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.
  # ==============================================================================
  """Keras convolution layers and image transformation layers.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.keras._impl.keras import activations
  from tensorflow.python.keras._impl.keras import backend as K
  from tensorflow.python.keras._impl.keras import constraints
  from tensorflow.python.keras._impl.keras import initializers
  from tensorflow.python.keras._impl.keras import regularizers
  from tensorflow.python.keras._impl.keras.engine import InputSpec
  from tensorflow.python.keras._impl.keras.engine import Layer
  # imports for backwards namespace compatibility
  # pylint: disable=unused-import
  from tensorflow.python.keras._impl.keras.layers.pooling import AveragePooling1D
  from tensorflow.python.keras._impl.keras.layers.pooling import AveragePooling2D
  from tensorflow.python.keras._impl.keras.layers.pooling import AveragePooling3D
  from tensorflow.python.keras._impl.keras.layers.pooling import MaxPooling1D
  from tensorflow.python.keras._impl.keras.layers.pooling import MaxPooling2D
  from tensorflow.python.keras._impl.keras.layers.pooling import MaxPooling3D
  # pylint: enable=unused-import
  from tensorflow.python.keras._impl.keras.utils import conv_utils
  from tensorflow.python.layers import convolutional as tf_convolutional_layers
  
  
  class Conv1D(tf_convolutional_layers.Conv1D, Layer):
    """1D convolution layer (e.g. temporal convolution).
  
    This layer creates a convolution kernel that is convolved
    with the layer input over a single spatial (or temporal) dimension
    to produce a tensor of outputs.
    If `use_bias` is True, a bias vector is created and added to the outputs.
    Finally, if `activation` is not `None`,
    it is applied to the outputs as well.
  
    When using this layer as the first layer in a model,
    provide an `input_shape` argument
    (tuple of integers or `None`, e.g.
    `(10, 128)` for sequences of 10 vectors of 128-dimensional vectors,
    or `(None, 128)` for variable-length sequences of 128-dimensional vectors.
  
    Arguments:
        filters: Integer, the dimensionality of the output space
            (i.e. the number output of filters in the convolution).
        kernel_size: An integer or tuple/list of a single integer,
            specifying the length of the 1D convolution window.
        strides: An integer or tuple/list of a single integer,
            specifying the stride length of the convolution.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: One of `"valid"`, `"causal"` or `"same"` (case-insensitive).
            `"causal"` results in causal (dilated) convolutions, e.g. output[t]
            does not depend on input[t+1:]. Useful when modeling temporal data
            where the model should not violate the temporal order.
            See [WaveNet: A Generative Model for Raw Audio, section
              2.1](https://arxiv.org/abs/1609.03499).
        dilation_rate: an integer or tuple/list of a single integer, specifying
            the dilation rate to use for dilated convolution.
            Currently, specifying any `dilation_rate` value != 1 is
            incompatible with specifying any `strides` value != 1.
        activation: Activation function to use.
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix.
        bias_initializer: Initializer for the bias vector.
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix.
        bias_regularizer: Regularizer function applied to the bias vector.
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation")..
        kernel_constraint: Constraint function applied to the kernel matrix.
        bias_constraint: Constraint function applied to the bias vector.
  
    Input shape:
        3D tensor with shape: `(batch_size, steps, input_dim)`
  
    Output shape:
        3D tensor with shape: `(batch_size, new_steps, filters)`
        `steps` value might have changed due to padding or strides.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 dilation_rate=1,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      super(Conv1D, self).__init__(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format='channels_last',
          dilation_rate=dilation_rate,
          activation=activations.get(activation),
          use_bias=use_bias,
          kernel_initializer=initializers.get(kernel_initializer),
          bias_initializer=initializers.get(bias_initializer),
          kernel_regularizer=regularizers.get(kernel_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          kernel_constraint=constraints.get(kernel_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
    def get_config(self):
      config = {
          'filters': self.filters,
          'kernel_size': self.kernel_size,
          'strides': self.strides,
          'padding': self.padding,
          'dilation_rate': self.dilation_rate,
          'activation': activations.serialize(self.activation),
          'use_bias': self.use_bias,
          'kernel_initializer': initializers.serialize(self.kernel_initializer),
          'bias_initializer': initializers.serialize(self.bias_initializer),
          'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
          'bias_regularizer': regularizers.serialize(self.bias_regularizer),
          'activity_regularizer':
              regularizers.serialize(self.activity_regularizer),
          'kernel_constraint': constraints.serialize(self.kernel_constraint),
          'bias_constraint': constraints.serialize(self.bias_constraint)
      }
      base_config = super(Conv1D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class Conv2D(tf_convolutional_layers.Conv2D, Layer):
    """2D convolution layer (e.g. spatial convolution over images).
  
    This layer creates a convolution kernel that is convolved
    with the layer input to produce a tensor of
    outputs. If `use_bias` is True,
    a bias vector is created and added to the outputs. Finally, if
    `activation` is not `None`, it is applied to the outputs as well.
  
    When using this layer as the first layer in a model,
    provide the keyword argument `input_shape`
    (tuple of integers, does not include the sample axis),
    e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
    in `data_format="channels_last"`.
  
    Arguments:
        filters: Integer, the dimensionality of the output space
            (i.e. the number output of filters in the convolution).
        kernel_size: An integer or tuple/list of 2 integers, specifying the
            width and height of the 2D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 2 integers,
            specifying the strides of the convolution along the width and height.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, height, width, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
        dilation_rate: an integer or tuple/list of 2 integers, specifying
            the dilation rate to use for dilated convolution.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Currently, specifying any `dilation_rate` value != 1 is
            incompatible with specifying any stride value != 1.
        activation: Activation function to use.
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix.
        bias_initializer: Initializer for the bias vector.
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix.
        bias_regularizer: Regularizer function applied to the bias vector.
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation")..
        kernel_constraint: Constraint function applied to the kernel matrix.
        bias_constraint: Constraint function applied to the bias vector.
  
    Input shape:
        4D tensor with shape:
        `(samples, channels, rows, cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(samples, rows, cols, channels)` if data_format='channels_last'.
  
    Output shape:
        4D tensor with shape:
        `(samples, filters, new_rows, new_cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(samples, new_rows, new_cols, filters)` if data_format='channels_last'.
        `rows` and `cols` values might have changed due to padding.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 dilation_rate=(1, 1),
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      if data_format is None:
        data_format = K.image_data_format()
      super(Conv2D, self).__init__(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          activation=activations.get(activation),
          use_bias=use_bias,
          kernel_initializer=initializers.get(kernel_initializer),
          bias_initializer=initializers.get(bias_initializer),
          kernel_regularizer=regularizers.get(kernel_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          kernel_constraint=constraints.get(kernel_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
    def get_config(self):
      config = {
          'filters': self.filters,
          'kernel_size': self.kernel_size,
          'strides': self.strides,
          'padding': self.padding,
          'data_format': self.data_format,
          'dilation_rate': self.dilation_rate,
          'activation': activations.serialize(self.activation),
          'use_bias': self.use_bias,
          'kernel_initializer': initializers.serialize(self.kernel_initializer),
          'bias_initializer': initializers.serialize(self.bias_initializer),
          'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
          'bias_regularizer': regularizers.serialize(self.bias_regularizer),
          'activity_regularizer':
              regularizers.serialize(self.activity_regularizer),
          'kernel_constraint': constraints.serialize(self.kernel_constraint),
          'bias_constraint': constraints.serialize(self.bias_constraint)
      }
      base_config = super(Conv2D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class Conv3D(tf_convolutional_layers.Conv3D, Layer):
    """3D convolution layer (e.g. spatial convolution over volumes).
  
    This layer creates a convolution kernel that is convolved
    with the layer input to produce a tensor of
    outputs. If `use_bias` is True,
    a bias vector is created and added to the outputs. Finally, if
    `activation` is not `None`, it is applied to the outputs as well.
  
    When using this layer as the first layer in a model,
    provide the keyword argument `input_shape`
    (tuple of integers, does not include the sample axis),
    e.g. `input_shape=(128, 128, 128, 1)` for 128x128x128 volumes
    with a single channel,
    in `data_format="channels_last"`.
  
    Arguments:
        filters: Integer, the dimensionality of the output space
            (i.e. the number output of filters in the convolution).
        kernel_size: An integer or tuple/list of 3 integers, specifying the
            depth, height and width of the 3D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 3 integers,
            specifying the strides of the convolution along each spatial
              dimension.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
            while `channels_first` corresponds to inputs with shape
            `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
        dilation_rate: an integer or tuple/list of 3 integers, specifying
            the dilation rate to use for dilated convolution.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Currently, specifying any `dilation_rate` value != 1 is
            incompatible with specifying any stride value != 1.
        activation: Activation function to use.
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix.
        bias_initializer: Initializer for the bias vector.
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix.
        bias_regularizer: Regularizer function applied to the bias vector.
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation")..
        kernel_constraint: Constraint function applied to the kernel matrix.
        bias_constraint: Constraint function applied to the bias vector.
  
    Input shape:
        5D tensor with shape:
        `(samples, channels, conv_dim1, conv_dim2, conv_dim3)` if
          data_format='channels_first'
        or 5D tensor with shape:
        `(samples, conv_dim1, conv_dim2, conv_dim3, channels)` if
          data_format='channels_last'.
  
    Output shape:
        5D tensor with shape:
        `(samples, filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)` if
          data_format='channels_first'
        or 5D tensor with shape:
        `(samples, new_conv_dim1, new_conv_dim2, new_conv_dim3, filters)` if
          data_format='channels_last'.
        `new_conv_dim1`, `new_conv_dim2` and `new_conv_dim3` values might have
          changed due to padding.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1, 1),
                 padding='valid',
                 data_format=None,
                 dilation_rate=(1, 1, 1),
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      if data_format is None:
        data_format = K.image_data_format()
      super(Conv3D, self).__init__(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          activation=activations.get(activation),
          use_bias=use_bias,
          kernel_initializer=initializers.get(kernel_initializer),
          bias_initializer=initializers.get(bias_initializer),
          kernel_regularizer=regularizers.get(kernel_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          kernel_constraint=constraints.get(kernel_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
    def get_config(self):
      config = {
          'filters': self.filters,
          'kernel_size': self.kernel_size,
          'strides': self.strides,
          'padding': self.padding,
          'data_format': self.data_format,
          'dilation_rate': self.dilation_rate,
          'activation': activations.serialize(self.activation),
          'use_bias': self.use_bias,
          'kernel_initializer': initializers.serialize(self.kernel_initializer),
          'bias_initializer': initializers.serialize(self.bias_initializer),
          'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
          'bias_regularizer': regularizers.serialize(self.bias_regularizer),
          'activity_regularizer':
              regularizers.serialize(self.activity_regularizer),
          'kernel_constraint': constraints.serialize(self.kernel_constraint),
          'bias_constraint': constraints.serialize(self.bias_constraint)
      }
      base_config = super(Conv3D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class Conv2DTranspose(tf_convolutional_layers.Conv2DTranspose, Layer):
    """Transposed convolution layer (sometimes called Deconvolution).
  
    The need for transposed convolutions generally arises
    from the desire to use a transformation going in the opposite direction
    of a normal convolution, i.e., from something that has the shape of the
    output of some convolution to something that has the shape of its input
    while maintaining a connectivity pattern that is compatible with
    said convolution.
  
    When using this layer as the first layer in a model,
    provide the keyword argument `input_shape`
    (tuple of integers, does not include the sample axis),
    e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
    in `data_format="channels_last"`.
  
    Arguments:
        filters: Integer, the dimensionality of the output space
            (i.e. the number of output filters in the convolution).
        kernel_size: An integer or tuple/list of 2 integers, specifying the
            width and height of the 2D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 2 integers,
            specifying the strides of the convolution along the width and height.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, height, width, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
        dilation_rate: an integer or tuple/list of 2 integers, specifying
            the dilation rate to use for dilated convolution.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Currently, specifying any `dilation_rate` value != 1 is
            incompatible with specifying any stride value != 1.
        activation: Activation function to use.
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix.
        bias_initializer: Initializer for the bias vector.
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix.
        bias_regularizer: Regularizer function applied to the bias vector.
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation")..
        kernel_constraint: Constraint function applied to the kernel matrix.
        bias_constraint: Constraint function applied to the bias vector.
  
    Input shape:
        4D tensor with shape:
        `(batch, channels, rows, cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(batch, rows, cols, channels)` if data_format='channels_last'.
  
    Output shape:
        4D tensor with shape:
        `(batch, filters, new_rows, new_cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(batch, new_rows, new_cols, filters)` if data_format='channels_last'.
        `rows` and `cols` values might have changed due to padding.
  
    References:
        - [A guide to convolution arithmetic for deep
          learning](https://arxiv.org/abs/1603.07285v1)
        - [Deconvolutional
          Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      if data_format is None:
        data_format = K.image_data_format()
      super(Conv2DTranspose, self).__init__(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          activation=activations.get(activation),
          use_bias=use_bias,
          kernel_initializer=initializers.get(kernel_initializer),
          bias_initializer=initializers.get(bias_initializer),
          kernel_regularizer=regularizers.get(kernel_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          kernel_constraint=constraints.get(kernel_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
    def get_config(self):
      config = {
          'filters': self.filters,
          'kernel_size': self.kernel_size,
          'strides': self.strides,
          'padding': self.padding,
          'data_format': self.data_format,
          'activation': activations.serialize(self.activation),
          'use_bias': self.use_bias,
          'kernel_initializer': initializers.serialize(self.kernel_initializer),
          'bias_initializer': initializers.serialize(self.bias_initializer),
          'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
          'bias_regularizer': regularizers.serialize(self.bias_regularizer),
          'activity_regularizer':
              regularizers.serialize(self.activity_regularizer),
          'kernel_constraint': constraints.serialize(self.kernel_constraint),
          'bias_constraint': constraints.serialize(self.bias_constraint)
      }
      base_config = super(Conv2DTranspose, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class Conv3DTranspose(tf_convolutional_layers.Conv3DTranspose, Layer):
    """Transposed convolution layer (sometimes called Deconvolution).
  
    The need for transposed convolutions generally arises
    from the desire to use a transformation going in the opposite direction
    of a normal convolution, i.e., from something that has the shape of the
    output of some convolution to something that has the shape of its input
    while maintaining a connectivity pattern that is compatible with
    said convolution.
  
    When using this layer as the first layer in a model,
    provide the keyword argument `input_shape`
    (tuple of integers, does not include the sample axis),
    e.g. `input_shape=(128, 128, 128, 3)` for a 128x128x128 volume with 3 channels
    if `data_format="channels_last"`.
  
    Arguments:
        filters: Integer, the dimensionality of the output space
            (i.e. the number of output filters in the convolution).
        kernel_size: An integer or tuple/list of 3 integers, specifying the
            depth, height and width of the 3D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 3 integers,
            specifying the strides of the convolution along the depth, height
              and width.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, depth, height, width, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, depth, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
        dilation_rate: an integer or tuple/list of 3 integers, specifying
            the dilation rate to use for dilated convolution.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Currently, specifying any `dilation_rate` value != 1 is
            incompatible with specifying any stride value != 1.
        activation: Activation function to use
            (see [activations](../activations.md)).
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix
            (see [regularizer](../regularizers.md)).
        bias_regularizer: Regularizer function applied to the bias vector
            (see [regularizer](../regularizers.md)).
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation").
            (see [regularizer](../regularizers.md)).
        kernel_constraint: Constraint function applied to the kernel matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).
  
    Input shape:
        5D tensor with shape:
        `(batch, channels, depth, rows, cols)` if data_format='channels_first'
        or 5D tensor with shape:
        `(batch, depth, rows, cols, channels)` if data_format='channels_last'.
  
    Output shape:
        5D tensor with shape:
        `(batch, filters, new_depth, new_rows, new_cols)` if
          data_format='channels_first'
        or 5D tensor with shape:
        `(batch, new_depth, new_rows, new_cols, filters)` if
          data_format='channels_last'.
        `depth` and `rows` and `cols` values might have changed due to padding.
  
    References:
        - [A guide to convolution arithmetic for deep
          learning](https://arxiv.org/abs/1603.07285v1)
        - [Deconvolutional
          Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1, 1),
                 padding='valid',
                 data_format=None,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      if data_format is None:
        data_format = K.image_data_format()
      super(Conv3DTranspose, self).__init__(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          activation=activations.get(activation),
          use_bias=use_bias,
          kernel_initializer=initializers.get(kernel_initializer),
          bias_initializer=initializers.get(bias_initializer),
          kernel_regularizer=regularizers.get(kernel_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          kernel_constraint=constraints.get(kernel_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
    def get_config(self):
      config = {
          'filters': self.filters,
          'kernel_size': self.kernel_size,
          'strides': self.strides,
          'padding': self.padding,
          'data_format': self.data_format,
          'activation': activations.serialize(self.activation),
          'use_bias': self.use_bias,
          'kernel_initializer': initializers.serialize(self.kernel_initializer),
          'bias_initializer': initializers.serialize(self.bias_initializer),
          'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
          'bias_regularizer': regularizers.serialize(self.bias_regularizer),
          'activity_regularizer':
              regularizers.serialize(self.activity_regularizer),
          'kernel_constraint': constraints.serialize(self.kernel_constraint),
          'bias_constraint': constraints.serialize(self.bias_constraint)
      }
      base_config = super(Conv3DTranspose, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class SeparableConv1D(tf_convolutional_layers.SeparableConv1D, Layer):
    """Depthwise separable 1D convolution.
  
    This layer performs a depthwise convolution that acts separately on
    channels, followed by a pointwise convolution that mixes channels.
    If `use_bias` is True and a bias initializer is provided,
    it adds a bias vector to the output.
    It then optionally applies an activation function to produce the final output.
  
    Arguments:
      filters: Integer, the dimensionality of the output space (i.e. the number
        of filters in the convolution).
      kernel_size: A single integer specifying the spatial
        dimensions of the filters.
      strides: A single integer specifying the strides
        of the convolution.
        Specifying any `stride` value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: One of `"valid"` or `"same"` (case-insensitive).
      data_format: A string, one of `channels_last` (default) or `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch, length, channels)` while `channels_first` corresponds to
        inputs with shape `(batch, channels, length)`.
      dilation_rate: A single integer, specifying
        the dilation rate to use for dilated convolution.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any stride value != 1.
      depth_multiplier: The number of depthwise convolution output channels for
        each input channel. The total number of depthwise convolution output
        channels will be equal to `num_filters_in * depth_multiplier`.
      activation: Activation function. Set it to None to maintain a
        linear activation.
      use_bias: Boolean, whether the layer uses a bias.
      depthwise_initializer: An initializer for the depthwise convolution kernel.
      pointwise_initializer: An initializer for the pointwise convolution kernel.
      bias_initializer: An initializer for the bias vector. If None, the default
        initializer will be used.
      depthwise_regularizer: Optional regularizer for the depthwise
        convolution kernel.
      pointwise_regularizer: Optional regularizer for the pointwise
        convolution kernel.
      bias_regularizer: Optional regularizer for the bias vector.
      activity_regularizer: Optional regularizer function for the output.
      depthwise_constraint: Optional projection function to be applied to the
          depthwise kernel after being updated by an `Optimizer` (e.g. used for
          norm constraints or value constraints for layer weights). The function
          must take as input the unprojected variable and must return the
          projected variable (which must have the same shape). Constraints are
          not safe to use when doing asynchronous distributed training.
      pointwise_constraint: Optional projection function to be applied to the
          pointwise kernel after being updated by an `Optimizer`.
      bias_constraint: Optional projection function to be applied to the
          bias after being updated by an `Optimizer`.
      trainable: Boolean, if `True` also add variables to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
      name: A string, the name of the layer.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 dilation_rate=1,
                 depth_multiplier=1,
                 activation=None,
                 use_bias=True,
                 depthwise_initializer='glorot_uniform',
                 pointwise_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 depthwise_regularizer=None,
                 pointwise_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 depthwise_constraint=None,
                 pointwise_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      if data_format is None:
        data_format = K.image_data_format()
      super(SeparableConv1D, self).__init__(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          activation=activations.get(activation),
          use_bias=use_bias,
          depthwise_initializer=initializers.get(depthwise_initializer),
          pointwise_initializer=initializers.get(pointwise_initializer),
          bias_initializer=initializers.get(bias_initializer),
          depthwise_regularizer=regularizers.get(depthwise_regularizer),
          pointwise_regularizer=regularizers.get(pointwise_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          depthwise_constraint=constraints.get(depthwise_constraint),
          pointwise_constraint=constraints.get(pointwise_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
    def get_config(self):
      config = {
          'filters': self.filters,
          'kernel_size': self.kernel_size,
          'strides': self.strides,
          'padding': self.padding,
          'data_format': self.data_format,
          'dilation_rate': self.dilation_rate,
          'activation': activations.serialize(self.activation),
          'use_bias': self.use_bias,
          'depthwise_initializer':
              initializers.serialize(self.depthwise_initializer),
          'pointwise_initializer':
              initializers.serialize(self.pointwise_initializer),
          'bias_initializer':
              initializers.serialize(self.bias_initializer),
          'depthwise_regularizer':
              regularizers.serialize(self.depthwise_regularizer),
          'pointwise_regularizer':
              regularizers.serialize(self.pointwise_regularizer),
          'bias_regularizer':
              regularizers.serialize(self.bias_regularizer),
          'activity_regularizer':
              regularizers.serialize(self.activity_regularizer),
          'depthwise_constraint':
              constraints.serialize(self.depthwise_constraint),
          'pointwise_constraint':
              constraints.serialize(self.pointwise_constraint),
          'bias_constraint':
              constraints.serialize(self.bias_constraint)
      }
      base_config = super(SeparableConv1D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class SeparableConv2D(tf_convolutional_layers.SeparableConv2D, Layer):
    """Depthwise separable 2D convolution.
  
    Separable convolutions consist in first performing
    a depthwise spatial convolution
    (which acts on each input channel separately)
    followed by a pointwise convolution which mixes together the resulting
    output channels. The `depth_multiplier` argument controls how many
    output channels are generated per input channel in the depthwise step.
  
    Intuitively, separable convolutions can be understood as
    a way to factorize a convolution kernel into two smaller kernels,
    or as an extreme version of an Inception block.
  
    Arguments:
        filters: Integer, the dimensionality of the output space
            (i.e. the number output of filters in the convolution).
        kernel_size: An integer or tuple/list of 2 integers, specifying the
            width and height of the 2D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 2 integers,
            specifying the strides of the convolution along the width and height.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, height, width, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
        depth_multiplier: The number of depthwise convolution output channels
            for each input channel.
            The total number of depthwise convolution output
            channels will be equal to `filterss_in * depth_multiplier`.
        activation: Activation function to use.
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        depthwise_initializer: Initializer for the depthwise kernel matrix.
        pointwise_initializer: Initializer for the pointwise kernel matrix.
        bias_initializer: Initializer for the bias vector.
        depthwise_regularizer: Regularizer function applied to
            the depthwise kernel matrix.
        pointwise_regularizer: Regularizer function applied to
            the pointwise kernel matrix.
        bias_regularizer: Regularizer function applied to the bias vector.
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation")..
        depthwise_constraint: Constraint function applied to
            the depthwise kernel matrix.
        pointwise_constraint: Constraint function applied to
            the pointwise kernel matrix.
        bias_constraint: Constraint function applied to the bias vector.
  
    Input shape:
        4D tensor with shape:
        `(batch, channels, rows, cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(batch, rows, cols, channels)` if data_format='channels_last'.
  
    Output shape:
        4D tensor with shape:
        `(batch, filters, new_rows, new_cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(batch, new_rows, new_cols, filters)` if data_format='channels_last'.
        `rows` and `cols` values might have changed due to padding.
    """
  
    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 dilation_rate=1,
                 depth_multiplier=1,
                 activation=None,
                 use_bias=True,
                 depthwise_initializer='glorot_uniform',
                 pointwise_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 depthwise_regularizer=None,
                 pointwise_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 depthwise_constraint=None,
                 pointwise_constraint=None,
                 bias_constraint=None,
                 **kwargs):
      if data_format is None:
        data_format = K.image_data_format()
      super(SeparableConv2D, self).__init__(
          filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilation_rate=dilation_rate,
          activation=activations.get(activation),
          use_bias=use_bias,
          depthwise_initializer=initializers.get(depthwise_initializer),
          pointwise_initializer=initializers.get(pointwise_initializer),
          bias_initializer=initializers.get(bias_initializer),
          depthwise_regularizer=regularizers.get(depthwise_regularizer),
          pointwise_regularizer=regularizers.get(pointwise_regularizer),
          bias_regularizer=regularizers.get(bias_regularizer),
          activity_regularizer=regularizers.get(activity_regularizer),
          depthwise_constraint=constraints.get(depthwise_constraint),
          pointwise_constraint=constraints.get(pointwise_constraint),
          bias_constraint=constraints.get(bias_constraint),
          **kwargs)
  
    def get_config(self):
      config = {
          'filters':
              self.filters,
          'kernel_size':
              self.kernel_size,
          'strides':
              self.strides,
          'padding':
              self.padding,
          'data_format':
              self.data_format,
          'dilation_rate':
              self.dilation_rate,
          'activation':
              activations.serialize(self.activation),
          'use_bias':
              self.use_bias,
          'depthwise_initializer':
              initializers.serialize(self.depthwise_initializer),
          'pointwise_initializer':
              initializers.serialize(self.pointwise_initializer),
          'bias_initializer':
              initializers.serialize(self.bias_initializer),
          'depthwise_regularizer':
              regularizers.serialize(self.depthwise_regularizer),
          'pointwise_regularizer':
              regularizers.serialize(self.pointwise_regularizer),
          'bias_regularizer':
              regularizers.serialize(self.bias_regularizer),
          'activity_regularizer':
              regularizers.serialize(self.activity_regularizer),
          'depthwise_constraint':
              constraints.serialize(self.depthwise_constraint),
          'pointwise_constraint':
              constraints.serialize(self.pointwise_constraint),
          'bias_constraint':
              constraints.serialize(self.bias_constraint)
      }
      base_config = super(SeparableConv2D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class UpSampling1D(Layer):
    """Upsampling layer for 1D inputs.
  
    Repeats each temporal step `size` times along the time axis.
  
    Arguments:
        size: integer. Upsampling factor.
  
    Input shape:
        3D tensor with shape: `(batch, steps, features)`.
  
    Output shape:
        3D tensor with shape: `(batch, upsampled_steps, features)`.
    """
  
    def __init__(self, size=2, **kwargs):
      super(UpSampling1D, self).__init__(**kwargs)
      self.size = int(size)
      self.input_spec = InputSpec(ndim=3)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      size = self.size * input_shape[1] if input_shape[1] is not None else None
      return tensor_shape.TensorShape([input_shape[0], size, input_shape[2]])
  
    def call(self, inputs):
      output = K.repeat_elements(inputs, self.size, axis=1)
      return output
  
    def get_config(self):
      config = {'size': self.size}
      base_config = super(UpSampling1D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class UpSampling2D(Layer):
    """Upsampling layer for 2D inputs.
  
    Repeats the rows and columns of the data
    by size[0] and size[1] respectively.
  
    Arguments:
        size: int, or tuple of 2 integers.
            The upsampling factors for rows and columns.
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, height, width, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
  
    Input shape:
        4D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, rows, cols, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, rows, cols)`
  
    Output shape:
        4D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, upsampled_rows, upsampled_cols, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, upsampled_rows, upsampled_cols)`
    """
  
    def __init__(self, size=(2, 2), data_format=None, **kwargs):
      super(UpSampling2D, self).__init__(**kwargs)
      self.data_format = conv_utils.normalize_data_format(data_format)
      self.size = conv_utils.normalize_tuple(size, 2, 'size')
      self.input_spec = InputSpec(ndim=4)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if self.data_format == 'channels_first':
        height = self.size[0] * input_shape[
            2] if input_shape[2] is not None else None
        width = self.size[1] * input_shape[
            3] if input_shape[3] is not None else None
        return tensor_shape.TensorShape(
            [input_shape[0], input_shape[1], height, width])
      else:
        height = self.size[0] * input_shape[
            1] if input_shape[1] is not None else None
        width = self.size[1] * input_shape[
            2] if input_shape[2] is not None else None
        return tensor_shape.TensorShape(
            [input_shape[0], height, width, input_shape[3]])
  
    def call(self, inputs):
      return K.resize_images(inputs, self.size[0], self.size[1], self.data_format)
  
    def get_config(self):
      config = {'size': self.size, 'data_format': self.data_format}
      base_config = super(UpSampling2D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class UpSampling3D(Layer):
    """Upsampling layer for 3D inputs.
  
    Repeats the 1st, 2nd and 3rd dimensions
    of the data by size[0], size[1] and size[2] respectively.
  
    Arguments:
        size: int, or tuple of 3 integers.
            The upsampling factors for dim1, dim2 and dim3.
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
            while `channels_first` corresponds to inputs with shape
            `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
  
    Input shape:
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, dim1, dim2, dim3, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, dim1, dim2, dim3)`
  
    Output shape:
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)`
    """
  
    def __init__(self, size=(2, 2, 2), data_format=None, **kwargs):
      self.data_format = conv_utils.normalize_data_format(data_format)
      self.size = conv_utils.normalize_tuple(size, 3, 'size')
      self.input_spec = InputSpec(ndim=5)
      super(UpSampling3D, self).__init__(**kwargs)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if self.data_format == 'channels_first':
        dim1 = self.size[0] * input_shape[
            2] if input_shape[2] is not None else None
        dim2 = self.size[1] * input_shape[
            3] if input_shape[3] is not None else None
        dim3 = self.size[2] * input_shape[
            4] if input_shape[4] is not None else None
        return tensor_shape.TensorShape(
            [input_shape[0], input_shape[1], dim1, dim2, dim3])
      else:
        dim1 = self.size[0] * input_shape[
            1] if input_shape[1] is not None else None
        dim2 = self.size[1] * input_shape[
            2] if input_shape[2] is not None else None
        dim3 = self.size[2] * input_shape[
            3] if input_shape[3] is not None else None
        return tensor_shape.TensorShape(
            [input_shape[0], dim1, dim2, dim3, input_shape[4]])
  
    def call(self, inputs):
      return K.resize_volumes(inputs, self.size[0], self.size[1], self.size[2],
                              self.data_format)
  
    def get_config(self):
      config = {'size': self.size, 'data_format': self.data_format}
      base_config = super(UpSampling3D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class ZeroPadding1D(Layer):
    """Zero-padding layer for 1D input (e.g. temporal sequence).
  
    Arguments:
        padding: int, or tuple of int (length 2), or dictionary.
            - If int:
            How many zeros to add at the beginning and end of
            the padding dimension (axis 1).
            - If tuple of int (length 2):
            How many zeros to add at the beginning and at the end of
            the padding dimension (`(left_pad, right_pad)`).
  
    Input shape:
        3D tensor with shape `(batch, axis_to_pad, features)`
  
    Output shape:
        3D tensor with shape `(batch, padded_axis, features)`
    """
  
    def __init__(self, padding=1, **kwargs):
      super(ZeroPadding1D, self).__init__(**kwargs)
      self.padding = conv_utils.normalize_tuple(padding, 2, 'padding')
      self.input_spec = InputSpec(ndim=3)
  
    def compute_output_shape(self, input_shape):
      if input_shape[1] is not None:
        length = input_shape[1] + self.padding[0] + self.padding[1]
      else:
        length = None
      return tensor_shape.TensorShape([input_shape[0], length, input_shape[2]])
  
    def call(self, inputs):
      return K.temporal_padding(inputs, padding=self.padding)
  
    def get_config(self):
      config = {'padding': self.padding}
      base_config = super(ZeroPadding1D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class ZeroPadding2D(Layer):
    """Zero-padding layer for 2D input (e.g. picture).
  
    This layer can add rows and columns of zeros
    at the top, bottom, left and right side of an image tensor.
  
    Arguments:
        padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
            - If int: the same symmetric padding
                is applied to width and height.
            - If tuple of 2 ints:
                interpreted as two different
                symmetric padding values for height and width:
                `(symmetric_height_pad, symmetric_width_pad)`.
            - If tuple of 2 tuples of 2 ints:
                interpreted as
                `((top_pad, bottom_pad), (left_pad, right_pad))`
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, height, width, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
  
    Input shape:
        4D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, rows, cols, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, rows, cols)`
  
    Output shape:
        4D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, padded_rows, padded_cols, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, padded_rows, padded_cols)`
    """
  
    def __init__(self, padding=(1, 1), data_format=None, **kwargs):
      super(ZeroPadding2D, self).__init__(**kwargs)
      self.data_format = conv_utils.normalize_data_format(data_format)
      if isinstance(padding, int):
        self.padding = ((padding, padding), (padding, padding))
      elif hasattr(padding, '__len__'):
        if len(padding) != 2:
          raise ValueError('`padding` should have two elements. '
                           'Found: ' + str(padding))
        height_padding = conv_utils.normalize_tuple(padding[0], 2,
                                                    '1st entry of padding')
        width_padding = conv_utils.normalize_tuple(padding[1], 2,
                                                   '2nd entry of padding')
        self.padding = (height_padding, width_padding)
      else:
        raise ValueError('`padding` should be either an int, '
                         'a tuple of 2 ints '
                         '(symmetric_height_pad, symmetric_width_pad), '
                         'or a tuple of 2 tuples of 2 ints '
                         '((top_pad, bottom_pad), (left_pad, right_pad)). '
                         'Found: ' + str(padding))
      self.input_spec = InputSpec(ndim=4)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if self.data_format == 'channels_first':
        if input_shape[2] is not None:
          rows = input_shape[2] + self.padding[0][0] + self.padding[0][1]
        else:
          rows = None
        if input_shape[3] is not None:
          cols = input_shape[3] + self.padding[1][0] + self.padding[1][1]
        else:
          cols = None
        return tensor_shape.TensorShape(
            [input_shape[0], input_shape[1], rows, cols])
      elif self.data_format == 'channels_last':
        if input_shape[1] is not None:
          rows = input_shape[1] + self.padding[0][0] + self.padding[0][1]
        else:
          rows = None
        if input_shape[2] is not None:
          cols = input_shape[2] + self.padding[1][0] + self.padding[1][1]
        else:
          cols = None
        return tensor_shape.TensorShape(
            [input_shape[0], rows, cols, input_shape[3]])
  
    def call(self, inputs):
      return K.spatial_2d_padding(
          inputs, padding=self.padding, data_format=self.data_format)
  
    def get_config(self):
      config = {'padding': self.padding, 'data_format': self.data_format}
      base_config = super(ZeroPadding2D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class ZeroPadding3D(Layer):
    """Zero-padding layer for 3D data (spatial or spatio-temporal).
  
    Arguments:
        padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
            - If int: the same symmetric padding
                is applied to width and height.
            - If tuple of 2 ints:
                interpreted as two different
                symmetric padding values for height and width:
                `(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)`.
            - If tuple of 2 tuples of 2 ints:
                interpreted as
                `((left_dim1_pad, right_dim1_pad), (left_dim2_pad,
                  right_dim2_pad), (left_dim3_pad, right_dim3_pad))`
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
            while `channels_first` corresponds to inputs with shape
            `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
  
    Input shape:
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad,
              depth)`
        - If `data_format` is `"channels_first"`:
            `(batch, depth, first_axis_to_pad, second_axis_to_pad,
              third_axis_to_pad)`
  
    Output shape:
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, first_padded_axis, second_padded_axis, third_axis_to_pad,
              depth)`
        - If `data_format` is `"channels_first"`:
            `(batch, depth, first_padded_axis, second_padded_axis,
              third_axis_to_pad)`
    """
  
    def __init__(self, padding=(1, 1, 1), data_format=None, **kwargs):
      super(ZeroPadding3D, self).__init__(**kwargs)
      self.data_format = conv_utils.normalize_data_format(data_format)
      if isinstance(padding, int):
        self.padding = ((padding, padding), (padding, padding), (padding,
                                                                 padding))
      elif hasattr(padding, '__len__'):
        if len(padding) != 3:
          raise ValueError('`padding` should have 3 elements. '
                           'Found: ' + str(padding))
        dim1_padding = conv_utils.normalize_tuple(padding[0], 2,
                                                  '1st entry of padding')
        dim2_padding = conv_utils.normalize_tuple(padding[1], 2,
                                                  '2nd entry of padding')
        dim3_padding = conv_utils.normalize_tuple(padding[2], 2,
                                                  '3rd entry of padding')
        self.padding = (dim1_padding, dim2_padding, dim3_padding)
      else:
        raise ValueError(
            '`padding` should be either an int, '
            'a tuple of 3 ints '
            '(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad), '
            'or a tuple of 3 tuples of 2 ints '
            '((left_dim1_pad, right_dim1_pad),'
            ' (left_dim2_pad, right_dim2_pad),'
            ' (left_dim3_pad, right_dim2_pad)). '
            'Found: ' + str(padding))
      self.input_spec = InputSpec(ndim=5)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if self.data_format == 'channels_first':
        if input_shape[2] is not None:
          dim1 = input_shape[2] + 2 * self.padding[0][0]
        else:
          dim1 = None
        if input_shape[3] is not None:
          dim2 = input_shape[3] + 2 * self.padding[1][0]
        else:
          dim2 = None
        if input_shape[4] is not None:
          dim3 = input_shape[4] + 2 * self.padding[2][0]
        else:
          dim3 = None
        return tensor_shape.TensorShape(
            [input_shape[0], input_shape[1], dim1, dim2, dim3])
      elif self.data_format == 'channels_last':
        if input_shape[1] is not None:
          dim1 = input_shape[1] + 2 * self.padding[0][1]
        else:
          dim1 = None
        if input_shape[2] is not None:
          dim2 = input_shape[2] + 2 * self.padding[1][1]
        else:
          dim2 = None
        if input_shape[3] is not None:
          dim3 = input_shape[3] + 2 * self.padding[2][1]
        else:
          dim3 = None
        return tensor_shape.TensorShape(
            [input_shape[0], dim1, dim2, dim3, input_shape[4]])
  
    def call(self, inputs):
      return K.spatial_3d_padding(
          inputs, padding=self.padding, data_format=self.data_format)
  
    def get_config(self):
      config = {'padding': self.padding, 'data_format': self.data_format}
      base_config = super(ZeroPadding3D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class Cropping1D(Layer):
    """Cropping layer for 1D input (e.g. temporal sequence).
  
    It crops along the time dimension (axis 1).
  
    Arguments:
        cropping: int or tuple of int (length 2)
            How many units should be trimmed off at the beginning and end of
            the cropping dimension (axis 1).
            If a single int is provided,
            the same value will be used for both.
  
    Input shape:
        3D tensor with shape `(batch, axis_to_crop, features)`
  
    Output shape:
        3D tensor with shape `(batch, cropped_axis, features)`
    """
  
    def __init__(self, cropping=(1, 1), **kwargs):
      super(Cropping1D, self).__init__(**kwargs)
      self.cropping = conv_utils.normalize_tuple(cropping, 2, 'cropping')
      self.input_spec = InputSpec(ndim=3)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      if input_shape[1] is not None:
        length = input_shape[1] - self.cropping[0] - self.cropping[1]
      else:
        length = None
      return tensor_shape.TensorShape([input_shape[0], length, input_shape[2]])
  
    def call(self, inputs):
      if self.cropping[1] == 0:
        return inputs[:, self.cropping[0]:, :]
      else:
        return inputs[:, self.cropping[0]:-self.cropping[1], :]
  
    def get_config(self):
      config = {'cropping': self.cropping}
      base_config = super(Cropping1D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class Cropping2D(Layer):
    """Cropping layer for 2D input (e.g. picture).
  
    It crops along spatial dimensions, i.e. width and height.
  
    Arguments:
        cropping: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
            - If int: the same symmetric cropping
                is applied to width and height.
            - If tuple of 2 ints:
                interpreted as two different
                symmetric cropping values for height and width:
                `(symmetric_height_crop, symmetric_width_crop)`.
            - If tuple of 2 tuples of 2 ints:
                interpreted as
                `((top_crop, bottom_crop), (left_crop, right_crop))`
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, height, width, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
  
    Input shape:
        4D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, rows, cols, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, rows, cols)`
  
    Output shape:
        4D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, cropped_rows, cropped_cols, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, cropped_rows, cropped_cols)`
  
    Examples:
  
    ```python
        # Crop the input 2D images or feature maps
        model = Sequential()
        model.add(Cropping2D(cropping=((2, 2), (4, 4)),
                             input_shape=(28, 28, 3)))
        # now model.output_shape == (None, 24, 20, 3)
        model.add(Conv2D(64, (3, 3), padding='same))
        model.add(Cropping2D(cropping=((2, 2), (2, 2))))
        # now model.output_shape == (None, 20, 16. 64)
    ```
    """
  
    def __init__(self, cropping=((0, 0), (0, 0)), data_format=None, **kwargs):
      super(Cropping2D, self).__init__(**kwargs)
      self.data_format = conv_utils.normalize_data_format(data_format)
      if isinstance(cropping, int):
        self.cropping = ((cropping, cropping), (cropping, cropping))
      elif hasattr(cropping, '__len__'):
        if len(cropping) != 2:
          raise ValueError('`cropping` should have two elements. '
                           'Found: ' + str(cropping))
        height_cropping = conv_utils.normalize_tuple(cropping[0], 2,
                                                     '1st entry of cropping')
        width_cropping = conv_utils.normalize_tuple(cropping[1], 2,
                                                    '2nd entry of cropping')
        self.cropping = (height_cropping, width_cropping)
      else:
        raise ValueError('`cropping` should be either an int, '
                         'a tuple of 2 ints '
                         '(symmetric_height_crop, symmetric_width_crop), '
                         'or a tuple of 2 tuples of 2 ints '
                         '((top_crop, bottom_crop), (left_crop, right_crop)). '
                         'Found: ' + str(cropping))
      self.input_spec = InputSpec(ndim=4)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      # pylint: disable=invalid-unary-operand-type
      if self.data_format == 'channels_first':
        return tensor_shape.TensorShape([
            input_shape[0], input_shape[1],
            input_shape[2] - self.cropping[0][0] - self.cropping[0][1]
            if input_shape[2] else None,
            input_shape[3] - self.cropping[1][0] - self.cropping[1][1]
            if input_shape[3] else None
        ])
      else:
        return tensor_shape.TensorShape([
            input_shape[0],
            input_shape[1] - self.cropping[0][0] - self.cropping[0][1]
            if input_shape[1] else None,
            input_shape[2] - self.cropping[1][0] - self.cropping[1][1]
            if input_shape[2] else None, input_shape[3]
        ])
      # pylint: enable=invalid-unary-operand-type
  
    def call(self, inputs):
      # pylint: disable=invalid-unary-operand-type
      if self.data_format == 'channels_first':
        if self.cropping[0][1] == self.cropping[1][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:]
        elif self.cropping[0][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:
                        -self.cropping[1][1]]
        elif self.cropping[1][1] == 0:
          return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
                        self.cropping[1][0]:]
        return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
                      self.cropping[1][0]:-self.cropping[1][1]]
      else:
        if self.cropping[0][1] == self.cropping[1][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:, :]
        elif self.cropping[0][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:
                        -self.cropping[1][1], :]
        elif self.cropping[1][1] == 0:
          return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
                        self.cropping[1][0]:, :]
        return inputs[:, self.cropping[0][0]:-self.cropping[0][1], self.cropping[
            1][0]:-self.cropping[1][1], :]  # pylint: disable=invalid-unary-operand-type
      # pylint: enable=invalid-unary-operand-type
  
    def get_config(self):
      config = {'cropping': self.cropping, 'data_format': self.data_format}
      base_config = super(Cropping2D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  class Cropping3D(Layer):
    """Cropping layer for 3D data (e.g.
  
    spatial or spatio-temporal).
  
    Arguments:
        cropping: int, or tuple of 23ints, or tuple of 3 tuples of 2 ints.
            - If int: the same symmetric cropping
                is applied to depth, height, and width.
            - If tuple of 3 ints:
                interpreted as two different
                symmetric cropping values for depth, height, and width:
                `(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)`.
            - If tuple of 3 tuples of 2 ints:
                interpreted as
                `((left_dim1_crop, right_dim1_crop), (left_dim2_crop,
                  right_dim2_crop), (left_dim3_crop, right_dim3_crop))`
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
            while `channels_first` corresponds to inputs with shape
            `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
  
    Input shape:
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop,
              depth)`
        - If `data_format` is `"channels_first"`:
            `(batch, depth, first_axis_to_crop, second_axis_to_crop,
              third_axis_to_crop)`
  
    Output shape:
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, first_cropped_axis, second_cropped_axis, third_cropped_axis,
              depth)`
        - If `data_format` is `"channels_first"`:
            `(batch, depth, first_cropped_axis, second_cropped_axis,
              third_cropped_axis)`
    """
  
    def __init__(self,
                 cropping=((1, 1), (1, 1), (1, 1)),
                 data_format=None,
                 **kwargs):
      super(Cropping3D, self).__init__(**kwargs)
      self.data_format = conv_utils.normalize_data_format(data_format)
      if isinstance(cropping, int):
        self.cropping = ((cropping, cropping), (cropping, cropping), (cropping,
                                                                      cropping))
      elif hasattr(cropping, '__len__'):
        if len(cropping) != 3:
          raise ValueError('`cropping` should have 3 elements. '
                           'Found: ' + str(cropping))
        dim1_cropping = conv_utils.normalize_tuple(cropping[0], 2,
                                                   '1st entry of cropping')
        dim2_cropping = conv_utils.normalize_tuple(cropping[1], 2,
                                                   '2nd entry of cropping')
        dim3_cropping = conv_utils.normalize_tuple(cropping[2], 2,
                                                   '3rd entry of cropping')
        self.cropping = (dim1_cropping, dim2_cropping, dim3_cropping)
      else:
        raise ValueError(
            '`cropping` should be either an int, '
            'a tuple of 3 ints '
            '(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop), '
            'or a tuple of 3 tuples of 2 ints '
            '((left_dim1_crop, right_dim1_crop),'
            ' (left_dim2_crop, right_dim2_crop),'
            ' (left_dim3_crop, right_dim2_crop)). '
            'Found: ' + str(cropping))
      self.input_spec = InputSpec(ndim=5)
  
    def compute_output_shape(self, input_shape):
      input_shape = tensor_shape.TensorShape(input_shape).as_list()
      # pylint: disable=invalid-unary-operand-type
      if self.data_format == 'channels_first':
        if input_shape[2] is not None:
          dim1 = input_shape[2] - self.cropping[0][0] - self.cropping[0][1]
        else:
          dim1 = None
        if input_shape[3] is not None:
          dim2 = input_shape[3] - self.cropping[1][0] - self.cropping[1][1]
        else:
          dim2 = None
        if input_shape[4] is not None:
          dim3 = input_shape[4] - self.cropping[2][0] - self.cropping[2][1]
        else:
          dim3 = None
        return tensor_shape.TensorShape(
            [input_shape[0], input_shape[1], dim1, dim2, dim3])
      elif self.data_format == 'channels_last':
        if input_shape[1] is not None:
          dim1 = input_shape[1] - self.cropping[0][0] - self.cropping[0][1]
        else:
          dim1 = None
        if input_shape[2] is not None:
          dim2 = input_shape[2] - self.cropping[1][0] - self.cropping[1][1]
        else:
          dim2 = None
        if input_shape[3] is not None:
          dim3 = input_shape[3] - self.cropping[2][0] - self.cropping[2][1]
        else:
          dim3 = None
        return tensor_shape.TensorShape(
            [input_shape[0], dim1, dim2, dim3, input_shape[4]])
      # pylint: enable=invalid-unary-operand-type
  
    def call(self, inputs):
      # pylint: disable=invalid-unary-operand-type
      if self.data_format == 'channels_first':
        if self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:,
                        self.cropping[2][0]:]
        elif self.cropping[0][1] == self.cropping[1][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:,
                        self.cropping[2][0]:-self.cropping[2][1]]
        elif self.cropping[1][1] == self.cropping[2][1] == 0:
          return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
                        self.cropping[1][0]:, self.cropping[2][0]:]
        elif self.cropping[0][1] == self.cropping[2][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][0]:
                        -self.cropping[1][1], self.cropping[2][0]:]
        elif self.cropping[0][1] == 0:
          return inputs[:, :, self.cropping[0][0]:, self.cropping[1][
              0]:-self.cropping[1][1], self.cropping[2][0]:-self.cropping[2][1]]
        elif self.cropping[1][1] == 0:
          return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1], self.
                        cropping[1][0]:, self.cropping[2][0]:-self.cropping[2][1]]
        elif self.cropping[2][1] == 0:
          return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1], self.
                        cropping[1][0]:-self.cropping[1][1], self.cropping[2][0]:]
        return inputs[:, :, self.cropping[0][0]:-self.cropping[0][1],
                      self.cropping[1][0]:-self.cropping[1][1], self.cropping[2][
                          0]:-self.cropping[2][1]]
      else:
        if self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:,
                        self.cropping[2][0]:, :]
        elif self.cropping[0][1] == self.cropping[1][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:,
                        self.cropping[2][0]:-self.cropping[2][1], :]
        elif self.cropping[1][1] == self.cropping[2][1] == 0:
          return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
                        self.cropping[1][0]:, self.cropping[2][0]:, :]
        elif self.cropping[0][1] == self.cropping[2][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][0]:
                        -self.cropping[1][1], self.cropping[2][0]:, :]
        elif self.cropping[0][1] == 0:
          return inputs[:, self.cropping[0][0]:, self.cropping[1][
              0]:-self.cropping[1][1], self.cropping[2][0]:
                        -self.cropping[2][1], :]
        elif self.cropping[1][1] == 0:
          return inputs[:, self.cropping[0][
              0]:-self.cropping[0][1], self.cropping[1][0]:, self.cropping[2][0]:
                        -self.cropping[2][1], :]
        elif self.cropping[2][1] == 0:
          return inputs[:, self.cropping[0][0]:-self.cropping[0][1],
                        self.cropping[1][0]:-self.cropping[1][1], self.cropping[
                            2][0]:, :]
        return inputs[:, self.cropping[0][0]:-self.cropping[0][1], self.cropping[
            1][0]:-self.cropping[1][1], self.cropping[2][0]:  # pylint: disable=invalid-unary-operand-type
                      -self.cropping[2][1], :]  # pylint: disable=invalid-unary-operand-type
      # pylint: enable=invalid-unary-operand-type
  
    def get_config(self):
      config = {'cropping': self.cropping, 'data_format': self.data_format}
      base_config = super(Cropping3D, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
  
  # Aliases
  
  Convolution1D = Conv1D
  Convolution2D = Conv2D
  Convolution3D = Conv3D
  SeparableConvolution1D = SeparableConv1D
  SeparableConvolution2D = SeparableConv2D
  Convolution2DTranspose = Conv2DTranspose
  Convolution3DTranspose = Conv3DTranspose
  Deconvolution2D = Deconv2D = Conv2DTranspose
  Deconvolution3D = Deconv3D = Conv3DTranspose
  