# -*- coding: utf-8 -*-
from __future__ import absolute_import

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

# 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


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 output of 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` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, ..., channels)` while `channels_first` corresponds to
            inputs with shape `(batch, channels, ...)`.
            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(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((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.kernel_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 output of filters in the convolution).
        kernel_size: An integer or tuple/list of a single integer,
            specifying the length of the 1D convolution window.
        strides: An integer or tuple/list of a single integer,
            specifying the stride length of the convolution.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: One of `"valid"`, `"causal"` or `"same"` (case-insensitive).
            `"causal"` results in causal (dilated) convolutions, e.g. output[t]
            depends solely on input[:t-1]. Useful when modeling temporal data
            where the model should not violate the temporal order.
            See [WaveNet: A Generative Model for Raw Audio, section 2.1](https://arxiv.org/abs/1609.03499).
        dilation_rate: an integer or tuple/list of a single integer, specifying
            the dilation rate to use for dilated convolution.
            Currently, specifying any `dilation_rate` value != 1 is
            incompatible with specifying any `strides` value != 1.
        activation: Activation function to use
            (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.
    """

    def __init__(self, filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 dilation_rate=1,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        super(Conv1D, self).__init__(
            rank=1,
            filters=filters,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
            data_format='channels_last',
            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')
        config.pop('data_format')
        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 output of filters in the convolution).
        kernel_size: An integer or tuple/list of 2 integers, specifying the
            width and height of the 2D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 2 integers,
            specifying the strides of the convolution along the width and height.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, width, height, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, width, height)`.
            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='channels_first'
        or 4D tensor with shape:
        `(samples, rows, cols, channels)` if data_format='channels_last'.

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

    def __init__(self, filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 dilation_rate=(1, 1),
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        super(Conv2D, self).__init__(
            rank=2,
            filters=filters,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            dilation_rate=dilation_rate,
            activation=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, 3)` for 128x128x128 volumes
    with a single channel,
    in `data_format="channels_last"`.

    # Arguments
        filters: Integer, the dimensionality of the output space
            (i.e. the number output of filters in the convolution).
        kernel_size: An integer or tuple/list of 3 integers, specifying the
            width and height of the 3D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 3 integers,
            specifying the strides of the convolution along each spatial dimension.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
            while `channels_first` corresponds to inputs with shape
            `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
        dilation_rate: an integer or tuple/list of 3 integers, specifying
            the dilation rate to use for dilated convolution.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Currently, specifying any `dilation_rate` value != 1 is
            incompatible with specifying any stride value != 1.
        activation: Activation function to use
            (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='channels_first'
        or 5D tensor with shape:
        `(samples, conv_dim1, conv_dim2, conv_dim3, channels)` if data_format='channels_last'.

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

    def __init__(self, filters,
                 kernel_size,
                 strides=(1, 1, 1),
                 padding='valid',
                 data_format=None,
                 dilation_rate=(1, 1, 1),
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        super(Conv3D, self).__init__(
            rank=3,
            filters=filters,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            dilation_rate=dilation_rate,
            activation=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 output of filters in the convolution).
        kernel_size: An integer or tuple/list of 2 integers, specifying the
            width and height of the 2D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 2 integers,
            specifying the strides of the convolution along the width and height.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, width, height, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, width, height)`.
            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='channels_first'
        or 4D tensor with shape:
        `(batch, rows, cols, channels)` if data_format='channels_last'.

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

    # References
        - [A guide to convolution arithmetic for deep learning](https://arxiv.org/abs/1603.07285v1)
        - [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
    """

    def __init__(self, filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format='channels_last',
                 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)

    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(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((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

        # Infer the dynamic output shape:
        out_height = conv_utils.deconv_length(height,
                                              stride_h, kernel_h,
                                              self.padding)
        out_width = conv_utils.deconv_length(width,
                                             stride_w, kernel_w,
                                             self.padding)
        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

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

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


class SeparableConv2D(Conv2D):
    """Depthwise separable 2D convolution.

    Separable convolutions consist in first performing
    a depthwise spatial convolution
    (which acts on each input channel separately)
    followed by a pointwise convolution which mixes together the resulting
    output channels. The `depth_multiplier` argument controls how many
    output channels are generated per input channel in the depthwise step.

    Intuitively, separable convolutions can be understood as
    a way to factorize a convolution kernel into two smaller kernels,
    or as an extreme version of an Inception block.

    # Arguments
        filters: Integer, the dimensionality of the output space
            (i.e. the number output of filters in the convolution).
        kernel_size: An integer or tuple/list of 2 integers, specifying the
            width and height of the 2D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 2 integers,
            specifying the strides of the convolution along the width and height.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, width, height, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, width, height)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
        depth_multiplier: The number of depthwise convolution output channels
            for each input channel.
            The total number of depthwise convolution output
            channels will be equal to `filterss_in * depth_multiplier`.
        activation: Activation function to use
            (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 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)).
        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='channels_first'
        or 4D tensor with shape:
        `(batch, rows, cols, channels)` if data_format='channels_last'.

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

    def __init__(self, filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 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__(
            filters=filters,
            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.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) < 4:
            raise ValueError('Inputs to `SeparableConv2D` 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 '
                             '`SeparableConv2D` '
                             '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)
        pointwise_kernel_shape = (1, 1,
                                  self.depth_multiplier * input_dim,
                                  self.filters)

        self.depthwise_kernel = self.add_weight(
            depthwise_kernel_shape,
            initializer=self.depthwise_initializer,
            name='depthwise_kernel',
            regularizer=self.depthwise_regularizer,
            constraint=self.depthwise_constraint)
        self.pointwise_kernel = self.add_weight(
            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((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):
        outputs = K.separable_conv2d(
            inputs,
            self.depthwise_kernel,
            self.pointwise_kernel,
            data_format=self.data_format,
            strides=self.strides,
            padding=self.padding)

        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]
        elif self.data_format == 'channels_last':
            rows = input_shape[1]
            cols = input_shape[2]

        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], self.filters, rows, cols)
        elif self.data_format == 'channels_last':
            return (input_shape[0], rows, cols, self.filters)

    def get_config(self):
        config = super(SeparableConv2D, self).get_config()
        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 UpSampling1D(Layer):
    """Upsampling layer for 1D inputs.

    Repeats each temporal step `size` times along the time axis.

    # Arguments
        size: integer. Upsampling factor.

    # Input shape
        3D tensor with shape: `(batch, steps, features)`.

    # Output shape
        3D tensor with shape: `(batch, upsampled_steps, features)`.
    """

    def __init__(self, size=2, **kwargs):
        super(UpSampling1D, self).__init__(**kwargs)
        self.size = int(size)
        self.input_spec = InputSpec(ndim=3)

    def compute_output_shape(self, input_shape):
        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` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, width, height, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, width, height)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".

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

    # Output shape
        4D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, upsampled_rows, upsampled_cols, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, upsampled_rows, upsampled_cols)`
    """

    def __init__(self, size=(2, 2), data_format=None, **kwargs):
        super(UpSampling2D, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.size = conv_utils.normalize_tuple(size, 2, 'size')
        self.input_spec = InputSpec(ndim=4)

    def compute_output_shape(self, input_shape):
        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` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
            while `channels_first` corresponds to inputs with shape
            `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".

    # Input shape
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, dim1, dim2, dim3, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, dim1, dim2, dim3)`

    # Output shape
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)`
    """

    def __init__(self, size=(2, 2, 2), data_format=None, **kwargs):
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.size = conv_utils.normalize_tuple(size, 3, 'size')
        self.input_spec = InputSpec(ndim=5)
        super(UpSampling3D, self).__init__(**kwargs)

    def compute_output_shape(self, input_shape):
        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):
        length = input_shape[1] + self.padding[0] + self.padding[1] if input_shape[1] is not None else 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 or zeros
    at the top, bottom, left and right side of an image tensor.

    # Arguments
        padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
            - If int: the same symmetric padding
                is applied to width and height.
            - If tuple of 2 ints:
                interpreted as two different
                symmetric padding values for height and width:
                `(symmetric_height_pad, symmetrc_width_pad)`.
            - If tuple of 2 tuples of 2 ints:
                interpreted as
                `((top_pad, bottom_pad), (left_pad, right_pad))`
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, width, height, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, width, height)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".

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

    # Output shape
        4D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, padded_rows, padded_cols, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, padded_rows, padded_cols)`
    """

    def __init__(self,
                 padding=(1, 1),
                 data_format=None,
                 **kwargs):
        super(ZeroPadding2D, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        if isinstance(padding, int):
            self.padding = ((padding, padding), (padding, padding))
        elif hasattr(padding, '__len__'):
            if len(padding) != 2:
                raise ValueError('`padding` should have two elements. '
                                 'Found: ' + str(padding))
            height_padding = conv_utils.normalize_tuple(padding[0], 2,
                                                        '1st entry of padding')
            width_padding = conv_utils.normalize_tuple(padding[1], 2,
                                                       '2nd entry of padding')
            self.padding = (height_padding, width_padding)
        else:
            raise ValueError('`padding` should be either an int, '
                             'a tuple of 2 ints '
                             '(symmetric_height_pad, symmetric_width_pad), '
                             'or a tuple of 2 tuples of 2 ints '
                             '((top_pad, bottom_pad), (left_pad, right_pad)). '
                             'Found: ' + str(padding))
        self.input_spec = InputSpec(ndim=4)

    def compute_output_shape(self, input_shape):
        if self.data_format == 'channels_first':
            rows = input_shape[2] + self.padding[0][0] + self.padding[0][1] if input_shape[2] is not None else None
            cols = input_shape[3] + self.padding[1][0] + self.padding[1][1] if input_shape[3] is not None else None
            return (input_shape[0],
                    input_shape[1],
                    rows,
                    cols)
        elif self.data_format == 'channels_last':
            rows = input_shape[1] + self.padding[0][0] + self.padding[0][1] if input_shape[1] is not None else None
            cols = input_shape[2] + self.padding[1][0] + self.padding[1][1] if input_shape[2] is not None else 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 2 ints, or tuple of 2 tuples of 2 ints.
            - If int: the same symmetric padding
                is applied to width and height.
            - If tuple of 2 ints:
                interpreted as two different
                symmetric padding values for height and width:
                `(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)`.
            - If tuple of 2 tuples of 2 ints:
                interpreted as
                `((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad))`
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
            while `channels_first` corresponds to inputs with shape
            `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".

    # Input shape
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad, depth)`
        - If `data_format` is `"channels_first"`:
            `(batch, depth, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad)`

    # Output shape
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, first_padded_axis, second_padded_axis, third_axis_to_pad, depth)`
        - If `data_format` is `"channels_first"`:
            `(batch, depth, first_padded_axis, second_padded_axis, third_axis_to_pad)`
    """

    def __init__(self, padding=(1, 1, 1), data_format=None, **kwargs):
        super(ZeroPadding3D, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        if isinstance(padding, int):
            self.padding = ((padding, padding), (padding, padding), (padding, padding))
        elif hasattr(padding, '__len__'):
            if len(padding) != 3:
                raise ValueError('`padding` should have 3 elements. '
                                 'Found: ' + str(padding))
            dim1_padding = conv_utils.normalize_tuple(padding[0], 2,
                                                      '1st entry of padding')
            dim2_padding = conv_utils.normalize_tuple(padding[1], 2,
                                                      '2nd entry of padding')
            dim3_padding = conv_utils.normalize_tuple(padding[2], 2,
                                                      '3rd entry of padding')
            self.padding = (dim1_padding, dim2_padding, dim3_padding)
        else:
            raise ValueError('`padding` should be either an int, '
                             'a tuple of 3 ints '
                             '(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad), '
                             'or a tuple of 3 tuples of 2 ints '
                             '((left_dim1_pad, right_dim1_pad),'
                             ' (left_dim2_pad, right_dim2_pad),'
                             ' (left_dim3_pad, right_dim2_pad)). '
                             'Found: ' + str(padding))
        self.input_spec = InputSpec(ndim=5)

    def compute_output_shape(self, input_shape):
        if self.data_format == 'channels_first':
            dim1 = input_shape[2] + 2 * self.padding[0][0] if input_shape[2] is not None else None
            dim2 = input_shape[3] + 2 * self.padding[1][0] if input_shape[3] is not None else None
            dim3 = input_shape[4] + 2 * self.padding[2][0] 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 = input_shape[1] + 2 * self.padding[0][1] if input_shape[1] is not None else None
            dim2 = input_shape[2] + 2 * self.padding[1][1] if input_shape[2] is not None else None
            dim3 = input_shape[3] + 2 * self.padding[2][1] 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.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. width and height.

    # Arguments
        cropping: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
            - If int: the same symmetric cropping
                is applied to width and height.
            - If tuple of 2 ints:
                interpreted as two different
                symmetric cropping values for height and width:
                `(symmetric_height_crop, symmetrc_width_crop)`.
            - If tuple of 2 tuples of 2 ints:
                interpreted as
                `((top_crop, bottom_crop), (left_crop, right_crop))`
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, width, height, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, width, height)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".

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

    # Output shape
        4D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, cropped_rows, cropped_cols, channels)`
        - If `data_format` is `"channels_first"`:
            `(batch, channels, cropped_rows, cropped_cols)`

    # Examples

    ```python
        # Crop the input 2D images or feature maps
        model = Sequential()
        model.add(Cropping2D(cropping=((2, 2), (4, 4)),
                             input_shape=(28, 28, 3)))
        # now model.output_shape == (None, 24, 20, 3)
        model.add(Conv2D(64, (3, 3), padding='same))
        model.add(Cropping2D(cropping=((2, 2), (2, 2))))
        # now model.output_shape == (None, 20, 16. 64)
    ```
    """

    def __init__(self, cropping=((0, 0), (0, 0)),
                 data_format=None, **kwargs):
        super(Cropping2D, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        if isinstance(cropping, int):
            self.cropping = ((cropping, cropping), (cropping, cropping))
        elif hasattr(cropping, '__len__'):
            if len(cropping) != 2:
                raise ValueError('`cropping` should have two elements. '
                                 'Found: ' + str(cropping))
            height_cropping = conv_utils.normalize_tuple(
                cropping[0], 2,
                '1st entry of cropping')
            width_cropping = conv_utils.normalize_tuple(
                cropping[1], 2,
                '2nd entry of cropping')
            self.cropping = (height_cropping, width_cropping)
        else:
            raise ValueError('`cropping` should be either an int, '
                             'a tuple of 2 ints '
                             '(symmetric_height_crop, symmetric_width_crop), '
                             'or a tuple of 2 tuples of 2 ints '
                             '((top_crop, bottom_crop), (left_crop, right_crop)). '
                             'Found: ' + str(cropping))
        self.input_spec = InputSpec(ndim=4)

    def compute_output_shape(self, input_shape):
        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 2 ints, or tuple of 2 tuples of 2 ints.
            - If int: the same symmetric cropping
                is applied to width and height.
            - If tuple of 2 ints:
                interpreted as two different
                symmetric cropping values for height and width:
                `(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)`.
            - If tuple of 2 tuples of 2 ints:
                interpreted as
                `((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop))`
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
            while `channels_first` corresponds to inputs with shape
            `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".

    # Input shape
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, depth)`
        - If `data_format` is `"channels_first"`:
            `(batch, depth, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)`

    # Output shape
        5D tensor with shape:
        - If `data_format` is `"channels_last"`:
            `(batch, first_cropped_axis, second_cropped_axis, third_cropped_axis, depth)`
        - If `data_format` is `"channels_first"`:
            `(batch, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis)`
    """

    def __init__(self, cropping=((1, 1), (1, 1), (1, 1)),
                 data_format=None, **kwargs):
        super(Cropping3D, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        if isinstance(cropping, int):
            self.cropping = ((cropping, cropping),
                             (cropping, cropping),
                             (cropping, cropping))
        elif hasattr(cropping, '__len__'):
            if len(cropping) != 3:
                raise ValueError('`cropping` should have 3 elements. '
                                 'Found: ' + str(cropping))
            dim1_cropping = conv_utils.normalize_tuple(cropping[0], 2,
                                                       '1st entry of cropping')
            dim2_cropping = conv_utils.normalize_tuple(cropping[1], 2,
                                                       '2nd entry of cropping')
            dim3_cropping = conv_utils.normalize_tuple(cropping[2], 2,
                                                       '3rd entry of cropping')
            self.cropping = (dim1_cropping, dim2_cropping, dim3_cropping)
        else:
            raise ValueError('`cropping` should be either an int, '
                             'a tuple of 3 ints '
                             '(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop), '
                             'or a tuple of 3 tuples of 2 ints '
                             '((left_dim1_crop, right_dim1_crop),'
                             ' (left_dim2_crop, right_dim2_crop),'
                             ' (left_dim3_crop, right_dim2_crop)). '
                             'Found: ' + str(cropping))
        self.input_spec = InputSpec(ndim=5)

    def compute_output_shape(self, input_shape):
        if self.data_format == 'channels_first':
            dim1 = input_shape[2] - self.cropping[0][0] - self.cropping[0][1] if input_shape[2] is not None else None
            dim2 = input_shape[3] - self.cropping[1][0] - self.cropping[1][1] if input_shape[3] is not None else None
            dim3 = input_shape[4] - self.cropping[2][0] - self.cropping[2][1] 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 = input_shape[1] - self.cropping[0][0] - self.cropping[0][1] if input_shape[1] is not None else None
            dim2 = input_shape[2] - self.cropping[1][0] - self.cropping[1][1] if input_shape[2] is not None else None
            dim3 = input_shape[3] - self.cropping[2][0] - self.cropping[2][1] if input_shape[3] is not None else 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
SeparableConvolution2D = SeparableConv2D
Convolution2DTranspose = Conv2DTranspose
Deconvolution2D = Deconv2D = Conv2DTranspose
