fccc0ae3f8
Initializer classes are fundamentally Keras objects (e.g. featuring Keras serialization logic), that were implemented in the TF codebase because of technical details. This meant that the docstrings were disconnected from Keras usage and signatures could not conform to Keras defaults. For instance, the docstring for `tf.keras.initializers.Zeros()` would show examples using `tf.zeros_initializer()`, which made no sense. We are keeping the logic in TF (to avoid code redundancy), but moving class definitions to Keras. Benefits: - Docstrings now corresponding to the objects in `tf.keras.initializers` and show Keras use cases. - Call signature now defaults `dtype` to `floatx()`. - Class names stay the same independently of TF version (e.g. keras.initializers.RandomNormal.__name__ == 'RandomNormal', not 'RandomNormalV2'). - Various edge cases surrounding classes aliased to functions are now fixed (lecun_uniform & friends). PiperOrigin-RevId: 303156255 Change-Id: Idf4171e12e6a9afe231eb38892737d603bcbb851
113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
# Copyright 2020 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 initializers for TF 1.
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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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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops import init_ops
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from tensorflow.python.util.tf_export import keras_export
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keras_export(v1=['keras.initializers.Zeros', 'keras.initializers.zeros'])(
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init_ops.Zeros)
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keras_export(v1=['keras.initializers.Ones', 'keras.initializers.ones'])(
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init_ops.Ones)
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keras_export(v1=['keras.initializers.Constant', 'keras.initializers.constant'])(
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init_ops.Constant)
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keras_export(v1=['keras.initializers.VarianceScaling'])(
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init_ops.VarianceScaling)
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keras_export(v1=['keras.initializers.Orthogonal',
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'keras.initializers.orthogonal'])(init_ops.Orthogonal)
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keras_export(v1=['keras.initializers.Identity',
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'keras.initializers.identity'])(init_ops.Identity)
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keras_export(v1=['keras.initializers.glorot_uniform'])(init_ops.GlorotUniform)
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keras_export(v1=['keras.initializers.glorot_normal'])(init_ops.GlorotNormal)
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@keras_export(v1=['keras.initializers.RandomNormal',
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'keras.initializers.random_normal',
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'keras.initializers.normal'])
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class RandomNormal(init_ops.RandomNormal):
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def __init__(self, mean=0.0, stddev=0.05, seed=None, dtype=dtypes.float32):
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super(RandomNormal, self).__init__(
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mean=mean, stddev=stddev, seed=seed, dtype=dtype)
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@keras_export(v1=['keras.initializers.RandomUniform',
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'keras.initializers.random_uniform',
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'keras.initializers.uniform'])
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class RandomUniform(init_ops.RandomUniform):
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def __init__(self, minval=-0.05, maxval=0.05, seed=None,
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dtype=dtypes.float32):
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super(RandomUniform, self).__init__(
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minval=minval, maxval=maxval, seed=seed, dtype=dtype)
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@keras_export(v1=['keras.initializers.TruncatedNormal',
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'keras.initializers.truncated_normal'])
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class TruncatedNormal(init_ops.TruncatedNormal):
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def __init__(self, mean=0.0, stddev=0.05, seed=None, dtype=dtypes.float32):
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super(TruncatedNormal, self).__init__(
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mean=mean, stddev=stddev, seed=seed, dtype=dtype)
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@keras_export(v1=['keras.initializers.lecun_normal'])
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class LecunNormal(init_ops.VarianceScaling):
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def __init__(self, seed=None):
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super(LecunNormal, self).__init__(
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scale=1., mode='fan_in', distribution='truncated_normal', seed=seed)
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def get_config(self):
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return {'seed': self.seed}
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@keras_export(v1=['keras.initializers.lecun_uniform'])
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class LecunUniform(init_ops.VarianceScaling):
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def __init__(self, seed=None):
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super(LecunUniform, self).__init__(
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scale=1., mode='fan_in', distribution='uniform', seed=seed)
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def get_config(self):
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return {'seed': self.seed}
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@keras_export(v1=['keras.initializers.he_normal'])
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class HeNormal(init_ops.VarianceScaling):
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def __init__(self, seed=None):
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super(HeNormal, self).__init__(
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scale=2., mode='fan_in', distribution='truncated_normal', seed=seed)
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def get_config(self):
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return {'seed': self.seed}
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@keras_export(v1=['keras.initializers.he_uniform'])
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class HeUniform(init_ops.VarianceScaling):
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def __init__(self, seed=None):
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super(HeUniform, self).__init__(
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scale=2., mode='fan_in', distribution='uniform', seed=seed)
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def get_config(self):
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return {'seed': self.seed}
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