Iterates over the time dimension of a tensor


Iterates over the time dimension of a tensor


  go_backwards = FALSE, 
  mask = NULL, 
  constants = NULL, 
  unroll = FALSE, 
  input_length = NULL 


Arguments Description
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.


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.


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, …).