You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
108 lines
3.7 KiB
108 lines
3.7 KiB
# Copyright (c) 2018 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 initializer import Initializer, Xavier, Constant
|
|
from regularizer import WeightDecayRegularizer
|
|
|
|
__all__ = [
|
|
'ParamAttr',
|
|
'WeightNormParamAttr',
|
|
]
|
|
|
|
|
|
class ParamAttr(object):
|
|
def __init__(self,
|
|
name=None,
|
|
initializer=None,
|
|
learning_rate=1.0,
|
|
regularizer=None,
|
|
trainable=True,
|
|
gradient_clip=None,
|
|
do_model_average=None):
|
|
self.name = name
|
|
self.initializer = initializer
|
|
self.learning_rate = learning_rate
|
|
self.regularizer = regularizer
|
|
self.trainable = trainable
|
|
self.gradient_clip = gradient_clip
|
|
self.model_average = do_model_average
|
|
|
|
def set_default_initializer(self, initializer):
|
|
if initializer is None:
|
|
if self.initializer is None:
|
|
raise ValueError("ParamAttr.initializer is not set")
|
|
return
|
|
|
|
if self.initializer is not None:
|
|
return
|
|
|
|
self.initializer = initializer
|
|
|
|
def set_default_param_initializer(self):
|
|
self.set_default_initializer(Xavier())
|
|
|
|
def set_default_bias_initializer(self):
|
|
self.set_default_initializer(Constant(0.0))
|
|
|
|
@staticmethod
|
|
def to_attr(arg):
|
|
if arg is None:
|
|
return ParamAttr()
|
|
elif isinstance(arg, list) or isinstance(arg, tuple):
|
|
return [ParamAttr.to_attr(a) for a in arg]
|
|
elif isinstance(arg, ParamAttr):
|
|
return arg
|
|
elif isinstance(arg, str) or isinstance(arg, unicode):
|
|
return ParamAttr(name=arg)
|
|
elif isinstance(arg, Initializer):
|
|
return ParamAttr(initializer=arg)
|
|
elif isinstance(arg, WeightDecayRegularizer):
|
|
return ParamAttr(regularizer=arg)
|
|
elif isinstance(arg, bool):
|
|
return ParamAttr.to_attr(None) if arg else False
|
|
else:
|
|
raise TypeError("{0} cast to ParamAttr".format(type(arg)))
|
|
|
|
def to_kwargs(self, with_initializer=False):
|
|
kwargs = {
|
|
'name': self.name,
|
|
'optimize_attr': {
|
|
'learning_rate': self.learning_rate
|
|
},
|
|
'regularizer': self.regularizer,
|
|
'trainable': self.trainable,
|
|
'gradient_clip_attr': self.gradient_clip,
|
|
'model_average': self.model_average
|
|
}
|
|
if with_initializer:
|
|
kwargs['initializer'] = self.initializer
|
|
return kwargs
|
|
|
|
|
|
class WeightNormParamAttr(ParamAttr):
|
|
"""
|
|
Used for weight normalization. Any field in ParamAttr can also be set here.
|
|
Besides, an extra field dim can be set to indicate the dimension except
|
|
which to normalize.
|
|
"""
|
|
# List to record the parameters reparameterized by weight normalization.
|
|
# If these parameters are treated as Variable rather than Parameter,
|
|
# it can be used to discriminate these parameters and help to serialize
|
|
# these paramters for inference.
|
|
params_with_weight_norm = []
|
|
|
|
def __init__(self, dim=None, **kwargs):
|
|
super(WeightNormParamAttr, self).__init__(**kwargs)
|
|
self.dim = dim
|