193 lines
6.2 KiB
Python
193 lines
6.2 KiB
Python
# 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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"""Layers that operate regularization via the addition of noise.
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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 numpy as np
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras.engine.base_layer import Layer
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from tensorflow.python.keras.utils import tf_utils
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.util.tf_export import tf_export
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@tf_export('keras.layers.GaussianNoise')
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class GaussianNoise(Layer):
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"""Apply additive zero-centered Gaussian noise.
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This is useful to mitigate overfitting
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(you could see it as a form of random data augmentation).
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Gaussian Noise (GS) is a natural choice as corruption process
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for real valued inputs.
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As it is a regularization layer, it is only active at training time.
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Arguments:
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stddev: float, standard deviation of the noise distribution.
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Input shape:
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Arbitrary. Use the keyword argument `input_shape`
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(tuple of integers, does not include the samples axis)
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when using this layer as the first layer in a model.
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Output shape:
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Same shape as input.
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"""
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def __init__(self, stddev, **kwargs):
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super(GaussianNoise, self).__init__(**kwargs)
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self.supports_masking = True
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self.stddev = stddev
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def call(self, inputs, training=None):
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def noised():
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return inputs + K.random_normal(
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shape=array_ops.shape(inputs), mean=0., stddev=self.stddev)
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return K.in_train_phase(noised, inputs, training=training)
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def get_config(self):
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config = {'stddev': self.stddev}
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base_config = super(GaussianNoise, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@tf_utils.shape_type_conversion
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def compute_output_shape(self, input_shape):
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return input_shape
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@tf_export('keras.layers.GaussianDropout')
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class GaussianDropout(Layer):
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"""Apply multiplicative 1-centered Gaussian noise.
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As it is a regularization layer, it is only active at training time.
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Arguments:
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rate: float, drop probability (as with `Dropout`).
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The multiplicative noise will have
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standard deviation `sqrt(rate / (1 - rate))`.
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Input shape:
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Arbitrary. Use the keyword argument `input_shape`
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(tuple of integers, does not include the samples axis)
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when using this layer as the first layer in a model.
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Output shape:
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Same shape as input.
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"""
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def __init__(self, rate, **kwargs):
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super(GaussianDropout, self).__init__(**kwargs)
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self.supports_masking = True
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self.rate = rate
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def call(self, inputs, training=None):
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if 0 < self.rate < 1:
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def noised():
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stddev = np.sqrt(self.rate / (1.0 - self.rate))
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return inputs * K.random_normal(
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shape=array_ops.shape(inputs), mean=1.0, stddev=stddev)
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return K.in_train_phase(noised, inputs, training=training)
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return inputs
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def get_config(self):
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config = {'rate': self.rate}
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base_config = super(GaussianDropout, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@tf_utils.shape_type_conversion
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def compute_output_shape(self, input_shape):
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return input_shape
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@tf_export('keras.layers.AlphaDropout')
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class AlphaDropout(Layer):
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"""Applies Alpha Dropout to the input.
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Alpha Dropout is a `Dropout` that keeps mean and variance of inputs
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to their original values, in order to ensure the self-normalizing property
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even after this dropout.
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Alpha Dropout fits well to Scaled Exponential Linear Units
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by randomly setting activations to the negative saturation value.
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Arguments:
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rate: float, drop probability (as with `Dropout`).
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The multiplicative noise will have
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standard deviation `sqrt(rate / (1 - rate))`.
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seed: A Python integer to use as random seed.
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Input shape:
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Arbitrary. Use the keyword argument `input_shape`
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(tuple of integers, does not include the samples axis)
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when using this layer as the first layer in a model.
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Output shape:
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Same shape as input.
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"""
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def __init__(self, rate, noise_shape=None, seed=None, **kwargs):
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super(AlphaDropout, self).__init__(**kwargs)
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self.rate = rate
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self.noise_shape = noise_shape
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self.seed = seed
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self.supports_masking = True
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def _get_noise_shape(self, inputs):
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return self.noise_shape if self.noise_shape else array_ops.shape(inputs)
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def call(self, inputs, training=None):
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if 0. < self.rate < 1.:
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noise_shape = self._get_noise_shape(inputs)
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def dropped_inputs(inputs=inputs, rate=self.rate, seed=self.seed): # pylint: disable=missing-docstring
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alpha = 1.6732632423543772848170429916717
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scale = 1.0507009873554804934193349852946
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alpha_p = -alpha * scale
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kept_idx = math_ops.greater_equal(
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K.random_uniform(noise_shape, seed=seed), rate)
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kept_idx = math_ops.cast(kept_idx, K.floatx())
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# Get affine transformation params
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a = ((1 - rate) * (1 + rate * alpha_p**2))**-0.5
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b = -a * alpha_p * rate
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# Apply mask
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x = inputs * kept_idx + alpha_p * (1 - kept_idx)
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# Do affine transformation
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return a * x + b
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return K.in_train_phase(dropped_inputs, inputs, training=training)
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return inputs
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def get_config(self):
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config = {'rate': self.rate}
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base_config = super(AlphaDropout, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@tf_utils.shape_type_conversion
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def compute_output_shape(self, input_shape):
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return input_shape
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