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)

## Arguments

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. |

## Value

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, ...).

## Keras Backend

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.