blob: 2b9239ee4a46f1882501513ec52c5c04cb555846 [file] [log] [blame]
# Copyright (c) 2016-present, Facebook, Inc.
#
# 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.
##############################################################################
# @package optimizer
# Module caffe2.python.optimizer
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core
class Regularizer(object):
def __init__(self):
pass
'''
Adds regularization to train_net for given parameter. Its factor ahead of
regularization is given when initialization.
The param should be a BlobReference.
'''
def __call__(self, train_net, param):
assert isinstance(param, core.BlobReference)
return self._run(train_net, param)
def _run(self, train_net, param):
raise Exception("Not Impelemented")
class L1Norm(Regularizer):
def __init__(self, reg_lambda):
super(L1Norm, self).__init__()
assert reg_lambda >= 0,\
'factor ahead of regularization should be 0 or positive'
self.reg_lambda = reg_lambda
def _run(self, train_net, param):
output_blob = train_net.NextScopedBlob(param + '_l1_regularization')
train_net.LpNorm([param], [output_blob], p=1)
train_net.Scale([output_blob], [output_blob], scale=self.reg_lambda)
return output_blob
class L2Norm(Regularizer):
def __init__(self, reg_lambda):
super(L2Norm, self).__init__()
assert reg_lambda >= 0,\
'factor ahead of regularization should be 0 or positive'
self.reg_lambda = reg_lambda
def _run(self, train_net, param):
output_blob = train_net.NextScopedBlob(param + '_l2_regularization')
train_net.LpNorm([param], [output_blob], p=2)
train_net.Scale([output_blob], [output_blob], scale=self.reg_lambda)
return output_blob