tf.contrib.rnn.SRUCell

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Class SRUCell

SRU, Simple Recurrent Unit.

Inherits From: LayerRNNCell

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.

__init__

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__init__(
    num_units,
    activation=None,
    reuse=None,
    name=None,
    **kwargs
)

Properties

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

tf.contrib.rnn.SRUCell.get_initial_state

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

tf.contrib.rnn.SRUCell.zero_state

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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.