Cell class for the LSTM layer

layer_lstm_cell(
units,
activation = "tanh",
recurrent_activation = "sigmoid",
use_bias = TRUE,
kernel_initializer = "glorot_uniform",
recurrent_initializer = "orthogonal",
bias_initializer = "zeros",
unit_forget_bias = TRUE,
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 function to use. Default: hyperbolic tangent (tanh). If you pass NULL, no activation is applied (ie. "linear" activation: a(x) = x). Activation function to use for the recurrent step. Default: sigmoid (sigmoid). 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. Boolean (default TRUE). If TRUE, add 1 to the bias of the forget gate at initialization. Setting it to true will also force bias_initializer="zeros". This is recommended in Jozefowicz et al. 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.

## 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\$LSTM processes the whole sequence.

For example:

inputs <- k_random_normal(c(32, 10, 8))
rnn <- layer_rnn(cell = layer_lstm_cell(units = 4))
output <- rnn(inputs)
dim(output) # (32, 4)

rnn <- layer_rnn(cell = layer_lstm_cell(units = 4),
return_sequences = TRUE,
return_state = TRUE)
c(whole_seq_output, final_memory_state, final_carry_state) %<-% rnn(inputs)

dim(whole_seq_output)   # (32, 10, 4)
dim(final_memory_state) # (32, 4)
dim(final_carry_state)  # (32, 4)


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