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Independently Gated Recurrent Unit cell.
Inherits From: LayerRNNCell
tf.contrib.rnn.IndyGRUCell(
num_units, activation=None, reuse=None, kernel_initializer=None,
bias_initializer=None, name=None, dtype=None
)
Based on IndRNNs (https://arxiv.org/abs/1803.04831) and similar to GRUCell, yet with the \(U_r\), \(U_z\), and \(U\) matrices in equations 5, 6, and 8 of http://arxiv.org/abs/1406.1078 respectively replaced by diagonal matrices, i.e. a Hadamard product with a single vector:
where \(\circ\) denotes the Hadamard operator. This means that each IndyGRU node sees only its own state, as opposed to seeing all states in the same layer.
Args | |
---|---|
num_units
|
int, The number of units in the GRU 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.
|
kernel_initializer
|
(optional) The initializer to use for the weight matrices applied to the input. |
bias_initializer
|
(optional) The initializer to use for the bias. |
name
|
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. |
dtype
|
Default dtype of the layer (default of None means use the type
of the first input). Required when build is called before call .
|
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 |