R/layers-recurrent-cells.R

layer_gru_cell

Cell class for the GRU layer

Description

Cell class for the GRU layer

Usage

 
layer_gru_cell( 
  units, 
  activation = "tanh", 
  recurrent_activation = "sigmoid", 
  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, 
  reset_after = TRUE, 
  ... 
)

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.
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.
reset_after GRU convention (whether to apply reset gate after or before matrix multiplication). FALSE = “before”, TRUE = “after” (default and CuDNN compatible).
… 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.GRU processes the whole sequence. For example: ```

inputs <- k_random_uniform(c(32, 10, 8))

output <- inputs %>% layer_rnn(layer_gru_cell(4))

output$shape # TensorShape([32, 4])

rnn <- layer_rnn(cell = layer_gru_cell(4),

             return_sequence = TRUE, 

             return_state = TRUE)

c(whole_sequence_output, final_state) %<-% rnn(inputs)

whole_sequence_output$shape # TensorShape([32, 10, 4])

final_state$shape # TensorShape([32, 4])

```

See Also