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Abstract object representing an RNN cell.

Inherits From: Layer

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).


  class MinimalRNNCell(AbstractRNNCell):

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

    def state_size(self):
      return self.units

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

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

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.



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