121 lines
4.3 KiB
Python
121 lines
4.3 KiB
Python
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# 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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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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_v1_zeros_initializer = init_ops.Zeros
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_v1_ones_initializer = init_ops.Ones
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_v1_constant_initializer = init_ops.Constant
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_v1_variance_scaling_initializer = init_ops.VarianceScaling
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_v1_orthogonal_initializer = init_ops.Orthogonal
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_v1_identity = init_ops.Identity
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_v1_glorot_uniform_initializer = init_ops.GlorotUniform
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_v1_glorot_normal_initializer = init_ops.GlorotNormal
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keras_export(v1=['keras.initializers.Zeros', 'keras.initializers.zeros'])(
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_v1_zeros_initializer)
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keras_export(v1=['keras.initializers.Ones', 'keras.initializers.ones'])(
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_v1_ones_initializer)
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keras_export(v1=['keras.initializers.Constant', 'keras.initializers.constant'])(
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_v1_constant_initializer)
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keras_export(v1=['keras.initializers.VarianceScaling'])(
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_v1_variance_scaling_initializer)
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keras_export(v1=['keras.initializers.Orthogonal',
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'keras.initializers.orthogonal'])(_v1_orthogonal_initializer)
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keras_export(v1=['keras.initializers.Identity',
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'keras.initializers.identity'])(_v1_identity)
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keras_export(v1=['keras.initializers.glorot_uniform'])(
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_v1_glorot_uniform_initializer)
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keras_export(v1=['keras.initializers.glorot_normal'])(
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_v1_glorot_normal_initializer)
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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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