Cell class for the GRU layer

```
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,
...
)
```

- 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.

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])

Other RNN cell layers:
`layer_lstm_cell()`

,
`layer_simple_rnn_cell()`

,
`layer_stacked_rnn_cells()`