layer_simple_rnn_cell: Cell class for SimpleRNN

In keras: R Interface to 'Keras'

Cell class for SimpleRNN

Description

Cell class for SimpleRNN

Usage

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,
  ...
)

Arguments

units	Positive integer, dimensionality of the output space.
activation	Activation function to use. Default: hyperbolic tangent (tanh). If you pass NULL, 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: NULL.
recurrent_regularizer	Regularizer function applied to the recurrent_kernel weights matrix. Default: NULL.
bias_regularizer	Regularizer function applied to the bias vector. Default: NULL.
kernel_constraint	Constraint function applied to the kernel weights matrix. Default: NULL.
recurrent_constraint	Constraint function applied to the recurrent_kernel weights matrix. Default: NULL.
bias_constraint	Constraint function applied to the bias vector. Default: NULL.
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.
...	standard layer arguments.

Details

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.

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/layers/SimpleRNNCell

• https://keras.io/api/layers

Other RNN cell layers: layer_gru_cell(), layer_lstm_cell(), layer_stacked_rnn_cells()

keras documentation built on Aug. 16, 2023, 1:07 a.m.