Fully-connected RNN where the output is to be fed back to input.
Inherits From: RNN
, Layer
, Module
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,
**kwargs
)
See the Keras RNN API guide
for details about the usage of RNN API.
Args |
units
|
Positive integer, dimensionality of the output space.
|
activation
|
Activation function to use.
Default: hyperbolic tangent (tanh ).
If you pass None, no activation is applied
(ie. "linear" activation: a(x) = x ).
|
use_bias
|
Boolean, (default True ), whether the layer uses a bias vector.
|
kernel_initializer
|
Initializer for the kernel weights matrix,
used for the linear transformation of the inputs. Default:
glorot_uniform .
|
recurrent_initializer
|
Initializer for the recurrent_kernel
weights matrix, used for the linear transformation of the recurrent
state. Default: orthogonal .
|
bias_initializer
|
Initializer for the bias vector. Default: zeros .
|
kernel_regularizer
|
Regularizer function applied to the kernel weights
matrix. Default: None .
|
recurrent_regularizer
|
Regularizer function applied to the
recurrent_kernel weights matrix. Default: None .
|
bias_regularizer
|
Regularizer function applied to the bias vector.
Default: None .
|
activity_regularizer
|
Regularizer function applied to the output of the
layer (its "activation"). Default: None .
|
kernel_constraint
|
Constraint function applied to the kernel weights
matrix. Default: None .
|
recurrent_constraint
|
Constraint function applied to the
recurrent_kernel weights matrix. Default: None .
|
bias_constraint
|
Constraint function applied to the bias vector. Default:
None .
|
dropout
|
Float between 0 and 1.
Fraction of the units to drop for the linear transformation of the
inputs. Default: 0.
|
recurrent_dropout
|
Float between 0 and 1.
Fraction of the units to drop for the linear transformation of the
recurrent state. Default: 0.
|
return_sequences
|
Boolean. Whether to return the last output
in the output sequence, or the full sequence. Default: False .
|
return_state
|
Boolean. Whether to return the last state
in addition to the output. Default: False
|
go_backwards
|
Boolean (default False).
If True, process the input sequence backwards and return the
reversed sequence.
|
stateful
|
Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
|
unroll
|
Boolean (default False).
If True, the network will be unrolled,
else a symbolic loop will be used.
Unrolling can speed-up a RNN,
although it tends to be more memory-intensive.
Unrolling is only suitable for short sequences.
|
Call arguments |
inputs
|
A 3D tensor, with shape [batch, timesteps, feature] .
|
mask
|
Binary tensor of shape [batch, timesteps] indicating whether
a given timestep should be masked. An individual True entry indicates
that the corresponding timestep should be utilized, while a False
entry indicates that the corresponding timestep should be ignored.
|
training
|
Python boolean indicating whether the layer should behave in
training mode or in inference mode. This argument is passed to the cell
when calling it. This is only relevant if dropout or
recurrent_dropout is used.
|
initial_state
|
List of initial state tensors to be passed to the first
call of the cell.
|
Examples:
inputs = np.random.random([32, 10, 8]).astype(np.float32)
simple_rnn = tf.keras.layers.SimpleRNN(4)
output = simple_rnn(inputs) # The output has shape `[32, 4]`.
simple_rnn = tf.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)
Attributes |
activation
|
|
bias_constraint
|
|
bias_initializer
|
|
bias_regularizer
|
|
dropout
|
|
kernel_constraint
|
|
kernel_initializer
|
|
kernel_regularizer
|
|
recurrent_constraint
|
|
recurrent_dropout
|
|
recurrent_initializer
|
|
recurrent_regularizer
|
|
states
|
|
units
|
|
use_bias
|
|
Methods
reset_states
View source
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.
|