layer_gru_cell: Cell class for the GRU layer
In keras: R Interface to 'Keras'

layer_gru_cell

R Documentation

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])