Cell class for the GRU layer.
Inherits From: Layer, Operation
tf.keras.layers.GRUCell(
units,
activation='tanh',
recurrent_activation='sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.0,
recurrent_dropout=0.0,
reset_after=True,
seed=None,
**kwargs
)
This class processes one step within the whole time sequence input, whereas
keras.layer.GRU processes the whole sequence.
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).
|
recurrent_activation
|
Activation function to use for the recurrent step.
Default: sigmoid (sigmoid). If you pass None, no activation is
applied (ie. "linear" activation: a(x) = x).
|
use_bias
|
Boolean, (default True), whether the layer
should use 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.
|
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.
|
reset_after
|
GRU convention (whether to apply reset gate after or
before matrix multiplication). False = "before",
True = "after" (default and cuDNN compatible).
|
seed
|
Random seed for dropout.
|
Call arguments |
|---|
inputs
|
A 2D tensor, with shape (batch, features).
|
states
|
A 2D tensor with shape (batch, units), which is the state
from the previous time step.
|
training
|
Python boolean indicating whether the layer should behave in
training mode or in inference mode. Only relevant when dropout or
recurrent_dropout is used.
|
Example:
inputs = np.random.random((32, 10, 8))
rnn = keras.layers.RNN(keras.layers.GRUCell(4))
output = rnn(inputs)
output.shape
(32, 4)
rnn = keras.layers.RNN(
keras.layers.GRUCell(4),
return_sequences=True,
return_state=True)
whole_sequence_output, final_state = rnn(inputs)
whole_sequence_output.shape
(32, 10, 4)
final_state.shape
(32, 4)
Attributes |
|---|
input
|
Retrieves the input tensor(s) of a symbolic operation.
Only returns the tensor(s) corresponding to the first time
the operation was called.
|
output
|
Retrieves the output tensor(s) of a layer.
Only returns the tensor(s) corresponding to the first time
the operation was called.
|
Methods
from_config
View source
@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_dropout_mask
View source
get_dropout_mask(
step_input
)
get_initial_state
View source
get_initial_state(
batch_size=None
)
get_recurrent_dropout_mask
View source
get_recurrent_dropout_mask(
step_input
)
reset_dropout_mask
View source
reset_dropout_mask()
Reset the cached dropout mask if any.
The RNN layer invokes this in the call() method
so that the cached mask is cleared after calling cell.call(). The
mask should be cached across all timestep within the same batch, but
shouldn't be cached between batches.
reset_recurrent_dropout_mask
View source
reset_recurrent_dropout_mask()
symbolic_call
View source
symbolic_call(
*args, **kwargs
)