# Sneak peek into ‘sauron’ package – XAI for Convolutional Neural Networks.

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Explainable Artificial Intelligence, or **XAI** for short, is a set of tools that helps us understand and interpret complicated **“black box”** machine and deep learning models and their predictions. Today I would like to show you a sneak peek of my newest package called **sauron**, which allows you to explain decisions of Convolutional Neural Networks.

# What exactly does CNN see?

Let’s start with the basic. We’re gonna need a model, test images for which we want to generate explanations and image preprocessing function (if needed).

library(sauron) input_imgs_paths <- list.files(system.file("extdata", "images", package = "sauron"), full.names = TRUE) model <- application_xception() preprocessing_function <- xception_preprocess_input

There’s a ton of different methods to explain CNNs, but for now with `sauron`

you have access to 6 **gradient based** ones. You can check full list using `sauron_available_methods`

function:

sauron_available_methods # # A tibble: 6 x 2 # method name # <chr> <chr> # 1 V Vanilla gradient # 2 GI Gradient x Input # 3 SG SmoothGrad # 4 SGI SmoothGrad x Input # 5 IG Integrated Gradients # 6 GB Guided Backpropagation

Package is still in development so I won’t talk about the theory of those methods today. I will leave it for another post (or more probably multiple posts 🙂 ).

To generate any set of explanations simply use `generate_explanations`

function. Beside, images paths, model and optional preprocessing function you have to pass class indexes for which explanation should be made (`NULL`

means select class with highest probability for this image), some method specific arguments and if you want to generate grayscale o RGB explanation maps.

explanations <- generate_explanations( model, input_imgs_paths, preprocessing_function, class_index = NULL, methods = sauron_available_methods$method, num_samples = 5, # SmoothGrad samples noise_sd = 0.1, # SmoothGrad noise standard divination steps = 10, # Integrated Gradients steps grayscale = FALSE)

Now we can plot our results:

plot_explanations(explanations, FALSE) # $Input

# # $V

# # $GI

# # $SG

# # $SGI

# # $IG

# # $GB

# # $Input

# # $V

# # $GI

# # $SG

# # $SGI

# # $IG

# # $GB

# What next?

First of all, as I said in the beginning, `sauron`

is still in development and it should be available at the end of 2020. So if this topic is interesting for you be sure to visit my github from time to time.

Second of all I’m planning to expand `sauron`

capabilities. The first step will be to add methods like: **Grad-CAM**, **Guided Grad-CAM**, **Occlusion** and **Layer-wise Relevance Propagation (LRP)**.

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