There are two variants. The default one is based on 1406.1078v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original 1406.1078v1 and has the order reversed.

```
layer_gru(
object,
units,
activation = "tanh",
recurrent_activation = "sigmoid",
use_bias = TRUE,
return_sequences = FALSE,
return_state = FALSE,
go_backwards = FALSE,
stateful = FALSE,
unroll = FALSE,
time_major = FALSE,
reset_after = TRUE,
kernel_initializer = "glorot_uniform",
recurrent_initializer = "orthogonal",
bias_initializer = "zeros",
kernel_regularizer = NULL,
recurrent_regularizer = NULL,
bias_regularizer = NULL,
activity_regularizer = NULL,
kernel_constraint = NULL,
recurrent_constraint = NULL,
bias_constraint = NULL,
dropout = 0,
recurrent_dropout = 0,
...
)
```

- object
What to compose the new

`Layer`

instance with. Typically a Sequential model or a Tensor (e.g., as returned by`layer_input()`

). The return value depends on`object`

. If`object`

is:missing or

`NULL`

, the`Layer`

instance is returned.a

`Sequential`

model, the model with an additional layer is returned.a Tensor, the output tensor from

`layer_instance(object)`

is returned.

- 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.

- use_bias
Boolean, whether the layer uses a bias vector.

- return_sequences
Boolean. Whether to return the last output in the output sequence, or the full sequence.

- return_state
Boolean (default FALSE). Whether to return the last state in addition to the output.

- go_backwards
Boolean (default FALSE). If TRUE, process the input sequence backwards and return the reversed sequence.

- stateful
Boolean (default FALSE). If TRUE, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.

- unroll
Boolean (default FALSE). If TRUE, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.

- time_major
If True, the inputs and outputs will be in shape

`[timesteps, batch, feature]`

, whereas in the False case, it will be`[batch, timesteps, feature]`

. Using`time_major = TRUE`

is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form.- reset_after
GRU convention (whether to apply reset gate after or before matrix multiplication). FALSE = "before" (default), TRUE = "after" (CuDNN compatible).

- kernel_initializer
Initializer for the

`kernel`

weights matrix, used for the linear transformation of the inputs.- recurrent_initializer
Initializer for the

`recurrent_kernel`

weights matrix, used for the linear transformation of the recurrent state.- bias_initializer
Initializer for the bias vector.

- kernel_regularizer
Regularizer function applied to the

`kernel`

weights matrix.- recurrent_regularizer
Regularizer function applied to the

`recurrent_kernel`

weights matrix.- bias_regularizer
Regularizer function applied to the bias vector.

- activity_regularizer
Regularizer function applied to the output of the layer (its "activation")..

- kernel_constraint
Constraint function applied to the

`kernel`

weights matrix.- recurrent_constraint
Constraint function applied to the

`recurrent_kernel`

weights matrix.- bias_constraint
Constraint function applied to the bias vector.

- dropout
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.

- recurrent_dropout
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.

- ...
Standard Layer args.

The second variant is compatible with CuDNNGRU (GPU-only) and allows
inference on CPU. Thus it has separate biases for `kernel`

and
`recurrent_kernel`

. Use `reset_after = TRUE`

and
`recurrent_activation = "sigmoid"`

.

N-D tensor with shape `(batch_size, timesteps, ...)`

,
or `(timesteps, batch_size, ...)`

when `time_major = TRUE`

.

if

`return_state`

: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each with shape`(batch_size, state_size)`

, where`state_size`

could be a high dimension tensor shape.if

`return_sequences`

: N-D tensor with shape`[batch_size, timesteps, output_size]`

, where`output_size`

could be a high dimension tensor shape, or`[timesteps, batch_size, output_size]`

when`time_major`

is`TRUE`

else, N-D tensor with shape

`[batch_size, output_size]`

, where`output_size`

could be a high dimension tensor shape.

This layer supports masking for input data with a variable number of
timesteps. To introduce masks to your data, use
`layer_embedding()`

with the `mask_zero`

parameter set to `TRUE`

.

You can set RNN layers to be 'stateful', which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches.

For intuition behind statefulness, there is a helpful blog post here: https://philipperemy.github.io/keras-stateful-lstm/

To enable statefulness:

Specify

`stateful = TRUE`

in the layer constructor.Specify a fixed batch size for your model. For sequential models, pass

`batch_input_shape = list(...)`

to the first layer in your model. For functional models with 1 or more Input layers, pass`batch_shape = list(...)`

to all the first layers in your model. This is the expected shape of your inputs*including the batch size*. It should be a list of integers, e.g.`list(32, 10, 100)`

. For dimensions which can vary (are not known ahead of time), use`NULL`

in place of an integer, e.g.`list(32, NULL, NULL)`

.Specify

`shuffle = FALSE`

when calling`fit()`

.

To reset the states of your model, call `layer$reset_states()`

on either
a specific layer, or on your entire model.

You can specify the initial state of RNN layers symbolically by calling them
with the keyword argument `initial_state.`

The value of initial_state should
be a tensor or list of tensors representing the initial state of the RNN
layer.

You can specify the initial state of RNN layers numerically by calling
`reset_states`

with the named argument `states.`

The value of `states`

should
be an array or list of arrays representing the initial state of the RNN
layer.

You can pass "external" constants to the cell using the `constants`

named
argument of `RNN$__call__`

(as well as `RNN$call`

) method. This requires that the
`cell$call`

method accepts the same keyword argument `constants`

. Such constants
can be used to condition the cell transformation on additional static inputs
(not changing over time), a.k.a. an attention mechanism.

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

Other recurrent layers:
`layer_cudnn_gru()`

,
`layer_cudnn_lstm()`

,
`layer_lstm()`

,
`layer_rnn()`

,
`layer_simple_rnn()`