GRU.py

import numpy as np

from tensorflow.python.keras import activations
from tensorflow.python.keras import backend
from tensorflow.python.keras import constraints
from tensorflow.python.keras import initializers
from tensorflow.python.keras import regularizers
from tensorflow.python.keras.engine.base_layer import Layer
from tensorflow.python.keras.engine.input_spec import InputSpec
from tensorflow.python.keras.layers.recurrent import DropoutRNNCellMixin
from tensorflow.python.keras.layers.recurrent import RNN
from tensorflow.python.keras.utils import conv_utils
from tensorflow.python.keras.utils import generic_utils
from tensorflow.python.keras.utils import tf_utils
from tensorflow.python.ops import array_ops

class ConvRNN2D(RNN):
  """Base class for convolutional-recurrent layers.

  Args:
    cell: A RNN cell instance. A RNN cell is a class that has:
      - a `call(input_at_t, states_at_t)` method, returning
        `(output_at_t, states_at_t_plus_1)`. The call method of the
        cell can also take the optional argument `constants`, see
        section "Note on passing external constants" below.
      - a `state_size` attribute. This can be a single integer
        (single state) in which case it is
        the number of channels of the recurrent state
        (which should be the same as the number of channels of the cell
        output). This can also be a list/tuple of integers
        (one size per state). In this case, the first entry
        (`state_size[0]`) should be the same as
        the size of the cell output.
    return_sequences: Boolean. Whether to return the last output.
      in the output sequence, or the full sequence.
    return_state: Boolean. Whether to return the last state
      in addition to the output.
    go_backwards: Boolean (default False).
      If True, process the input sequence backwards and return the
      reversed sequence.
    stateful: Boolean (default False). If True, the last state
      for each sample at index i in a batch will be used as initial
      state for the sample of index i in the following batch.
    input_shape: Use this argument to specify the shape of the
      input when this layer is the first one in a model.

  Call arguments:
    inputs: A 5D tensor.
    mask: Binary tensor of shape `(samples, timesteps)` indicating whether
      a given timestep should be masked.
    training: Python boolean indicating whether the layer should behave in
      training mode or in inference mode. This argument is passed to the cell
      when calling it. This is for use with cells that use dropout.
    initial_state: List of initial state tensors to be passed to the first
      call of the cell.
    constants: List of constant tensors to be passed to the cell at each
      timestep.

  Input shape:
    5D tensor with shape:
    `(samples, timesteps, channels, rows, cols)`
    if data_format='channels_first' or 5D tensor with shape:
    `(samples, timesteps, rows, cols, channels)`
    if data_format='channels_last'.

  Output shape:
    - If `return_state`: a list of tensors. The first tensor is
      the output. The remaining tensors are the last states,
      each 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.
    - If `return_sequences`: 5D tensor with shape:
      `(samples, timesteps, filters, new_rows, new_cols)`
      if data_format='channels_first'
      or 5D tensor with shape:
      `(samples, timesteps, new_rows, new_cols, filters)`
      if data_format='channels_last'.
    - Else, 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'.

  Masking:
    This layer supports masking for input data with a variable number
    of timesteps.

  Note on using statefulness in RNNs:
    You can set RNN layers to be 'stateful', which means that the states
    computed for the samples in one batch will be reused as initial states
    for the samples in the next batch. This assumes a one-to-one mapping
    between samples in different successive batches.
    To enable statefulness:
      - Specify `stateful=True` in the layer constructor.
      - Specify a fixed batch size for your model, by passing
         - If sequential model:
            `batch_input_shape=(...)` to the first layer in your model.
         - If functional model with 1 or more Input layers:
            `batch_shape=(...)` to all the first layers in your model.
            This is the expected shape of your inputs
            *including the batch size*.
            It should be a tuple of integers,
            e.g. `(32, 10, 100, 100, 32)`.
            Note that the number of rows and columns should be specified
            too.
      - Specify `shuffle=False` when calling fit().
    To reset the states of your model, call `.reset_states()` on either
    a specific layer, or on your entire model.

  Note on specifying the initial state of RNNs:
    You can specify the initial state of RNN layers symbolically by
    calling them with the keyword argument `initial_state`. The value of
    `initial_state` should be a tensor or list of tensors representing
    the initial state of the RNN layer.
    You can specify the initial state of RNN layers numerically by
    calling `reset_states` with the keyword argument `states`. The value of
    `states` should be a numpy array or list of numpy arrays representing
    the initial state of the RNN layer.

  Note on passing external constants to RNNs:
    You can pass "external" constants to the cell using the `constants`
    keyword argument of `RNN.__call__` (as well as `RNN.call`) method. This
    requires that the `cell.call` method accepts the same keyword argument
    `constants`. Such constants can be used to condition the cell
    transformation on additional static inputs (not changing over time),
    a.k.a. an attention mechanism.
  """

  def __init__(self,
               cell,
               return_sequences=False,
               return_state=False,
               go_backwards=False,
               stateful=False,
               unroll=False,
               **kwargs):
    if unroll:
      raise TypeError('Unrolling isn\'t possible with '
                      'convolutional RNNs.')
    if isinstance(cell, (list, tuple)):
      # The StackedConvRNN2DCells isn't implemented yet.
      raise TypeError('It is not possible at the moment to'
                      'stack convolutional cells.')
    super(ConvRNN2D, self).__init__(cell,
                                    return_sequences,
                                    return_state,
                                    go_backwards,
                                    stateful,
                                    unroll,
                                    **kwargs)
    self.input_spec = [InputSpec(ndim=5)]
    self.states = None
    self._num_constants = None

  @tf_utils.shape_type_conversion
  def compute_output_shape(self, input_shape):
    if isinstance(input_shape, list):
      input_shape = input_shape[0]

    cell = self.cell
    if cell.data_format == 'channels_first':
      rows = input_shape[3]
      cols = input_shape[4]
    elif cell.data_format == 'channels_last':
      rows = input_shape[2]
      cols = input_shape[3]
    rows = conv_utils.conv_output_length(rows,
                                         cell.kernel_size[0],
                                         padding=cell.padding,
                                         stride=cell.strides[0],
                                         dilation=cell.dilation_rate[0])
    cols = conv_utils.conv_output_length(cols,
                                         cell.kernel_size[1],
                                         padding=cell.padding,
                                         stride=cell.strides[1],
                                         dilation=cell.dilation_rate[1])

    if cell.data_format == 'channels_first':
      output_shape = input_shape[:2] + (cell.filters, rows, cols)
    elif cell.data_format == 'channels_last':
      output_shape = input_shape[:2] + (rows, cols, cell.filters)

    if not self.return_sequences:
      output_shape = output_shape[:1] + output_shape[2:]

    if self.return_state:
      output_shape = [output_shape]
      if cell.data_format == 'channels_first':
        output_shape += [(input_shape[0], cell.filters, rows, cols)
                         for _ in range(2)]
      elif cell.data_format == 'channels_last':
        output_shape += [(input_shape[0], rows, cols, cell.filters)
                         for _ in range(2)]
    return output_shape

  @tf_utils.shape_type_conversion
  def build(self, input_shape):
    # Note input_shape will be list of shapes of initial states and
    # constants if these are passed in __call__.
    if self._num_constants is not None:
      constants_shape = input_shape[-self._num_constants:]  # pylint: disable=E1130
    else:
      constants_shape = None

    if isinstance(input_shape, list):
      input_shape = input_shape[0]

    batch_size = input_shape[0] if self.stateful else None
    self.input_spec[0] = InputSpec(shape=(batch_size, None) + input_shape[2:5])

    # allow cell (if layer) to build before we set or validate state_spec
    if isinstance(self.cell, Layer):
      step_input_shape = (input_shape[0],) + input_shape[2:]
      if constants_shape is not None:
        self.cell.build([step_input_shape] + constants_shape)
      else:
        self.cell.build(step_input_shape)

    # set or validate state_spec
    if hasattr(self.cell.state_size, '__len__'):
      state_size = list(self.cell.state_size)
    else:
      state_size = [self.cell.state_size]

    if self.state_spec is not None:
      # initial_state was passed in call, check compatibility
      if self.cell.data_format == 'channels_first':
        ch_dim = 1
      elif self.cell.data_format == 'channels_last':
        ch_dim = 3
      if [spec.shape[ch_dim] for spec in self.state_spec] != state_size:
        raise ValueError(
            'An initial_state was passed that is not compatible with '
            '`cell.state_size`. Received `state_spec`={}; '
            'However `cell.state_size` is '
            '{}'.format([spec.shape for spec in self.state_spec],
                        self.cell.state_size))
    else:
      if self.cell.data_format == 'channels_first':
        self.state_spec = [InputSpec(shape=(None, dim, None, None))
                           for dim in state_size]
      elif self.cell.data_format == 'channels_last':
        self.state_spec = [InputSpec(shape=(None, None, None, dim))
                           for dim in state_size]
    if self.stateful:
      self.reset_states()
    self.built = True

  def get_initial_state(self, inputs):
    # (samples, timesteps, rows, cols, filters)
    initial_state = backend.zeros_like(inputs)
    # (samples, rows, cols, filters)
    initial_state = backend.sum(initial_state, axis=1)
    shape = list(self.cell.kernel_shape)
    shape[-1] = self.cell.filters
    initial_state = self.cell.input_conv(initial_state,
                                         array_ops.zeros(tuple(shape),
                                                         initial_state.dtype),
                                         padding=self.cell.padding)

    if hasattr(self.cell.state_size, '__len__'):
      return [initial_state for _ in self.cell.state_size]
    else:
      return [initial_state]

  def call(self,
           inputs,
           mask=None,
           training=None,
           initial_state=None,
           constants=None):
    # note that the .build() method of subclasses MUST define
    # self.input_spec and self.state_spec with complete input shapes.
    inputs, initial_state, constants = self._process_inputs(
        inputs, initial_state, constants)

    if isinstance(mask, list):
      mask = mask[0]
    timesteps = backend.int_shape(inputs)[1]

    kwargs = {}
    if generic_utils.has_arg(self.cell.call, 'training'):
      kwargs['training'] = training

    if constants:
      if not generic_utils.has_arg(self.cell.call, 'constants'):
        raise ValueError('RNN cell does not support constants')

      def step(inputs, states):
        constants = states[-self._num_constants:]  # pylint: disable=invalid-unary-operand-type
        states = states[:-self._num_constants]  # pylint: disable=invalid-unary-operand-type
        return self.cell.call(inputs, states, constants=constants, **kwargs)
    else:
      def step(inputs, states):
        return self.cell.call(inputs, states, **kwargs)

    last_output, outputs, states = backend.rnn(step,
                                               inputs,
                                               initial_state,
                                               constants=constants,
                                               go_backwards=self.go_backwards,
                                               mask=mask,
                                               input_length=timesteps)
    if self.stateful:
      updates = [
          backend.update(self_state, state)
          for self_state, state in zip(self.states, states)
      ]
      self.add_update(updates)

    if self.return_sequences:
      output = outputs
    else:
      output = last_output

    if self.return_state:
      if not isinstance(states, (list, tuple)):
        states = [states]
      else:
        states = list(states)
      return [output] + states
    else:
      return output

  def reset_states(self, states=None):
    if not self.stateful:
      raise AttributeError('Layer must be stateful.')
    input_shape = self.input_spec[0].shape
    state_shape = self.compute_output_shape(input_shape)
    if self.return_state:
      state_shape = state_shape[0]
    if self.return_sequences:
      state_shape = state_shape[:1].concatenate(state_shape[2:])
    if None in state_shape:
      raise ValueError('If a RNN is stateful, it needs to know '
                       'its batch size. Specify the batch size '
                       'of your input tensors: \n'
                       '- If using a Sequential model, '
                       'specify the batch size by passing '
                       'a `batch_input_shape` '
                       'argument to your first layer.\n'
                       '- If using the functional API, specify '
                       'the time dimension by passing a '
                       '`batch_shape` argument to your Input layer.\n'
                       'The same thing goes for the number of rows and '
                       'columns.')

    # helper function
    def get_tuple_shape(nb_channels):
      result = list(state_shape)
      if self.cell.data_format == 'channels_first':
        result[1] = nb_channels
      elif self.cell.data_format == 'channels_last':
        result[3] = nb_channels
      else:
        raise KeyError
      return tuple(result)

    # initialize state if None
    if self.states[0] is None:
      if hasattr(self.cell.state_size, '__len__'):
        self.states = [backend.zeros(get_tuple_shape(dim))
                       for dim in self.cell.state_size]
      else:
        self.states = [backend.zeros(get_tuple_shape(self.cell.state_size))]
    elif states is None:
      if hasattr(self.cell.state_size, '__len__'):
        for state, dim in zip(self.states, self.cell.state_size):
          backend.set_value(state, np.zeros(get_tuple_shape(dim)))
      else:
        backend.set_value(self.states[0],
                          np.zeros(get_tuple_shape(self.cell.state_size)))
    else:
      if not isinstance(states, (list, tuple)):
        states = [states]
      if len(states) != len(self.states):
        raise ValueError('Layer ' + self.name + ' expects ' +
                         str(len(self.states)) + ' states, ' +
                         'but it received ' + str(len(states)) +
                         ' state values. Input received: ' + str(states))
      for index, (value, state) in enumerate(zip(states, self.states)):
        if hasattr(self.cell.state_size, '__len__'):
          dim = self.cell.state_size[index]
        else:
          dim = self.cell.state_size
        if value.shape != get_tuple_shape(dim):
          raise ValueError('State ' + str(index) +
                           ' is incompatible with layer ' +
                           self.name + ': expected shape=' +
                           str(get_tuple_shape(dim)) +
                           ', found shape=' + str(value.shape))
        # TODO(anjalisridhar): consider batch calls to `set_value`.
        backend.set_value(state, value)


class ConvGRU2DCell(DropoutRNNCellMixin, Layer):
    """Cell class for the ConvGRU2DCell layer.
    Args:
      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).
        `"valid"` means no padding. `"same"` results in padding evenly to
        the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      data_format: A string,
        one of `channels_last` (default) or `channels_first`.
        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.
        If you don't specify anything, no activation is applied
        (ie. "linear" activation: `a(x) = x`).
      recurrent_activation: Activation function to use
        for the recurrent step.
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix,
        used for the linear transformation of the inputs.
      recurrent_initializer: Initializer for the `recurrent_kernel`
        weights matrix,
        used for the linear transformation of the recurrent state.
      bias_initializer: Initializer for the bias vector.
      kernel_regularizer: Regularizer function applied to
        the `kernel` weights matrix.
      recurrent_regularizer: Regularizer function applied to
        the `recurrent_kernel` weights matrix.
      bias_regularizer: Regularizer function applied to the bias vector.
      kernel_constraint: Constraint function applied to
        the `kernel` weights matrix.
      recurrent_constraint: Constraint function applied to
        the `recurrent_kernel` weights matrix.
      bias_constraint: Constraint function applied to the bias vector.
      dropout: Float between 0 and 1.
        Fraction of the units to drop for
        the linear transformation of the inputs.
      recurrent_dropout: Float between 0 and 1.
        Fraction of the units to drop for
        the linear transformation of the recurrent state.
    Call arguments:
      inputs: A 4D tensor.
      states:  List of state tensors corresponding to the previous timestep.
      training: Python boolean indicating whether the layer should behave in
        training mode or in inference mode. Only relevant when `dropout` or
        `recurrent_dropout` is used.
    """

    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 dilation_rate=(1, 1),
                 activation='tanh',
                 recurrent_activation='hard_sigmoid',
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 recurrent_initializer='orthogonal',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 recurrent_regularizer=None,
                 bias_regularizer=None,
                 kernel_constraint=None,
                 recurrent_constraint=None,
                 bias_constraint=None,
                 dropout=0.,
                 recurrent_dropout=0.,
                 **kwargs):
        super(ConvGRU2DCell, self).__init__(**kwargs)
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, 2, '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, 2,
                                                        'dilation_rate')
        self.activation = activations.get(activation)
        self.recurrent_activation = activations.get(recurrent_activation)
        self.use_bias = use_bias

        self.kernel_initializer = initializers.get(kernel_initializer)
        self.recurrent_initializer = initializers.get(recurrent_initializer)
        self.bias_initializer = initializers.get(bias_initializer)

        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)

        self.kernel_constraint = constraints.get(kernel_constraint)
        self.recurrent_constraint = constraints.get(recurrent_constraint)
        self.bias_constraint = constraints.get(bias_constraint)

        self.dropout = min(1., max(0., dropout))
        self.recurrent_dropout = min(1., max(0., recurrent_dropout))
        self.state_size = (self.filters)

    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 * 3)
        self.kernel_shape = kernel_shape
        recurrent_kernel_shape = self.kernel_size + (self.filters, self.filters * 3)

        self.kernel = self.add_weight(shape=kernel_shape,
                                      initializer=self.kernel_initializer,
                                      name='kernel',
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        self.recurrent_kernel = self.add_weight(
            shape=recurrent_kernel_shape,
            initializer=self.recurrent_initializer,
            name='recurrent_kernel',
            regularizer=self.recurrent_regularizer,
            constraint=self.recurrent_constraint)

        if self.use_bias:
            bias_initializer = self.bias_initializer
            self.bias = self.add_weight(
                shape=(self.filters * 3,),
                name='bias',
                initializer=bias_initializer,
                regularizer=self.bias_regularizer,
                constraint=self.bias_constraint)
        else:
            self.bias = None
        self.built = True

    def call(self, inputs, states, training=None):
        h_tm1 = states[0]  # previous memory state

        # dropout matrices for input units
        dp_mask = self.get_dropout_mask_for_cell(inputs, training, count=3)
        # dropout matrices for recurrent units
        rec_dp_mask = self.get_recurrent_dropout_mask_for_cell(
            h_tm1, training, count=3)

        if 0 < self.dropout < 1.:
            inputs_z = inputs * dp_mask[0]
            inputs_r = inputs * dp_mask[1]
            inputs_h = inputs * dp_mask[2]

        else:
            inputs_z = inputs
            inputs_r = inputs
            inputs_h = inputs

        if 0 < self.recurrent_dropout < 1.:
            h_tm1_z = h_tm1 * rec_dp_mask[0]
            h_tm1_r = h_tm1 * rec_dp_mask[1]
            h_tm1_h = h_tm1 * rec_dp_mask[2]

        else:
            h_tm1_z = h_tm1
            h_tm1_r = h_tm1
            h_tm1_h = h_tm1

        (kernel_z, kernel_r,
         kernel_h) = array_ops.split(self.kernel, 3, axis=3)
        (recurrent_kernel_z,
         recurrent_kernel_r,
         recurrent_kernel_h) = array_ops.split(self.recurrent_kernel, 3, axis=3)

        if self.use_bias:
            bias_z, bias_r, bias_h = array_ops.split(self.bias, 3)
        else:
            bias_z, bias_r, bias_h = None, None, None

        x_z = self.input_conv(inputs_z, kernel_z, bias_z, padding=self.padding)
        x_r = self.input_conv(inputs_r, kernel_r, bias_r, padding=self.padding)
        x_h = self.input_conv(inputs_h, kernel_h, bias_h, padding=self.padding)

        h_z = self.recurrent_conv(h_tm1_z, recurrent_kernel_z)
        h_r = self.recurrent_conv(h_tm1_r, recurrent_kernel_r)
        h_h = self.recurrent_conv(h_tm1_h, recurrent_kernel_h)

        z = self.recurrent_activation(x_z + h_z)
        r = self.recurrent_activation(x_r + h_r)

        h = (1.0 - z) * h_tm1 + z * self.activation(x_h + h_h)
        return h, [h]

    def input_conv(self, x, w, b=None, padding='valid'):
        conv_out = backend.conv2d(x, w, strides=self.strides,
                                  padding=padding,
                                  data_format=self.data_format,
                                  dilation_rate=self.dilation_rate)
        if b is not None:
            conv_out = backend.bias_add(conv_out, b,
                                        data_format=self.data_format)
        return conv_out

    def recurrent_conv(self, x, w):
        conv_out = backend.conv2d(x, w, strides=(1, 1),
                                  padding='same',
                                  data_format=self.data_format)
        return conv_out

    def get_config(self):
        config = {'filters': self.filters,
                  'kernel_size': self.kernel_size,
                  'strides': self.strides,
                  'padding': self.padding,
                  'data_format': self.data_format,
                  'dilation_rate': self.dilation_rate,
                  'activation': activations.serialize(self.activation),
                  'recurrent_activation': activations.serialize(
                      self.recurrent_activation),
                  'use_bias': self.use_bias,
                  'kernel_initializer': initializers.serialize(
                      self.kernel_initializer),
                  'recurrent_initializer': initializers.serialize(
                      self.recurrent_initializer),
                  'bias_initializer': initializers.serialize(self.bias_initializer),
                  'kernel_regularizer': regularizers.serialize(
                      self.kernel_regularizer),
                  'recurrent_regularizer': regularizers.serialize(
                      self.recurrent_regularizer),
                  'bias_regularizer': regularizers.serialize(self.bias_regularizer),
                  'kernel_constraint': constraints.serialize(
                      self.kernel_constraint),
                  'recurrent_constraint': constraints.serialize(
                      self.recurrent_constraint),
                  'bias_constraint': constraints.serialize(self.bias_constraint),
                  'dropout': self.dropout,
                  'recurrent_dropout': self.recurrent_dropout}
        base_config = super(ConvGRU2DCell, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class ConvGRU2D(ConvRNN2D):
    """2D Convolutional GRU layer.
    A convolutional GRU is similar to an GRU, but the input transformations
    and recurrent transformations are both convolutional. This layer is typically
    used to process timeseries of images (i.e. video-like data).
    It is known to perform well for weather data forecasting,
    using inputs that are timeseries of 2D grids of sensor values.
    It isn't usually applied to regular video data, due to its high computational
    cost.
    Args:
      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).
        `"valid"` means no padding. `"same"` results in padding evenly to
        the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      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, time, ..., channels)`
        while `channels_first` corresponds to
        inputs with shape `(batch, time, 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.
        By default hyperbolic tangent activation function is applied
        (`tanh(x)`).
      recurrent_activation: Activation function to use
        for the recurrent step.
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix,
        used for the linear transformation of the inputs.
      recurrent_initializer: Initializer for the `recurrent_kernel`
        weights matrix,
        used for the linear transformation of the recurrent state.
      bias_initializer: Initializer for the bias vector.
      kernel_regularizer: Regularizer function applied to
        the `kernel` weights matrix.
      recurrent_regularizer: Regularizer function applied to
        the `recurrent_kernel` weights matrix.
      bias_regularizer: Regularizer function applied to the bias vector.
      activity_regularizer: Regularizer function applied to.
      kernel_constraint: Constraint function applied to
        the `kernel` weights matrix.
      recurrent_constraint: Constraint function applied to
        the `recurrent_kernel` weights matrix.
      bias_constraint: Constraint function applied to the bias vector.
      return_sequences: Boolean. Whether to return the last output
        in the output sequence, or the full sequence. (default False)
      return_state: Boolean Whether to return the last state
        in addition to the output. (default False)
      go_backwards: Boolean (default False).
        If True, process the input sequence backwards.
      stateful: Boolean (default False). If True, the last state
        for each sample at index i in a batch will be used as initial
        state for the sample of index i in the following batch.
      dropout: Float between 0 and 1.
        Fraction of the units to drop for
        the linear transformation of the inputs.
      recurrent_dropout: Float between 0 and 1.
        Fraction of the units to drop for
        the linear transformation of the recurrent state.
    Call arguments:
      inputs: A 5D float tensor (see input shape description below).
      mask: Binary tensor of shape `(samples, timesteps)` indicating whether
        a given timestep should be masked.
      training: Python boolean indicating whether the layer should behave in
        training mode or in inference mode. This argument is passed to the cell
        when calling it. This is only relevant if `dropout` or `recurrent_dropout`
        are set.
      initial_state: List of initial state tensors to be passed to the first
        call of the cell.
    Input shape:
      - If data_format='channels_first'
          5D tensor with shape:
          `(samples, time, channels, rows, cols)`
      - If data_format='channels_last'
          5D tensor with shape:
          `(samples, time, rows, cols, channels)`
    Output shape:
      - If `return_state`: a list of tensors. The first tensor is
        the output. The remaining tensors are the last states,
        each 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.
      - If `return_sequences`: 5D tensor with shape:
        `(samples, timesteps, filters, new_rows, new_cols)`
        if data_format='channels_first'
        or 5D tensor with shape:
        `(samples, timesteps, new_rows, new_cols, filters)`
        if data_format='channels_last'.
      - Else, 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'.
    Raises:
      ValueError: in case of invalid constructor arguments.
    References:
      - [Shi et al., 2015](http://arxiv.org/abs/1506.04214v1)
      (the current implementation does not include the feedback loop on the
      cells output).
    Example:
    ```python
    steps = 10
    height = 32
    width = 32
    input_channels = 3
    output_channels = 6
    inputs = tf.keras.Input(shape=(steps, height, width, input_channels))
    layer = ConvGRU2D.ConvGRU2D(filters=output_channels, kernel_size=3)
    outputs = layer(inputs)
    ```
    """

    def __init__(self,
                 filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 dilation_rate=(1, 1),
                 activation='tanh',
                 recurrent_activation='hard_sigmoid',
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 recurrent_initializer='orthogonal',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 recurrent_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 recurrent_constraint=None,
                 bias_constraint=None,
                 return_sequences=False,
                 return_state=False,
                 go_backwards=False,
                 stateful=False,
                 dropout=0.,
                 recurrent_dropout=0.,
                 **kwargs):
        cell = ConvGRU2DCell(filters=filters,
                             kernel_size=kernel_size,
                             strides=strides,
                             padding=padding,
                             data_format=data_format,
                             dilation_rate=dilation_rate,
                             activation=activation,
                             recurrent_activation=recurrent_activation,
                             use_bias=use_bias,
                             kernel_initializer=kernel_initializer,
                             recurrent_initializer=recurrent_initializer,
                             bias_initializer=bias_initializer,
                             kernel_regularizer=kernel_regularizer,
                             recurrent_regularizer=recurrent_regularizer,
                             bias_regularizer=bias_regularizer,
                             kernel_constraint=kernel_constraint,
                             recurrent_constraint=recurrent_constraint,
                             bias_constraint=bias_constraint,
                             dropout=dropout,
                             recurrent_dropout=recurrent_dropout,
                             dtype=kwargs.get('dtype'))
        super(ConvGRU2D, self).__init__(cell,
                                        return_sequences=return_sequences,
                                        return_state=return_state,
                                        go_backwards=go_backwards,
                                        stateful=stateful,
                                        **kwargs)
        self.activity_regularizer = regularizers.get(activity_regularizer)

    def call(self, inputs, mask=None, training=None, initial_state=None):
        return super(ConvGRU2D, self).call(inputs,
                                           mask=mask,
                                           training=training,
                                           initial_state=initial_state)

    @property
    def filters(self):
        return self.cell.filters

    @property
    def kernel_size(self):
        return self.cell.kernel_size

    @property
    def strides(self):
        return self.cell.strides

    @property
    def padding(self):
        return self.cell.padding

    @property
    def data_format(self):
        return self.cell.data_format

    @property
    def dilation_rate(self):
        return self.cell.dilation_rate

    @property
    def activation(self):
        return self.cell.activation

    @property
    def recurrent_activation(self):
        return self.cell.recurrent_activation

    @property
    def use_bias(self):
        return self.cell.use_bias

    @property
    def kernel_initializer(self):
        return self.cell.kernel_initializer

    @property
    def recurrent_initializer(self):
        return self.cell.recurrent_initializer

    @property
    def bias_initializer(self):
        return self.cell.bias_initializer

    @property
    def kernel_regularizer(self):
        return self.cell.kernel_regularizer

    @property
    def recurrent_regularizer(self):
        return self.cell.recurrent_regularizer

    @property
    def bias_regularizer(self):
        return self.cell.bias_regularizer

    @property
    def kernel_constraint(self):
        return self.cell.kernel_constraint

    @property
    def recurrent_constraint(self):
        return self.cell.recurrent_constraint

    @property
    def bias_constraint(self):
        return self.cell.bias_constraint

    @property
    def dropout(self):
        return self.cell.dropout

    @property
    def recurrent_dropout(self):
        return self.cell.recurrent_dropout

    def get_config(self):
        config = {'filters': self.filters,
                  'kernel_size': self.kernel_size,
                  'strides': self.strides,
                  'padding': self.padding,
                  'data_format': self.data_format,
                  'dilation_rate': self.dilation_rate,
                  'activation': activations.serialize(self.activation),
                  'recurrent_activation': activations.serialize(
                      self.recurrent_activation),
                  'use_bias': self.use_bias,
                  'kernel_initializer': initializers.serialize(
                      self.kernel_initializer),
                  'recurrent_initializer': initializers.serialize(
                      self.recurrent_initializer),
                  'bias_initializer': initializers.serialize(self.bias_initializer),
                  'kernel_regularizer': regularizers.serialize(
                      self.kernel_regularizer),
                  'recurrent_regularizer': regularizers.serialize(
                      self.recurrent_regularizer),
                  'bias_regularizer': regularizers.serialize(self.bias_regularizer),
                  'activity_regularizer': regularizers.serialize(
                      self.activity_regularizer),
                  'kernel_constraint': constraints.serialize(
                      self.kernel_constraint),
                  'recurrent_constraint': constraints.serialize(
                      self.recurrent_constraint),
                  'bias_constraint': constraints.serialize(self.bias_constraint),
                  'dropout': self.dropout,
                  'recurrent_dropout': self.recurrent_dropout}
        base_config = super(ConvGRU2D, self).get_config()
        del base_config['cell']
        return dict(list(base_config.items()) + list(config.items()))

    @classmethod
    def from_config(cls, config):
        return cls(**config)