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 act as activation functions.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.keras import backend as K
  from tensorflow.python.keras import constraints
  from tensorflow.python.keras import initializers
  from tensorflow.python.keras import regularizers
  from tensorflow.python.keras.engine.base_layer import InputSpec
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import math_ops
  from tensorflow.python.util.tf_export import keras_export
  
  
  @keras_export('keras.layers.LeakyReLU')
  class LeakyReLU(Layer):
    """Leaky version of a Rectified Linear Unit.
  
    It allows a small gradient when the unit is not active:
    `f(x) = alpha * x for x < 0`,
    `f(x) = x for x >= 0`.
  
    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 the input.
  
    Arguments:
      alpha: Float >= 0. Negative slope coefficient.
  
    """
  
    def __init__(self, alpha=0.3, **kwargs):
      super(LeakyReLU, self).__init__(**kwargs)
      self.supports_masking = True
      self.alpha = K.cast_to_floatx(alpha)
  
    def call(self, inputs):
      return K.relu(inputs, alpha=self.alpha)
  
    def get_config(self):
      config = {'alpha': float(self.alpha)}
      base_config = super(LeakyReLU, 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.PReLU')
  class PReLU(Layer):
    """Parametric Rectified Linear Unit.
  
    It follows:
    `f(x) = alpha * x for x < 0`,
    `f(x) = x for x >= 0`,
    where `alpha` is a learned array with the same shape as x.
  
    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 the input.
  
    Arguments:
      alpha_initializer: Initializer function for the weights.
      alpha_regularizer: Regularizer for the weights.
      alpha_constraint: Constraint for the weights.
      shared_axes: The axes along which to share learnable
        parameters for the activation function.
        For example, if the incoming feature maps
        are from a 2D convolution
        with output shape `(batch, height, width, channels)`,
        and you wish to share parameters across space
        so that each filter only has one set of parameters,
        set `shared_axes=[1, 2]`.
    """
  
    def __init__(self,
                 alpha_initializer='zeros',
                 alpha_regularizer=None,
                 alpha_constraint=None,
                 shared_axes=None,
                 **kwargs):
      super(PReLU, self).__init__(**kwargs)
      self.supports_masking = True
      self.alpha_initializer = initializers.get(alpha_initializer)
      self.alpha_regularizer = regularizers.get(alpha_regularizer)
      self.alpha_constraint = constraints.get(alpha_constraint)
      if shared_axes is None:
        self.shared_axes = None
      elif not isinstance(shared_axes, (list, tuple)):
        self.shared_axes = [shared_axes]
      else:
        self.shared_axes = list(shared_axes)
  
    @tf_utils.shape_type_conversion
    def build(self, input_shape):
      param_shape = list(input_shape[1:])
      if self.shared_axes is not None:
        for i in self.shared_axes:
          param_shape[i - 1] = 1
      self.alpha = self.add_weight(
          shape=param_shape,
          name='alpha',
          initializer=self.alpha_initializer,
          regularizer=self.alpha_regularizer,
          constraint=self.alpha_constraint)
      # Set input spec
      axes = {}
      if self.shared_axes:
        for i in range(1, len(input_shape)):
          if i not in self.shared_axes:
            axes[i] = input_shape[i]
      self.input_spec = InputSpec(ndim=len(input_shape), axes=axes)
      self.built = True
  
    def call(self, inputs):
      pos = K.relu(inputs)
      neg = -self.alpha * K.relu(-inputs)
      return pos + neg
  
    def get_config(self):
      config = {
          'alpha_initializer': initializers.serialize(self.alpha_initializer),
          'alpha_regularizer': regularizers.serialize(self.alpha_regularizer),
          'alpha_constraint': constraints.serialize(self.alpha_constraint),
          'shared_axes': self.shared_axes
      }
      base_config = super(PReLU, 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.ELU')
  class ELU(Layer):
    """Exponential Linear Unit.
  
    It follows:
    `f(x) =  alpha * (exp(x) - 1.) for x < 0`,
    `f(x) = x for x >= 0`.
  
    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 the input.
  
    Arguments:
      alpha: Scale for the negative factor.
    """
  
    def __init__(self, alpha=1.0, **kwargs):
      super(ELU, self).__init__(**kwargs)
      self.supports_masking = True
      self.alpha = K.cast_to_floatx(alpha)
  
    def call(self, inputs):
      return K.elu(inputs, self.alpha)
  
    def get_config(self):
      config = {'alpha': float(self.alpha)}
      base_config = super(ELU, 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.ThresholdedReLU')
  class ThresholdedReLU(Layer):
    """Thresholded Rectified Linear Unit.
  
    It follows:
    `f(x) = x for x > theta`,
    `f(x) = 0 otherwise`.
  
    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 the input.
  
    Arguments:
      theta: Float >= 0. Threshold location of activation.
    """
  
    def __init__(self, theta=1.0, **kwargs):
      super(ThresholdedReLU, self).__init__(**kwargs)
      self.supports_masking = True
      self.theta = K.cast_to_floatx(theta)
  
    def call(self, inputs):
      return inputs * math_ops.cast(
          math_ops.greater(inputs, self.theta), K.floatx())
  
    def get_config(self):
      config = {'theta': float(self.theta)}
      base_config = super(ThresholdedReLU, 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.Softmax')
  class Softmax(Layer):
    """Softmax activation function.
  
    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 the input.
  
    Arguments:
      axis: Integer, axis along which the softmax normalization is applied.
    """
  
    def __init__(self, axis=-1, **kwargs):
      super(Softmax, self).__init__(**kwargs)
      self.supports_masking = True
      self.axis = axis
  
    def call(self, inputs):
      return K.softmax(inputs, axis=self.axis)
  
    def get_config(self):
      config = {'axis': self.axis}
      base_config = super(Softmax, 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.ReLU')
  class ReLU(Layer):
    """Rectified Linear Unit activation function.
  
    With default values, it returns element-wise `max(x, 0)`.
  
    Otherwise, it follows:
    `f(x) = max_value` for `x >= max_value`,
    `f(x) = x` for `threshold <= x < max_value`,
    `f(x) = negative_slope * (x - threshold)` otherwise.
  
    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 the input.
  
    Arguments:
      max_value: Float >= 0. Maximum activation value.
      negative_slope: Float >= 0. Negative slope coefficient.
      threshold: Float. Threshold value for thresholded activation.
    """
  
    def __init__(self, max_value=None, negative_slope=0, threshold=0, **kwargs):
      super(ReLU, self).__init__(**kwargs)
      if max_value is not None and max_value < 0.:
        raise ValueError('max_value of Relu layer '
                         'cannot be negative value: ' + str(max_value))
      if negative_slope < 0.:
        raise ValueError('negative_slope of Relu layer '
                         'cannot be negative value: ' + str(negative_slope))
  
      self.support_masking = True
      if max_value is not None:
        max_value = K.cast_to_floatx(max_value)
      self.max_value = max_value
      self.negative_slope = K.cast_to_floatx(negative_slope)
      self.threshold = K.cast_to_floatx(threshold)
  
    def call(self, inputs):
      # alpha is used for leaky relu slope in activations instead of
      # negative_slope.
      return K.relu(inputs,
                    alpha=self.negative_slope,
                    max_value=self.max_value,
                    threshold=self.threshold)
  
    def get_config(self):
      config = {
          'max_value': self.max_value,
          'negative_slope': self.negative_slope,
          'threshold': self.threshold
      }
      base_config = super(ReLU, 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
  