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