Iterates over the time dimension of a tensor

```
k_rnn(
step_function,
inputs,
initial_states,
go_backwards = FALSE,
mask = NULL,
constants = NULL,
unroll = FALSE,
input_length = NULL
)
```

- step_function
RNN step function.

- inputs
Tensor with shape (samples, ...) (no time dimension), representing input for the batch of samples at a certain time step.

- initial_states
Tensor with shape (samples, output_dim) (no time dimension), containing the initial values for the states used in the step function.

- go_backwards
Logical If

`TRUE`

, do the iteration over the time dimension in reverse order and return the reversed sequence.- mask
Binary tensor with shape (samples, time, 1), with a zero for every element that is masked.

- constants
A list of constant values passed at each step.

- unroll
Whether to unroll the RNN or to use a symbolic loop (while_loop or scan depending on backend).

- input_length
Not relevant in the TensorFlow implementation. Must be specified if using unrolling with Theano.

A list with:

`last_output`

: the latest output of the rnn, of shape (samples, ...)`outputs`

: tensor with shape (samples, time, ...) where each entry`outputs[s, t]`

is the output of the step function at time t for sample s.`new_states`

: list of tensors, latest states returned by the step function, of shape (samples, ...).

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://keras.rstudio.com/articles/backend.html#backend-functions.