Cell class for SimpleRNN
layer_simple_rnn_cell(
units,
activation = "tanh",
use_bias = TRUE,
kernel_initializer = "glorot_uniform",
recurrent_initializer = "orthogonal",
bias_initializer = "zeros",
kernel_regularizer = NULL,
recurrent_regularizer = NULL,
bias_regularizer = NULL,
kernel_constraint = NULL,
recurrent_constraint = NULL,
bias_constraint = NULL,
dropout = 0,
recurrent_dropout = 0,
...
)
Positive integer, dimensionality of the output space.
Activation function to use.
Default: hyperbolic tangent (tanh
).
If you pass NULL
, no activation is applied
(ie. "linear" activation: a(x) = x
).
Boolean, (default TRUE
), whether the layer uses a bias vector.
Initializer for the kernel
weights matrix,
used for the linear transformation of the inputs. Default:
glorot_uniform
.
Initializer for the recurrent_kernel
weights matrix, used for the linear transformation of the recurrent state.
Default: orthogonal
.
Initializer for the bias vector. Default: zeros
.
Regularizer function applied to the kernel
weights
matrix. Default: NULL
.
Regularizer function applied to the
recurrent_kernel
weights matrix. Default: NULL
.
Regularizer function applied to the bias vector. Default:
NULL
.
Constraint function applied to the kernel
weights
matrix. Default: NULL
.
Constraint function applied to the recurrent_kernel
weights matrix. Default: NULL
.
Constraint function applied to the bias vector. Default:
NULL
.
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0.
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0.
standard layer arguments.
See the Keras RNN API guide for details about the usage of RNN API.
This class processes one step within the whole time sequence input, whereas
tf.keras.layer.SimpleRNN
processes the whole sequence.
Other RNN cell layers:
layer_gru_cell()
,
layer_lstm_cell()
,
layer_stacked_rnn_cells()