175 lines
5.4 KiB
Python
175 lines
5.4 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Keras initializer classes (soon to be replaced with core TF initializers).
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"""
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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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import six
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from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
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from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
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from tensorflow.python.ops.init_ops import Constant
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from tensorflow.python.ops.init_ops import glorot_normal_initializer
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from tensorflow.python.ops.init_ops import glorot_uniform_initializer
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from tensorflow.python.ops.init_ops import Identity
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from tensorflow.python.ops.init_ops import Initializer # pylint: disable=unused-import
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from tensorflow.python.ops.init_ops import Ones
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from tensorflow.python.ops.init_ops import Orthogonal
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from tensorflow.python.ops.init_ops import RandomNormal
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from tensorflow.python.ops.init_ops import RandomUniform
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from tensorflow.python.ops.init_ops import TruncatedNormal
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from tensorflow.python.ops.init_ops import VarianceScaling
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from tensorflow.python.ops.init_ops import Zeros
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from tensorflow.python.util.tf_export import tf_export
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@tf_export('keras.initializers.lecun_normal')
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def lecun_normal(seed=None):
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"""LeCun normal initializer.
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It draws samples from a truncated normal distribution centered on 0
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with `stddev = sqrt(1 / fan_in)`
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where `fan_in` is the number of input units in the weight tensor.
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Arguments:
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seed: A Python integer. Used to seed the random generator.
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Returns:
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An initializer.
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References:
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- [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
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- [Efficient
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Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
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"""
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return VarianceScaling(
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scale=1., mode='fan_in', distribution='normal', seed=seed)
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@tf_export('keras.initializers.lecun_uniform')
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def lecun_uniform(seed=None):
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"""LeCun uniform initializer.
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It draws samples from a uniform distribution within [-limit, limit]
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where `limit` is `sqrt(3 / fan_in)`
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where `fan_in` is the number of input units in the weight tensor.
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Arguments:
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seed: A Python integer. Used to seed the random generator.
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Returns:
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An initializer.
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References:
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LeCun 98, Efficient Backprop,
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http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf
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"""
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return VarianceScaling(
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scale=1., mode='fan_in', distribution='uniform', seed=seed)
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@tf_export('keras.initializers.he_normal')
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def he_normal(seed=None):
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"""He normal initializer.
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It draws samples from a truncated normal distribution centered on 0
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with `stddev = sqrt(2 / fan_in)`
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where `fan_in` is the number of input units in the weight tensor.
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Arguments:
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seed: A Python integer. Used to seed the random generator.
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Returns:
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An initializer.
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References:
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He et al., http://arxiv.org/abs/1502.01852
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"""
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return VarianceScaling(
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scale=2., mode='fan_in', distribution='normal', seed=seed)
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@tf_export('keras.initializers.he_uniform')
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def he_uniform(seed=None):
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"""He uniform variance scaling initializer.
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It draws samples from a uniform distribution within [-limit, limit]
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where `limit` is `sqrt(6 / fan_in)`
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where `fan_in` is the number of input units in the weight tensor.
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Arguments:
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seed: A Python integer. Used to seed the random generator.
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Returns:
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An initializer.
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References:
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He et al., http://arxiv.org/abs/1502.01852
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"""
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return VarianceScaling(
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scale=2., mode='fan_in', distribution='uniform', seed=seed)
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# Compatibility aliases
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# pylint: disable=invalid-name
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zero = zeros = Zeros
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one = ones = Ones
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constant = Constant
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uniform = random_uniform = RandomUniform
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normal = random_normal = RandomNormal
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truncated_normal = TruncatedNormal
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identity = Identity
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orthogonal = Orthogonal
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glorot_normal = glorot_normal_initializer
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glorot_uniform = glorot_uniform_initializer
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# pylint: enable=invalid-name
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# Utility functions
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@tf_export('keras.initializers.serialize')
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def serialize(initializer):
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return serialize_keras_object(initializer)
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@tf_export('keras.initializers.deserialize')
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def deserialize(config, custom_objects=None):
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return deserialize_keras_object(
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config,
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module_objects=globals(),
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custom_objects=custom_objects,
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printable_module_name='initializer')
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@tf_export('keras.initializers.get')
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def get(identifier):
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if identifier is None:
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return None
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if isinstance(identifier, dict):
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return deserialize(identifier)
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elif isinstance(identifier, six.string_types):
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config = {'class_name': str(identifier), 'config': {}}
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return deserialize(config)
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elif callable(identifier):
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return identifier
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else:
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raise ValueError('Could not interpret initializer identifier: ' +
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str(identifier))
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