k_rnn: Iterates over the time dimension of a tensor

In dfalbel/keras: R Interface to 'Keras'

Description

Iterates over the time dimension of a tensor

Usage

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.

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.