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class Momentum(object):
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"""
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Simple Momentum optimizer with velocity state.
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Args:
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learning_rate (float|Variable) - The learning rate used to update parameters.
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Can be a float value or a Variable with one float value as data element.
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momentum (float) - Momentum factor.
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regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
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"""
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def __init__(self,
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learning_rate,
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momentum,
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weight_decay=None,
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grad_clip=None,
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**args):
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super(Momentum, self).__init__()
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self.learning_rate = learning_rate
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self.momentum = momentum
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self.weight_decay = weight_decay
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self.grad_clip = grad_clip
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def __call__(self, parameters):
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opt = optim.Momentum(
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learning_rate=self.learning_rate,
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momentum=self.momentum,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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parameters=parameters)
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return opt
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class Adam(object):
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def __init__(self,
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learning_rate=0.001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-08,
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parameter_list=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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lazy_mode=False,
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**kwargs):
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self.learning_rate = learning_rate
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self.beta1 = beta1
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self.beta2 = beta2
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self.epsilon = epsilon
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self.parameter_list = parameter_list
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self.learning_rate = learning_rate
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self.weight_decay = weight_decay
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self.grad_clip = grad_clip
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self.name = name
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self.lazy_mode = lazy_mode
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def __call__(self, parameters):
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opt = optim.Adam(
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learning_rate=self.learning_rate,
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beta1=self.beta1,
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beta2=self.beta2,
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epsilon=self.epsilon,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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name=self.name,
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lazy_mode=self.lazy_mode,
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parameters=parameters)
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return opt
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class RMSProp(object):
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"""
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Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning rate method.
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Args:
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learning_rate (float|Variable) - The learning rate used to update parameters.
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Can be a float value or a Variable with one float value as data element.
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momentum (float) - Momentum factor.
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rho (float) - rho value in equation.
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epsilon (float) - avoid division by zero, default is 1e-6.
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regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
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"""
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def __init__(self,
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learning_rate,
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momentum=0.0,
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rho=0.95,
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epsilon=1e-6,
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weight_decay=None,
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grad_clip=None,
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**args):
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super(RMSProp, self).__init__()
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self.learning_rate = learning_rate
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self.momentum = momentum
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self.rho = rho
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self.epsilon = epsilon
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self.weight_decay = weight_decay
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self.grad_clip = grad_clip
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def __call__(self, parameters):
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opt = optim.RMSProp(
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learning_rate=self.learning_rate,
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momentum=self.momentum,
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rho=self.rho,
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epsilon=self.epsilon,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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parameters=parameters)
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return opt
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