tf.keras.layers.AbstractRNNCell

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Abstract object representing an RNN cell.

Inherits From: Layer

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.AbstractRNNCell, `tf.compat.v2.keras.layers.AbstractRNNCell`

tf.keras.layers.AbstractRNNCell(
    trainable=True, name=None, dtype=None, dynamic=False, **kwargs
)

This is the base class for implementing RNN cells with custom behavior.

Every RNNCell must have the properties below and implement call with the signature (output, next_state) = call(input, state).

Examples:

class MinimalRNNCell(AbstractRNNCell):

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

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

  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

This definition of cell differs from the definition used in the literature. In the literature, 'cell' refers to an object with a single scalar output. This definition refers to a horizontal array of such units.

An RNN cell, in the most abstract setting, is anything that has a state and performs some operation that takes a matrix of inputs. This operation results in an output matrix with self.output_size columns. If self.state_size is an integer, this operation also results in a new state matrix with self.state_size columns. If self.state_size is a (possibly nested tuple of) TensorShape object(s), then it should return a matching structure of Tensors having shape [batch_size].concatenate(s) for each s in self.batch_size.

Attributes
output_size	Integer or TensorShape: size of outputs produced by this cell.
state_size	size(s) of state(s) used by this cell. It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.

Methods

get_initial_state

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get_initial_state(
    inputs=None, batch_size=None, dtype=None
)