Iterates over the time dimension of a tensor
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 |
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.
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