Base interface for all RNN Cells

class tf.nn.rnn_cell.RNNCell

Abstract object representing an RNN cell.

The definition of cell in this package differs from the definition used in the literature. In the literature, cell refers to an object with a single scalar output. The definition in this package 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 tuple of integers, then it results in a tuple of len(state_size) state matrices, each with a column size corresponding to values in state_size.

This module provides a number of basic commonly used RNN cells, such as LSTM (Long Short Term Memory) or GRU (Gated Recurrent Unit), and a number of operators that allow add dropouts, projections, or embeddings for inputs. Constructing multi-layer cells is supported by the class MultiRNNCell, or by calling the rnn ops several times. Every RNNCell must have the properties below and and implement __call__ with the following signature.


tf.nn.rnn_cell.RNNCell.__call__(inputs, state, scope=None) {:#RNNCell.call}

Run this RNN cell on inputs, starting from the given state.

Args:
  • inputs: 2-D tensor with shape [batch_size x input_size].
  • state: if self.state_size is an integer, this should be a 2-D Tensor with shape [batch_size x self.state_size]. Otherwise, if self.state_size is a tuple of integers, this should be a tuple with shapes [batch_size x s] for s in self.state_size.
  • scope: VariableScope for the created subgraph; defaults to class name.
Returns:

A pair containing: - Output: A 2-D tensor with shape [batch_size x self.output_size]. - New state: Either a single 2-D tensor, or a tuple of tensors matching the arity and shapes of state.


tf.nn.rnn_cell.RNNCell.output_size

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


tf.nn.rnn_cell.RNNCell.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.


tf.nn.rnn_cell.RNNCell.zero_state(batch_size, dtype)

Return zero-filled state tensor(s).

Args:
  • batch_size: int, float, or unit Tensor representing the batch size.
  • dtype: the data type to use for the state.
Returns:

If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros.

If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [batch_size x s] for each s in state_size.