Move Keras code out of _impl folder and remove API files.

PiperOrigin-RevId: 197097430

1 files changed, 2580 insertions, 0 deletions

diff --git a/tensorflow/python/keras/layers/convolutional.py b/tensorflow/python/keras/layers/convolutional.py
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+++ b/tensorflow/python/keras/layers/convolutional.py
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+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Keras convolution layers and image transformation layers.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.eager import context
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.keras import activations
+from tensorflow.python.keras import backend
+from tensorflow.python.keras import constraints
+from tensorflow.python.keras import initializers
+from tensorflow.python.keras import regularizers
+from tensorflow.python.keras.engine import InputSpec
+from tensorflow.python.keras.engine import Layer
+# imports for backwards namespace compatibility
+# pylint: disable=unused-import
+from tensorflow.python.keras.layers.pooling import AveragePooling1D
+from tensorflow.python.keras.layers.pooling import AveragePooling2D
+from tensorflow.python.keras.layers.pooling import AveragePooling3D
+from tensorflow.python.keras.layers.pooling import MaxPooling1D
+from tensorflow.python.keras.layers.pooling import MaxPooling2D
+from tensorflow.python.keras.layers.pooling import MaxPooling3D
+# pylint: enable=unused-import
+from tensorflow.python.keras.utils import conv_utils
+from tensorflow.python.keras.utils import tf_utils
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import nn
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.util.tf_export import tf_export
+
+
+class Conv(Layer):
+  """Abstract nD convolution layer (private, used as implementation base).
+
+  This layer creates a convolution kernel that is convolved
+  (actually cross-correlated) with the layer input to produce a tensor of
+  outputs. If `use_bias` is True (and a `bias_initializer` is provided),
+  a bias vector is created and added to the outputs. Finally, if
+  `activation` is not `None`, it is applied to the outputs as well.
+
+  Arguments:
+    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
+    filters: Integer, the dimensionality of the output space (i.e. the number
+      of filters in the convolution).
+    kernel_size: An integer or tuple/list of n integers, specifying the
+      length of the convolution window.
+    strides: An integer or tuple/list of n integers,
+      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"` 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, ..., channels)` while `channels_first` corresponds to
+      inputs with shape `(batch, channels, ...)`.
+    dilation_rate: An integer or tuple/list of n integers, 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. Set it to None to maintain a
+      linear activation.
+    use_bias: Boolean, whether the layer uses a bias.
+    kernel_initializer: An initializer for the convolution kernel.
+    bias_initializer: An initializer for the bias vector. If None, the default
+      initializer will be used.
+    kernel_regularizer: Optional regularizer for the convolution kernel.
+    bias_regularizer: Optional regularizer for the bias vector.
+    activity_regularizer: Optional regularizer function for the output.
+    kernel_constraint: Optional projection function to be applied to the
+        kernel after being updated by an `Optimizer` (e.g. used to implement
+        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.
+    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, rank,
+               filters,
+               kernel_size,
+               strides=1,
+               padding='valid',
+               data_format=None,
+               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,
+               trainable=True,
+               name=None,
+               **kwargs):
+    super(Conv, self).__init__(
+        trainable=trainable,
+        name=name,
+        activity_regularizer=regularizers.get(activity_regularizer),
+        **kwargs)
+    self.rank = rank
+    self.filters = filters
+    self.kernel_size = conv_utils.normalize_tuple(
+        kernel_size, rank, 'kernel_size')
+    self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
+    self.padding = conv_utils.normalize_padding(padding)
+    self.data_format = conv_utils.normalize_data_format(data_format)
+    self.dilation_rate = conv_utils.normalize_tuple(
+        dilation_rate, rank, 'dilation_rate')
+    self.activation = activations.get(activation)
+    self.use_bias = use_bias
+    self.kernel_initializer = initializers.get(kernel_initializer)
+    self.bias_initializer = initializers.get(bias_initializer)
+    self.kernel_regularizer = regularizers.get(kernel_regularizer)
+    self.bias_regularizer = regularizers.get(bias_regularizer)
+    self.kernel_constraint = constraints.get(kernel_constraint)
+    self.bias_constraint = constraints.get(bias_constraint)
+    self.input_spec = InputSpec(ndim=self.rank + 2)
+
+  def build(self, input_shape):
+    input_shape = tensor_shape.TensorShape(input_shape)
+    if self.data_format == 'channels_first':
+      channel_axis = 1
+    else:
+      channel_axis = -1
+    if input_shape[channel_axis].value is None:
+      raise ValueError('The channel dimension of the inputs '
+                       'should be defined. Found `None`.')
+    input_dim = int(input_shape[channel_axis])
+    kernel_shape = self.kernel_size + (input_dim, self.filters)
+
+    self.kernel = self.add_variable(name='kernel',
+                                    shape=kernel_shape,
+                                    initializer=self.kernel_initializer,
+                                    regularizer=self.kernel_regularizer,
+                                    constraint=self.kernel_constraint,
+                                    trainable=True,
+                                    dtype=self.dtype)
+    if self.use_bias:
+      self.bias = self.add_variable(name='bias',
+                                    shape=(self.filters,),
+                                    initializer=self.bias_initializer,
+                                    regularizer=self.bias_regularizer,
+                                    constraint=self.bias_constraint,
+                                    trainable=True,
+                                    dtype=self.dtype)
+    else:
+      self.bias = None
+    self.input_spec = InputSpec(ndim=self.rank + 2,
+                                axes={channel_axis: input_dim})
+    self._convolution_op = nn_ops.Convolution(
+        input_shape,
+        filter_shape=self.kernel.get_shape(),
+        dilation_rate=self.dilation_rate,
+        strides=self.strides,
+        padding=self.padding.upper(),
+        data_format=conv_utils.convert_data_format(self.data_format,
+                                                   self.rank + 2))
+    self.built = True
+
+  def call(self, inputs):
+    outputs = self._convolution_op(inputs, self.kernel)
+
+    if self.use_bias:
+      if self.data_format == 'channels_first':
+        if self.rank == 1:
+          # nn.bias_add does not accept a 1D input tensor.
+          bias = array_ops.reshape(self.bias, (1, self.filters, 1))
+          outputs += bias
+        if self.rank == 2:
+          outputs = nn.bias_add(outputs, self.bias, data_format='NCHW')
+        if self.rank == 3:
+          # As of Mar 2017, direct addition is significantly slower than
+          # bias_add when computing gradients. To use bias_add, we collapse Z
+          # and Y into a single dimension to obtain a 4D input tensor.
+          outputs_shape = outputs.shape.as_list()
+          if outputs_shape[0] is None:
+            outputs_shape[0] = -1
+          outputs_4d = array_ops.reshape(outputs,
+                                         [outputs_shape[0], outputs_shape[1],
+                                          outputs_shape[2] * outputs_shape[3],
+                                          outputs_shape[4]])
+          outputs_4d = nn.bias_add(outputs_4d, self.bias, data_format='NCHW')
+          outputs = array_ops.reshape(outputs_4d, outputs_shape)
+      else:
+        outputs = nn.bias_add(outputs, self.bias, data_format='NHWC')
+
+    if self.activation is not None:
+      return self.activation(outputs)
+    return outputs
+
+  def compute_output_shape(self, input_shape):
+    input_shape = tensor_shape.TensorShape(input_shape).as_list()
+    if self.data_format == 'channels_last':
+      space = input_shape[1:-1]
+      new_space = []
+      for i in range(len(space)):
+        new_dim = conv_utils.conv_output_length(
+            space[i],
+            self.kernel_size[i],
+            padding=self.padding,
+            stride=self.strides[i],
+            dilation=self.dilation_rate[i])
+        new_space.append(new_dim)
+      return tensor_shape.TensorShape([input_shape[0]] + new_space +
+                                      [self.filters])
+    else:
+      space = input_shape[2:]
+      new_space = []
+      for i in range(len(space)):
+        new_dim = conv_utils.conv_output_length(
+            space[i],
+            self.kernel_size[i],
+            padding=self.padding,
+            stride=self.strides[i],
+            dilation=self.dilation_rate[i])
+        new_space.append(new_dim)
+      return tensor_shape.TensorShape([input_shape[0], self.filters] +
+                                      new_space)
+
+  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(Conv, self).get_config()
+    return dict(list(base_config.items()) + list(config.items()))
+
+
+@tf_export('keras.layers.Conv1D', 'keras.layers.Convolution1D')
+class Conv1D(Conv):
+  """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 of output 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).
+      data_format: A string,
+          one of `channels_last` (default) or `channels_first`.
+      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',
+               data_format='channels_last',
+               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__(
+        rank=1,
+        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)
+
+
+@tf_export('keras.layers.Conv2D', 'keras.layers.Convolution2D')
+class Conv2D(Conv):
+  """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 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:
+      `(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):
+    super(Conv2D, self).__init__(
+        rank=2,
+        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)
+
+
+@tf_export('keras.layers.Conv3D', 'keras.layers.Convolution3D')
+class Conv3D(Conv):
+  """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 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 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):
+    super(Conv3D, self).__init__(
+        rank=3,
+        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)
+
+
+@tf_export('keras.layers.Conv2DTranspose',
+           'keras.layers.Convolution2DTranspose')
+class Conv2DTranspose(Conv2D):
+  """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):
+    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 build(self, input_shape):
+    input_shape = tensor_shape.TensorShape(input_shape)
+    if len(input_shape) != 4:
+      raise ValueError('Inputs should have rank 4. Received input shape: ' +
+                       str(input_shape))
+    if self.data_format == 'channels_first':
+      channel_axis = 1
+    else:
+      channel_axis = -1
+    if input_shape[channel_axis].value is None:
+      raise ValueError('The channel dimension of the inputs '
+                       'should be defined. Found `None`.')
+    input_dim = int(input_shape[channel_axis])
+    self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
+    kernel_shape = self.kernel_size + (self.filters, input_dim)
+
+    self.kernel = self.add_variable(name='kernel',
+                                    shape=kernel_shape,
+                                    initializer=self.kernel_initializer,
+                                    regularizer=self.kernel_regularizer,
+                                    constraint=self.kernel_constraint,
+                                    trainable=True,
+                                    dtype=self.dtype)
+    if self.use_bias:
+      self.bias = self.add_variable(name='bias',
+                                    shape=(self.filters,),
+                                    initializer=self.bias_initializer,
+                                    regularizer=self.bias_regularizer,
+                                    constraint=self.bias_constraint,
+                                    trainable=True,
+                                    dtype=self.dtype)
+    else:
+      self.bias = None
+    self.built = True
+
+  def call(self, inputs):
+    inputs_shape = array_ops.shape(inputs)
+    batch_size = inputs_shape[0]
+    if self.data_format == 'channels_first':
+      c_axis, h_axis, w_axis = 1, 2, 3
+    else:
+      c_axis, h_axis, w_axis = 3, 1, 2
+
+    height, width = inputs_shape[h_axis], inputs_shape[w_axis]
+    kernel_h, kernel_w = self.kernel_size
+    stride_h, stride_w = self.strides
+
+    # Infer the dynamic output shape:
+    out_height = conv_utils.deconv_output_length(height,
+                                                 kernel_h,
+                                                 self.padding,
+                                                 stride_h)
+    out_width = conv_utils.deconv_output_length(width,
+                                                kernel_w,
+                                                self.padding,
+                                                stride_w)
+    if self.data_format == 'channels_first':
+      output_shape = (batch_size, self.filters, out_height, out_width)
+      strides = (1, 1, stride_h, stride_w)
+    else:
+      output_shape = (batch_size, out_height, out_width, self.filters)
+      strides = (1, stride_h, stride_w, 1)
+
+    output_shape_tensor = array_ops.stack(output_shape)
+    outputs = nn.conv2d_transpose(
+        inputs,
+        self.kernel,
+        output_shape_tensor,
+        strides,
+        padding=self.padding.upper(),
+        data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
+
+    if not context.executing_eagerly():
+      # Infer the static output shape:
+      out_shape = inputs.get_shape().as_list()
+      out_shape[c_axis] = self.filters
+      out_shape[h_axis] = conv_utils.deconv_output_length(out_shape[h_axis],
+                                                          kernel_h,
+                                                          self.padding,
+                                                          stride_h)
+      out_shape[w_axis] = conv_utils.deconv_output_length(out_shape[w_axis],
+                                                          kernel_w,
+                                                          self.padding,
+                                                          stride_w)
+      outputs.set_shape(out_shape)
+
+    if self.use_bias:
+      outputs = nn.bias_add(
+          outputs,
+          self.bias,
+          data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
+
+    if self.activation is not None:
+      return self.activation(outputs)
+    return outputs
+
+  def compute_output_shape(self, input_shape):
+    input_shape = tensor_shape.TensorShape(input_shape).as_list()
+    output_shape = list(input_shape)
+    if self.data_format == 'channels_first':
+      c_axis, h_axis, w_axis = 1, 2, 3
+    else:
+      c_axis, h_axis, w_axis = 3, 1, 2
+
+    kernel_h, kernel_w = self.kernel_size
+    stride_h, stride_w = self.strides
+
+    output_shape[c_axis] = self.filters
+    output_shape[h_axis] = conv_utils.deconv_output_length(
+        output_shape[h_axis], kernel_h, self.padding, stride_h)
+    output_shape[w_axis] = conv_utils.deconv_output_length(
+        output_shape[w_axis], kernel_w, self.padding, stride_w)
+    return tensor_shape.TensorShape(output_shape)
+
+
+@tf_export('keras.layers.Conv3DTranspose',
+           'keras.layers.Convolution3DTranspose')
+class Conv3DTranspose(Conv3D):
+  """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):
+    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 build(self, input_shape):
+    input_shape = tensor_shape.TensorShape(input_shape)
+    if len(input_shape) != 5:
+      raise ValueError('Inputs should have rank 5, received input shape:',
+                       str(input_shape))
+    if self.data_format == 'channels_first':
+      channel_axis = 1
+    else:
+      channel_axis = -1
+    if input_shape[channel_axis].value is None:
+      raise ValueError('The channel dimension of the inputs '
+                       'should be defined, found None: ' + str(input_shape))
+    input_dim = int(input_shape[channel_axis])
+    kernel_shape = self.kernel_size + (self.filters, input_dim)
+    self.input_spec = InputSpec(ndim=5, axes={channel_axis: input_dim})
+
+    self.kernel = self.add_variable(
+        'kernel',
+        shape=kernel_shape,
+        initializer=self.kernel_initializer,
+        regularizer=self.kernel_regularizer,
+        constraint=self.kernel_constraint,
+        trainable=True,
+        dtype=self.dtype)
+    if self.use_bias:
+      self.bias = self.add_variable(
+          'bias',
+          shape=(self.filters,),
+          initializer=self.bias_initializer,
+          regularizer=self.bias_regularizer,
+          constraint=self.bias_constraint,
+          trainable=True,
+          dtype=self.dtype)
+    else:
+      self.bias = None
+    self.built = True
+
+  def call(self, inputs):
+    inputs_shape = array_ops.shape(inputs)
+    batch_size = inputs_shape[0]
+    if self.data_format == 'channels_first':
+      c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
+    else:
+      c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
+
+    self.input_spec = InputSpec(ndim=5, axes={c_axis: inputs_shape[c_axis]})
+
+    depth = inputs_shape[d_axis]
+    height = inputs_shape[h_axis]
+    width = inputs_shape[w_axis]
+
+    kernel_d, kernel_h, kernel_w = self.kernel_size
+    stride_d, stride_h, stride_w = self.strides
+
+    # Infer the dynamic output shape:
+    out_depth = conv_utils.deconv_output_length(depth,
+                                                kernel_d,
+                                                self.padding,
+                                                stride_d)
+    out_height = conv_utils.deconv_output_length(height,
+                                                 kernel_h,
+                                                 self.padding,
+                                                 stride_h)
+    out_width = conv_utils.deconv_output_length(width,
+                                                kernel_w,
+                                                self.padding,
+                                                stride_w)
+    if self.data_format == 'channels_first':
+      output_shape = (batch_size, self.filters, out_depth, out_height,
+                      out_width)
+      strides = (1, 1, stride_d, stride_h, stride_w)
+    else:
+      output_shape = (batch_size, out_depth, out_height, out_width,
+                      self.filters)
+      strides = (1, stride_d, stride_h, stride_w, 1)
+
+    output_shape_tensor = array_ops.stack(output_shape)
+    outputs = nn.conv3d_transpose(
+        inputs,
+        self.kernel,
+        output_shape_tensor,
+        strides,
+        data_format=conv_utils.convert_data_format(self.data_format, ndim=5),
+        padding=self.padding.upper())
+
+    if not context.executing_eagerly():
+      # Infer the static output shape:
+      out_shape = inputs.get_shape().as_list()
+      out_shape[c_axis] = self.filters
+      out_shape[d_axis] = conv_utils.deconv_output_length(out_shape[d_axis],
+                                                          kernel_d,
+                                                          self.padding,
+                                                          stride_d)
+      out_shape[h_axis] = conv_utils.deconv_output_length(out_shape[h_axis],
+                                                          kernel_h,
+                                                          self.padding,
+                                                          stride_h)
+      out_shape[w_axis] = conv_utils.deconv_output_length(out_shape[w_axis],
+                                                          kernel_w,
+                                                          self.padding,
+                                                          stride_w)
+      outputs.set_shape(out_shape)
+
+    if self.use_bias:
+      outputs_shape = outputs.shape.as_list()
+      if outputs_shape[0] is None:
+        outputs_shape[0] = -1
+      if self.data_format == 'channels_first':
+        outputs_4d = array_ops.reshape(outputs, [
+            outputs_shape[0], outputs_shape[1],
+            outputs_shape[2] * outputs_shape[3], outputs_shape[4]
+        ])
+      else:
+        outputs_4d = array_ops.reshape(outputs, [
+            outputs_shape[0], outputs_shape[1] * outputs_shape[2],
+            outputs_shape[3], outputs_shape[4]
+        ])
+      outputs_4d = nn.bias_add(
+          outputs_4d,
+          self.bias,
+          data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
+      outputs = array_ops.reshape(outputs_4d, outputs_shape)
+
+    if self.activation is not None:
+      return self.activation(outputs)
+    return outputs
+
+  def compute_output_shape(self, input_shape):
+    input_shape = tensor_shape.TensorShape(input_shape).as_list()
+    output_shape = list(input_shape)
+    if self.data_format == 'channels_first':
+      c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
+    else:
+      c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
+
+    kernel_d, kernel_h, kernel_w = self.kernel_size
+    stride_d, stride_h, stride_w = self.strides
+
+    output_shape[c_axis] = self.filters
+    output_shape[d_axis] = conv_utils.deconv_output_length(
+        output_shape[d_axis], kernel_d, self.padding, stride_d)
+    output_shape[h_axis] = conv_utils.deconv_output_length(
+        output_shape[h_axis], kernel_h, self.padding, stride_h)
+    output_shape[w_axis] = conv_utils.deconv_output_length(
+        output_shape[w_axis], kernel_w, self.padding, stride_w)
+    return tensor_shape.TensorShape(output_shape)
+
+
+class SeparableConv(Conv):
+  """Abstract base layer for separable nD 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:
+    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
+    filters: Integer, the dimensionality of the output space (i.e. the number
+      of filters in the convolution).
+    kernel_size: A tuple or list of integers specifying the spatial
+      dimensions of the filters. Can be a single integer to specify the same
+      value for all spatial dimensions.
+    strides: A tuple or list of integers specifying the strides
+      of the convolution. 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, ..., channels)` while `channels_first` corresponds to
+      inputs with shape `(batch, channels, ...)`.
+    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.
+    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,
+               rank,
+               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,
+               trainable=True,
+               name=None,
+               **kwargs):
+    super(SeparableConv, self).__init__(
+        rank=rank,
+        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,
+        bias_regularizer=regularizers.get(bias_regularizer),
+        activity_regularizer=regularizers.get(activity_regularizer),
+        bias_constraint=bias_constraint,
+        trainable=trainable,
+        name=name,
+        **kwargs)
+    self.depth_multiplier = depth_multiplier
+    self.depthwise_initializer = initializers.get(depthwise_initializer)
+    self.pointwise_initializer = initializers.get(pointwise_initializer)
+    self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
+    self.pointwise_regularizer = regularizers.get(pointwise_regularizer)
+    self.depthwise_constraint = constraints.get(depthwise_constraint)
+    self.pointwise_constraint = constraints.get(pointwise_constraint)
+
+  def build(self, input_shape):
+    input_shape = tensor_shape.TensorShape(input_shape)
+    if self.data_format == 'channels_first':
+      channel_axis = 1
+    else:
+      channel_axis = -1
+    if input_shape[channel_axis].value is None:
+      raise ValueError('The channel dimension of the inputs '
+                       'should be defined. Found `None`.')
+    input_dim = int(input_shape[channel_axis])
+    self.input_spec = InputSpec(ndim=self.rank + 2,
+                                axes={channel_axis: input_dim})
+    depthwise_kernel_shape = self.kernel_size + (input_dim,
+                                                 self.depth_multiplier)
+    pointwise_kernel_shape = (
+        1,) * self.rank + (self.depth_multiplier * input_dim, self.filters)
+
+    self.depthwise_kernel = self.add_variable(
+        name='depthwise_kernel',
+        shape=depthwise_kernel_shape,
+        initializer=self.depthwise_initializer,
+        regularizer=self.depthwise_regularizer,
+        constraint=self.depthwise_constraint,
+        trainable=True,
+        dtype=self.dtype)
+    self.pointwise_kernel = self.add_variable(
+        name='pointwise_kernel',
+        shape=pointwise_kernel_shape,
+        initializer=self.pointwise_initializer,
+        regularizer=self.pointwise_regularizer,
+        constraint=self.pointwise_constraint,
+        trainable=True,
+        dtype=self.dtype)
+    if self.use_bias:
+      self.bias = self.add_variable(name='bias',
+                                    shape=(self.filters,),
+                                    initializer=self.bias_initializer,
+                                    regularizer=self.bias_regularizer,
+                                    constraint=self.bias_constraint,
+                                    trainable=True,
+                                    dtype=self.dtype)
+    else:
+      self.bias = None
+    self.built = True
+
+  def call(self, inputs):
+    raise NotImplementedError
+
+  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(SeparableConv, self).get_config()
+    return dict(list(base_config.items()) + list(config.items()))
+
+
+@tf_export('keras.layers.SeparableConv1D',
+           'keras.layers.SeparableConvolution1D')
+class SeparableConv1D(SeparableConv):
+  """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):
+    super(SeparableConv1D, self).__init__(
+        rank=1,
+        filters=filters,
+        kernel_size=kernel_size,
+        strides=strides,
+        padding=padding,
+        data_format=data_format,
+        dilation_rate=dilation_rate,
+        depth_multiplier=depth_multiplier,
+        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 call(self, inputs):
+    if self.data_format == 'channels_last':
+      strides = (1,) + self.strides * 2 + (1,)
+      spatial_start_dim = 1
+    else:
+      strides = (1, 1) + self.strides * 2
+      spatial_start_dim = 2
+
+    # Explicitly broadcast inputs and kernels to 4D.
+    # TODO(fchollet): refactor when a native separable_conv1d op is available.
+    inputs = array_ops.expand_dims(inputs, spatial_start_dim)
+    depthwise_kernel = array_ops.expand_dims(self.depthwise_kernel, 0)
+    pointwise_kernel = array_ops.expand_dims(self.pointwise_kernel, 0)
+    dilation_rate = (1,) + self.dilation_rate
+
+    outputs = nn.separable_conv2d(
+        inputs,
+        depthwise_kernel,
+        pointwise_kernel,
+        strides=strides,
+        padding=self.padding.upper(),
+        rate=dilation_rate,
+        data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
+
+    if self.use_bias:
+      outputs = nn.bias_add(
+          outputs,
+          self.bias,
+          data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
+
+    outputs = array_ops.squeeze(outputs, [spatial_start_dim])
+
+    if self.activation is not None:
+      return self.activation(outputs)
+    return outputs
+
+
+@tf_export('keras.layers.SeparableConv2D',
+           'keras.layers.SeparableConvolution2D')
+class SeparableConv2D(SeparableConv):
+  """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 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.
+          Currently, specifying any `dilation_rate` value != 1 is
+          incompatible with specifying any `strides` 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 `filters_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, 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):
+    super(SeparableConv2D, self).__init__(
+        rank=2,
+        filters=filters,
+        kernel_size=kernel_size,
+        strides=strides,
+        padding=padding,
+        data_format=data_format,
+        dilation_rate=dilation_rate,
+        depth_multiplier=depth_multiplier,
+        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 call(self, inputs):
+    # Apply the actual ops.
+    if self.data_format == 'channels_last':
+      strides = (1,) + self.strides + (1,)
+    else:
+      strides = (1, 1) + self.strides
+    outputs = nn.separable_conv2d(
+        inputs,
+        self.depthwise_kernel,
+        self.pointwise_kernel,
+        strides=strides,
+        padding=self.padding.upper(),
+        rate=self.dilation_rate,
+        data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
+
+    if self.use_bias:
+      outputs = nn.bias_add(
+          outputs,
+          self.bias,
+          data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
+
+    if self.activation is not None:
+      return self.activation(outputs)
+    return outputs
+
+
+@tf_export('keras.layers.DepthwiseConv2D')
+class DepthwiseConv2D(Conv2D):
+  """Depthwise separable 2D convolution.
+
+  Depthwise Separable convolutions consists in performing
+  just the first step in a depthwise spatial convolution
+  (which acts on each input channel separately).
+  The `depth_multiplier` argument controls how many
+  output channels are generated per input channel in the depthwise step.
+
+  Arguments:
+    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).
+    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 `filters_in * depth_multiplier`.
+    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'.
+    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.
+    bias_initializer: Initializer for the bias vector.
+    depthwise_regularizer: Regularizer function applied to
+        the depthwise 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.
+    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,
+               kernel_size,
+               strides=(1, 1),
+               padding='valid',
+               depth_multiplier=1,
+               data_format=None,
+               activation=None,
+               use_bias=True,
+               depthwise_initializer='glorot_uniform',
+               bias_initializer='zeros',
+               depthwise_regularizer=None,
+               bias_regularizer=None,
+               activity_regularizer=None,
+               depthwise_constraint=None,
+               bias_constraint=None,
+               **kwargs):
+    super(DepthwiseConv2D, self).__init__(
+        filters=None,
+        kernel_size=kernel_size,
+        strides=strides,
+        padding=padding,
+        data_format=data_format,
+        activation=activation,
+        use_bias=use_bias,
+        bias_regularizer=bias_regularizer,
+        activity_regularizer=activity_regularizer,
+        bias_constraint=bias_constraint,
+        **kwargs)
+    self.depth_multiplier = depth_multiplier
+    self.depthwise_initializer = initializers.get(depthwise_initializer)
+    self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
+    self.depthwise_constraint = constraints.get(depthwise_constraint)
+    self.bias_initializer = initializers.get(bias_initializer)
+
+  def build(self, input_shape):
+    if len(input_shape) < 4:
+      raise ValueError('Inputs to `DepthwiseConv2D` should have rank 4. '
+                       'Received input shape:', str(input_shape))
+    if self.data_format == 'channels_first':
+      channel_axis = 1
+    else:
+      channel_axis = 3
+    if input_shape[channel_axis] is None:
+      raise ValueError('The channel dimension of the inputs to '
+                       '`DepthwiseConv2D` '
+                       'should be defined. Found `None`.')
+    input_dim = int(input_shape[channel_axis])
+    depthwise_kernel_shape = (self.kernel_size[0],
+                              self.kernel_size[1],
+                              input_dim,
+                              self.depth_multiplier)
+
+    self.depthwise_kernel = self.add_weight(
+        shape=depthwise_kernel_shape,
+        initializer=self.depthwise_initializer,
+        name='depthwise_kernel',
+        regularizer=self.depthwise_regularizer,
+        constraint=self.depthwise_constraint)
+
+    if self.use_bias:
+      self.bias = self.add_weight(shape=(input_dim * self.depth_multiplier,),
+                                  initializer=self.bias_initializer,
+                                  name='bias',
+                                  regularizer=self.bias_regularizer,
+                                  constraint=self.bias_constraint)
+    else:
+      self.bias = None
+    # Set input spec.
+    self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
+    self.built = True
+
+  def call(self, inputs, training=None):
+    outputs = backend.depthwise_conv2d(
+        inputs,
+        self.depthwise_kernel,
+        strides=self.strides,
+        padding=self.padding,
+        dilation_rate=self.dilation_rate,
+        data_format=self.data_format)
+
+    if self.bias:
+      outputs = backend.bias_add(
+          outputs,
+          self.bias,
+          data_format=self.data_format)
+
+    if self.activation is not None:
+      return self.activation(outputs)
+
+    return outputs
+
+  @tf_utils.shape_type_conversion
+  def compute_output_shape(self, input_shape):
+    if self.data_format == 'channels_first':
+      rows = input_shape[2]
+      cols = input_shape[3]
+      out_filters = input_shape[1] * self.depth_multiplier
+    elif self.data_format == 'channels_last':
+      rows = input_shape[1]
+      cols = input_shape[2]
+      out_filters = input_shape[3] * self.depth_multiplier
+
+    rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
+                                         self.padding,
+                                         self.strides[0])
+    cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
+                                         self.padding,
+                                         self.strides[1])
+    if self.data_format == 'channels_first':
+      return (input_shape[0], out_filters, rows, cols)
+    elif self.data_format == 'channels_last':
+      return (input_shape[0], rows, cols, out_filters)
+
+  def get_config(self):
+    config = super(DepthwiseConv2D, self).get_config()
+    config.pop('filters')
+    config.pop('kernel_initializer')
+    config.pop('kernel_regularizer')
+    config.pop('kernel_constraint')
+    config['depth_multiplier'] = self.depth_multiplier
+    config['depthwise_initializer'] = initializers.serialize(
+        self.depthwise_initializer)
+    config['depthwise_regularizer'] = regularizers.serialize(
+        self.depthwise_regularizer)
+    config['depthwise_constraint'] = constraints.serialize(
+        self.depthwise_constraint)
+    return config
+
+
+@tf_export('keras.layers.UpSampling1D')
+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 = backend.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()))
+
+
+@tf_export('keras.layers.UpSampling2D')
+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 backend.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()))
+
+
+@tf_export('keras.layers.UpSampling3D')
+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 backend.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()))
+
+
+@tf_export('keras.layers.ZeroPadding1D')
+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 backend.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()))
+
+
+@tf_export('keras.layers.ZeroPadding2D')
+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 backend.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()))
+
+
+@tf_export('keras.layers.ZeroPadding3D')
+class ZeroPadding3D(Layer):
+  """Zero-padding layer for 3D data (spatial or spatio-temporal).
+
+  Arguments:
+      padding: int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
+          - If int: the same symmetric padding
+              is applied to width and height.
+          - If tuple of 3 ints:
+              interpreted as two different
+              symmetric padding values for height and width:
+              `(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)`.
+          - If tuple of 3 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 backend.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()))
+
+
+@tf_export('keras.layers.Cropping1D')
+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()))
+
+
+@tf_export('keras.layers.Cropping2D')
+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()))
+
+
+@tf_export('keras.layers.Cropping3D')
+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