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e76015040a
Summary: Regularization added for caffe2 and dper. This regularization is intended for `dense feature `only. Sparse feature would serve as individual optimizer, see ` D5618405 ` and `D5534579` for details. The implementation of dense regularization is similar to the ones in optimizer. we now support `l1 norm` and ` l2 norm` in regularizer. In dper, we would call different regularization based on regularization type defined in model_definition.thrift. Reviewed By: xianjiec Differential Revision: D5724851 fbshipit-source-id: 0fbee698cfeff1ac477fc9d07785406069f8d9c8
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
# @package optimizer
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# Module caffe2.python.optimizer
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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 caffe2.python import core
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class Regularizer(object):
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def __init__(self):
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pass
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'''
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Adds regularization to train_net for given parameter. Its factor ahead of
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regularization is given when initialization.
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The param should be a BlobReference.
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'''
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def __call__(self, train_net, param):
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assert isinstance(param, core.BlobReference)
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return self._run(train_net, param)
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def _run(self, train_net, param):
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raise Exception("Not Impelemented")
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class L1Norm(Regularizer):
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def __init__(self, reg_lambda):
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super(L1Norm, self).__init__()
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assert reg_lambda >= 0,\
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'factor ahead of regularization should be 0 or positive'
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self.reg_lambda = reg_lambda
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def _run(self, train_net, param):
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output_blob = train_net.NextScopedBlob(param + '_l1_regularization')
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train_net.LpNorm([param], [output_blob], p=1)
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train_net.Scale([output_blob], [output_blob], scale=self.reg_lambda)
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return output_blob
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class L2Norm(Regularizer):
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def __init__(self, reg_lambda):
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super(L2Norm, self).__init__()
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assert reg_lambda >= 0,\
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'factor ahead of regularization should be 0 or positive'
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self.reg_lambda = reg_lambda
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def _run(self, train_net, param):
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output_blob = train_net.NextScopedBlob(param + '_l2_regularization')
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train_net.LpNorm([param], [output_blob], p=2)
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train_net.Scale([output_blob], [output_blob], scale=self.reg_lambda)
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return output_blob
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