--- a
+++ b/man/integrated_gradients.Rd
@@ -0,0 +1,55 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/visualization.R
+\name{integrated_gradients}
+\alias{integrated_gradients}
+\title{Compute integrated gradients}
+\usage{
+integrated_gradients(
+  m_steps = 50,
+  baseline_type = "zero",
+  input_seq,
+  target_class_idx,
+  model,
+  pred_stepwise = FALSE,
+  num_baseline_repeats = 1
+)
+}
+\arguments{
+\item{m_steps}{Number of steps between baseline and original input.}
+
+\item{baseline_type}{Baseline sequence, either \code{"zero"} for all zeros or \code{"shuffle"} for random permutation of \code{input_seq}.}
+
+\item{input_seq}{Input tensor.}
+
+\item{target_class_idx}{Index of class to compute gradient for}
+
+\item{model}{Model to compute gradient for.}
+
+\item{pred_stepwise}{Whether to do predictions with batch size 1 rather than all at once. Can be used if
+input is too big to handle at once. Only supported for single input layer.}
+
+\item{num_baseline_repeats}{Number of different baseline estimations if baseline_type is \code{"shuffle"} (estimate integrated
+gradient repeatedly for different shuffles). Final result is average of \code{num_baseline} single calculations.}
+}
+\value{
+A tensorflow tensor.
+}
+\description{
+Computes integrated gradients scores for model and an input sequence.
+This can be used to visualize what part of the input is import for the models decision.
+Code is R implementation of python code from \href{https://www.tensorflow.org/tutorials/interpretability/integrated_gradients}{here}.
+Tensorflow implementation is based on this \href{https://arxiv.org/abs/1703.01365}{paper}.
+}
+\examples{
+\dontshow{if (reticulate::py_module_available("tensorflow")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+library(reticulate)
+model <- create_model_lstm_cnn(layer_lstm = 8, layer_dense = 3, maxlen = 20, verbose = FALSE)
+random_seq <- sample(0:3, 20, replace = TRUE)
+input_seq <- array(keras::to_categorical(random_seq), dim = c(1, 20, 4))
+integrated_gradients(
+  input_seq = input_seq,
+  target_class_idx = 3,
+  model = model)
+  
+\dontshow{\}) # examplesIf}
+}