# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=protected-access
"""Recurrent layers.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

from tensorflow.python.framework import tensor_shape
from tensorflow.python.keras._impl.keras import activations
from tensorflow.python.keras._impl.keras import backend as K
from tensorflow.python.keras._impl.keras import constraints
from tensorflow.python.keras._impl.keras import initializers
from tensorflow.python.keras._impl.keras import regularizers
from tensorflow.python.keras._impl.keras.engine import InputSpec
from tensorflow.python.keras._impl.keras.engine import Layer
from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg
from tensorflow.python.platform import tf_logging as logging


class StackedRNNCells(Layer):
  """Wrapper allowing a stack of RNN cells to behave as a single cell.

  Used to implement efficient stacked RNNs.

  Arguments:
      cells: List of RNN cell instances.

  Examples:

  ```python
      cells = [
          keras.layers.LSTMCell(output_dim),
          keras.layers.LSTMCell(output_dim),
          keras.layers.LSTMCell(output_dim),
      ]

      inputs = keras.Input((timesteps, input_dim))
      x = keras.layers.RNN(cells)(inputs)
  ```
  """

  def __init__(self, cells, **kwargs):
    for cell in cells:
      if not hasattr(cell, 'call'):
        raise ValueError('All cells must have a `call` method. '
                         'received cells:', cells)
      if not hasattr(cell, 'state_size'):
        raise ValueError('All cells must have a '
                         '`state_size` attribute. '
                         'received cells:', cells)
    self.cells = cells
    super(StackedRNNCells, self).__init__(**kwargs)

  @property
  def state_size(self):
    # States are a flat list
    # in reverse order of the cell stack.
    # This allows to preserve the requirement
    # `stack.state_size[0] == output_dim`.
    # e.g. states of a 2-layer LSTM would be
    # `[h2, c2, h1, c1]`
    # (assuming one LSTM has states [h, c])
    state_size = []
    for cell in self.cells[::-1]:
      if hasattr(cell.state_size, '__len__'):
        state_size += list(cell.state_size)
      else:
        state_size.append(cell.state_size)
    return tuple(state_size)

  def call(self, inputs, states, **kwargs):
    # Recover per-cell states.
    nested_states = []
    for cell in self.cells[::-1]:
      if hasattr(cell.state_size, '__len__'):
        nested_states.append(states[:len(cell.state_size)])
        states = states[len(cell.state_size):]
      else:
        nested_states.append([states[0]])
        states = states[1:]
    nested_states = nested_states[::-1]

    # Call the cells in order and store the returned states.
    new_nested_states = []
    for cell, states in zip(self.cells, nested_states):
      inputs, states = cell.call(inputs, states, **kwargs)
      new_nested_states.append(states)

    # Format the new states as a flat list
    # in reverse cell order.
    states = []
    for cell_states in new_nested_states[::-1]:
      states += cell_states
    return inputs, states

  def build(self, input_shape):
    for cell in self.cells:
      if isinstance(cell, Layer):
        cell.build(input_shape)
      if hasattr(cell.state_size, '__len__'):
        output_dim = cell.state_size[0]
      else:
        output_dim = cell.state_size
      input_shape = (input_shape[0], input_shape[1], output_dim)
    self.built = True

  def get_config(self):
    cells = []
    for cell in self.cells:
      cells.append({
          'class_name': cell.__class__.__name__,
          'config': cell.get_config()
      })
    config = {'cells': cells}
    base_config = super(StackedRNNCells, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  @classmethod
  def from_config(cls, config, custom_objects=None):
    from tensorflow.python.keras._impl.keras.layers import deserialize as deserialize_layer  # pylint: disable=g-import-not-at-top
    cells = []
    for cell_config in config.pop('cells'):
      cells.append(
          deserialize_layer(cell_config, custom_objects=custom_objects))
    return cls(cells, **config)

  @property
  def trainable_weights(self):
    if not self.trainable:
      return []
    weights = []
    for cell in self.cells:
      if isinstance(cell, Layer):
        weights += cell.trainable_weights
    return weights

  @property
  def non_trainable_weights(self):
    weights = []
    for cell in self.cells:
      if isinstance(cell, Layer):
        weights += cell.non_trainable_weights
    if not self.trainable:
      trainable_weights = []
      for cell in self.cells:
        if isinstance(cell, Layer):
          trainable_weights += cell.trainable_weights
      return trainable_weights + weights
    return weights

  def get_weights(self):
    """Retrieves the weights of the model.

    Returns:
        A flat list of Numpy arrays.
    """
    weights = []
    for cell in self.cells:
      if isinstance(cell, Layer):
        weights += cell.weights
    return K.batch_get_value(weights)

  def set_weights(self, weights):
    """Sets the weights of the model.

    Arguments:
        weights: A list of Numpy arrays with shapes and types matching
            the output of `model.get_weights()`.
    """
    tuples = []
    for cell in self.cells:
      if isinstance(cell, Layer):
        num_param = len(cell.weights)
        weights = weights[:num_param]
        for sw, w in zip(cell.weights, weights):
          tuples.append((sw, w))
        weights = weights[num_param:]
    K.batch_set_value(tuples)

  @property
  def losses(self):
    losses = []
    for cell in self.cells:
      if isinstance(cell, Layer):
        cell_losses = cell.losses
        losses += cell_losses
    return losses

  def get_losses_for(self, inputs=None):
    losses = []
    for cell in self.cells:
      if isinstance(cell, Layer):
        cell_losses = cell.get_losses_for(inputs)
        losses += cell_losses
    return losses


class RNN(Layer):
  """Base class for recurrent layers.

  Arguments:
      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 size of the recurrent state
              (which should be the same as the size 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.
          It is also possible for `cell` to be a list of RNN cell instances,
          in which cases the cells get stacked on after the other in the RNN,
          implementing an efficient stacked RNN.
      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.
      unroll: Boolean (default False).
          If True, the network will be unrolled,
          else a symbolic loop will be used.
          Unrolling can speed-up a RNN,
          although it tends to be more memory-intensive.
          Unrolling is only suitable for short sequences.
      input_dim: dimensionality of the input (integer).
          This argument (or alternatively,
          the keyword argument `input_shape`)
          is required when using this layer as the first layer in a model.
      input_length: Length of input sequences, to be specified
          when it is constant.
          This argument is required if you are going to connect
          `Flatten` then `Dense` layers upstream
          (without it, the shape of the dense outputs cannot be computed).
          Note that if the recurrent layer is not the first layer
          in your model, you would need to specify the input length
          at the level of the first layer
          (e.g. via the `input_shape` argument)

  Input shape:
      3D tensor with shape `(batch_size, timesteps, input_dim)`,
      (Optional) 2D tensors with shape `(batch_size, output_dim)`.

  Output shape:
      - if `return_state`: a list of tensors. The first tensor is
          the output. The remaining tensors are the last states,
          each with shape `(batch_size, units)`.
      - if `return_sequences`: 3D tensor with shape
          `(batch_size, timesteps, units)`.
      - else, 2D tensor with shape `(batch_size, units)`.

  # Masking
      This layer supports masking for input data with a variable number
      of timesteps. To introduce masks to your data,
      use an [Embedding](embeddings.md) layer with the `mask_zero` parameter
      set to `True`.

  # 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.
              else for 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)`.
          - 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.

  Examples:

  ```python
      # First, let's define a RNN Cell, as a layer subclass.

      class MinimalRNNCell(keras.layers.Layer):

          def __init__(self, units, **kwargs):
              self.units = units
              self.state_size = units
              super(MinimalRNNCell, self).__init__(**kwargs)

          def build(self, input_shape):
              self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
                                            initializer='uniform',
                                            name='kernel')
              self.recurrent_kernel = self.add_weight(
                  shape=(self.units, self.units),
                  initializer='uniform',
                  name='recurrent_kernel')
              self.built = True

          def call(self, inputs, states):
              prev_output = states[0]
              h = K.dot(inputs, self.kernel)
              output = h + K.dot(prev_output, self.recurrent_kernel)
              return output, [output]

      # Let's use this cell in a RNN layer:

      cell = MinimalRNNCell(32)
      x = keras.Input((None, 5))
      layer = RNN(cell)
      y = layer(x)

      # Here's how to use the cell to build a stacked RNN:

      cells = [MinimalRNNCell(32), MinimalRNNCell(64)]
      x = keras.Input((None, 5))
      layer = RNN(cells)
      y = layer(x)
  ```
  """

  def __init__(self,
               cell,
               return_sequences=False,
               return_state=False,
               go_backwards=False,
               stateful=False,
               unroll=False,
               activity_regularizer=None,
               **kwargs):
    if isinstance(cell, (list, tuple)):
      cell = StackedRNNCells(cell)
    if not hasattr(cell, 'call'):
      raise ValueError('`cell` should have a `call` method. '
                       'The RNN was passed:', cell)
    if not hasattr(cell, 'state_size'):
      raise ValueError('The RNN cell should have '
                       'an attribute `state_size` '
                       '(tuple of integers, '
                       'one integer per RNN state).')
    super(RNN, self).__init__(
        activity_regularizer=regularizers.get(activity_regularizer), **kwargs)
    self.cell = cell
    self.return_sequences = return_sequences
    self.return_state = return_state
    self.go_backwards = go_backwards
    self.stateful = stateful
    self.unroll = unroll

    self.supports_masking = True
    self.input_spec = [InputSpec(ndim=3)]
    self.state_spec = None
    self._states = None
    self.constants_spec = None
    self._num_constants = None

  @property
  def states(self):
    if self._states is None:
      if isinstance(self.cell.state_size, int):
        num_states = 1
      else:
        num_states = len(self.cell.state_size)
      return [None for _ in range(num_states)]
    return self._states

  @states.setter
  def states(self, states):
    self._states = states

  def _compute_output_shape(self, input_shape):
    if isinstance(input_shape, list):
      input_shape = input_shape[0]
    input_shape = tensor_shape.TensorShape(input_shape).as_list()

    if hasattr(self.cell.state_size, '__len__'):
      output_dim = self.cell.state_size[0]
    else:
      output_dim = self.cell.state_size

    if self.return_sequences:
      output_shape = (input_shape[0], input_shape[1], output_dim)
    else:
      output_shape = (input_shape[0], output_dim)

    if self.return_state:
      state_shape = [(input_shape[0], output_dim) for _ in self.states]
      output_shape = [output_shape] + state_shape
    else:
      output_shape = output_shape
    return tensor_shape.TensorShape(output_shape)

  def compute_mask(self, inputs, mask):
    if isinstance(mask, list):
      mask = mask[0]
    output_mask = mask if self.return_sequences else None
    if self.return_state:
      state_mask = [None for _ in self.states]
      return [output_mask] + state_mask
    else:
      return output_mask

  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=invalid-unary-operand-type
    else:
      constants_shape = None

    if isinstance(input_shape, list):
      input_shape = input_shape[0]
    input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list())

    batch_size = input_shape[0] if self.stateful else None
    input_dim = input_shape[-1]
    self.input_spec[0] = InputSpec(shape=(batch_size, None, input_dim))

    # 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 [spec.shape[-1] 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(self.state_spec, self.cell.state_size))
    else:
      self.state_spec = [InputSpec(shape=(None, dim)) for dim in state_size]
    if self.stateful:
      self.reset_states()

  def get_initial_state(self, inputs):
    # build an all-zero tensor of shape (samples, output_dim)
    initial_state = K.zeros_like(inputs)  # (samples, timesteps, input_dim)
    initial_state = K.sum(initial_state, axis=(1, 2))  # (samples,)
    initial_state = K.expand_dims(initial_state)  # (samples, 1)
    if hasattr(self.cell.state_size, '__len__'):
      return [K.tile(initial_state, [1, dim]) for dim in self.cell.state_size]
    else:
      return [K.tile(initial_state, [1, self.cell.state_size])]

  def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
    inputs, initial_state, constants = self._standardize_args(
        inputs, initial_state, constants)

    if initial_state is None and constants is None:
      return super(RNN, self).__call__(inputs, **kwargs)

    # If any of `initial_state` or `constants` are specified and are Keras
    # tensors, then add them to the inputs and temporarily modify the
    # input_spec to include them.

    additional_inputs = []
    additional_specs = []
    if initial_state is not None:
      kwargs['initial_state'] = initial_state
      additional_inputs += initial_state
      self.state_spec = [
          InputSpec(shape=K.int_shape(state)) for state in initial_state
      ]
      additional_specs += self.state_spec
    if constants is not None:
      kwargs['constants'] = constants
      additional_inputs += constants
      self.constants_spec = [
          InputSpec(shape=K.int_shape(constant)) for constant in constants
      ]
      self._num_constants = len(constants)
      additional_specs += self.constants_spec
    # at this point additional_inputs cannot be empty
    is_keras_tensor = hasattr(additional_inputs[0], '_keras_history')
    for tensor in additional_inputs:
      if hasattr(tensor, '_keras_history') != is_keras_tensor:
        raise ValueError('The initial state or constants of an RNN'
                         ' layer cannot be specified with a mix of'
                         ' Keras tensors and non-Keras tensors')

    if is_keras_tensor:
      # Compute the full input spec, including state and constants
      full_input = [inputs] + additional_inputs
      full_input_spec = self.input_spec + additional_specs
      # Perform the call with temporarily replaced input_spec
      original_input_spec = self.input_spec
      self.input_spec = full_input_spec
      output = super(RNN, self).__call__(full_input, **kwargs)
      self.input_spec = original_input_spec
      return output
    else:
      return super(RNN, self).__call__(inputs, **kwargs)

  def call(self,
           inputs,
           mask=None,
           training=None,
           initial_state=None,
           constants=None):
    # input shape: `(samples, time (padded with zeros), input_dim)`
    # note that the .build() method of subclasses MUST define
    # self.input_spec and self.state_spec with complete input shapes.
    if isinstance(inputs, list):
      inputs = inputs[0]
    if initial_state is not None:
      pass
    elif self.stateful:
      initial_state = self.states
    else:
      initial_state = self.get_initial_state(inputs)

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

    if len(initial_state) != len(self.states):
      raise ValueError(
          'Layer has ' + str(len(self.states)) + ' states but was passed ' +
          str(len(initial_state)) + ' initial states.')
    input_shape = K.int_shape(inputs)
    timesteps = input_shape[1]
    if self.unroll and timesteps in [None, 1]:
      raise ValueError('Cannot unroll a RNN if the '
                       'time dimension is undefined or equal to 1. \n'
                       '- If using a Sequential model, '
                       'specify the time dimension by passing '
                       'an `input_shape` or `batch_input_shape` '
                       'argument to your first layer. If your '
                       'first layer is an Embedding, you can '
                       'also use the `input_length` argument.\n'
                       '- If using the functional API, specify '
                       'the time dimension by passing a `shape` '
                       'or `batch_shape` argument to your Input layer.')

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

    if constants:
      if not 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 = K.rnn(
        step,
        inputs,
        initial_state,
        constants=constants,
        go_backwards=self.go_backwards,
        mask=mask,
        unroll=self.unroll)
    if self.stateful:
      updates = []
      for i in range(len(states)):
        updates.append((self.states[i], states[i]))
      self.add_update(updates, inputs)

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

    # Properly set learning phase
    if getattr(last_output, '_uses_learning_phase', False):
      output._uses_learning_phase = True

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

  def _standardize_args(self, inputs, initial_state, constants):
    """Standardize `__call__` arguments to a single list of tensor inputs.

    When running a model loaded from file, the input tensors
    `initial_state` and `constants` can be passed to `RNN.__call__` as part
    of `inputs` instead of by the dedicated keyword arguments. This method
    makes sure the arguments are separated and that `initial_state` and
    `constants` are lists of tensors (or None).

    Arguments:
        inputs: tensor or list/tuple of tensors
        initial_state: tensor or list of tensors or None
        constants: tensor or list of tensors or None

    Returns:
        inputs: tensor
        initial_state: list of tensors or None
        constants: list of tensors or None
    """
    if isinstance(inputs, list):
      assert initial_state is None and constants is None
      if self._num_constants is not None:
        constants = inputs[-self._num_constants:]  # pylint: disable=invalid-unary-operand-type
        inputs = inputs[:-self._num_constants]  # pylint: disable=invalid-unary-operand-type
      if len(inputs) > 1:
        initial_state = inputs[1:]
      inputs = inputs[0]

    def to_list_or_none(x):
      if x is None or isinstance(x, list):
        return x
      if isinstance(x, tuple):
        return list(x)
      return [x]

    initial_state = to_list_or_none(initial_state)
    constants = to_list_or_none(constants)

    return inputs, initial_state, constants

  def reset_states(self, states=None):
    if not self.stateful:
      raise AttributeError('Layer must be stateful.')
    batch_size = self.input_spec[0].shape[0]
    if not batch_size:
      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.')
    # initialize state if None
    if self.states[0] is None:
      if hasattr(self.cell.state_size, '__len__'):
        self.states = [
            K.zeros((batch_size, dim)) for dim in self.cell.state_size
        ]
      else:
        self.states = [K.zeros((batch_size, 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):
          K.set_value(state, np.zeros((batch_size, dim)))
      else:
        K.set_value(self.states[0], np.zeros((batch_size,
                                              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 != (batch_size, dim):
          raise ValueError(
              'State ' + str(index) + ' is incompatible with layer ' +
              self.name + ': expected shape=' + str(
                  (batch_size, dim)) + ', found shape=' + str(value.shape))
        # TODO(fchollet): consider batch calls to `set_value`.
        K.set_value(state, value)

  def get_config(self):
    config = {
        'return_sequences': self.return_sequences,
        'return_state': self.return_state,
        'go_backwards': self.go_backwards,
        'stateful': self.stateful,
        'unroll': self.unroll
    }
    if self._num_constants is not None:
      config['num_constants'] = self._num_constants

    cell_config = self.cell.get_config()
    config['cell'] = {
        'class_name': self.cell.__class__.__name__,
        'config': cell_config
    }
    base_config = super(RNN, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  @classmethod
  def from_config(cls, config, custom_objects=None):
    from tensorflow.python.keras._impl.keras.layers import deserialize as deserialize_layer  # pylint: disable=g-import-not-at-top
    cell = deserialize_layer(config.pop('cell'), custom_objects=custom_objects)
    num_constants = config.pop('num_constants', None)
    layer = cls(cell, **config)
    layer._num_constants = num_constants
    return layer

  @property
  def trainable_weights(self):
    if not self.trainable:
      return []
    if isinstance(self.cell, Layer):
      return self.cell.trainable_weights
    return []

  @property
  def non_trainable_weights(self):
    if isinstance(self.cell, Layer):
      if not self.trainable:
        return self.cell.weights
      return self.cell.non_trainable_weights
    return []

  @property
  def losses(self):
    if isinstance(self.cell, Layer):
      return self.cell.losses
    return []

  def get_losses_for(self, inputs=None):
    if isinstance(self.cell, Layer):
      cell_losses = self.cell.get_losses_for(inputs)
      return cell_losses + super(RNN, self).get_losses_for(inputs)
    return super(RNN, self).get_losses_for(inputs)


class SimpleRNNCell(Layer):
  """Cell class for SimpleRNN.

  Arguments:
      units: Positive integer, dimensionality of the output space.
      activation: Activation function to use
          (see [activations](../activations.md)).
          If you pass None, 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,
          used for the linear transformation of the inputs.
          (see [initializers](../initializers.md)).
      recurrent_initializer: Initializer for the `recurrent_kernel`
          weights matrix,
          used for the linear transformation of the recurrent state.
          (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)).
      recurrent_regularizer: Regularizer function applied to
          the `recurrent_kernel` weights matrix
          (see [regularizer](../regularizers.md)).
      bias_regularizer: Regularizer function applied to the bias vector
          (see [regularizer](../regularizers.md)).
      kernel_constraint: Constraint function applied to
          the `kernel` weights matrix
          (see [constraints](../constraints.md)).
      recurrent_constraint: Constraint function applied to
          the `recurrent_kernel` weights matrix
          (see [constraints](../constraints.md)).
      bias_constraint: Constraint function applied to the bias vector
          (see [constraints](../constraints.md)).
      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.
  """

  def __init__(self,
               units,
               activation='tanh',
               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(SimpleRNNCell, self).__init__(**kwargs)
    self.units = units
    self.activation = activations.get(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.units
    self._dropout_mask = None
    self._recurrent_dropout_mask = None

  def build(self, input_shape):
    self.kernel = self.add_weight(
        shape=(input_shape[-1], self.units),
        name='kernel',
        initializer=self.kernel_initializer,
        regularizer=self.kernel_regularizer,
        constraint=self.kernel_constraint)
    self.recurrent_kernel = self.add_weight(
        shape=(self.units, self.units),
        name='recurrent_kernel',
        initializer=self.recurrent_initializer,
        regularizer=self.recurrent_regularizer,
        constraint=self.recurrent_constraint)
    if self.use_bias:
      self.bias = self.add_weight(
          shape=(self.units,),
          name='bias',
          initializer=self.bias_initializer,
          regularizer=self.bias_regularizer,
          constraint=self.bias_constraint)
    else:
      self.bias = None
    self.built = True

  def _generate_dropout_mask(self, inputs, training=None):
    if 0 < self.dropout < 1:
      ones = K.ones_like(K.squeeze(inputs[:, 0:1, :], axis=1))

      def dropped_inputs():
        return K.dropout(ones, self.dropout)

      self._dropout_mask = K.in_train_phase(
          dropped_inputs, ones, training=training)
    else:
      self._dropout_mask = None

  def _generate_recurrent_dropout_mask(self, inputs, training=None):
    if 0 < self.recurrent_dropout < 1:
      ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
      ones = K.tile(ones, (1, self.units))

      def dropped_inputs():
        return K.dropout(ones, self.dropout)

      self._recurrent_dropout_mask = K.in_train_phase(
          dropped_inputs, ones, training=training)
    else:
      self._recurrent_dropout_mask = None

  def call(self, inputs, states, training=None):
    prev_output = states[0]
    dp_mask = self._dropout_mask
    rec_dp_mask = self._recurrent_dropout_mask

    if dp_mask is not None:
      h = K.dot(inputs * dp_mask, self.kernel)
    else:
      h = K.dot(inputs, self.kernel)
    if self.bias is not None:
      h = K.bias_add(h, self.bias)

    if rec_dp_mask is not None:
      prev_output *= rec_dp_mask
    output = h + K.dot(prev_output, self.recurrent_kernel)
    if self.activation is not None:
      output = self.activation(output)

    # Properly set learning phase on output tensor.
    if 0 < self.dropout + self.recurrent_dropout:
      if training is None:
        output._uses_learning_phase = True
    return output, [output]


class SimpleRNN(RNN):
  """Fully-connected RNN where the output is to be fed back to input.

  Arguments:
      units: Positive integer, dimensionality of the output space.
      activation: Activation function to use
          (see [activations](../activations.md)).
          If you pass None, 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,
          used for the linear transformation of the inputs.
          (see [initializers](../initializers.md)).
      recurrent_initializer: Initializer for the `recurrent_kernel`
          weights matrix,
          used for the linear transformation of the recurrent state.
          (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)).
      recurrent_regularizer: Regularizer function applied to
          the `recurrent_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` weights matrix
          (see [constraints](../constraints.md)).
      recurrent_constraint: Constraint function applied to
          the `recurrent_kernel` weights matrix
          (see [constraints](../constraints.md)).
      bias_constraint: Constraint function applied to the bias vector
          (see [constraints](../constraints.md)).
      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.
      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.
      unroll: Boolean (default False).
          If True, the network will be unrolled,
          else a symbolic loop will be used.
          Unrolling can speed-up a RNN,
          although it tends to be more memory-intensive.
          Unrolling is only suitable for short sequences.
  """

  def __init__(self,
               units,
               activation='tanh',
               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,
               dropout=0.,
               recurrent_dropout=0.,
               return_sequences=False,
               return_state=False,
               go_backwards=False,
               stateful=False,
               unroll=False,
               **kwargs):
    if 'implementation' in kwargs:
      kwargs.pop('implementation')
      logging.warning('The `implementation` argument '
                      'in `SimpleRNN` has been deprecated. '
                      'Please remove it from your layer call.')
    cell = SimpleRNNCell(
        units,
        activation=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)
    super(SimpleRNN, self).__init__(
        cell,
        return_sequences=return_sequences,
        return_state=return_state,
        go_backwards=go_backwards,
        stateful=stateful,
        unroll=unroll,
        activity_regularizer=regularizers.get(activity_regularizer),
        **kwargs)

  def call(self, inputs, mask=None, training=None, initial_state=None):
    self.cell._generate_dropout_mask(inputs, training=training)
    self.cell._generate_recurrent_dropout_mask(inputs, training=training)
    return super(SimpleRNN, self).call(
        inputs, mask=mask, training=training, initial_state=initial_state)

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

  @property
  def activation(self):
    return self.cell.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 = {
        'units': self.units,
        'activation': activations.serialize(self.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(SimpleRNN, self).get_config()
    del base_config['cell']
    return dict(list(base_config.items()) + list(config.items()))

  @classmethod
  def from_config(cls, config):
    if 'implementation' in config:
      config.pop('implementation')
    return cls(**config)


class GRUCell(Layer):
  """Cell class for the GRU layer.

  Arguments:
      units: Positive integer, dimensionality of the output space.
      activation: Activation function to use
          (see [activations](../activations.md)).
          If you pass None, no activation is applied
          (ie. "linear" activation: `a(x) = x`).
      recurrent_activation: Activation function to use
          for the recurrent step
          (see [activations](../activations.md)).
      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.
          (see [initializers](../initializers.md)).
      recurrent_initializer: Initializer for the `recurrent_kernel`
          weights matrix,
          used for the linear transformation of the recurrent state.
          (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)).
      recurrent_regularizer: Regularizer function applied to
          the `recurrent_kernel` weights matrix
          (see [regularizer](../regularizers.md)).
      bias_regularizer: Regularizer function applied to the bias vector
          (see [regularizer](../regularizers.md)).
      kernel_constraint: Constraint function applied to
          the `kernel` weights matrix
          (see [constraints](../constraints.md)).
      recurrent_constraint: Constraint function applied to
          the `recurrent_kernel` weights matrix
          (see [constraints](../constraints.md)).
      bias_constraint: Constraint function applied to the bias vector
          (see [constraints](../constraints.md)).
      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.
      implementation: Implementation mode, either 1 or 2.
          Mode 1 will structure its operations as a larger number of
          smaller dot products and additions, whereas mode 2 will
          batch them into fewer, larger operations. These modes will
          have different performance profiles on different hardware and
          for different applications.
  """

  def __init__(self,
               units,
               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.,
               implementation=1,
               **kwargs):
    super(GRUCell, self).__init__(**kwargs)
    self.units = units
    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.implementation = implementation
    self.state_size = self.units
    self._dropout_mask = None
    self._recurrent_dropout_mask = None

  def build(self, input_shape):
    input_dim = input_shape[-1]
    self.kernel = self.add_weight(
        shape=(input_dim, self.units * 3),
        name='kernel',
        initializer=self.kernel_initializer,
        regularizer=self.kernel_regularizer,
        constraint=self.kernel_constraint)
    self.recurrent_kernel = self.add_weight(
        shape=(self.units, self.units * 3),
        name='recurrent_kernel',
        initializer=self.recurrent_initializer,
        regularizer=self.recurrent_regularizer,
        constraint=self.recurrent_constraint)

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

    self.kernel_z = self.kernel[:, :self.units]
    self.recurrent_kernel_z = self.recurrent_kernel[:, :self.units]
    self.kernel_r = self.kernel[:, self.units:self.units * 2]
    self.recurrent_kernel_r = self.recurrent_kernel[:, self.units:
                                                    self.units * 2]
    self.kernel_h = self.kernel[:, self.units * 2:]
    self.recurrent_kernel_h = self.recurrent_kernel[:, self.units * 2:]

    if self.use_bias:
      self.bias_z = self.bias[:self.units]
      self.bias_r = self.bias[self.units:self.units * 2]
      self.bias_h = self.bias[self.units * 2:]
    else:
      self.bias_z = None
      self.bias_r = None
      self.bias_h = None
    self.built = True

  def _generate_dropout_mask(self, inputs, training=None):
    if 0 < self.dropout < 1:
      ones = K.ones_like(K.squeeze(inputs[:, 0:1, :], axis=1))

      def dropped_inputs():
        return K.dropout(ones, self.dropout)

      self._dropout_mask = [
          K.in_train_phase(dropped_inputs, ones, training=training)
          for _ in range(3)
      ]
    else:
      self._dropout_mask = None

  def _generate_recurrent_dropout_mask(self, inputs, training=None):
    if 0 < self.recurrent_dropout < 1:
      ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
      ones = K.tile(ones, (1, self.units))

      def dropped_inputs():
        return K.dropout(ones, self.dropout)

      self._recurrent_dropout_mask = [
          K.in_train_phase(dropped_inputs, ones, training=training)
          for _ in range(3)
      ]
    else:
      self._recurrent_dropout_mask = None

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

    # dropout matrices for input units
    dp_mask = self._dropout_mask
    # dropout matrices for recurrent units
    rec_dp_mask = self._recurrent_dropout_mask

    if self.implementation == 1:
      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
      x_z = K.dot(inputs_z, self.kernel_z)
      x_r = K.dot(inputs_r, self.kernel_r)
      x_h = K.dot(inputs_h, self.kernel_h)
      if self.use_bias:
        x_z = K.bias_add(x_z, self.bias_z)
        x_r = K.bias_add(x_r, self.bias_r)
        x_h = K.bias_add(x_h, self.bias_h)

      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
      z = self.recurrent_activation(
          x_z + K.dot(h_tm1_z, self.recurrent_kernel_z))
      r = self.recurrent_activation(
          x_r + K.dot(h_tm1_r, self.recurrent_kernel_r))

      hh = self.activation(x_h + K.dot(r * h_tm1_h, self.recurrent_kernel_h))
    else:
      if 0. < self.dropout < 1.:
        inputs *= dp_mask[0]
      matrix_x = K.dot(inputs, self.kernel)
      if self.use_bias:
        matrix_x = K.bias_add(matrix_x, self.bias)
      if 0. < self.recurrent_dropout < 1.:
        h_tm1 *= rec_dp_mask[0]
      matrix_inner = K.dot(h_tm1, self.recurrent_kernel[:, :2 * self.units])

      x_z = matrix_x[:, :self.units]
      x_r = matrix_x[:, self.units:2 * self.units]
      recurrent_z = matrix_inner[:, :self.units]
      recurrent_r = matrix_inner[:, self.units:2 * self.units]

      z = self.recurrent_activation(x_z + recurrent_z)
      r = self.recurrent_activation(x_r + recurrent_r)

      x_h = matrix_x[:, 2 * self.units:]
      recurrent_h = K.dot(r * h_tm1, self.recurrent_kernel[:, 2 * self.units:])
      hh = self.activation(x_h + recurrent_h)
    h = z * h_tm1 + (1 - z) * hh
    if 0 < self.dropout + self.recurrent_dropout:
      if training is None:
        h._uses_learning_phase = True
    return h, [h]


class GRU(RNN):
  # pylint: disable=line-too-long
  """Gated Recurrent Unit - Cho et al.

  2014.

  Arguments:
      units: Positive integer, dimensionality of the output space.
      activation: Activation function to use
          (see [activations](../activations.md)).
          If you pass None, no activation is applied
          (ie. "linear" activation: `a(x) = x`).
      recurrent_activation: Activation function to use
          for the recurrent step
          (see [activations](../activations.md)).
      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.
          (see [initializers](../initializers.md)).
      recurrent_initializer: Initializer for the `recurrent_kernel`
          weights matrix,
          used for the linear transformation of the recurrent state.
          (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)).
      recurrent_regularizer: Regularizer function applied to
          the `recurrent_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` weights matrix
          (see [constraints](../constraints.md)).
      recurrent_constraint: Constraint function applied to
          the `recurrent_kernel` weights matrix
          (see [constraints](../constraints.md)).
      bias_constraint: Constraint function applied to the bias vector
          (see [constraints](../constraints.md)).
      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.
      implementation: Implementation mode, either 1 or 2.
          Mode 1 will structure its operations as a larger number of
          smaller dot products and additions, whereas mode 2 will
          batch them into fewer, larger operations. These modes will
          have different performance profiles on different hardware and
          for different applications.
      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.
      unroll: Boolean (default False).
          If True, the network will be unrolled,
          else a symbolic loop will be used.
          Unrolling can speed-up a RNN,
          although it tends to be more memory-intensive.
          Unrolling is only suitable for short sequences.

  References:
      - [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://arxiv.org/abs/1409.1259)
      - [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](http://arxiv.org/abs/1412.3555v1)
      - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287)
  """
  # pylint: enable=line-too-long

  def __init__(self,
               units,
               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,
               dropout=0.,
               recurrent_dropout=0.,
               implementation=1,
               return_sequences=False,
               return_state=False,
               go_backwards=False,
               stateful=False,
               unroll=False,
               **kwargs):
    if implementation == 0:
      logging.warning('`implementation=0` has been deprecated, '
                      'and now defaults to `implementation=1`.'
                      'Please update your layer call.')
    cell = GRUCell(
        units,
        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,
        implementation=implementation)
    super(GRU, self).__init__(
        cell,
        return_sequences=return_sequences,
        return_state=return_state,
        go_backwards=go_backwards,
        stateful=stateful,
        unroll=unroll,
        **kwargs)
    self.activity_regularizer = regularizers.get(activity_regularizer)

  def call(self, inputs, mask=None, training=None, initial_state=None):
    self.cell._generate_dropout_mask(inputs, training=training)
    self.cell._generate_recurrent_dropout_mask(inputs, training=training)
    return super(GRU, self).call(
        inputs, mask=mask, training=training, initial_state=initial_state)

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

  @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

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

  def get_config(self):
    config = {
        'units': self.units,
        '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,
        'implementation': self.implementation
    }
    base_config = super(GRU, self).get_config()
    del base_config['cell']
    return dict(list(base_config.items()) + list(config.items()))

  @classmethod
  def from_config(cls, config):
    if 'implementation' in config and config['implementation'] == 0:
      config['implementation'] = 1
    return cls(**config)


class LSTMCell(Layer):
  """Cell class for the LSTM layer.

  Arguments:
      units: Positive integer, dimensionality of the output space.
      activation: Activation function to use
          (see [activations](../activations.md)).
          If you pass None, no activation is applied
          (ie. "linear" activation: `a(x) = x`).
      recurrent_activation: Activation function to use
          for the recurrent step
          (see [activations](../activations.md)).
      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.
          (see [initializers](../initializers.md)).
      recurrent_initializer: Initializer for the `recurrent_kernel`
          weights matrix,
          used for the linear transformation of the recurrent state.
          (see [initializers](../initializers.md)).
      bias_initializer: Initializer for the bias vector
          (see [initializers](../initializers.md)).
      unit_forget_bias: Boolean.
          If True, add 1 to the bias of the forget gate at initialization.
          Setting it to true will also force `bias_initializer="zeros"`.
          This is recommended in [Jozefowicz et
            al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
      kernel_regularizer: Regularizer function applied to
          the `kernel` weights matrix
          (see [regularizer](../regularizers.md)).
      recurrent_regularizer: Regularizer function applied to
          the `recurrent_kernel` weights matrix
          (see [regularizer](../regularizers.md)).
      bias_regularizer: Regularizer function applied to the bias vector
          (see [regularizer](../regularizers.md)).
      kernel_constraint: Constraint function applied to
          the `kernel` weights matrix
          (see [constraints](../constraints.md)).
      recurrent_constraint: Constraint function applied to
          the `recurrent_kernel` weights matrix
          (see [constraints](../constraints.md)).
      bias_constraint: Constraint function applied to the bias vector
          (see [constraints](../constraints.md)).
      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.
      implementation: Implementation mode, either 1 or 2.
          Mode 1 will structure its operations as a larger number of
          smaller dot products and additions, whereas mode 2 will
          batch them into fewer, larger operations. These modes will
          have different performance profiles on different hardware and
          for different applications.
  """

  def __init__(self,
               units,
               activation='tanh',
               recurrent_activation='hard_sigmoid',
               use_bias=True,
               kernel_initializer='glorot_uniform',
               recurrent_initializer='orthogonal',
               bias_initializer='zeros',
               unit_forget_bias=True,
               kernel_regularizer=None,
               recurrent_regularizer=None,
               bias_regularizer=None,
               kernel_constraint=None,
               recurrent_constraint=None,
               bias_constraint=None,
               dropout=0.,
               recurrent_dropout=0.,
               implementation=1,
               **kwargs):
    super(LSTMCell, self).__init__(**kwargs)
    self.units = units
    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.unit_forget_bias = unit_forget_bias

    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.implementation = implementation
    self.state_size = (self.units, self.units)
    self._dropout_mask = None
    self._recurrent_dropout_mask = None

  def build(self, input_shape):
    input_dim = input_shape[-1]
    self.kernel = self.add_weight(
        shape=(input_dim, self.units * 4),
        name='kernel',
        initializer=self.kernel_initializer,
        regularizer=self.kernel_regularizer,
        constraint=self.kernel_constraint)
    self.recurrent_kernel = self.add_weight(
        shape=(self.units, self.units * 4),
        name='recurrent_kernel',
        initializer=self.recurrent_initializer,
        regularizer=self.recurrent_regularizer,
        constraint=self.recurrent_constraint)

    if self.use_bias:
      if self.unit_forget_bias:

        def bias_initializer(_, *args, **kwargs):
          return K.concatenate([
              self.bias_initializer((self.units,), *args, **kwargs),
              initializers.Ones()((self.units,), *args, **kwargs),
              self.bias_initializer((self.units * 2,), *args, **kwargs),
          ])
      else:
        bias_initializer = self.bias_initializer
      self.bias = self.add_weight(
          shape=(self.units * 4,),
          name='bias',
          initializer=bias_initializer,
          regularizer=self.bias_regularizer,
          constraint=self.bias_constraint)
    else:
      self.bias = None

    self.kernel_i = self.kernel[:, :self.units]
    self.kernel_f = self.kernel[:, self.units:self.units * 2]
    self.kernel_c = self.kernel[:, self.units * 2:self.units * 3]
    self.kernel_o = self.kernel[:, self.units * 3:]

    self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units]
    self.recurrent_kernel_f = self.recurrent_kernel[:, self.units:
                                                    self.units * 2]
    self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2:
                                                    self.units * 3]
    self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:]

    if self.use_bias:
      self.bias_i = self.bias[:self.units]
      self.bias_f = self.bias[self.units:self.units * 2]
      self.bias_c = self.bias[self.units * 2:self.units * 3]
      self.bias_o = self.bias[self.units * 3:]
    else:
      self.bias_i = None
      self.bias_f = None
      self.bias_c = None
      self.bias_o = None
    self.built = True

  def _generate_dropout_mask(self, inputs, training=None):
    if 0 < self.dropout < 1:
      ones = K.ones_like(K.squeeze(inputs[:, 0:1, :], axis=1))

      def dropped_inputs():
        return K.dropout(ones, self.dropout)

      self._dropout_mask = [
          K.in_train_phase(dropped_inputs, ones, training=training)
          for _ in range(4)
      ]
    else:
      self._dropout_mask = None

  def _generate_recurrent_dropout_mask(self, inputs, training=None):
    if 0 < self.recurrent_dropout < 1:
      ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
      ones = K.tile(ones, (1, self.units))

      def dropped_inputs():
        return K.dropout(ones, self.dropout)

      self._recurrent_dropout_mask = [
          K.in_train_phase(dropped_inputs, ones, training=training)
          for _ in range(4)
      ]
    else:
      self._recurrent_dropout_mask = None

  def call(self, inputs, states, training=None):
    # dropout matrices for input units
    dp_mask = self._dropout_mask
    # dropout matrices for recurrent units
    rec_dp_mask = self._recurrent_dropout_mask

    h_tm1 = states[0]  # previous memory state
    c_tm1 = states[1]  # previous carry state

    if self.implementation == 1:
      if 0 < self.dropout < 1.:
        inputs_i = inputs * dp_mask[0]
        inputs_f = inputs * dp_mask[1]
        inputs_c = inputs * dp_mask[2]
        inputs_o = inputs * dp_mask[3]
      else:
        inputs_i = inputs
        inputs_f = inputs
        inputs_c = inputs
        inputs_o = inputs
      x_i = K.dot(inputs_i, self.kernel_i)
      x_f = K.dot(inputs_f, self.kernel_f)
      x_c = K.dot(inputs_c, self.kernel_c)
      x_o = K.dot(inputs_o, self.kernel_o)
      if self.use_bias:
        x_i = K.bias_add(x_i, self.bias_i)
        x_f = K.bias_add(x_f, self.bias_f)
        x_c = K.bias_add(x_c, self.bias_c)
        x_o = K.bias_add(x_o, self.bias_o)

      if 0 < self.recurrent_dropout < 1.:
        h_tm1_i = h_tm1 * rec_dp_mask[0]
        h_tm1_f = h_tm1 * rec_dp_mask[1]
        h_tm1_c = h_tm1 * rec_dp_mask[2]
        h_tm1_o = h_tm1 * rec_dp_mask[3]
      else:
        h_tm1_i = h_tm1
        h_tm1_f = h_tm1
        h_tm1_c = h_tm1
        h_tm1_o = h_tm1
      i = self.recurrent_activation(
          x_i + K.dot(h_tm1_i, self.recurrent_kernel_i))
      f = self.recurrent_activation(
          x_f + K.dot(h_tm1_f, self.recurrent_kernel_f))
      c = f * c_tm1 + i * self.activation(
          x_c + K.dot(h_tm1_c, self.recurrent_kernel_c))
      o = self.recurrent_activation(
          x_o + K.dot(h_tm1_o, self.recurrent_kernel_o))
    else:
      if 0. < self.dropout < 1.:
        inputs *= dp_mask[0]
      z = K.dot(inputs, self.kernel)
      if 0. < self.recurrent_dropout < 1.:
        h_tm1 *= rec_dp_mask[0]
      z += K.dot(h_tm1, self.recurrent_kernel)
      if self.use_bias:
        z = K.bias_add(z, self.bias)

      z0 = z[:, :self.units]
      z1 = z[:, self.units:2 * self.units]
      z2 = z[:, 2 * self.units:3 * self.units]
      z3 = z[:, 3 * self.units:]

      i = self.recurrent_activation(z0)
      f = self.recurrent_activation(z1)
      c = f * c_tm1 + i * self.activation(z2)
      o = self.recurrent_activation(z3)

    h = o * self.activation(c)
    if 0 < self.dropout + self.recurrent_dropout:
      if training is None:
        h._uses_learning_phase = True
    return h, [h, c]


class LSTM(RNN):
  # pylint: disable=line-too-long
  """Long-Short Term Memory layer - Hochreiter 1997.

  Arguments:
      units: Positive integer, dimensionality of the output space.
      activation: Activation function to use
          (see [activations](../activations.md)).
          If you pass None, no activation is applied
          (ie. "linear" activation: `a(x) = x`).
      recurrent_activation: Activation function to use
          for the recurrent step
          (see [activations](../activations.md)).
      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.
          (see [initializers](../initializers.md)).
      recurrent_initializer: Initializer for the `recurrent_kernel`
          weights matrix,
          used for the linear transformation of the recurrent state.
          (see [initializers](../initializers.md)).
      bias_initializer: Initializer for the bias vector
          (see [initializers](../initializers.md)).
      unit_forget_bias: Boolean.
          If True, add 1 to the bias of the forget gate at initialization.
          Setting it to true will also force `bias_initializer="zeros"`.
          This is recommended in [Jozefowicz et
            al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
      kernel_regularizer: Regularizer function applied to
          the `kernel` weights matrix
          (see [regularizer](../regularizers.md)).
      recurrent_regularizer: Regularizer function applied to
          the `recurrent_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` weights matrix
          (see [constraints](../constraints.md)).
      recurrent_constraint: Constraint function applied to
          the `recurrent_kernel` weights matrix
          (see [constraints](../constraints.md)).
      bias_constraint: Constraint function applied to the bias vector
          (see [constraints](../constraints.md)).
      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.
      implementation: Implementation mode, either 1 or 2.
          Mode 1 will structure its operations as a larger number of
          smaller dot products and additions, whereas mode 2 will
          batch them into fewer, larger operations. These modes will
          have different performance profiles on different hardware and
          for different applications.
      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.
      unroll: Boolean (default False).
          If True, the network will be unrolled,
          else a symbolic loop will be used.
          Unrolling can speed-up a RNN,
          although it tends to be more memory-intensive.
          Unrolling is only suitable for short sequences.

  References:
      - [Long short-term memory](http://www.bioinf.jku.at/publications/older/2604.pdf)
      - [Learning to forget: Continual prediction with LSTM](http://www.mitpressjournals.org/doi/pdf/10.1162/089976600300015015)
      - [Supervised sequence labeling with recurrent neural networks](http://www.cs.toronto.edu/~graves/preprint.pdf)
      - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287)
  """
  # pylint: enable=line-too-long

  def __init__(self,
               units,
               activation='tanh',
               recurrent_activation='hard_sigmoid',
               use_bias=True,
               kernel_initializer='glorot_uniform',
               recurrent_initializer='orthogonal',
               bias_initializer='zeros',
               unit_forget_bias=True,
               kernel_regularizer=None,
               recurrent_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               kernel_constraint=None,
               recurrent_constraint=None,
               bias_constraint=None,
               dropout=0.,
               recurrent_dropout=0.,
               implementation=1,
               return_sequences=False,
               return_state=False,
               go_backwards=False,
               stateful=False,
               unroll=False,
               **kwargs):
    if implementation == 0:
      logging.warning('`implementation=0` has been deprecated, '
                      'and now defaults to `implementation=1`.'
                      'Please update your layer call.')
    cell = LSTMCell(
        units,
        activation=activation,
        recurrent_activation=recurrent_activation,
        use_bias=use_bias,
        kernel_initializer=kernel_initializer,
        recurrent_initializer=recurrent_initializer,
        unit_forget_bias=unit_forget_bias,
        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,
        implementation=implementation)
    super(LSTM, self).__init__(
        cell,
        return_sequences=return_sequences,
        return_state=return_state,
        go_backwards=go_backwards,
        stateful=stateful,
        unroll=unroll,
        **kwargs)
    self.activity_regularizer = regularizers.get(activity_regularizer)

  def call(self, inputs, mask=None, training=None, initial_state=None):
    self.cell._generate_dropout_mask(inputs, training=training)
    self.cell._generate_recurrent_dropout_mask(inputs, training=training)
    return super(LSTM, self).call(
        inputs, mask=mask, training=training, initial_state=initial_state)

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

  @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 unit_forget_bias(self):
    return self.cell.unit_forget_bias

  @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

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

  def get_config(self):
    config = {
        'units': self.units,
        '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),
        'unit_forget_bias': self.unit_forget_bias,
        '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,
        'implementation': self.implementation
    }
    base_config = super(LSTM, self).get_config()
    del base_config['cell']
    return dict(list(base_config.items()) + list(config.items()))

  @classmethod
  def from_config(cls, config):
    if 'implementation' in config and config['implementation'] == 0:
      config['implementation'] = 1
    return cls(**config)


class Recurrent(Layer):
  """Deprecated abstract base class for recurrent layers.

  It still exists because it is leveraged by the convolutional-recurrent layers.
  It will be removed entirely in the future.
  It was never part of the public API.
  Do not use.

  Arguments:
      weights: list of Numpy arrays to set as initial weights.
          The list should have 3 elements, of shapes:
          `[(input_dim, output_dim), (output_dim, output_dim), (output_dim,)]`.
      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.
      unroll: Boolean (default False).
          If True, the network will be unrolled,
          else a symbolic loop will be used.
          Unrolling can speed-up a RNN,
          although it tends to be more memory-intensive.
          Unrolling is only suitable for short sequences.
      implementation: one of {0, 1, or 2}.
          If set to 0, the RNN will use
          an implementation that uses fewer, larger matrix products,
          thus running faster on CPU but consuming more memory.
          If set to 1, the RNN will use more matrix products,
          but smaller ones, thus running slower
          (may actually be faster on GPU) while consuming less memory.
          If set to 2 (LSTM/GRU only),
          the RNN will combine the input gate,
          the forget gate and the output gate into a single matrix,
          enabling more time-efficient parallelization on the GPU.
          Note: RNN dropout must be shared for all gates,
          resulting in a slightly reduced regularization.
      input_dim: dimensionality of the input (integer).
          This argument (or alternatively, the keyword argument `input_shape`)
          is required when using this layer as the first layer in a model.
      input_length: Length of input sequences, to be specified
          when it is constant.
          This argument is required if you are going to connect
          `Flatten` then `Dense` layers upstream
          (without it, the shape of the dense outputs cannot be computed).
          Note that if the recurrent layer is not the first layer
          in your model, you would need to specify the input length
          at the level of the first layer
          (e.g. via the `input_shape` argument)

  Input shape:
      3D tensor with shape `(batch_size, timesteps, input_dim)`,
      (Optional) 2D tensors with shape `(batch_size, output_dim)`.

  Output shape:
      - if `return_state`: a list of tensors. The first tensor is
          the output. The remaining tensors are the last states,
          each with shape `(batch_size, units)`.
      - if `return_sequences`: 3D tensor with shape
          `(batch_size, timesteps, units)`.
      - else, 2D tensor with shape `(batch_size, units)`.

  # Masking
      This layer supports masking for input data with a variable number
      of timesteps. To introduce masks to your data,
      use an `Embedding` layer with the `mask_zero` parameter
      set to `True`.

  # 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.
              else for 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)`.
          - 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.
  """

  def __init__(self,
               return_sequences=False,
               return_state=False,
               go_backwards=False,
               stateful=False,
               unroll=False,
               implementation=0,
               **kwargs):
    super(Recurrent, self).__init__(**kwargs)
    self.return_sequences = return_sequences
    self.return_state = return_state
    self.go_backwards = go_backwards
    self.stateful = stateful
    self.unroll = unroll
    self.implementation = implementation
    self.supports_masking = True
    self.input_spec = [InputSpec(ndim=3)]
    self.state_spec = None
    self.dropout = 0
    self.recurrent_dropout = 0

  def _compute_output_shape(self, input_shape):
    if isinstance(input_shape, list):
      input_shape = input_shape[0]
    input_shape = tensor_shape.TensorShape(input_shape).as_list()
    if self.return_sequences:
      output_shape = (input_shape[0], input_shape[1], self.units)
    else:
      output_shape = (input_shape[0], self.units)

    if self.return_state:
      state_shape = [tensor_shape.TensorShape(
          (input_shape[0], self.units)) for _ in self.states]
      return [tensor_shape.TensorShape(output_shape)] + state_shape
    return tensor_shape.TensorShape(output_shape)

  def compute_mask(self, inputs, mask):
    if isinstance(mask, list):
      mask = mask[0]
    output_mask = mask if self.return_sequences else None
    if self.return_state:
      state_mask = [None for _ in self.states]
      return [output_mask] + state_mask
    return output_mask

  def step(self, inputs, states):
    raise NotImplementedError

  def get_constants(self, inputs, training=None):
    return []

  def get_initial_state(self, inputs):
    # build an all-zero tensor of shape (samples, output_dim)
    initial_state = K.zeros_like(inputs)  # (samples, timesteps, input_dim)
    initial_state = K.sum(initial_state, axis=(1, 2))  # (samples,)
    initial_state = K.expand_dims(initial_state)  # (samples, 1)
    initial_state = K.tile(initial_state, [1,
                                           self.units])  # (samples, output_dim)
    initial_state = [initial_state for _ in range(len(self.states))]
    return initial_state

  def preprocess_input(self, inputs, training=None):
    return inputs

  def __call__(self, inputs, initial_state=None, **kwargs):
    if (isinstance(inputs, (list, tuple)) and
        len(inputs) > 1
        and initial_state is None):
      initial_state = inputs[1:]
      inputs = inputs[0]

    # If `initial_state` is specified,
    # and if it a Keras tensor,
    # then add it to the inputs and temporarily
    # modify the input spec to include the state.
    if initial_state is None:
      return super(Recurrent, self).__call__(inputs, **kwargs)

    if not isinstance(initial_state, (list, tuple)):
      initial_state = [initial_state]

    is_keras_tensor = hasattr(initial_state[0], '_keras_history')
    for tensor in initial_state:
      if hasattr(tensor, '_keras_history') != is_keras_tensor:
        raise ValueError('The initial state of an RNN layer cannot be'
                         ' specified with a mix of Keras tensors and'
                         ' non-Keras tensors')

    if is_keras_tensor:
      # Compute the full input spec, including state
      input_spec = self.input_spec
      state_spec = self.state_spec
      if not isinstance(input_spec, list):
        input_spec = [input_spec]
      if not isinstance(state_spec, list):
        state_spec = [state_spec]
      self.input_spec = input_spec + state_spec

      # Compute the full inputs, including state
      inputs = [inputs] + list(initial_state)

      # Perform the call
      output = super(Recurrent, self).__call__(inputs, **kwargs)

      # Restore original input spec
      self.input_spec = input_spec
      return output
    else:
      kwargs['initial_state'] = initial_state
      return super(Recurrent, self).__call__(inputs, **kwargs)

  def call(self, inputs, mask=None, training=None, initial_state=None):
    # input shape: `(samples, time (padded with zeros), input_dim)`
    # note that the .build() method of subclasses MUST define
    # self.input_spec and self.state_spec with complete input shapes.
    if isinstance(inputs, list):
      initial_state = inputs[1:]
      inputs = inputs[0]
    elif initial_state is not None:
      pass
    elif self.stateful:
      initial_state = self.states
    else:
      initial_state = self.get_initial_state(inputs)

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

    if len(initial_state) != len(self.states):
      raise ValueError('Layer has ' + str(len(self.states)) +
                       ' states but was passed ' + str(len(initial_state)) +
                       ' initial states.')
    input_shape = K.int_shape(inputs)
    if self.unroll and input_shape[1] is None:
      raise ValueError('Cannot unroll a RNN if the '
                       'time dimension is undefined. \n'
                       '- If using a Sequential model, '
                       'specify the time dimension by passing '
                       'an `input_shape` or `batch_input_shape` '
                       'argument to your first layer. If your '
                       'first layer is an Embedding, you can '
                       'also use the `input_length` argument.\n'
                       '- If using the functional API, specify '
                       'the time dimension by passing a `shape` '
                       'or `batch_shape` argument to your Input layer.')
    constants = self.get_constants(inputs, training=None)
    preprocessed_input = self.preprocess_input(inputs, training=None)
    last_output, outputs, states = K.rnn(
        self.step,
        preprocessed_input,
        initial_state,
        go_backwards=self.go_backwards,
        mask=mask,
        constants=constants,
        unroll=self.unroll)
    if self.stateful:
      updates = []
      for i in range(len(states)):
        updates.append((self.states[i], states[i]))
      self.add_update(updates, inputs)

    # Properly set learning phase
    if 0 < self.dropout + self.recurrent_dropout:
      last_output._uses_learning_phase = True
      outputs._uses_learning_phase = True

    if not self.return_sequences:
      outputs = last_output

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

  def reset_states(self, states=None):
    if not self.stateful:
      raise AttributeError('Layer must be stateful.')
    batch_size = self.input_spec[0].shape[0]
    if not batch_size:
      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.')
    # initialize state if None
    if self.states[0] is None:
      self.states = [K.zeros((batch_size, self.units)) for _ in self.states]
    elif states is None:
      for state in self.states:
        K.set_value(state, np.zeros((batch_size, self.units)))
    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 value.shape != (batch_size, self.units):
          raise ValueError('State ' + str(index) +
                           ' is incompatible with layer ' + self.name +
                           ': expected shape=' + str((batch_size, self.units)) +
                           ', found shape=' + str(value.shape))
        K.set_value(state, value)

  def get_config(self):
    config = {
        'return_sequences': self.return_sequences,
        'return_state': self.return_state,
        'go_backwards': self.go_backwards,
        'stateful': self.stateful,
        'unroll': self.unroll,
        'implementation': self.implementation
    }
    base_config = super(Recurrent, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))
