layer_lstm_cell
Cell class for the LSTM layer
Description
Cell class for the LSTM layer
Usage
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
Arguments | Description |
---|---|
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 ). |
recurrent_activation | 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 ). |
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 . |
unit_forget_bias | 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. |
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$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)
```
See Also
https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTMCell
Other RNN cell layers:
layer_gru_cell()
,layer_simple_rnn_cell()
,layer_stacked_rnn_cells()