|View source on GitHub|
Abstract object representing an RNN cell.
This is the base class for implementing RNN cells with custom behavior.
RNNCell must have the properties below and implement
(output, next_state) = call(input, state).
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 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.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
__init__( trainable=True, name=None, dtype=None, dynamic=False, **kwargs )
Integer or TensorShape: size of outputs produced by this cell.
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.
get_initial_state( inputs=None, batch_size=None, dtype=None )