View source: R/layersrecurrentcells.R
layer_lstm_cell  R Documentation 
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,
...
)
units 
Positive integer, dimensionality of the output space. 
activation 
Activation function to use. Default: hyperbolic tangent
( 
recurrent_activation 
Activation function to use for the recurrent step.
Default: sigmoid ( 
use_bias 
Boolean, (default 
kernel_initializer 
Initializer for the 
recurrent_initializer 
Initializer for the 
bias_initializer 
Initializer for the bias vector. Default: 
unit_forget_bias 
Boolean (default 
kernel_regularizer 
Regularizer function applied to the 
recurrent_regularizer 
Regularizer function applied to
the 
bias_regularizer 
Regularizer function applied to the bias vector. Default:

kernel_constraint 
Constraint function applied to the 
recurrent_constraint 
Constraint function applied to the 
bias_constraint 
Constraint function applied to the bias vector. Default:

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. 
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()
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