{ }
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Fully-connected RNN where the output is to be fed back as the new input.
Inherits From: RNN
, Layer
, Operation
tf.keras.layers.SimpleRNN(
units,
activation='tanh',
use_bias=True,
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,
dropout=0.0,
recurrent_dropout=0.0,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
seed=None,
**kwargs
)
Example:
inputs = np.random.random((32, 10, 8))
simple_rnn = keras.layers.SimpleRNN(4)
output = simple_rnn(inputs) # The output has shape `(32, 4)`.
simple_rnn = keras.layers.SimpleRNN(
4, return_sequences=True, return_state=True
)
# whole_sequence_output has shape `(32, 10, 4)`.
# final_state has shape `(32, 4)`.
whole_sequence_output, final_state = simple_rnn(inputs)
Methods
from_config
@classmethod
from_config( config )
Creates a layer from its config.
This method is the reverse of get_config
,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights
).
Args | |
---|---|
config
|
A Python dictionary, typically the output of get_config. |
Returns | |
---|---|
A layer instance. |
get_initial_state
get_initial_state(
batch_size
)
inner_loop
inner_loop(
sequences, initial_state, mask, training=False
)
reset_state
reset_state()
reset_states
reset_states()
symbolic_call
symbolic_call(
*args, **kwargs
)