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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.
# ==============================================================================
"""Keras initializer serialization / deserialization.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from tensorflow.python.framework import dtypes
from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
# These imports are brought in so that keras.initializers.deserialize
# has them available in module_objects.
from tensorflow.python.ops.init_ops import Constant
from tensorflow.python.ops.init_ops import GlorotNormal
from tensorflow.python.ops.init_ops import GlorotUniform
from tensorflow.python.ops.init_ops import he_normal # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import he_uniform # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import Identity
from tensorflow.python.ops.init_ops import Initializer # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import lecun_normal # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import lecun_uniform # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import Ones
from tensorflow.python.ops.init_ops import Orthogonal
from tensorflow.python.ops.init_ops import RandomNormal as TFRandomNormal
from tensorflow.python.ops.init_ops import RandomUniform as TFRandomUniform
from tensorflow.python.ops.init_ops import TruncatedNormal as TFTruncatedNormal
from tensorflow.python.ops.init_ops import VarianceScaling # pylint: disable=unused-import
from tensorflow.python.ops.init_ops import Zeros
from tensorflow.python.util.tf_export import tf_export
@tf_export('keras.initializers.TruncatedNormal',
'keras.initializers.truncated_normal')
class TruncatedNormal(TFTruncatedNormal):
"""Initializer that generates a truncated normal distribution.
These values are similar to values from a `random_normal_initializer`
except that values more than two standard deviations from the mean
are discarded and re-drawn. This is the recommended initializer for
neural network weights and filters.
Args:
mean: a python scalar or a scalar tensor. Mean of the random values to
generate. Defaults to 0.
stddev: a python scalar or a scalar tensor. Standard deviation of the random
values to generate. Defaults to 0.05.
seed: A Python integer. Used to create random seeds. See
`tf.set_random_seed` for behavior.
dtype: The data type. Only floating point types are supported.
"""
def __init__(self, mean=0.0, stddev=0.05, seed=None, dtype=dtypes.float32):
super(TruncatedNormal, self).__init__(
mean=mean, stddev=stddev, seed=seed, dtype=dtype)
@tf_export('keras.initializers.RandomUniform', 'keras.initializers.uniform',
'keras.initializers.random_uniform')
class RandomUniform(TFRandomUniform):
"""Initializer that generates tensors with a uniform distribution.
Args:
minval: A python scalar or a scalar tensor. Lower bound of the range of
random values to generate. Defaults to -0.05.
maxval: A python scalar or a scalar tensor. Upper bound of the range of
random values to generate. Defaults to 0.05.
seed: A Python integer. Used to create random seeds. See
`tf.set_random_seed` for behavior.
dtype: The data type.
"""
def __init__(self, minval=-0.05, maxval=0.05, seed=None,
dtype=dtypes.float32):
super(RandomUniform, self).__init__(
minval=minval, maxval=maxval, seed=seed, dtype=dtype)
@tf_export('keras.initializers.RandomNormal', 'keras.initializers.normal',
'keras.initializers.random_normal')
class RandomNormal(TFRandomNormal):
"""Initializer that generates tensors with a normal distribution.
Args:
mean: a python scalar or a scalar tensor. Mean of the random values to
generate. Defaults to 0.
stddev: a python scalar or a scalar tensor. Standard deviation of the random
values to generate. Defaults to 0.05.
seed: A Python integer. Used to create random seeds. See
`tf.set_random_seed` for behavior.
dtype: The data type. Only floating point types are supported.
Returns:
RandomNormal instance.
"""
def __init__(self, mean=0.0, stddev=0.05, seed=None, dtype=dtypes.float32):
super(RandomNormal, self).__init__(
mean=mean, stddev=stddev, seed=seed, dtype=dtype)
# Compatibility aliases
# pylint: disable=invalid-name
zero = zeros = Zeros
one = ones = Ones
constant = Constant
uniform = random_uniform = RandomUniform
normal = random_normal = RandomNormal
truncated_normal = TruncatedNormal
identity = Identity
orthogonal = Orthogonal
glorot_normal = GlorotNormal
glorot_uniform = GlorotUniform
# Utility functions
@tf_export('keras.initializers.serialize')
def serialize(initializer):
return serialize_keras_object(initializer)
@tf_export('keras.initializers.deserialize')
def deserialize(config, custom_objects=None):
return deserialize_keras_object(
config,
module_objects=globals(),
custom_objects=custom_objects,
printable_module_name='initializer')
@tf_export('keras.initializers.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 initializer identifier: ' +
str(identifier))
# pylint: enable=invalid-name
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