PaddleClas/ppcls/optimizer/optimizer.py

56 lines
2.0 KiB
Python

# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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 __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.fluid.optimizer as pfopt
import paddle.fluid.regularizer as pfreg
__all__ = ['OptimizerBuilder']
class OptimizerBuilder(object):
"""
Build optimizer with fluid api in fluid.layers.optimizer,
such as fluid.layers.optimizer.Momentum()
https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/optimizer_cn.html
https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/regularizer_cn.html
Args:
function(str): optimizer name of learning rate
params(dict): parameters used for init the class
regularizer (dict): parameters used for create regularization
"""
def __init__(self,
function='Momentum',
params={'momentum': 0.9},
regularizer=None):
self.function = function
self.params = params
# create regularizer
if regularizer is not None:
reg_func = regularizer['function'] + 'Decay'
reg_factor = regularizer['factor']
reg = getattr(pfreg, reg_func)(reg_factor)
self.params['regularization'] = reg
def __call__(self, learning_rate, parameter_list):
opt = getattr(pfopt, self.function)
return opt(learning_rate=learning_rate,
parameter_list=parameter_list,
**self.params)