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Gated Recurrent Unit cell.
Inherits From: RNNCell
, Layer
, Layer
, Module
tf.compat.v1.nn.rnn_cell.GRUCell(
num_units,
activation=None,
reuse=None,
kernel_initializer=None,
bias_initializer=None,
name=None,
dtype=None,
**kwargs
)
Note that this cell is not optimized for performance. Please use
tf.contrib.cudnn_rnn.CudnnGRU
for better performance on GPU, or
tf.contrib.rnn.GRUBlockCellV2
for better performance on CPU.
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 and projection matrices. |
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 .
|
**kwargs
|
Dict, keyword named properties for common layer attributes, like
trainable etc when constructing the cell from configs of get_config().
References: Learning Phrase Representations using RNN Encoder Decoder for Statistical Machine Translation: Cho et al., 2014 (pdf) |
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 |