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# Copyright 2015 The TensorFlow 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.
# ==============================================================================
"""Built-in regularizers.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from tensorflow.python.keras._impl.keras import backend as K
from tensorflow.python.keras._impl.keras.utils.generic_utils import deserialize_keras_object
from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_keras_object
from tensorflow.python.ops import math_ops
from tensorflow.python.util.tf_export import tf_export
@tf_export('keras.regularizers.Regularizer')
class Regularizer(object):
"""Regularizer base class.
"""
def __call__(self, x):
return 0.
@classmethod
def from_config(cls, config):
return cls(**config)
@tf_export('keras.regularizers.L1L2')
class L1L2(Regularizer):
"""Regularizer for L1 and L2 regularization.
Arguments:
l1: Float; L1 regularization factor.
l2: Float; L2 regularization factor.
"""
def __init__(self, l1=0., l2=0.): # pylint: disable=redefined-outer-name
self.l1 = K.cast_to_floatx(l1)
self.l2 = K.cast_to_floatx(l2)
def __call__(self, x):
regularization = 0.
if self.l1:
regularization += math_ops.reduce_sum(self.l1 * math_ops.abs(x))
if self.l2:
regularization += math_ops.reduce_sum(self.l2 * math_ops.square(x))
return regularization
def get_config(self):
return {'l1': float(self.l1), 'l2': float(self.l2)}
# Aliases.
@tf_export('keras.regularizers.l1')
def l1(l=0.01):
return L1L2(l1=l)
@tf_export('keras.regularizers.l2')
def l2(l=0.01):
return L1L2(l2=l)
@tf_export('keras.regularizers.l1_l2')
def l1_l2(l1=0.01, l2=0.01): # pylint: disable=redefined-outer-name
return L1L2(l1=l1, l2=l2)
@tf_export('keras.regularizers.serialize')
def serialize(regularizer):
return serialize_keras_object(regularizer)
@tf_export('keras.regularizers.deserialize')
def deserialize(config, custom_objects=None):
return deserialize_keras_object(
config,
module_objects=globals(),
custom_objects=custom_objects,
printable_module_name='regularizer')
@tf_export('keras.regularizers.get')
def get(identifier):
if identifier is None:
return None
if isinstance(identifier, dict):
return deserialize(identifier)
elif isinstance(identifier, six.string_types):
config = {'class_name': str(identifier), 'config': {}}
return deserialize(config)
elif callable(identifier):
return identifier
else:
raise ValueError('Could not interpret regularizer identifier:', identifier)