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
)RNN step function.
Tensor with shape (samples, ...) (no time dimension), representing input for the batch of samples at a certain time step.
Tensor with shape (samples, output_dim) (no time dimension), containing the initial values for the states used in the step function.
Logical If TRUE, do the iteration over the time
dimension in reverse order and return the reversed sequence.
Binary tensor with shape (samples, time, 1), with a zero for every element that is masked.
A list of constant values passed at each step.
Whether to unroll the RNN or to use a symbolic loop (while_loop or scan depending on backend).
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.