| # 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 |