tf.contrib.rnn.SRUCell

SRU, Simple Recurrent Unit.

Inherits From: LayerRNNCell

tf.contrib.rnn.SRUCell(
    num_units, activation=None, reuse=None, name=None, **kwargs
)

Implementation based on Training RNNs as Fast as CNNs (cf. https://arxiv.org/abs/1709.02755).

This variation of RNN cell is characterized by the simplified data dependence between hidden states of two consecutive time steps. Traditionally, hidden states from a cell at time step t-1 needs to be multiplied with a matrix Whh before being fed into the ensuing cell at time step t. This flavor of RNN replaces the matrix multiplication between h{t-1} and W_hh with a pointwise multiplication, resulting in performance gain.

Args
num_units	int, The number of units in the SRU cell.
activation	Nonlinearity to use. Default: tanh.
reuse	(optional) Python boolean describing whether to reuse variables in an existing scope. If not True, and the existing scope already has the given variables, an error is raised.
name	(optional) String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.
**kwargs	Additional keyword arguments.

Attributes
graph	DEPRECATED FUNCTION
output_size	Integer or TensorShape: size of outputs produced by this cell.
scope_name
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

View source

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

zero_state

View source

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, 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, s] for each s in state_size.