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keras (version 2.8.0)

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

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

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