google-research

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# coding=utf-8
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# Copyright 2024 The Google Research Authors.
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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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"""Implementations of different initialization methods."""
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import numpy as np
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import tensorflow.compat.v1 as tf
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def uniform(shape, scale=0.05, name=None):
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  """Uniform init."""
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  initial = tf.random_uniform(
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      shape, minval=-scale, maxval=scale, dtype=tf.float32)
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  return tf.Variable(initial, name=name)
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def glorot(shape, name=None):
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  """Glorot & Bengio (AISTATS 2010) init."""
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  init_range = np.sqrt(6.0 / (shape[0] + shape[1]))
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  initial = tf.random_uniform(
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      shape, minval=-init_range, maxval=init_range, dtype=tf.float32)
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  return tf.Variable(initial, name=name)
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def zeros(shape, name=None):
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  """All zeros."""
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  initial = tf.zeros(shape, dtype=tf.float32)
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  return tf.Variable(initial, name=name)
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def ones(shape, name=None):
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  """All ones."""
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  initial = tf.ones(shape, dtype=tf.float32)
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  return tf.Variable(initial, name=name)
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