convolutional.py

# -*- coding: utf-8 -*-
"""Convolutional layers.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from .. import backend as K
from .. import activations
from .. import initializers
from .. import regularizers
from .. import constraints
from ..engine.base_layer import Layer
from ..engine.base_layer import InputSpec
from ..utils import conv_utils
from ..legacy import interfaces

# imports for backwards namespace compatibility
from .pooling import AveragePooling1D
from .pooling import AveragePooling2D
from .pooling import AveragePooling3D
from .pooling import MaxPooling1D
from .pooling import MaxPooling2D
from .pooling import MaxPooling3D

from ..legacy.layers import AtrousConvolution1D
from ..legacy.layers import AtrousConvolution2D


class _Conv(Layer):
    """Abstract nD convolution layer (private, used as implementation base).

    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.

    # 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 output filters in the convolution).
        kernel_size: An integer or tuple/list of n integers, specifying the
            dimensions of the convolution window.
        strides: An integer or tuple/list of n integers,
            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"` 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, ...)`.
            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 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 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)).
    """

    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,
                 **kwargs):
        super(_Conv, self).__init__(**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.activity_regularizer = regularizers.get(activity_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):
        if self.data_format == 'channels_first':
            channel_axis = 1
        else:
            channel_axis = -1
        if input_shape[channel_axis] is None:
            raise ValueError('The channel dimension of the inputs '
                             'should be defined. Found `None`.')
        input_dim = input_shape[channel_axis]
        kernel_shape = self.kernel_size + (input_dim, self.filters)

        self.kernel = self.add_weight(shape=kernel_shape,
                                      initializer=self.kernel_initializer,
                                      name='kernel',
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        if self.use_bias:
            self.bias = self.add_weight(shape=(self.filters,),
                                        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=self.rank + 2,
                                    axes={channel_axis: input_dim})
        self.built = True

    def call(self, inputs):
        if self.rank == 1:
            outputs = K.conv1d(
                inputs,
                self.kernel,
                strides=self.strides[0],
                padding=self.padding,
                data_format=self.data_format,
                dilation_rate=self.dilation_rate[0])
        if self.rank == 2:
            outputs = K.conv2d(
                inputs,
                self.kernel,
                strides=self.strides,
                padding=self.padding,
                data_format=self.data_format,
                dilation_rate=self.dilation_rate)
        if self.rank == 3:
            outputs = K.conv3d(
                inputs,
                self.kernel,
                strides=self.strides,
                padding=self.padding,
                data_format=self.data_format,
                dilation_rate=self.dilation_rate)

        if self.use_bias:
            outputs = K.bias_add(
                outputs,
                self.bias,
                data_format=self.data_format)

        if self.activation is not None:
            return self.activation(outputs)
        return outputs

    def compute_output_shape(self, input_shape):
        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 (input_shape[0],) + tuple(new_space) + (self.filters,)
        if self.data_format == 'channels_first':
            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 (input_shape[0], self.filters) + tuple(new_space)

    def get_config(self):
        config = {
            'rank': self.rank,
            '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()))


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).
            `"valid"` means "no padding".
            `"same"` results in padding the input such that
            the output has the same length as the original input.
            `"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"`.
            The ordering of the dimensions in the inputs.
            `"channels_last"` corresponds to inputs with shape
            `(batch, length, channels)`
            (default format for temporal data in Keras)
            while `"channels_first"` corresponds to inputs
            with shape `(batch, channels, length)`.
        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
            (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
        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.
    """

    @interfaces.legacy_conv1d_support
    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):
        if padding == 'causal':
            if data_format != 'channels_last':
                raise ValueError('When using causal padding in `Conv1D`, '
                                 '`data_format` must be "channels_last" '
                                 '(temporal data).')
        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=activation,
            use_bias=use_bias,
            kernel_initializer=kernel_initializer,
            bias_initializer=bias_initializer,
            kernel_regularizer=kernel_regularizer,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            kernel_constraint=kernel_constraint,
            bias_constraint=bias_constraint,
            **kwargs)
        self.input_spec = InputSpec(ndim=3)

    def get_config(self):
        config = super(Conv1D, self).get_config()
        config.pop('rank')
        return config


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
            height and width 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 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).
            Note that `"same"` is slightly inconsistent across backends with
            `strides` != 1, as described
            [here](https://github.com/keras-team/keras/pull/9473#issuecomment-372166860)
        data_format: A string,
            one of `"channels_last"` 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
            (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
        4D tensor with shape:
        `(samples, channels, rows, cols)`
        if `data_format` is `"channels_first"`
        or 4D tensor with shape:
        `(samples, rows, cols, channels)`
        if `data_format` is `"channels_last"`.

    # Output shape
        4D tensor with shape:
        `(samples, filters, new_rows, new_cols)`
        if `data_format` is `"channels_first"`
        or 4D tensor with shape:
        `(samples, new_rows, new_cols, filters)`
        if `data_format` is `"channels_last"`.
        `rows` and `cols` values might have changed due to padding.
    """

    @interfaces.legacy_conv2d_support
    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=activation,
            use_bias=use_bias,
            kernel_initializer=kernel_initializer,
            bias_initializer=bias_initializer,
            kernel_regularizer=kernel_regularizer,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            kernel_constraint=kernel_constraint,
            bias_constraint=bias_constraint,
            **kwargs)
        self.input_spec = InputSpec(ndim=4)

    def get_config(self):
        config = super(Conv2D, self).get_config()
        config.pop('rank')
        return config


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"` 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
            (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:
        `(samples, channels, conv_dim1, conv_dim2, conv_dim3)`
        if `data_format` is `"channels_first"`
        or 5D tensor with shape:
        `(samples, conv_dim1, conv_dim2, conv_dim3, channels)`
        if `data_format` is `"channels_last"`.

    # Output shape
        5D tensor with shape:
        `(samples, filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)`
        if `data_format` is `"channels_first"`
        or 5D tensor with shape:
        `(samples, new_conv_dim1, new_conv_dim2, new_conv_dim3, filters)`
        if `data_format` is `"channels_last"`.
        `new_conv_dim1`, `new_conv_dim2` and `new_conv_dim3` values might have changed due to padding.
    """

    @interfaces.legacy_conv3d_support
    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=activation,
            use_bias=use_bias,
            kernel_initializer=kernel_initializer,
            bias_initializer=bias_initializer,
            kernel_regularizer=kernel_regularizer,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            kernel_constraint=kernel_constraint,
            bias_constraint=bias_constraint,
            **kwargs)
        self.input_spec = InputSpec(ndim=5)

    def get_config(self):
        config = super(Conv3D, self).get_config()
        config.pop('rank')
        return config


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
            height and width 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 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).
        output_padding: An integer or tuple/list of 2 integers,
            specifying the amount of padding along the height and width
            of the output tensor.
            Can be a single integer to specify the same value for all
            spatial dimensions.
            The amount of output padding along a givern dimension must be
            lower than the stride along that same dimension.
            If set to `None`, defaults to 1 if padding is `"same"` and to
            0 if padding is '"valid"'.
        data_format: A string,
            one of `"channels_last"` 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
            (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
        4D tensor with shape:
        `(batch, channels, rows, cols)`
        if `data_format` is `"channels_first"`
        or 4D tensor with shape:
        `(batch, rows, cols, channels)`
        if `data_format` is `"channels_last"`.

    # Output shape
        4D tensor with shape:
        `(batch, filters, new_rows, new_cols)`
        if `data_format` is `"channels_first"`
        or 4D tensor with shape:
        `(batch, new_rows, new_cols, filters)`
        if `data_format` is `"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)
    """

    @interfaces.legacy_deconv2d_support
    def __init__(self, filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 output_padding=None,
                 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,
            kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            activation=activation,
            use_bias=use_bias,
            kernel_initializer=kernel_initializer,
            bias_initializer=bias_initializer,
            kernel_regularizer=kernel_regularizer,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            kernel_constraint=kernel_constraint,
            bias_constraint=bias_constraint,
            **kwargs)
        self.input_spec = InputSpec(ndim=4)

        self.output_padding = output_padding
        if self.output_padding is not None:
            self.output_padding = conv_utils.normalize_tuple(self.output_padding, 2, 'output_padding')
            for stride, out_pad in zip(self.strides, self.output_padding):
                if out_pad >= stride:
                    raise ValueError('Stride ' + str(self.strides) + ' must be ' +
                                     'greater than output padding ' +
                                     str(self.output_padding))

    def build(self, input_shape):
        if len(input_shape) != 4:
            raise ValueError('Inputs should have rank ' +
                             str(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] is None:
            raise ValueError('The channel dimension of the inputs '
                             'should be defined. Found `None`.')
        input_dim = input_shape[channel_axis]
        kernel_shape = self.kernel_size + (self.filters, input_dim)

        self.kernel = self.add_weight(shape=kernel_shape,
                                      initializer=self.kernel_initializer,
                                      name='kernel',
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        if self.use_bias:
            self.bias = self.add_weight(shape=(self.filters,),
                                        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):
        input_shape = K.shape(inputs)
        batch_size = input_shape[0]
        if self.data_format == 'channels_first':
            h_axis, w_axis = 2, 3
        else:
            h_axis, w_axis = 1, 2

        height, width = input_shape[h_axis], input_shape[w_axis]
        kernel_h, kernel_w = self.kernel_size
        stride_h, stride_w = self.strides
        if self.output_padding is None:
            out_pad_h = out_pad_w = None
        else:
            out_pad_h, out_pad_w = self.output_padding

        # Infer the dynamic output shape:
        out_height = conv_utils.deconv_length(height,
                                              stride_h, kernel_h,
                                              self.padding,
                                              out_pad_h)
        out_width = conv_utils.deconv_length(width,
                                             stride_w, kernel_w,
                                             self.padding,
                                             out_pad_w)
        if self.data_format == 'channels_first':
            output_shape = (batch_size, self.filters, out_height, out_width)
        else:
            output_shape = (batch_size, out_height, out_width, self.filters)

        outputs = K.conv2d_transpose(
            inputs,
            self.kernel,
            output_shape,
            self.strides,
            padding=self.padding,
            data_format=self.data_format)

        if self.bias:
            outputs = K.bias_add(
                outputs,
                self.bias,
                data_format=self.data_format)

        if self.activation is not None:
            return self.activation(outputs)
        return outputs

    def compute_output_shape(self, input_shape):
        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
        if self.output_padding is None:
            out_pad_h = out_pad_w = None
        else:
            out_pad_h, out_pad_w = self.output_padding

        output_shape[c_axis] = self.filters
        output_shape[h_axis] = conv_utils.deconv_length(output_shape[h_axis],
                                                        stride_h,
                                                        kernel_h,
                                                        self.padding,
                                                        out_pad_h)
        output_shape[w_axis] = conv_utils.deconv_length(output_shape[w_axis],
                                                        stride_w,
                                                        kernel_w,
                                                        self.padding,
                                                        out_pad_w)
        return tuple(output_shape)

    def get_config(self):
        config = super(Conv2DTranspose, self).get_config()
        config.pop('dilation_rate')
        config['output_padding'] = self.output_padding
        return config


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
            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 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).
        output_padding: An integer or tuple/list of 3 integers,
            specifying the amount of padding along the depth, height, and
            width.
            Can be a single integer to specify the same value for all
            spatial dimensions.
            The amount of output padding along a givern dimension must be
            lower than the stride along that same dimension.
            If set to `None`, defaults to 1 if padding is `"same"` and to
            0 if padding is '"valid"'.
        data_format: A string,
            one of `"channels_last"` 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` is `"channels_first"`
        or 5D tensor with shape:
        `(batch, depth, rows, cols, channels)`
        if `data_format` is `"channels_last"`.

    # Output shape
        5D tensor with shape:
        `(batch, filters, new_depth, new_rows, new_cols)`
        if `data_format` is `"channels_first"`
        or 5D tensor with shape:
        `(batch, new_depth, new_rows, new_cols, filters)`
        if `data_format` is `"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',
                 output_padding=None,
                 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,
            kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            activation=activation,
            use_bias=use_bias,
            kernel_initializer=kernel_initializer,
            bias_initializer=bias_initializer,
            kernel_regularizer=kernel_regularizer,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            kernel_constraint=kernel_constraint,
            bias_constraint=bias_constraint,
            **kwargs)
        self.input_spec = InputSpec(ndim=5)
        self.output_padding = output_padding
        if self.output_padding is not None:
            self.output_padding = conv_utils.normalize_tuple(self.output_padding, 3, 'output_padding')
            for stride, out_pad in zip(self.strides, self.output_padding):
                if out_pad >= stride:
                    raise ValueError('Stride ' + str(self.strides) + ' must be ' +
                                     'greater than output padding ' +
                                     str(self.output_padding))

    def build(self, input_shape):
        if len(input_shape) != 5:
            raise ValueError('Inputs should have rank ' +
                             str(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] is None:
            raise ValueError('The channel dimension of the inputs '
                             'should be defined. Found `None`.')
        input_dim = input_shape[channel_axis]
        kernel_shape = self.kernel_size + (self.filters, input_dim)

        self.kernel = self.add_weight(shape=kernel_shape,
                                      initializer=self.kernel_initializer,
                                      name='kernel',
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        if self.use_bias:
            self.bias = self.add_weight(shape=(self.filters,),
                                        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=5, axes={channel_axis: input_dim})
        self.built = True

    def call(self, inputs):
        input_shape = K.shape(inputs)
        batch_size = input_shape[0]
        if self.data_format == 'channels_first':
            d_axis, h_axis, w_axis = 2, 3, 4
        else:
            d_axis, h_axis, w_axis = 1, 2, 3

        depth = input_shape[d_axis]
        height = input_shape[h_axis]
        width = input_shape[w_axis]

        kernel_d, kernel_h, kernel_w = self.kernel_size
        stride_d, stride_h, stride_w = self.strides
        if self.output_padding is None:
            out_pad_d = out_pad_h = out_pad_w = None
        else:
            out_pad_d, out_pad_h, out_pad_w = self.output_padding

        # Infer the dynamic output shape:
        out_depth = conv_utils.deconv_length(depth,
                                             stride_d, kernel_d,
                                             self.padding,
                                             out_pad_d)
        out_height = conv_utils.deconv_length(height,
                                              stride_h, kernel_h,
                                              self.padding,
                                              out_pad_h)
        out_width = conv_utils.deconv_length(width,
                                             stride_w, kernel_w,
                                             self.padding,
                                             out_pad_w)

        if self.data_format == 'channels_first':
            output_shape = (batch_size, self.filters, out_depth, out_height, out_width)
        else:
            output_shape = (batch_size, out_depth, out_height, out_width, self.filters)

        outputs = K.conv3d_transpose(inputs,
                                     self.kernel,
                                     output_shape,
                                     self.strides,
                                     padding=self.padding,
                                     data_format=self.data_format)

        if self.bias:
            outputs = K.bias_add(
                outputs,
                self.bias,
                data_format=self.data_format)

        if self.activation is not None:
            return self.activation(outputs)
        return outputs

    def compute_output_shape(self, input_shape):
        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
        if self.output_padding is None:
            out_pad_d = out_pad_h = out_pad_w = None
        else:
            out_pad_d, out_pad_h, out_pad_w = self.output_padding

        output_shape[c_axis] = self.filters
        output_shape[d_axis] = conv_utils.deconv_length(output_shape[d_axis],
                                                        stride_d,
                                                        kernel_d,
                                                        self.padding,
                                                        out_pad_d)
        output_shape[h_axis] = conv_utils.deconv_length(output_shape[h_axis],
                                                        stride_h,
                                                        kernel_h,
                                                        self.padding,
                                                        out_pad_h)
        output_shape[w_axis] = conv_utils.deconv_length(output_shape[w_axis],
                                                        stride_w,
                                                        kernel_w,
                                                        self.padding,
                                                        out_pad_w)

        return tuple(output_shape)

    def get_config(self):
        config = super(Conv3DTranspose, self).get_config()
        config.pop('dilation_rate')
        config['output_padding'] = self.output_padding
        return config


class _SeparableConv(_Conv):
    """Abstract nD depthwise separable convolution layer (private).

    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
        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 output filters in the convolution).
        kernel_size: An integer or tuple/list of 2 integers, specifying the
            height and width 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 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"` 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 n 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 `filters_in * depth_multiplier`.
        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.
        depthwise_initializer: Initializer for the depthwise kernel matrix
            (see [initializers](../initializers.md)).
        pointwise_initializer: Initializer for the pointwise kernel matrix
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        depthwise_regularizer: Regularizer function applied to
            the depthwise kernel matrix
            (see [regularizer](../regularizers.md)).
        pointwise_regularizer: Regularizer function applied to
            the pointwise kernel 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)).
        depthwise_constraint: Constraint function applied to
            the depthwise kernel matrix
            (see [constraints](../constraints.md)).
        pointwise_constraint: Constraint function applied to
            the pointwise kernel matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).

    # Input shape
        4D tensor with shape:
        `(batch, channels, rows, cols)`
        if `data_format` is `"channels_first"`
        or 4D tensor with shape:
        `(batch, rows, cols, channels)`
        if `data_format` is `"channels_last"`.

    # Output shape
        4D tensor with shape:
        `(batch, filters, new_rows, new_cols)`
        if `data_format` is `"channels_first"`
        or 4D tensor with shape:
        `(batch, new_rows, new_cols, filters)`
        if `data_format` is `"channels_last"`.
        `rows` and `cols` values might have changed due to padding.
    """

    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,
                 **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=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.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):
        if len(input_shape) < self.rank + 2:
            raise ValueError('Inputs to `SeparableConv' + str(self.rank) + 'D` '
                             'should have rank ' + str(self.rank + 2) + '. '
                             'Received input shape:', str(input_shape))
        channel_axis = 1 if self.data_format == 'channels_first' else -1
        if input_shape[channel_axis] is None:
            raise ValueError('The channel dimension of the inputs '
                             'should be defined. Found `None`.')
        input_dim = int(input_shape[channel_axis])
        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_weight(
            shape=depthwise_kernel_shape,
            initializer=self.depthwise_initializer,
            name='depthwise_kernel',
            regularizer=self.depthwise_regularizer,
            constraint=self.depthwise_constraint)
        self.pointwise_kernel = self.add_weight(
            shape=pointwise_kernel_shape,
            initializer=self.pointwise_initializer,
            name='pointwise_kernel',
            regularizer=self.pointwise_regularizer,
            constraint=self.pointwise_constraint)

        if self.use_bias:
            self.bias = self.add_weight(shape=(self.filters,),
                                        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=self.rank + 2,
                                    axes={channel_axis: input_dim})
        self.built = True

    def call(self, inputs):
        if self.rank == 1:
            outputs = K.separable_conv1d(
                inputs,
                self.depthwise_kernel,
                self.pointwise_kernel,
                data_format=self.data_format,
                strides=self.strides,
                padding=self.padding,
                dilation_rate=self.dilation_rate)
        if self.rank == 2:
            outputs = K.separable_conv2d(
                inputs,
                self.depthwise_kernel,
                self.pointwise_kernel,
                data_format=self.data_format,
                strides=self.strides,
                padding=self.padding,
                dilation_rate=self.dilation_rate)

        if self.bias:
            outputs = K.bias_add(
                outputs,
                self.bias,
                data_format=self.data_format)

        if self.activation is not None:
            return self.activation(outputs)
        return outputs

    def get_config(self):
        config = super(_SeparableConv, self).get_config()
        config.pop('rank')
        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['pointwise_initializer'] = initializers.serialize(self.pointwise_initializer)
        config['depthwise_regularizer'] = regularizers.serialize(self.depthwise_regularizer)
        config['pointwise_regularizer'] = regularizers.serialize(self.pointwise_regularizer)
        config['depthwise_constraint'] = constraints.serialize(self.depthwise_constraint)
        config['pointwise_constraint'] = constraints.serialize(self.pointwise_constraint)
        return config


class SeparableConv1D(_SeparableConv):
    """Depthwise separable 1D 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 single integer,
            specifying the length of the 1D convolution window.
        strides: An integer or tuple/list of 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"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `"channels_last"` 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 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.
        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
            (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.
        depthwise_initializer: Initializer for the depthwise kernel matrix
            (see [initializers](../initializers.md)).
        pointwise_initializer: Initializer for the pointwise kernel matrix
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        depthwise_regularizer: Regularizer function applied to
            the depthwise kernel matrix
            (see [regularizer](../regularizers.md)).
        pointwise_regularizer: Regularizer function applied to
            the pointwise kernel 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)).
        depthwise_constraint: Constraint function applied to
            the depthwise kernel matrix
            (see [constraints](../constraints.md)).
        pointwise_constraint: Constraint function applied to
            the pointwise kernel matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).

    # Input shape
        3D tensor with shape:
        `(batch, channels, steps)`
        if `data_format` is `"channels_first"`
        or 3D tensor with shape:
        `(batch, steps, channels)`
        if `data_format` is `"channels_last"`.

    # Output shape
        3D tensor with shape:
        `(batch, filters, new_steps)`
        if `data_format` is `"channels_first"`
        or 3D tensor with shape:
        `(batch, new_steps, filters)`
        if `data_format` is `"channels_last"`.
        `new_steps` values might have changed due to padding or strides.
    """

    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=activation,
            use_bias=use_bias,
            depthwise_initializer=depthwise_initializer,
            pointwise_initializer=pointwise_initializer,
            bias_initializer=bias_initializer,
            depthwise_regularizer=depthwise_regularizer,
            pointwise_regularizer=pointwise_regularizer,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            depthwise_constraint=depthwise_constraint,
            pointwise_constraint=pointwise_constraint,
            bias_constraint=bias_constraint,
            **kwargs)


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
            height and width 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 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"` 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
            (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.
        depthwise_initializer: Initializer for the depthwise kernel matrix
            (see [initializers](../initializers.md)).
        pointwise_initializer: Initializer for the pointwise kernel matrix
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        depthwise_regularizer: Regularizer function applied to
            the depthwise kernel matrix
            (see [regularizer](../regularizers.md)).
        pointwise_regularizer: Regularizer function applied to
            the pointwise kernel 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)).
        depthwise_constraint: Constraint function applied to
            the depthwise kernel matrix
            (see [constraints](../constraints.md)).
        pointwise_constraint: Constraint function applied to
            the pointwise kernel matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).

    # Input shape
        4D tensor with shape:
        `(batch, channels, rows, cols)`
        if `data_format` is `"channels_first"`
        or 4D tensor with shape:
        `(batch, rows, cols, channels)`
        if `data_format` is `"channels_last"`.

    # Output shape
        4D tensor with shape:
        `(batch, filters, new_rows, new_cols)`
        if `data_format` is `"channels_first"`
        or 4D tensor with shape:
        `(batch, new_rows, new_cols, filters)`
        if `data_format` is `"channels_last"`.
        `rows` and `cols` values might have changed due to padding.
    """

    @interfaces.legacy_separable_conv2d_support
    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=activation,
            use_bias=use_bias,
            depthwise_initializer=depthwise_initializer,
            pointwise_initializer=pointwise_initializer,
            bias_initializer=bias_initializer,
            depthwise_regularizer=depthwise_regularizer,
            pointwise_regularizer=pointwise_regularizer,
            bias_regularizer=bias_regularizer,
            activity_regularizer=activity_regularizer,
            depthwise_constraint=depthwise_constraint,
            pointwise_constraint=pointwise_constraint,
            bias_constraint=bias_constraint,
            **kwargs)


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
            height and width 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 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).
        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"` 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
            (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.
        depthwise_initializer: Initializer for the depthwise kernel matrix
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        depthwise_regularizer: Regularizer function applied to
            the depthwise kernel 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)).
        depthwise_constraint: Constraint function applied to
            the depthwise kernel matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).

    # Input shape
        4D tensor with shape:
        `[batch, channels, rows, cols]`
        if `data_format` is `"channels_first"`
        or 4D tensor with shape:
        `[batch, rows, cols, channels]`
        if `data_format` is `"channels_last"`.

    # Output shape
        4D tensor with shape:
        `[batch, filters, new_rows, new_cols]`
        if `data_format` is `"channels_first"`
        or 4D tensor with shape:
        `[batch, new_rows, new_cols, filters]`
        if `data_format` is `"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 = K.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 = K.bias_add(
                outputs,
                self.bias,
                data_format=self.data_format)

        if self.activation is not None:
            return self.activation(outputs)

        return outputs

    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


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)`.
    """

    @interfaces.legacy_upsampling1d_support
    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):
        size = self.size * input_shape[1] if input_shape[1] is not None else None
        return (input_shape[0], size, input_shape[2])

    def call(self, inputs):
        output = K.repeat_elements(inputs, self.size, axis=1)
        return output

    def get_config(self):
        config = {'size': self.size}
        base_config = super(UpSampling1D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class UpSampling2D(Layer):
    """Upsampling layer for 2D inputs.

    Repeats the rows and columns of the data
    by size[0] and size[1] respectively.

    # Arguments
        size: int, or tuple of 2 integers.
            The upsampling factors for rows and columns.
        data_format: A string,
            one of `"channels_last"` 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)`
    """

    @interfaces.legacy_upsampling2d_support
    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):
        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 (input_shape[0],
                    input_shape[1],
                    height,
                    width)
        elif self.data_format == 'channels_last':
            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 (input_shape[0],
                    height,
                    width,
                    input_shape[3])

    def call(self, inputs):
        return K.resize_images(inputs, self.size[0], self.size[1],
                               self.data_format)

    def get_config(self):
        config = {'size': self.size,
                  'data_format': self.data_format}
        base_config = super(UpSampling2D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class UpSampling3D(Layer):
    """Upsampling layer for 3D inputs.

    Repeats the 1st, 2nd and 3rd dimensions
    of the data by size[0], size[1] and size[2] respectively.

    # Arguments
        size: int, or tuple of 3 integers.
            The upsampling factors for dim1, dim2 and dim3.
        data_format: A string,
            one of `"channels_last"` 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)`
    """

    @interfaces.legacy_upsampling3d_support
    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):
        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 (input_shape[0],
                    input_shape[1],
                    dim1,
                    dim2,
                    dim3)
        elif self.data_format == 'channels_last':
            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 (input_shape[0],
                    dim1,
                    dim2,
                    dim3,
                    input_shape[4])

    def call(self, inputs):
        return K.resize_volumes(inputs,
                                self.size[0], self.size[1], self.size[2],
                                self.data_format)

    def get_config(self):
        config = {'size': self.size,
                  'data_format': self.data_format}
        base_config = super(UpSampling3D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class ZeroPadding1D(Layer):
    """Zero-padding layer for 1D input (e.g. temporal sequence).

    # Arguments
        padding: int, or tuple of int (length 2), or dictionary.
            - If int:
            How many zeros to add at the beginning and end of
            the padding dimension (axis 1).
            - If tuple of int (length 2):
            How many zeros to add at the beginning and at the end of
            the padding dimension (`(left_pad, right_pad)`).

    # Input shape
        3D tensor with shape `(batch, axis_to_pad, features)`

    # Output shape
        3D tensor with shape `(batch, padded_axis, features)`
    """

    def __init__(self, padding=1, **kwargs):
        super(ZeroPadding1D, self).__init__(**kwargs)
        self.padding = conv_utils.normalize_tuple(padding, 2, 'padding')
        self.input_spec = InputSpec(ndim=3)

    def compute_output_shape(self, input_shape):
        if input_shape[1] is not None:
            length = input_shape[1] + self.padding[0] + self.padding[1]
        else:
            length = None
        return (input_shape[0],
                length,
                input_shape[2])

    def call(self, inputs):
        return K.temporal_padding(inputs, padding=self.padding)

    def get_config(self):
        config = {'padding': self.padding}
        base_config = super(ZeroPadding1D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class ZeroPadding2D(Layer):
    """Zero-padding layer for 2D input (e.g. picture).

    This layer can add rows and columns of zeros
    at the top, bottom, left and right side of an image tensor.

    # Arguments
        padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
            - If int: the same symmetric padding
                is applied to height and width.
            - 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"` 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)`
    """

    @interfaces.legacy_zeropadding2d_support
    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):
        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 (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 (input_shape[0],
                    rows,
                    cols,
                    input_shape[3])

    def call(self, inputs):
        return K.spatial_2d_padding(inputs,
                                    padding=self.padding,
                                    data_format=self.data_format)

    def get_config(self):
        config = {'padding': self.padding,
                  'data_format': self.data_format}
        base_config = super(ZeroPadding2D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class ZeroPadding3D(Layer):
    """Zero-padding layer for 3D data (spatial or spatio-temporal).

    # Arguments
        padding: int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
            - If int: the same symmetric padding
                is applied to height and width.
            - 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"` 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)`
    """

    @interfaces.legacy_zeropadding3d_support
    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):
        if self.data_format == 'channels_first':
            if input_shape[2] is not None:
                dim1 = input_shape[2] + self.padding[0][0] + self.padding[0][1]
            else:
                dim1 = None
            if input_shape[3] is not None:
                dim2 = input_shape[3] + self.padding[1][0] + self.padding[1][1]
            else:
                dim2 = None
            if input_shape[4] is not None:
                dim3 = input_shape[4] + self.padding[2][0] + self.padding[2][1]
            else:
                dim3 = None
            return (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.padding[0][0] + self.padding[0][1]
            else:
                dim1 = None
            if input_shape[2] is not None:
                dim2 = input_shape[2] + self.padding[1][0] + self.padding[1][1]
            else:
                dim2 = None
            if input_shape[3] is not None:
                dim3 = input_shape[3] + self.padding[2][0] + self.padding[2][1]
            else:
                dim3 = None
            return (input_shape[0],
                    dim1,
                    dim2,
                    dim3,
                    input_shape[4])

    def call(self, inputs):
        return K.spatial_3d_padding(inputs,
                                    padding=self.padding,
                                    data_format=self.data_format)

    def get_config(self):
        config = {'padding': self.padding,
                  'data_format': self.data_format}
        base_config = super(ZeroPadding3D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class Cropping1D(Layer):
    """Cropping layer for 1D input (e.g. temporal sequence).

    It crops along the time dimension (axis 1).

    # Arguments
        cropping: int or tuple of int (length 2)
            How many units should be trimmed off at the beginning and end of
            the cropping dimension (axis 1).
            If a single int is provided,
            the same value will be used for both.

    # Input shape
        3D tensor with shape `(batch, axis_to_crop, features)`

    # Output shape
        3D tensor with shape `(batch, cropped_axis, features)`
    """

    def __init__(self, cropping=(1, 1), **kwargs):
        super(Cropping1D, self).__init__(**kwargs)
        self.cropping = conv_utils.normalize_tuple(cropping, 2, 'cropping')
        self.input_spec = InputSpec(ndim=3)

    def compute_output_shape(self, input_shape):
        if input_shape[1] is not None:
            length = input_shape[1] - self.cropping[0] - self.cropping[1]
        else:
            length = None
        return (input_shape[0],
                length,
                input_shape[2])

    def call(self, inputs):
        if self.cropping[1] == 0:
            return inputs[:, self.cropping[0]:, :]
        else:
            return inputs[:, self.cropping[0]: -self.cropping[1], :]

    def get_config(self):
        config = {'cropping': self.cropping}
        base_config = super(Cropping1D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class Cropping2D(Layer):
    """Cropping layer for 2D input (e.g. picture).

    It crops along spatial dimensions, i.e. height and width.

    # 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 height and width.
            - 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"` 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)
    ```
    """

    @interfaces.legacy_cropping2d_support
    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):
        if self.data_format == 'channels_first':
            return (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)
        elif self.data_format == 'channels_last':
            return (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])

    def call(self, inputs):
        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]]
        elif self.data_format == 'channels_last':
            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],
                          :]

    def get_config(self):
        config = {'cropping': self.cropping,
                  'data_format': self.data_format}
        base_config = super(Cropping2D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class Cropping3D(Layer):
    """Cropping layer for 3D data (e.g. spatial or spatio-temporal).

    # Arguments
        cropping: int, or tuple of 3 ints, 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"` 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)`
    """

    @interfaces.legacy_cropping3d_support
    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):
        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 (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 (input_shape[0],
                    dim1,
                    dim2,
                    dim3,
                    input_shape[4])

    def call(self, inputs):
        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]]

        elif self.data_format == 'channels_last':
            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],
                          :]

    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
Deconvolution2D = Deconv2D = Conv2DTranspose
Deconvolution3D = Deconv3D = Conv3DTranspose

# Legacy aliases
AtrousConv1D = AtrousConvolution1D
AtrousConv2D = AtrousConvolution2D