text: # 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.
  # ==============================================================================
  """Layers that operate regularization via the addition of noise."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.python.keras import backend as K
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.util.tf_export import keras_export
  
  
  @keras_export('keras.layers.GaussianNoise')
  class GaussianNoise(Layer):
    """Apply additive zero-centered Gaussian noise.
  
    This is useful to mitigate overfitting
    (you could see it as a form of random data augmentation).
    Gaussian Noise (GS) is a natural choice as corruption process
    for real valued inputs.
  
    As it is a regularization layer, it is only active at training time.
  
    Arguments:
      stddev: Float, standard deviation of the noise distribution.
  
    Call arguments:
      inputs: Input tensor (of any rank).
      training: Python boolean indicating whether the layer should behave in
        training mode (adding noise) or in inference mode (doing nothing).
  
    Input shape:
      Arbitrary. Use the keyword argument `input_shape`
      (tuple of integers, does not include the samples axis)
      when using this layer as the first layer in a model.
  
    Output shape:
      Same shape as input.
    """
  
    def __init__(self, stddev, **kwargs):
      super(GaussianNoise, self).__init__(**kwargs)
      self.supports_masking = True
      self.stddev = stddev
  
    def call(self, inputs, training=None):
  
      def noised():
        return inputs + K.random_normal(
            shape=array_ops.shape(inputs),
            mean=0.,
            stddev=self.stddev,
            dtype=inputs.dtype)
  
      return K.in_train_phase(noised, inputs, training=training)
  
    def get_config(self):
      config = {'stddev': self.stddev}
      base_config = super(GaussianNoise, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
    @tf_utils.shape_type_conversion
    def compute_output_shape(self, input_shape):
      return input_shape
  
  
  @keras_export('keras.layers.GaussianDropout')
  class GaussianDropout(Layer):
    """Apply multiplicative 1-centered Gaussian noise.
  
    As it is a regularization layer, it is only active at training time.
  
    Arguments:
      rate: Float, drop probability (as with `Dropout`).
        The multiplicative noise will have
        standard deviation `sqrt(rate / (1 - rate))`.
  
    Call arguments:
      inputs: Input tensor (of any rank).
      training: Python boolean indicating whether the layer should behave in
        training mode (adding dropout) or in inference mode (doing nothing).
  
    Input shape:
      Arbitrary. Use the keyword argument `input_shape`
      (tuple of integers, does not include the samples axis)
      when using this layer as the first layer in a model.
  
    Output shape:
      Same shape as input.
    """
  
    def __init__(self, rate, **kwargs):
      super(GaussianDropout, self).__init__(**kwargs)
      self.supports_masking = True
      self.rate = rate
  
    def call(self, inputs, training=None):
      if 0 < self.rate < 1:
  
        def noised():
          stddev = np.sqrt(self.rate / (1.0 - self.rate))
          return inputs * K.random_normal(
              shape=array_ops.shape(inputs),
              mean=1.0,
              stddev=stddev,
              dtype=inputs.dtype)
  
        return K.in_train_phase(noised, inputs, training=training)
      return inputs
  
    def get_config(self):
      config = {'rate': self.rate}
      base_config = super(GaussianDropout, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
    @tf_utils.shape_type_conversion
    def compute_output_shape(self, input_shape):
      return input_shape
  
  
  @keras_export('keras.layers.AlphaDropout')
  class AlphaDropout(Layer):
    """Applies Alpha Dropout to the input.
  
    Alpha Dropout is a `Dropout` that keeps mean and variance of inputs
    to their original values, in order to ensure the self-normalizing property
    even after this dropout.
    Alpha Dropout fits well to Scaled Exponential Linear Units
    by randomly setting activations to the negative saturation value.
  
    Arguments:
      rate: float, drop probability (as with `Dropout`).
        The multiplicative noise will have
        standard deviation `sqrt(rate / (1 - rate))`.
      seed: A Python integer to use as random seed.
  
    Call arguments:
      inputs: Input tensor (of any rank).
      training: Python boolean indicating whether the layer should behave in
        training mode (adding dropout) or in inference mode (doing nothing).
  
    Input shape:
      Arbitrary. Use the keyword argument `input_shape`
      (tuple of integers, does not include the samples axis)
      when using this layer as the first layer in a model.
  
    Output shape:
      Same shape as input.
    """
  
    def __init__(self, rate, noise_shape=None, seed=None, **kwargs):
      super(AlphaDropout, self).__init__(**kwargs)
      self.rate = rate
      self.noise_shape = noise_shape
      self.seed = seed
      self.supports_masking = True
  
    def _get_noise_shape(self, inputs):
      return self.noise_shape if self.noise_shape else array_ops.shape(inputs)
  
    def call(self, inputs, training=None):
      if 0. < self.rate < 1.:
        noise_shape = self._get_noise_shape(inputs)
  
        def dropped_inputs(inputs=inputs, rate=self.rate, seed=self.seed):  # pylint: disable=missing-docstring
          alpha = 1.6732632423543772848170429916717
          scale = 1.0507009873554804934193349852946
          alpha_p = -alpha * scale
  
          kept_idx = math_ops.greater_equal(
              K.random_uniform(noise_shape, seed=seed), rate)
          kept_idx = math_ops.cast(kept_idx, inputs.dtype)
  
          # Get affine transformation params
          a = ((1 - rate) * (1 + rate * alpha_p**2))**-0.5
          b = -a * alpha_p * rate
  
          # Apply mask
          x = inputs * kept_idx + alpha_p * (1 - kept_idx)
  
          # Do affine transformation
          return a * x + b
  
        return K.in_train_phase(dropped_inputs, inputs, training=training)
      return inputs
  
    def get_config(self):
      config = {'rate': self.rate}
      base_config = super(AlphaDropout, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
    @tf_utils.shape_type_conversion
    def compute_output_shape(self, input_shape):
      return input_shape
  