AbstractRNNCell - LostTech.TensorFlow Documentation

Type AbstractRNNCell

Namespace tensorflow.keras.layers

Parent Layer

Abstract object representing an RNN cell.

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: 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`.

Show Example

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

Properties

Public properties

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

bool built get; set;

object dtype get;

object dtype_dyn get;

bool dynamic get;

object dynamic_dyn get;

IList<Node> inbound_nodes get;

object inbound_nodes_dyn get;

IList<object> input get;

object input_dyn get;

object input_mask get;

object input_mask_dyn get;

IList<object> input_shape get;

object input_shape_dyn get;

object input_spec get; set;

object input_spec_dyn get; set;

IList<object> losses get;

object losses_dyn get;

IList<object> metrics get;

object metrics_dyn get;

object name get;

object name_dyn get;

object name_scope get;

object name_scope_dyn get;

IList<object> non_trainable_variables get;

object non_trainable_variables_dyn get;

IList<object> non_trainable_weights get;

object non_trainable_weights_dyn get;

IList<object> outbound_nodes get;

object outbound_nodes_dyn get;

IList<object> output get;

object output_dyn get;

object output_mask get;

object output_mask_dyn get;

object output_shape get;

object output_shape_dyn get;

object output_size get;

Integer or TensorShape: size of outputs produced by this cell.

object output_size_dyn get;

Integer or TensorShape: size of outputs produced by this cell.

object PythonObject get;

object state_size get;

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.

object state_size_dyn get;