View source on GitHub |
Fast GRU implementation backed by cuDNN.
Inherits From: RNN
, Layer
, Module
tf.compat.v1.keras.layers.CuDNNGRU(
units,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
**kwargs
)
More information about cuDNN can be found on the NVIDIA developer website. Can only be run on GPU.
Attributes | |
---|---|
cell
|
|
states
|
Methods
get_losses_for
get_losses_for(
inputs=None
)
reset_states
reset_states(
states=None
)
Reset the recorded states for the stateful RNN layer.
Can only be used when RNN layer is constructed with stateful
= True
.
Args:
states: Numpy arrays that contains the value for the initial state,
which will be feed to cell at the first time step. When the value is
None, zero filled numpy array will be created based on the cell
state size.
Raises | |
---|---|
AttributeError
|
When the RNN layer is not stateful. |
ValueError
|
When the batch size of the RNN layer is unknown. |
ValueError
|
When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise. |