diff --git a/stable b/stable index 4ea5caf..349ca0e 120000 --- a/stable +++ b/stable @@ -1 +1 @@ -v0.8.0 \ No newline at end of file +v0.8.1 \ No newline at end of file diff --git a/v0.8 b/v0.8 index 4ea5caf..349ca0e 120000 --- a/v0.8 +++ b/v0.8 @@ -1 +1 @@ -v0.8.0 \ No newline at end of file +v0.8.1 \ No newline at end of file diff --git a/v0.8.1/.documenter-siteinfo.json b/v0.8.1/.documenter-siteinfo.json new file mode 100644 index 0000000..256bc80 --- /dev/null +++ b/v0.8.1/.documenter-siteinfo.json @@ -0,0 +1 @@ +{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-06-24T17:05:55","documenter_version":"1.4.1"}} \ No newline at end of file diff --git a/v0.8.1/api/index.html b/v0.8.1/api/index.html new file mode 100644 index 0000000..77e393e --- /dev/null +++ b/v0.8.1/api/index.html @@ -0,0 +1,6 @@ + +API Reference · ExplainableAI.jl
GitHub

Basic API

All methods in ExplainableAI.jl work by calling analyze on an input and an analyzer:

XAIBase.analyze — Function
analyze(input, method)
+analyze(input, method, output_selection)

Apply the analyzer method for the given input, returning an Explanation. If output_selection is specified, the explanation will be calculated for that output. Otherwise, the output with the highest activation is automatically chosen.

See also Explanation.

Keyword arguments

  • add_batch_dim: add batch dimension to the input without allocating. Default is false.
source
XAIBase.Explanation — Type
Explanation(val, output, output_selection, analyzer, heatmap, extras)

Return type of analyzers when calling analyze.

Fields

  • val: numerical output of the analyzer, e.g. an attribution or gradient
  • output: model output for the given analyzer input
  • output_selection: index of the output used for the explanation
  • analyzer: symbol corresponding the used analyzer, e.g. :Gradient or :LRP
  • heatmap: symbol indicating a preset heatmapping style, e.g. :attribution, :sensitivity or :cam
  • extras: optional named tuple that can be used by analyzers to return additional information.
source

For heatmapping functionality, take a look at either VisionHeatmaps.jl or TextHeatmaps.jl. Both provide heatmap methods for visualizing explanations, either for images or text, respectively.

Analyzers

ExplainableAI.Gradient — Type
Gradient(model)

Analyze model by calculating the gradient of a neuron activation with respect to the input.

source
ExplainableAI.InputTimesGradient — Type
InputTimesGradient(model)

Analyze model by calculating the gradient of a neuron activation with respect to the input. This gradient is then multiplied element-wise with the input.

source
ExplainableAI.SmoothGrad — Function
SmoothGrad(analyzer, [n=50, std=0.1, rng=GLOBAL_RNG])
+SmoothGrad(analyzer, [n=50, distribution=Normal(0, σ²=0.01), rng=GLOBAL_RNG])

Analyze model by calculating a smoothed sensitivity map. This is done by averaging sensitivity maps of a Gradient analyzer over random samples in a neighborhood of the input, typically by adding Gaussian noise with mean 0.

References

  • Smilkov et al., SmoothGrad: removing noise by adding noise
source
ExplainableAI.IntegratedGradients — Function
IntegratedGradients(analyzer, [n=50])
+IntegratedGradients(analyzer, [n=50])

Analyze model by using the Integrated Gradients method.

References

  • Sundararajan et al., Axiomatic Attribution for Deep Networks
source
ExplainableAI.GradCAM — Type
GradCAM(feature_layers, adaptation_layers)

Calculates the Gradient-weighted Class Activation Map (GradCAM). GradCAM provides a visual explanation of the regions with significant neuron importance for the model's classification decision.

Parameters

  • feature_layers: The layers of a convolutional neural network (CNN) responsible for extracting feature maps.
  • adaptation_layers: The layers of the CNN used for adaptation and classification.

Note

Flux is not required for GradCAM. GradCAM is compatible with a wide variety of CNN model-families.

References

  • Selvaraju et al., Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
source

Input augmentations

SmoothGrad and IntegratedGradients are special cases of the input augmentations NoiseAugmentation and InterpolationAugmentation, which can be applied as a wrapper to any analyzer:

ExplainableAI.NoiseAugmentation — Type
NoiseAugmentation(analyzer, n, [std=1, rng=GLOBAL_RNG])
+NoiseAugmentation(analyzer, n, distribution, [rng=GLOBAL_RNG])

A wrapper around analyzers that augments the input with n samples of additive noise sampled from distribution. This input augmentation is then averaged to return an Explanation.

source
ExplainableAI.InterpolationAugmentation — Type
InterpolationAugmentation(model, [n=50])

A wrapper around analyzers that augments the input with n steps of linear interpolation between the input and a reference input (typically zero(input)). The gradients w.r.t. this augmented input are then averaged and multiplied with the difference between the input and the reference input.

source

Index

diff --git a/v0.8.1/assets/documenter.js b/v0.8.1/assets/documenter.js new file mode 100644 index 0000000..c6562b5 --- /dev/null +++ b/v0.8.1/assets/documenter.js @@ -0,0 +1,1050 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min', + 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min', + 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/contrib/auto-render.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min', + 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', + 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min', + }, + shim: { + "highlight-julia": { + "deps": [ + "highlight" + ] + }, + "katex-auto-render": { + "deps": [ + "katex" + ] + }, + "headroom-jquery": { + "deps": [ + "jquery", + "headroom" + ] + }, + "highlight-julia-repl": { + "deps": [ + "highlight" + ] + } +} +}); +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'katex', 'katex-auto-render'], function($, katex, renderMathInElement) { +$(document).ready(function() { + renderMathInElement( + document.body, + { + "delimiters": [ + { + "left": "$", + "right": "$", + "display": false + }, + { + "left": "$$", + "right": "$$", + "display": true + }, + { + "left": "\\[", + "right": "\\]", + "display": true + } + ] +} + + ); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) { +$(document).ready(function() { + hljs.highlightAll(); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let timer = 0; +var isExpanded = true; + +$(document).on("click", ".docstring header", function () { + let articleToggleTitle = "Expand docstring"; + + debounce(() => { + if ($(this).siblings("section").is(":visible")) { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + } else { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + articleToggleTitle = "Collapse docstring"; + } + + $(this) + .find(".docstring-article-toggle-button") + .prop("title", articleToggleTitle); + $(this).siblings("section").slideToggle(); + }); +}); + +$(document).on("click", ".docs-article-toggle-button", function (event) { + let articleToggleTitle = "Expand docstring"; + let navArticleToggleTitle = "Expand all docstrings"; + let animationSpeed = event.noToggleAnimation ? 0 : 400; + + debounce(() => { + if (isExpanded) { + $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + + isExpanded = false; + + $(".docstring section").slideUp(animationSpeed); + } else { + $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + isExpanded = true; + articleToggleTitle = "Collapse docstring"; + navArticleToggleTitle = "Collapse all docstrings"; + + $(".docstring section").slideDown(animationSpeed); + } + + $(this).prop("title", navArticleToggleTitle); + $(".docstring-article-toggle-button").prop("title", articleToggleTitle); + }); +}); + +function debounce(callback, timeout = 300) { + if (Date.now() - timer > timeout) { + callback(); + } + + clearTimeout(timer); + + timer = Date.now(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require([], function() { +function addCopyButtonCallbacks() { + for (const el of document.getElementsByTagName("pre")) { + const button = document.createElement("button"); + button.classList.add("copy-button", "fa-solid", "fa-copy"); + button.setAttribute("aria-label", "Copy this code block"); + button.setAttribute("title", "Copy"); + + el.appendChild(button); + + const success = function () { + button.classList.add("success", "fa-check"); + button.classList.remove("fa-copy"); + }; + + const failure = function () { + button.classList.add("error", "fa-xmark"); + button.classList.remove("fa-copy"); + }; + + button.addEventListener("click", function () { + copyToClipboard(el.innerText).then(success, failure); + + setTimeout(function () { + button.classList.add("fa-copy"); + button.classList.remove("success", "fa-check", "fa-xmark"); + }, 5000); + }); + } +} + +function copyToClipboard(text) { + // clipboard API is only available in secure contexts + if (window.navigator && window.navigator.clipboard) { + return window.navigator.clipboard.writeText(text); + } else { + return new Promise(function (resolve, reject) { + try { + const el = document.createElement("textarea"); + el.textContent = text; + el.style.position = "fixed"; + el.style.opacity = 0; + document.body.appendChild(el); + el.select(); + document.execCommand("copy"); + + resolve(); + } catch (err) { + reject(err); + } finally { + document.body.removeChild(el); + } + }); + } +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks); +} else { + addCopyButtonCallbacks(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { + +// Manages the top navigation bar (hides it when the user starts scrolling down on the +// mobile). +window.Headroom = Headroom; // work around buggy module loading? +$(document).ready(function () { + $("#documenter .docs-navbar").headroom({ + tolerance: { up: 10, down: 10 }, + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let meta = $("div[data-docstringscollapsed]").data(); + + if (meta?.docstringscollapsed) { + $("#documenter-article-toggle-button").trigger({ + type: "click", + noToggleAnimation: true, + }); + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +/* +To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc + +PSEUDOCODE: + +Searching happens automatically as the user types or adjusts the selected filters. +To preserve responsiveness, as much as possible of the slow parts of the search are done +in a web worker. Searching and result generation are done in the worker, and filtering and +DOM updates are done in the main thread. The filters are in the main thread as they should +be very quick to apply. This lets filters be changed without re-searching with minisearch +(which is possible even if filtering is on the worker thread) and also lets filters be +changed _while_ the worker is searching and without message passing (neither of which are +possible if filtering is on the worker thread) + +SEARCH WORKER: + +Import minisearch + +Build index + +On message from main thread + run search + find the first 200 unique results from each category, and compute their divs for display + note that this is necessary and sufficient information for the main thread to find the + first 200 unique results from any given filter set + post results to main thread + +MAIN: + +Launch worker + +Declare nonconstant globals (worker_is_running, last_search_text, unfiltered_results) + +On text update + if worker is not running, launch_search() + +launch_search + set worker_is_running to true, set last_search_text to the search text + post the search query to worker + +on message from worker + if last_search_text is not the same as the text in the search field, + the latest search result is not reflective of the latest search query, so update again + launch_search() + otherwise + set worker_is_running to false + + regardless, display the new search results to the user + save the unfiltered_results as a global + update_search() + +on filter click + adjust the filter selection + update_search() + +update_search + apply search filters by looping through the unfiltered_results and finding the first 200 + unique results that match the filters + + Update the DOM +*/ + +/////// SEARCH WORKER /////// + +function worker_function(documenterSearchIndex, documenterBaseURL, filters) { + importScripts( + "https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min.js" + ); + + let data = documenterSearchIndex.map((x, key) => { + x["id"] = key; // minisearch requires a unique for each object + return x; + }); + + // list below is the lunr 2.1.3 list minus the intersect with names(Base) + // (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) + // ideally we'd just filter the original list but it's not available as a variable + const stopWords = new Set([ + "a", + "able", + "about", + "across", + "after", + "almost", + "also", + "am", + "among", + "an", + "and", + "are", + "as", + "at", + "be", + "because", + "been", + "but", + "by", + "can", + "cannot", + "could", + "dear", + "did", + "does", + "either", + "ever", + "every", + "from", + "got", + "had", + "has", + "have", + "he", + "her", + "hers", + "him", + "his", + "how", + "however", + "i", + "if", + "into", + "it", + "its", + "just", + "least", + "like", + "likely", + "may", + "me", + "might", + "most", + "must", + "my", + "neither", + "no", + "nor", + "not", + "of", + "off", + "often", + "on", + "or", + "other", + "our", + "own", + "rather", + "said", + "say", + "says", + "she", + "should", + "since", + "so", + "some", + "than", + "that", + "the", + "their", + "them", + "then", + "there", + "these", + "they", + "this", + "tis", + "to", + "too", + "twas", + "us", + "wants", + "was", + "we", + "were", + "what", + "when", + "who", + "whom", + "why", + "will", + "would", + "yet", + "you", + "your", + ]); + + let index = new MiniSearch({ + fields: ["title", "text"], // fields to index for full-text search + storeFields: ["location", "title", "text", "category", "page"], // fields to return with results + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + + word = word.toLowerCase(); + } + + return word ?? null; + }, + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not + // find anything if searching for "add!", only for the entire qualification + tokenize: (string) => string.split(/[\s\-\.]+/), + // options which will be applied during the search + searchOptions: { + prefix: true, + boost: { title: 100 }, + fuzzy: 2, + }, + }); + + index.addAll(data); + + /** + * Used to map characters to HTML entities. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const htmlEscapes = { + "&": "&", + "<": "<", + ">": ">", + '"': """, + "'": "'", + }; + + /** + * Used to match HTML entities and HTML characters. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const reUnescapedHtml = /[&<>"']/g; + const reHasUnescapedHtml = RegExp(reUnescapedHtml.source); + + /** + * Escape function from lodash + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + function escape(string) { + return string && reHasUnescapedHtml.test(string) + ? string.replace(reUnescapedHtml, (chr) => htmlEscapes[chr]) + : string || ""; + } + + /** + * Make the result component given a minisearch result data object and the value + * of the search input as queryString. To view the result object structure, refer: + * https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult + * + * @param {object} result + * @param {string} querystring + * @returns string + */ + function make_search_result(result, querystring) { + let search_divider = `
`; + let display_link = + result.location.slice(Math.max(0), Math.min(50, result.location.length)) + + (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div + + if (result.page !== "") { + display_link += ` (${result.page})`; + } + + let textindex = new RegExp(`${querystring}`, "i").exec(result.text); + let text = + textindex !== null + ? result.text.slice( + Math.max(textindex.index - 100, 0), + Math.min( + textindex.index + querystring.length + 100, + result.text.length + ) + ) + : ""; // cut-off text before and after from the match + + text = text.length ? escape(text) : ""; + + let display_result = text.length + ? "..." + + text.replace( + new RegExp(`${escape(querystring)}`, "i"), // For first occurrence + '$&' + ) + + "..." + : ""; // highlights the match + + let in_code = false; + if (!["page", "section"].includes(result.category.toLowerCase())) { + in_code = true; + } + + // We encode the full url to escape some special characters which can lead to broken links + let result_div = ` + +
+
${escape(result.title)}
+
${result.category}
+
+

+ ${display_result} +

+
+ ${display_link} +
+ + ${search_divider} + `; + + return result_div; + } + + self.onmessage = function (e) { + let query = e.data; + let results = index.search(query, { + filter: (result) => { + // Only return relevant results + return result.score >= 1; + }, + }); + + // Pre-filter to deduplicate and limit to 200 per category to the extent + // possible without knowing what the filters are. + let filtered_results = []; + let counts = {}; + for (let filter of filters) { + counts[filter] = 0; + } + let present = {}; + + for (let result of results) { + cat = result.category; + cnt = counts[cat]; + if (cnt < 200) { + id = cat + "---" + result.location; + if (present[id]) { + continue; + } + present[id] = true; + filtered_results.push({ + location: result.location, + category: cat, + div: make_search_result(result, query), + }); + } + } + + postMessage(filtered_results); + }; +} + +// `worker = Threads.@spawn worker_function(documenterSearchIndex)`, but in JavaScript! +const filters = [ + ...new Set(documenterSearchIndex["docs"].map((x) => x.category)), +]; +const worker_str = + "(" + + worker_function.toString() + + ")(" + + JSON.stringify(documenterSearchIndex["docs"]) + + "," + + JSON.stringify(documenterBaseURL) + + "," + + JSON.stringify(filters) + + ")"; +const worker_blob = new Blob([worker_str], { type: "text/javascript" }); +const worker = new Worker(URL.createObjectURL(worker_blob)); + +/////// SEARCH MAIN /////// + +// Whether the worker is currently handling a search. This is a boolean +// as the worker only ever handles 1 or 0 searches at a time. +var worker_is_running = false; + +// The last search text that was sent to the worker. This is used to determine +// if the worker should be launched again when it reports back results. +var last_search_text = ""; + +// The results of the last search. This, in combination with the state of the filters +// in the DOM, is used compute the results to display on calls to update_search. +var unfiltered_results = []; + +// Which filter is currently selected +var selected_filter = ""; + +$(document).on("input", ".documenter-search-input", function (event) { + if (!worker_is_running) { + launch_search(); + } +}); + +function launch_search() { + worker_is_running = true; + last_search_text = $(".documenter-search-input").val(); + worker.postMessage(last_search_text); +} + +worker.onmessage = function (e) { + if (last_search_text !== $(".documenter-search-input").val()) { + launch_search(); + } else { + worker_is_running = false; + } + + unfiltered_results = e.data; + update_search(); +}; + +$(document).on("click", ".search-filter", function () { + if ($(this).hasClass("search-filter-selected")) { + selected_filter = ""; + } else { + selected_filter = $(this).text().toLowerCase(); + } + + // This updates search results and toggles classes for UI: + update_search(); +}); + +/** + * Make/Update the search component + */ +function update_search() { + let querystring = $(".documenter-search-input").val(); + + if (querystring.trim()) { + if (selected_filter == "") { + results = unfiltered_results; + } else { + results = unfiltered_results.filter((result) => { + return selected_filter == result.category.toLowerCase(); + }); + } + + let search_result_container = ``; + let modal_filters = make_modal_body_filters(); + let search_divider = `
`; + + if (results.length) { + let links = []; + let count = 0; + let search_results = ""; + + for (var i = 0, n = results.length; i < n && count < 200; ++i) { + let result = results[i]; + if (result.location && !links.includes(result.location)) { + search_results += result.div; + count++; + links.push(result.location); + } + } + + if (count == 1) { + count_str = "1 result"; + } else if (count == 200) { + count_str = "200+ results"; + } else { + count_str = count + " results"; + } + let result_count = `
${count_str}
`; + + search_result_container = ` +
+ ${modal_filters} + ${search_divider} + ${result_count} +
+ ${search_results} +
+
+ `; + } else { + search_result_container = ` +
+ ${modal_filters} + ${search_divider} +
0 result(s)
+
+
No result found!
+ `; + } + + if ($(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").removeClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(search_result_container); + } else { + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(` +
Type something to get started!
+ `); + } +} + +/** + * Make the modal filter html + * + * @returns string + */ +function make_modal_body_filters() { + let str = filters + .map((val) => { + if (selected_filter == val.toLowerCase()) { + return `${val}`; + } else { + return `${val}`; + } + }) + .join(""); + + return ` +
+ Filters: + ${str} +
`; +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Modal settings dialog +$(document).ready(function () { + var settings = $("#documenter-settings"); + $("#documenter-settings-button").click(function () { + settings.toggleClass("is-active"); + }); + // Close the dialog if X is clicked + $("#documenter-settings button.delete").click(function () { + settings.removeClass("is-active"); + }); + // Close dialog if ESC is pressed + $(document).keyup(function (e) { + if (e.keyCode == 27) settings.removeClass("is-active"); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let search_modal_header = ` +
+
+

+ + + + +

+
+
+ +
+
+ `; + + let initial_search_body = ` +
Type something to get started!
+ `; + + let search_modal_footer = ` + + `; + + $(document.body).append( + ` + + ` + ); + + document.querySelector(".docs-search-query").addEventListener("click", () => { + openModal(); + }); + + document + .querySelector(".close-search-modal") + .addEventListener("click", () => { + closeModal(); + }); + + $(document).on("click", ".search-result-link", function () { + closeModal(); + }); + + document.addEventListener("keydown", (event) => { + if ((event.ctrlKey || event.metaKey) && event.key === "/") { + openModal(); + } else if (event.key === "Escape") { + closeModal(); + } + + return false; + }); + + // Functions to open and close a modal + function openModal() { + let searchModal = document.querySelector("#search-modal"); + + searchModal.classList.add("is-active"); + document.querySelector(".documenter-search-input").focus(); + } + + function closeModal() { + let searchModal = document.querySelector("#search-modal"); + let initial_search_body = ` +
Type something to get started!
+ `; + + searchModal.classList.remove("is-active"); + document.querySelector(".documenter-search-input").blur(); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".documenter-search-input").val(""); + $(".search-modal-card-body").html(initial_search_body); + } + + document + .querySelector("#search-modal .modal-background") + .addEventListener("click", () => { + closeModal(); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Manages the showing and hiding of the sidebar. +$(document).ready(function () { + var sidebar = $("#documenter > .docs-sidebar"); + var sidebar_button = $("#documenter-sidebar-button"); + sidebar_button.click(function (ev) { + ev.preventDefault(); + sidebar.toggleClass("visible"); + if (sidebar.hasClass("visible")) { + // Makes sure that the current menu item is visible in the sidebar. + $("#documenter .docs-menu a.is-active").focus(); + } + }); + $("#documenter > .docs-main").bind("click", function (ev) { + if ($(ev.target).is(sidebar_button)) { + return; + } + if (sidebar.hasClass("visible")) { + sidebar.removeClass("visible"); + } + }); +}); + +// Resizes the package name / sitename in the sidebar if it is too wide. +// Inspired by: https://github.com/davatron5000/FitText.js +$(document).ready(function () { + e = $("#documenter .docs-autofit"); + function resize() { + var L = parseInt(e.css("max-width"), 10); + var L0 = e.width(); + if (L0 > L) { + var h0 = parseInt(e.css("font-size"), 10); + e.css("font-size", (L * h0) / L0); + // TODO: make sure it survives resizes? + } + } + // call once and then register events + resize(); + $(window).resize(resize); + $(window).on("orientationchange", resize); +}); + +// Scroll the navigation bar to the currently selected menu item +$(document).ready(function () { + var sidebar = $("#documenter .docs-menu").get(0); + var active = $("#documenter .docs-menu .is-active").get(0); + if (typeof active !== "undefined") { + sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Theme picker setup +$(document).ready(function () { + // onchange callback + $("#documenter-themepicker").change(function themepick_callback(ev) { + var themename = $("#documenter-themepicker option:selected").attr("value"); + if (themename === "auto") { + // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'); + window.localStorage.removeItem("documenter-theme"); + } else { + // set_theme(themename); + window.localStorage.setItem("documenter-theme", themename); + } + // We re-use the global function from themeswap.js to actually do the swapping. + set_theme_from_local_storage(); + }); + + // Make sure that the themepicker displays the correct theme when the theme is retrieved + // from localStorage + if (typeof window.localStorage !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if (theme !== null) { + $("#documenter-themepicker option").each(function (i, e) { + e.selected = e.value === theme; + }); + } + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// update the version selector with info from the siteinfo.js and ../versions.js files +$(document).ready(function () { + // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the + // siteinfo.js file, we just return immediately and not display the version selector. + if ( + typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" && + DOCUMENTER_VERSION_SELECTOR_DISABLED + ) { + return; + } + + var version_selector = $("#documenter .docs-version-selector"); + var version_selector_select = $("#documenter .docs-version-selector select"); + + version_selector_select.change(function (x) { + target_href = version_selector_select + .children("option:selected") + .get(0).value; + window.location.href = target_href; + }); + + // add the current version to the selector based on siteinfo.js, but only if the selector is empty + if ( + typeof DOCUMENTER_CURRENT_VERSION !== "undefined" && + $("#version-selector > option").length == 0 + ) { + var option = $( + "" + ); + version_selector_select.append(option); + } + + if (typeof DOC_VERSIONS !== "undefined") { + var existing_versions = version_selector_select.children("option"); + var existing_versions_texts = existing_versions.map(function (i, x) { + return x.text; + }); + DOC_VERSIONS.forEach(function (each) { + var version_url = documenterBaseURL + "/../" + each + "/"; + var existing_id = $.inArray(each, existing_versions_texts); + // if not already in the version selector, add it as a new option, + // otherwise update the old option with the URL and enable it + if (existing_id == -1) { + var option = $( + "" + ); + version_selector_select.append(option); + } else { + var option = existing_versions[existing_id]; + option.value = version_url; + option.disabled = false; + } + }); + } + + // only show the version selector if the selector has been populated + if (version_selector_select.children("option").length > 0) { + version_selector.toggleClass("visible"); + } +}); + +}) diff --git a/v0.8.1/assets/logo.png b/v0.8.1/assets/logo.png new file mode 100644 index 0000000..54bb485 Binary files /dev/null and b/v0.8.1/assets/logo.png differ diff --git a/v0.8.1/assets/themes/documenter-dark.css 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h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content 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.docs-logo>img.is-5by4{padding-top:80%}html.theme--documenter-dark .image.is-4by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3{padding-top:75%}html.theme--documenter-dark .image.is-3by2,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2{padding-top:66.6666%}html.theme--documenter-dark .image.is-5by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3{padding-top:60%}html.theme--documenter-dark .image.is-16by9,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9{padding-top:56.25%}html.theme--documenter-dark .image.is-2by1,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1{padding-top:50%}html.theme--documenter-dark .image.is-3by1,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1{padding-top:33.3333%}html.theme--documenter-dark .image.is-4by5,html.theme--documenter-dark #documenter .docs-sidebar 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.hljs-string{color:#38a}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold}.gap-4{gap:1rem} diff --git a/v0.8.1/assets/themeswap.js b/v0.8.1/assets/themeswap.js new file mode 100644 index 0000000..9f5eebe --- /dev/null +++ b/v0.8.1/assets/themeswap.js @@ -0,0 +1,84 @@ +// Small function to quickly swap out themes. Gets put into the tag.. +function set_theme_from_local_storage() { + // Initialize the theme to null, which means default + var theme = null; + // If the browser supports the localstorage and is not disabled then try to get the + // documenter theme + if (window.localStorage != null) { + // Get the user-picked theme from localStorage. May be `null`, which means the default + // theme. + theme = window.localStorage.getItem("documenter-theme"); + } + // Check if the users preference is for dark color scheme + var darkPreference = + window.matchMedia("(prefers-color-scheme: dark)").matches === true; + // Initialize a few variables for the loop: + // + // - active: will contain the index of the theme that should be active. Note that there + // is no guarantee that localStorage contains sane values. If `active` stays `null` + // we either could not find the theme or it is the default (primary) theme anyway. + // Either way, we then need to stick to the primary theme. + // + // - disabled: style sheets that should be disabled (i.e. all the theme style sheets + // that are not the currently active theme) + var active = null; + var disabled = []; + var primaryLightTheme = null; + var primaryDarkTheme = null; + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // To distinguish the default (primary) theme, it needs to have the data-theme-primary + // attribute set. + if (ss.ownerNode.getAttribute("data-theme-primary") !== null) { + primaryLightTheme = themename; + } + // Check if the theme is primary dark theme so that we could store its name in darkTheme + if (ss.ownerNode.getAttribute("data-theme-primary-dark") !== null) { + primaryDarkTheme = themename; + } + // If we find a matching theme (and it's not the default), we'll set active to non-null + if (themename === theme) active = i; + // Store the style sheets of inactive themes so that we could disable them + if (themename !== theme) disabled.push(ss); + } + var activeTheme = null; + if (active !== null) { + // If we did find an active theme, we'll (1) add the theme--$(theme) class to + document.getElementsByTagName("html")[0].className = "theme--" + theme; + activeTheme = theme; + } else { + // If we did _not_ find an active theme, then we need to fall back to the primary theme + // which can either be dark or light, depending on the user's OS preference. + var activeTheme = darkPreference ? primaryDarkTheme : primaryLightTheme; + // In case it somehow happens that the relevant primary theme was not found in the + // preceding loop, we abort without doing anything. + if (activeTheme === null) { + console.error("Unable to determine primary theme."); + return; + } + // When switching to the primary light theme, then we must not have a class name + // for the tag. That's only for non-primary or the primary dark theme. + if (darkPreference) { + document.getElementsByTagName("html")[0].className = + "theme--" + activeTheme; + } else { + document.getElementsByTagName("html")[0].className = ""; + } + } + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // we'll disable all the stylesheets, except for the active one + ss.disabled = !(themename == activeTheme); + } +} +set_theme_from_local_storage(); diff --git a/v0.8.1/assets/warner.js b/v0.8.1/assets/warner.js new file mode 100644 index 0000000..3f6f5d0 --- /dev/null +++ b/v0.8.1/assets/warner.js @@ -0,0 +1,52 @@ +function maybeAddWarning() { + // DOCUMENTER_NEWEST is defined in versions.js, DOCUMENTER_CURRENT_VERSION and DOCUMENTER_STABLE + // in siteinfo.js. + // If either of these are undefined something went horribly wrong, so we abort. + if ( + window.DOCUMENTER_NEWEST === undefined || + window.DOCUMENTER_CURRENT_VERSION === undefined || + window.DOCUMENTER_STABLE === undefined + ) { + return; + } + + // Current version is not a version number, so we can't tell if it's the newest version. Abort. + if (!/v(\d+\.)*\d+/.test(window.DOCUMENTER_CURRENT_VERSION)) { + return; + } + + // Current version is newest version, so no need to add a warning. + if (window.DOCUMENTER_NEWEST === window.DOCUMENTER_CURRENT_VERSION) { + return; + } + + // Add a noindex meta tag (unless one exists) so that search engines don't index this version of the docs. + if (document.body.querySelector('meta[name="robots"]') === null) { + const meta = document.createElement("meta"); + meta.name = "robots"; + meta.content = "noindex"; + + document.getElementsByTagName("head")[0].appendChild(meta); + } + + const div = document.createElement("div"); + div.classList.add("outdated-warning-overlay"); + const closer = document.createElement("button"); + closer.classList.add("outdated-warning-closer", "delete"); + closer.addEventListener("click", function () { + document.body.removeChild(div); + }); + const href = window.documenterBaseURL + "/../" + window.DOCUMENTER_STABLE; + div.innerHTML = + 'This documentation is not for the latest stable release, but for either the development version or an older release.
Click here to go to the documentation for the latest stable release.'; + div.appendChild(closer); + document.body.appendChild(div); +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", maybeAddWarning); +} else { + maybeAddWarning(); +} diff --git a/v0.8.1/generated/augmentations.ipynb b/v0.8.1/generated/augmentations.ipynb new file mode 100644 index 0000000..6253ee6 --- /dev/null +++ b/v0.8.1/generated/augmentations.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Analyzer augmentations\n", + "All analyzers implemented in ExplainableAI.jl can be augmented by two types of augmentations:\n", + "`NoiseAugmentation`s and `InterpolationAugmentation`s.\n", + "These augmentations are wrappers around analyzers\n", + "that modify the input before passing it to the analyzer.\n", + "\n", + "We build on the basics shown in the *Getting started* section\n", + "and start out by loading the same pre-trained LeNet5 model and MNIST input data:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Chain(\n Conv((5, 5), 1 => 6, relu), \u001b[90m# 156 parameters\u001b[39m\n MaxPool((2, 2)),\n Conv((5, 5), 6 => 16, relu), \u001b[90m# 2_416 parameters\u001b[39m\n MaxPool((2, 2)),\n Flux.flatten,\n Dense(256 => 120, relu), \u001b[90m# 30_840 parameters\u001b[39m\n Dense(120 => 84, relu), \u001b[90m# 10_164 parameters\u001b[39m\n Dense(84 => 10), \u001b[90m# 850 parameters\u001b[39m\n) \u001b[90m # Total: 10 arrays, \u001b[39m44_426 parameters, 174.867 KiB." + }, + "metadata": {}, + "execution_count": 1 + } + ], + "cell_type": "code", + "source": [ + "using ExplainableAI\n", + "using VisionHeatmaps\n", + "using Flux\n", + "\n", + "using BSON # hide\n", + "model = BSON.load(\"../model.bson\", @__MODULE__)[:model] # hide\n", + "model" + ], + "metadata": {}, + "execution_count": 1 + }, + { + "outputs": [ + { + "output_type": "execute_result", + 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"execution_count": 2 + } + ], + "cell_type": "code", + "source": [ + "using MLDatasets\n", + "using ImageCore, ImageIO, ImageShow\n", + "\n", + "index = 10\n", + "x, y = MNIST(Float32, :test)[10]\n", + "input = reshape(x, 28, 28, 1, :)\n", + "\n", + "convert2image(MNIST, x)" + ], + "metadata": {}, + "execution_count": 2 + }, + { + "cell_type": "markdown", + "source": [ + "## Noise augmentation\n", + "The `NoiseAugmentation` wrapper computes explanations averaged over noisy inputs.\n", + "Let's demonstrate this on the `Gradient` analyzer.\n", + "First, we compute the heatmap of an explanation without augmentation:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.461087,0.459224,0.457032) … RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.468989,0.467139,0.464901) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.479787,0.477956,0.475654) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.459915,0.45805,0.455864) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.445069,0.443181,0.441081) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.445034,0.443146,0.441047) … RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.457174,0.455305,0.453135) RGB{Float64}(0.463812,0.461953,0.459745)\n RGB{Float64}(0.466269,0.464414,0.462192) RGB{Float64}(0.463755,0.461896,0.459688)\n RGB{Float64}(0.470723,0.468876,0.466628) RGB{Float64}(0.452716,0.45084,0.448697)\n RGB{Float64}(0.485669,0.483847,0.481511) RGB{Float64}(0.450914,0.449035,0.446902)\n ⋮ ⋱ \n RGB{Float64}(0.46542,0.463564,0.461346) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.481108,0.479278,0.476969) … RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.456239,0.454368,0.452204) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.443836,0.441947,0.439855) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.435607,0.433706,0.431661) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.452624,0.450747,0.448604) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.451326,0.449448,0.447312) … RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.467146,0.465293,0.463065) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.464131,0.462273,0.460063) RGB{Float64}(0.464131,0.462273,0.460063)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ], + "cell_type": "code", + "source": [ + "analyzer = Gradient(model)\n", + "heatmap(input, analyzer)" + ], + "metadata": {}, + "execution_count": 3 + }, + { + "cell_type": "markdown", + "source": [ + "Now we wrap the analyzer in a `NoiseAugmentation` with 10 samples of noise.\n", + "By default, the noise is sampled from a Gaussian distribution\n", + "with mean 0 and standard deviation 1." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.448439,0.446556,0.444437) … RGB{Float64}(0.45011,0.44823,0.446102)\n RGB{Float64}(0.450416,0.448536,0.446406) RGB{Float64}(0.450035,0.448155,0.446027)\n RGB{Float64}(0.454668,0.452795,0.45064) RGB{Float64}(0.44808,0.446197,0.44408)\n RGB{Float64}(0.447673,0.44579,0.443675) RGB{Float64}(0.447804,0.445921,0.443805)\n RGB{Float64}(0.439141,0.437245,0.43518) RGB{Float64}(0.452162,0.450286,0.448145)\n RGB{Float64}(0.436617,0.434718,0.432667) … RGB{Float64}(0.453304,0.451428,0.449281)\n RGB{Float64}(0.442315,0.440424,0.43834) RGB{Float64}(0.454358,0.452484,0.450331)\n RGB{Float64}(0.446499,0.444614,0.442506) RGB{Float64}(0.446073,0.444187,0.442082)\n RGB{Float64}(0.445808,0.443922,0.441818) RGB{Float64}(0.440099,0.438205,0.436134)\n RGB{Float64}(0.45176,0.449882,0.447744) RGB{Float64}(0.440371,0.438476,0.436404)\n ⋮ ⋱ \n RGB{Float64}(0.447601,0.445717,0.443603) RGB{Float64}(0.454162,0.452288,0.450136)\n RGB{Float64}(0.453985,0.452111,0.44996) … RGB{Float64}(0.447594,0.44571,0.443596)\n RGB{Float64}(0.451135,0.449257,0.447122) RGB{Float64}(0.446818,0.444934,0.442824)\n RGB{Float64}(0.445032,0.443144,0.441045) RGB{Float64}(0.447403,0.445519,0.443405)\n RGB{Float64}(0.443146,0.441256,0.439167) RGB{Float64}(0.450957,0.449078,0.446944)\n RGB{Float64}(0.445533,0.443646,0.441543) RGB{Float64}(0.454958,0.453085,0.450929)\n RGB{Float64}(0.446702,0.444816,0.442707) … RGB{Float64}(0.452736,0.45086,0.448716)\n RGB{Float64}(0.448384,0.446501,0.444383) RGB{Float64}(0.449308,0.447427,0.445303)\n RGB{Float64}(0.450467,0.448587,0.446456) RGB{Float64}(0.449723,0.447842,0.445716)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "cell_type": "code", + "source": [ + "analyzer = NoiseAugmentation(Gradient(model), 50)\n", + "heatmap(input, analyzer)" + ], + "metadata": {}, + "execution_count": 4 + }, + { + "cell_type": "markdown", + "source": [ + "Note that a higher sample size is desired, as it will lead to a smoother heatmap.\n", + "However, this comes at the cost of a longer computation time.\n", + "\n", + "We can also set the standard deviation of the Gaussian distribution:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.449978,0.448098,0.44597) … RGB{Float64}(0.452173,0.450296,0.448156)\n RGB{Float64}(0.452705,0.450829,0.448685) RGB{Float64}(0.45192,0.450043,0.447904)\n RGB{Float64}(0.456123,0.454252,0.452088) RGB{Float64}(0.450424,0.448544,0.446414)\n RGB{Float64}(0.45265,0.450774,0.448631) RGB{Float64}(0.450698,0.448819,0.446686)\n RGB{Float64}(0.444293,0.442405,0.440309) RGB{Float64}(0.453718,0.451844,0.449694)\n RGB{Float64}(0.440023,0.438129,0.436058) … RGB{Float64}(0.454039,0.452165,0.450014)\n RGB{Float64}(0.441217,0.439324,0.437246) RGB{Float64}(0.45543,0.453558,0.451398)\n RGB{Float64}(0.446168,0.444282,0.442176) RGB{Float64}(0.447204,0.44532,0.443208)\n RGB{Float64}(0.446783,0.444898,0.442788) RGB{Float64}(0.444282,0.442394,0.440298)\n RGB{Float64}(0.449169,0.447288,0.445164) RGB{Float64}(0.443066,0.441175,0.439087)\n ⋮ ⋱ \n RGB{Float64}(0.450005,0.448125,0.445997) RGB{Float64}(0.454888,0.453015,0.450859)\n RGB{Float64}(0.457152,0.455283,0.453113) … RGB{Float64}(0.446942,0.445057,0.442947)\n RGB{Float64}(0.456044,0.454173,0.452009) RGB{Float64}(0.447434,0.44555,0.443437)\n RGB{Float64}(0.447761,0.445877,0.443762) RGB{Float64}(0.448295,0.446413,0.444294)\n RGB{Float64}(0.442367,0.440476,0.438392) RGB{Float64}(0.454562,0.452689,0.450534)\n RGB{Float64}(0.444085,0.442196,0.440102) RGB{Float64}(0.457341,0.455473,0.453302)\n RGB{Float64}(0.446014,0.444128,0.442023) … RGB{Float64}(0.454071,0.452197,0.450045)\n RGB{Float64}(0.450304,0.448425,0.446295) RGB{Float64}(0.450835,0.448956,0.446823)\n RGB{Float64}(0.452307,0.450431,0.448289) RGB{Float64}(0.45143,0.449552,0.447415)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "cell_type": "code", + "source": [ + "analyzer = NoiseAugmentation(Gradient(model), 50, 0.1)\n", + "heatmap(input, analyzer)" + ], + "metadata": {}, + "execution_count": 5 + }, + { + "cell_type": "markdown", + "source": [ + "When used with a `Gradient` analyzer, this is equivalent to `SmoothGrad`:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.450444,0.448564,0.446434) … RGB{Float64}(0.45208,0.450203,0.448063)\n RGB{Float64}(0.451525,0.449647,0.44751) RGB{Float64}(0.451511,0.449633,0.447496)\n RGB{Float64}(0.455755,0.453884,0.451722) RGB{Float64}(0.450817,0.448938,0.446805)\n RGB{Float64}(0.448593,0.446711,0.444591) RGB{Float64}(0.450455,0.448576,0.446445)\n RGB{Float64}(0.441146,0.439253,0.437176) RGB{Float64}(0.452042,0.450165,0.448025)\n RGB{Float64}(0.439896,0.438001,0.435931) … RGB{Float64}(0.453196,0.451321,0.449174)\n RGB{Float64}(0.44198,0.440088,0.438007) RGB{Float64}(0.456411,0.454541,0.452376)\n RGB{Float64}(0.444494,0.442606,0.44051) RGB{Float64}(0.447974,0.44609,0.443974)\n RGB{Float64}(0.444113,0.442224,0.44013) RGB{Float64}(0.441669,0.439777,0.437697)\n RGB{Float64}(0.452002,0.450125,0.447985) RGB{Float64}(0.443664,0.441775,0.439683)\n ⋮ ⋱ \n RGB{Float64}(0.447873,0.44599,0.443874) RGB{Float64}(0.451205,0.449327,0.447191)\n RGB{Float64}(0.456221,0.454351,0.452186) … RGB{Float64}(0.448932,0.44705,0.444928)\n RGB{Float64}(0.454016,0.452142,0.44999) RGB{Float64}(0.448083,0.4462,0.444083)\n RGB{Float64}(0.445034,0.443147,0.441047) RGB{Float64}(0.449519,0.447638,0.445513)\n RGB{Float64}(0.440373,0.438478,0.436406) RGB{Float64}(0.454007,0.452133,0.449982)\n RGB{Float64}(0.443401,0.441511,0.439421) RGB{Float64}(0.456432,0.454562,0.452397)\n RGB{Float64}(0.445844,0.443957,0.441853) … RGB{Float64}(0.454384,0.452511,0.450357)\n RGB{Float64}(0.450126,0.448246,0.446117) RGB{Float64}(0.45111,0.449232,0.447097)\n RGB{Float64}(0.452035,0.450158,0.448018) RGB{Float64}(0.451456,0.449578,0.447441)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "cell_type": "code", + "source": [ + "analyzer = SmoothGrad(model, 50)\n", + "heatmap(input, analyzer)" + ], + "metadata": {}, + "execution_count": 6 + }, + { + "cell_type": "markdown", + "source": [ + "We can also use any distribution from\n", + "[Distributions.jl](https://github.com/JuliaStats/Distributions.jl),\n", + "for example Poisson noise with rate $\\lambda=0.5$:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.385968,0.384011,0.382249) … RGB{Float64}(0.387228,0.385273,0.383504)\n RGB{Float64}(0.386976,0.38502,0.383252) RGB{Float64}(0.388324,0.386369,0.384594)\n RGB{Float64}(0.389137,0.387183,0.385403) RGB{Float64}(0.390234,0.388281,0.386495)\n RGB{Float64}(0.38535,0.383393,0.381635) RGB{Float64}(0.383153,0.381194,0.379448)\n RGB{Float64}(0.38339,0.381431,0.379684) RGB{Float64}(0.396267,0.394319,0.392499)\n RGB{Float64}(0.38226,0.3803,0.378559) … RGB{Float64}(0.39826,0.396314,0.394483)\n RGB{Float64}(0.381069,0.379108,0.377373) RGB{Float64}(0.393853,0.391903,0.390097)\n RGB{Float64}(0.382764,0.380804,0.37906) RGB{Float64}(0.387359,0.385403,0.383634)\n RGB{Float64}(0.376101,0.374136,0.37243) RGB{Float64}(0.372857,0.37089,0.369202)\n RGB{Float64}(0.384637,0.382679,0.380925) RGB{Float64}(0.377432,0.375469,0.373755)\n ⋮ ⋱ \n RGB{Float64}(0.388693,0.386738,0.384961) RGB{Float64}(0.379807,0.377846,0.376118)\n RGB{Float64}(0.390512,0.388559,0.386772) … RGB{Float64}(0.378634,0.376671,0.374951)\n RGB{Float64}(0.386639,0.384683,0.382917) RGB{Float64}(0.377059,0.375095,0.373383)\n RGB{Float64}(0.386524,0.384567,0.382802) RGB{Float64}(0.388142,0.386187,0.384413)\n RGB{Float64}(0.384815,0.382857,0.381102) RGB{Float64}(0.382534,0.380575,0.378832)\n RGB{Float64}(0.385302,0.383345,0.381587) RGB{Float64}(0.380766,0.378805,0.377072)\n RGB{Float64}(0.385349,0.383391,0.381633) … RGB{Float64}(0.384057,0.382098,0.380347)\n RGB{Float64}(0.385893,0.383936,0.382175) RGB{Float64}(0.382974,0.381015,0.37927)\n RGB{Float64}(0.386381,0.384425,0.382661) RGB{Float64}(0.385464,0.383507,0.381748)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "cell_type": "code", + "source": [ + "using Distributions\n", + "\n", + "analyzer = NoiseAugmentation(Gradient(model), 50, Poisson(0.5))\n", + "heatmap(input, analyzer)" + ], + "metadata": {}, + "execution_count": 7 + }, + { + "cell_type": "markdown", + "source": [ + "Is is also possible to define your own distributions or mixture distributions.\n", + "\n", + "`NoiseAugmentation` can be combined with any analyzer type from the Julia-XAI ecosystem,\n", + "for example `LRP` from [RelevancePropagation.jl](https://github.com/Julia-XAI/RelevancePropagation.jl)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## Integration augmentation\n", + "The `InterpolationAugmentation` wrapper computes explanations\n", + "averaged over `n` steps of linear interpolation between the input and a reference input,\n", + "which is set to `zero(input)` by default:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.308513,0.306531,0.305198) … RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) … RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n ⋮ ⋱ \n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) … RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) … RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "cell_type": "code", + "source": [ + "analyzer = InterpolationAugmentation(Gradient(model), 50)\n", + "heatmap(input, analyzer)" + ], + "metadata": {}, + "execution_count": 8 + }, + { + "cell_type": "markdown", + "source": [ + "When used with a `Gradient` analyzer, this is equivalent to `IntegratedGradients`:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.308513,0.306531,0.305198) … RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) … RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n ⋮ ⋱ \n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) … RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) … RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)\n RGB{Float64}(0.308513,0.306531,0.305198) RGB{Float64}(0.308513,0.306531,0.305198)", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHAAAABwCAIAAABJgmMcAAAABGdBTUEAALGPC/xhBQAAAAFzUkdCAK7OHOkAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAABDdJREFUeAHtwVtPFAcYx+HfzLwLu8uCsZFdUasQa1JpNepl/QrVxkO/qkn1thrvjFoktWAUU1DZBZVF9jDD9OqPNyZG88qheZ/Hfrt6leDHCK6M4MoIrozgygiujODKCK6M4MoIrozgygiujODKCK6M4MoIrozgygiujODKCK6M4MoIrozgygiujODKCK6M4MoIrox9rKREEhKk2WwirVYL2ehuICvLK0iv30MSEr4lI7gygisjuDL2g4SPSnZUR6vIaHUUSZIEGQ6HiGWGTByaQNKNFFl8tohkaYY0m00kSRK+lhFcGcGVEVwZu6gyUkH6vT6ysbGB1Go1pF6vI72tHlKxCvL69Wuk0WggU1NTyNvaW+TNmzeIVSrI1tYWUq/X+VpGcGUEV0ZwZXxjWZYh7XYb6ff6SLfbRY4dO4Z0u10kzVKk0+nwKXmeI0tLS0hZlsjs7CwyGA6QLM2QlZUVpCxLvoQRXBnBlRFcGd9AmqXIq1evkPn5eaRSqSA/nzuHjIyOIGmSsqPks+aezCHNZgupmCHdzS5yaOIQsra2xo6Ej0q+iBFcGcGVEVwZTvI8R1ZftZGxsToy+9MsMlIZQcYbDb5ESYkUeYGc/P4kMjk5iZgZ8vfTp8joyAhSr9eRcrvkaxnBlRFcGcGV4aRarSKXL/+CTB2dQmZmppHbd+4gMzMzyKlTp5Dbf9xGWq0WsrC4gJw5cwbp9/rIh60PSGetg2RZhrTbbaTZaiEVM76WEVwZwZURXBlO8iLnU6ZnppGbv99Ert+4gQwGfWR5eQV59PARcuXKr8itW7eQWr2ObG5uIkmaIO/fvUcmJsaRWrWKpGmKByO4MoIrI7gyvJTseLb4DHnxYglZXFhE3r17h5w4cRx5v7GB5HmO/Hn3LvLj2bPIgwcP+JSyLJEkSZBiWCBpmuLNCK6M4MoIroxvrMhz5P79+3zK/Pw8nzMYDJDz588jR48eRdbX15Hlf5eRwWDAbjGCKyO4MoIrYx8ryxIpigKZm5tD+r0+svDPAjIcDtkLRnBlBFdGcGXsM8+fP0cePnqEXL92DXn812NkYnwCySxDEhL2ghFcGcGVEVwZ+8D6+jqy9PIlMjM9jdy9dw8ZDofIpUsXkTEbY68ZwZURXBnBlbEPbPV6SK1WRbbLEjly5AjSbE4iY/Ux9hMjuDKCKyO4MvZIsV0ga2sdZHW1jYyPjyM/nD6NNBoN9isjuDKCKyO4MvbIh80PyPHjx5E0zZCxsTrSaDQ4CIzgygiujODK2EW1eg05/N1h5MmTJ8jFCxeQbrfLQWMEV0ZwZQRXxi5qTjaR1fYq0mq2kGK7QCqVCgeNEVwZwZURXBm7aKu3xY6SHUmaInmec5AZwZURXBnBlbGLOp0OkqUZUm5vI0macpAZwZURXBnBlbGLirxACgokTVP+L4zgygiujODqP7TeIGp427rdAAAAAElFTkSuQmCC", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "cell_type": "code", + "source": [ + "analyzer = IntegratedGradients(model, 50)\n", + "heatmap(input, analyzer)" + ], + "metadata": {}, + "execution_count": 9 + }, + { + "cell_type": "markdown", + "source": [ + "To select a different reference input, pass it to the `analyze` function\n", + "using the keyword argument `input_ref`.\n", + "Note that this is an arbitrary example for the sake of demonstration." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.409019,0.407085,0.405193) … RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.409518,0.407584,0.405689) RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.414041,0.412112,0.410191) RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.416505,0.414579,0.412644) RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.403693,0.401754,0.399891) RGB{Float64}(0.403858,0.401919,0.400055)\n RGB{Float64}(0.401287,0.399345,0.397496) … RGB{Float64}(0.413245,0.411316,0.409399)\n RGB{Float64}(0.410847,0.408914,0.407011) RGB{Float64}(0.40079,0.398847,0.397001)\n RGB{Float64}(0.415844,0.413917,0.411986) RGB{Float64}(0.412815,0.410885,0.408971)\n RGB{Float64}(0.435989,0.434089,0.432041) RGB{Float64}(0.412814,0.410884,0.40897)\n RGB{Float64}(0.412335,0.410405,0.408493) RGB{Float64}(0.401388,0.399446,0.397597)\n ⋮ ⋱ \n RGB{Float64}(0.392085,0.390133,0.388337) RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.404857,0.402919,0.40105) … RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.411016,0.409084,0.40718) RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.386425,0.384468,0.382704) RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.408805,0.40687,0.404979) RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.404115,0.402176,0.400311) RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.409222,0.407288,0.405394) … RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.411927,0.409996,0.408087) RGB{Float64}(0.405703,0.403765,0.401891)\n RGB{Float64}(0.405703,0.403765,0.401891) RGB{Float64}(0.405703,0.403765,0.401891)", + "image/png": 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+ "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ], + "cell_type": "code", + "source": [ + "matrix_of_ones = ones(Float32, size(input))\n", + "\n", + "analyzer = InterpolationAugmentation(Gradient(model), 50)\n", + "expl = analyzer(input; input_ref=matrix_of_ones)\n", + "heatmap(expl)" + ], + "metadata": {}, + "execution_count": 10 + }, + { + "cell_type": "markdown", + "source": [ + "Once again, `InterpolationAugmentation` can be combined with any analyzer type from the Julia-XAI ecosystem,\n", + "for example `LRP` from [RelevancePropagation.jl](https://github.com/Julia-XAI/RelevancePropagation.jl)." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.4" + }, + "kernelspec": { + "name": "julia-1.10", + "display_name": "Julia 1.10.4", + "language": "julia" + } + }, + "nbformat": 4 +} diff --git a/v0.8.1/generated/augmentations.jl b/v0.8.1/generated/augmentations.jl new file mode 100644 index 0000000..57463a5 --- /dev/null +++ b/v0.8.1/generated/augmentations.jl @@ -0,0 +1,47 @@ +using ExplainableAI +using VisionHeatmaps +using Flux + +using BSON # hide +model = BSON.load("../model.bson", @__MODULE__)[:model] # hide +model + +using MLDatasets +using ImageCore, ImageIO, ImageShow + +index = 10 +x, y = MNIST(Float32, :test)[10] +input = reshape(x, 28, 28, 1, :) + +convert2image(MNIST, x) + +analyzer = Gradient(model) +heatmap(input, analyzer) + +analyzer = NoiseAugmentation(Gradient(model), 50) +heatmap(input, analyzer) + +analyzer = NoiseAugmentation(Gradient(model), 50, 0.1) +heatmap(input, analyzer) + +analyzer = SmoothGrad(model, 50) +heatmap(input, analyzer) + +using Distributions + +analyzer = NoiseAugmentation(Gradient(model), 50, Poisson(0.5)) +heatmap(input, analyzer) + +analyzer = InterpolationAugmentation(Gradient(model), 50) +heatmap(input, analyzer) + +analyzer = IntegratedGradients(model, 50) +heatmap(input, analyzer) + +matrix_of_ones = ones(Float32, size(input)) + +analyzer = InterpolationAugmentation(Gradient(model), 50) +expl = analyzer(input; input_ref=matrix_of_ones) +heatmap(expl) + +# This file was generated using Literate.jl, https://github.com/fredrikekre/Literate.jl diff --git a/v0.8.1/generated/augmentations/index.html b/v0.8.1/generated/augmentations/index.html new file mode 100644 index 0000000..10059ae --- /dev/null +++ b/v0.8.1/generated/augmentations/index.html @@ -0,0 +1,35 @@ + +Input augmentations · ExplainableAI.jl
GitHub

Analyzer augmentations

All analyzers implemented in ExplainableAI.jl can be augmented by two types of augmentations: NoiseAugmentations and InterpolationAugmentations. These augmentations are wrappers around analyzers that modify the input before passing it to the analyzer.

We build on the basics shown in the Getting started section and start out by loading the same pre-trained LeNet5 model and MNIST input data:

using ExplainableAI
+using VisionHeatmaps
+using Flux
+
+model
Chain(
+  Conv((5, 5), 1 => 6, relu),           # 156 parameters
+  MaxPool((2, 2)),
+  Conv((5, 5), 6 => 16, relu),          # 2_416 parameters
+  MaxPool((2, 2)),
+  Flux.flatten,
+  Dense(256 => 120, relu),              # 30_840 parameters
+  Dense(120 => 84, relu),               # 10_164 parameters
+  Dense(84 => 10),                      # 850 parameters
+)                   # Total: 10 arrays, 44_426 parameters, 174.867 KiB.
using MLDatasets
+using ImageCore, ImageIO, ImageShow
+
+index = 10
+x, y = MNIST(Float32, :test)[10]
+input = reshape(x, 28, 28, 1, :)
+
+convert2image(MNIST, x)

Noise augmentation

The NoiseAugmentation wrapper computes explanations averaged over noisy inputs. Let's demonstrate this on the Gradient analyzer. First, we compute the heatmap of an explanation without augmentation:

analyzer = Gradient(model)
+heatmap(input, analyzer)

Now we wrap the analyzer in a NoiseAugmentation with 10 samples of noise. By default, the noise is sampled from a Gaussian distribution with mean 0 and standard deviation 1.

analyzer = NoiseAugmentation(Gradient(model), 50)
+heatmap(input, analyzer)

Note that a higher sample size is desired, as it will lead to a smoother heatmap. However, this comes at the cost of a longer computation time.

We can also set the standard deviation of the Gaussian distribution:

analyzer = NoiseAugmentation(Gradient(model), 50, 0.1)
+heatmap(input, analyzer)

When used with a Gradient analyzer, this is equivalent to SmoothGrad:

analyzer = SmoothGrad(model, 50)
+heatmap(input, analyzer)

We can also use any distribution from Distributions.jl, for example Poisson noise with rate $\lambda=0.5$:

using Distributions
+
+analyzer = NoiseAugmentation(Gradient(model), 50, Poisson(0.5))
+heatmap(input, analyzer)

Is is also possible to define your own distributions or mixture distributions.

NoiseAugmentation can be combined with any analyzer type from the Julia-XAI ecosystem, for example LRP from RelevancePropagation.jl.

Integration augmentation

The InterpolationAugmentation wrapper computes explanations averaged over n steps of linear interpolation between the input and a reference input, which is set to zero(input) by default:

analyzer = InterpolationAugmentation(Gradient(model), 50)
+heatmap(input, analyzer)

When used with a Gradient analyzer, this is equivalent to IntegratedGradients:

analyzer = IntegratedGradients(model, 50)
+heatmap(input, analyzer)

To select a different reference input, pass it to the analyze function using the keyword argument input_ref. Note that this is an arbitrary example for the sake of demonstration.

matrix_of_ones = ones(Float32, size(input))
+
+analyzer = InterpolationAugmentation(Gradient(model), 50)
+expl = analyzer(input; input_ref=matrix_of_ones)
+heatmap(expl)

Once again, InterpolationAugmentation can be combined with any analyzer type from the Julia-XAI ecosystem, for example LRP from RelevancePropagation.jl.

This page was generated using Literate.jl.

diff --git a/v0.8.1/generated/example.ipynb b/v0.8.1/generated/example.ipynb new file mode 100644 index 0000000..0a68bee --- /dev/null +++ b/v0.8.1/generated/example.ipynb @@ -0,0 +1,385 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Getting started" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "For this first example, we already have loaded a pre-trained LeNet5 model\n", + "to look at explanations on the MNIST dataset." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Chain(\n Conv((5, 5), 1 => 6, relu), \u001b[90m# 156 parameters\u001b[39m\n MaxPool((2, 2)),\n Conv((5, 5), 6 => 16, relu), \u001b[90m# 2_416 parameters\u001b[39m\n MaxPool((2, 2)),\n Flux.flatten,\n Dense(256 => 120, relu), \u001b[90m# 30_840 parameters\u001b[39m\n Dense(120 => 84, relu), \u001b[90m# 10_164 parameters\u001b[39m\n Dense(84 => 10), \u001b[90m# 850 parameters\u001b[39m\n) \u001b[90m # Total: 10 arrays, \u001b[39m44_426 parameters, 174.867 KiB." + }, + "metadata": {}, + "execution_count": 1 + } + ], + "cell_type": "code", + "source": [ + "using ExplainableAI\n", + "using Flux\n", + "\n", + "using BSON # hide\n", + "model = BSON.load(\"../model.bson\", @__MODULE__)[:model] # hide\n", + "model" + ], + "metadata": {}, + "execution_count": 1 + }, + { + "cell_type": "markdown", + "source": [ + "## Preparing the input data\n", + "We use MLDatasets to load a single image from the MNIST dataset:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 reinterpret(reshape, ColorTypes.Gray{Float32}, ::Matrix{Float32}) with eltype ColorTypes.Gray{Float32}:\n Gray{Float32}(0.0) Gray{Float32}(0.0) … Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) … Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n ⋮ ⋱ \n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) … Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) … Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ], + "cell_type": "code", + "source": [ + "using MLDatasets\n", + "using ImageCore, ImageIO, ImageShow\n", + "\n", + "index = 10\n", + "x, y = MNIST(Float32, :test)[10]\n", + "\n", + "convert2image(MNIST, x)" + ], + "metadata": {}, + "execution_count": 2 + }, + { + "cell_type": "markdown", + "source": [ + "By convention in Flux.jl, this input needs to be resized to WHCN format\n", + "by adding a color channel and batch dimensions." + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "input = reshape(x, 28, 28, 1, :);" + ], + "metadata": {}, + "execution_count": 3 + }, + { + "cell_type": "markdown", + "source": [ + "## Explanations\n", + "We can now select an analyzer of our choice and call `analyze`\n", + "to get an `Explanation`:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "analyzer = InputTimesGradient(model)\n", + "expl = analyze(input, analyzer);" + ], + "metadata": {}, + "execution_count": 4 + }, + { + "cell_type": "markdown", + "source": [ + "The return value `expl` is of type `Explanation` and bundles the following data:\n", + "* `expl.val`: numerical output of the analyzer, e.g. an attribution or gradient\n", + "* `expl.output`: model output for the given analyzer input\n", + "* `expl.output_selection`: index of the output used for the explanation\n", + "* `expl.analyzer`: symbol corresponding the used analyzer, e.g. `:Gradient` or `:LRP`\n", + "* `expl.heatmap`: symbol indicating a preset heatmapping style,\n", + " e.g. `:attibution`, `:sensitivity` or `:cam`\n", + "* `expl.extras`: optional named tuple that can be used by analyzers\n", + " to return additional information.\n", + "\n", + "We used `InputTimesGradient`, so `expl.analyzer` is `:InputTimesGradient`." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": ":InputTimesGradient" + }, + "metadata": {}, + "execution_count": 5 + } + ], + "cell_type": "code", + "source": [ + "expl.analyzer" + ], + "metadata": {}, + "execution_count": 5 + }, + { + "cell_type": "markdown", + "source": [ + "By default, the explanation is computed for the maximally activated output neuron.\n", + "Since our digit is a 9 and Julia's indexing is 1-based,\n", + "the output neuron at index `10` of our trained model is maximally activated." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "Finally, we obtain the result of the analyzer in form of an array." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28×1×1 Array{Float32, 4}:\n[:, :, 1, 1] =\n -0.0 0.0 0.0 -0.0 -0.0 -0.0 -0.0 … -0.0 0.0 0.0\n -0.0 0.0 0.0 -0.0 -0.0 -0.0 -0.0 -0.0 0.0 0.0\n -0.0 0.0 0.0 -0.0 -0.0 -0.0 -0.0 -0.0 0.0 0.0\n 0.0 0.0 0.0 -0.0 -0.0 -0.0 -0.0 0.0 0.0 0.0\n 0.0 0.0 -0.0 -0.0 -0.0 0.0 0.0 0.0 0.0 0.0\n -0.0 0.0 0.0 -0.0 0.0 -0.0 -0.0 … 0.0 0.0 0.0\n -0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n -0.0 0.0 0.0 0.0 -0.0 0.0 -0.0 0.0 0.0 -0.0\n -0.0 0.0 0.0 0.0 -0.0 -0.0 -0.0 0.0 0.0 -0.0\n -0.0 0.0 0.0 0.0 -0.0 0.0 -0.0 0.385115 0.0714216 0.0\n ⋮ ⋮ ⋱ ⋮ \n 0.0 -0.0 -0.0 0.0 0.0 0.0 -0.0 -0.0 0.0 0.0\n -0.0 -0.0 0.0 0.0 0.0 0.0 -0.0 … -0.0 0.0 0.0\n -0.0 0.0 0.0 0.0 0.0 0.0 -0.0 0.0 0.0 0.0\n -0.0 0.0 0.0 0.0 -0.0 0.0 -0.0 0.0 0.0 0.0\n -0.0 0.0 0.0 -0.0 -0.0 0.0 -0.0 0.0 0.0 0.0\n 0.0 0.0 -0.0 -0.0 -0.0 0.0 -0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 -0.0 -0.0 0.0 0.0 … 0.0 0.0 0.0\n 0.0 0.0 -0.0 -0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 -0.0 0.0 0.0 0.0" + }, + "metadata": {}, + "execution_count": 6 + } + ], + "cell_type": "code", + "source": [ + "expl.val" + ], + "metadata": {}, + "execution_count": 6 + }, + { + "cell_type": "markdown", + "source": [ + "## Heatmapping basics\n", + "Since the array `expl.val` is not very informative at first sight,\n", + "we can visualize `Explanation`s by computing a `heatmap` using either\n", + "[VisionHeatmaps.jl](https://julia-xai.github.io/XAIDocs/VisionHeatmaps/stable/) or\n", + "[TextHeatmaps.jl](https://julia-xai.github.io/XAIDocs/TextHeatmaps/stable/)." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n ⋮ ⋱ \n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "cell_type": "code", + "source": [ + "using VisionHeatmaps\n", + "\n", + "heatmap(expl)" + ], + "metadata": {}, + "execution_count": 7 + }, + { + "cell_type": "markdown", + "source": [ + "If we are only interested in the heatmap, we can combine analysis and heatmapping\n", + "into a single function call:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n ⋮ ⋱ \n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "cell_type": "code", + "source": [ + "heatmap(input, analyzer)" + ], + "metadata": {}, + "execution_count": 8 + }, + { + "cell_type": "markdown", + "source": [ + "For a more detailed explanation of the `heatmap` function,\n", + "refer to the heatmapping section." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## List of analyzers" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## Neuron selection\n", + "By passing an additional index to our call to `analyze`,\n", + "we can compute an explanation with respect to a specific output neuron.\n", + "Let's see why the output wasn't interpreted as a 4 (output neuron at index 5)" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n ⋮ ⋱ \n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)", + "image/png": 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+ "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "cell_type": "code", + "source": [ + "expl = analyze(input, analyzer, 5)\n", + "heatmap(expl)" + ], + "metadata": {}, + "execution_count": 9 + }, + { + "cell_type": "markdown", + "source": [ + "This heatmap shows us that the \"upper loop\" of the hand-drawn 9 has negative relevance\n", + "with respect to the output neuron corresponding to digit 4!" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## Analyzing batches\n", + "ExplainableAI also supports explanations of input batches:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "batchsize = 20\n", + "xs, _ = MNIST(Float32, :test)[1:batchsize]\n", + "batch = reshape(xs, 28, 28, 1, :) # reshape to WHCN format\n", + "expl = analyze(batch, analyzer);" + ], + "metadata": {}, + "execution_count": 10 + }, + { + "cell_type": "markdown", + "source": [ + "This will return a single `Explanation` `expl` for the entire batch.\n", + "Calling `heatmap` on `expl` will detect the batch dimension and return a vector of heatmaps." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "20-element Vector{Matrix{ColorTypes.RGB{Float64}}}:\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) 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RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0); RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) … RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0); … ; RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) … RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0); RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) … RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) 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RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) … RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093)]", + "text/html": [ + "
(a vector displayed as a row to save space)
" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ], + "cell_type": "code", + "source": [ + "heatmap(expl)" + ], + "metadata": {}, + "execution_count": 11 + }, + { + "cell_type": "markdown", + "source": [ + "For more information on heatmapping batches,\n", + "refer to the heatmapping documentation." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.4" + }, + "kernelspec": { + "name": "julia-1.10", + "display_name": "Julia 1.10.4", + "language": "julia" + } + }, + "nbformat": 4 +} diff --git a/v0.8.1/generated/example.jl b/v0.8.1/generated/example.jl new file mode 100644 index 0000000..0cef228 --- /dev/null +++ b/v0.8.1/generated/example.jl @@ -0,0 +1,41 @@ +using ExplainableAI +using Flux + +using BSON # hide +model = BSON.load("../model.bson", @__MODULE__)[:model] # hide +model + +using MLDatasets +using ImageCore, ImageIO, ImageShow + +index = 10 +x, y = MNIST(Float32, :test)[10] + +convert2image(MNIST, x) + +input = reshape(x, 28, 28, 1, :); + +analyzer = InputTimesGradient(model) +expl = analyze(input, analyzer); + +expl.analyzer + +expl.val + +using VisionHeatmaps + +heatmap(expl) + +heatmap(input, analyzer) + +expl = analyze(input, analyzer, 5) +heatmap(expl) + +batchsize = 20 +xs, _ = MNIST(Float32, :test)[1:batchsize] +batch = reshape(xs, 28, 28, 1, :) # reshape to WHCN format +expl = analyze(batch, analyzer); + +heatmap(expl) + +# This file was generated using Literate.jl, https://github.com/fredrikekre/Literate.jl diff --git a/v0.8.1/generated/example/index.html b/v0.8.1/generated/example/index.html new file mode 100644 index 0000000..c9db0ec --- /dev/null +++ b/v0.8.1/generated/example/index.html @@ -0,0 +1,48 @@ + +Getting started · ExplainableAI.jl
GitHub

Getting started

Note

This package is part of a wider Julia XAI ecosystem. For an introduction to this ecosystem, please refer to the Getting started guide.

For this first example, we already have loaded a pre-trained LeNet5 model to look at explanations on the MNIST dataset.

using ExplainableAI
+using Flux
+
+model
Chain(
+  Conv((5, 5), 1 => 6, relu),           # 156 parameters
+  MaxPool((2, 2)),
+  Conv((5, 5), 6 => 16, relu),          # 2_416 parameters
+  MaxPool((2, 2)),
+  Flux.flatten,
+  Dense(256 => 120, relu),              # 30_840 parameters
+  Dense(120 => 84, relu),               # 10_164 parameters
+  Dense(84 => 10),                      # 850 parameters
+)                   # Total: 10 arrays, 44_426 parameters, 174.867 KiB.
Supported models

ExplainableAI.jl can be used on any differentiable classifier.

Preparing the input data

We use MLDatasets to load a single image from the MNIST dataset:

using MLDatasets
+using ImageCore, ImageIO, ImageShow
+
+index = 10
+x, y = MNIST(Float32, :test)[10]
+
+convert2image(MNIST, x)

By convention in Flux.jl, this input needs to be resized to WHCN format by adding a color channel and batch dimensions.

input = reshape(x, 28, 28, 1, :);
Input format

For any explanation of a model, ExplainableAI.jl assumes the batch dimension to come last in the input.

For the purpose of heatmapping, the input is assumed to be in WHCN order (width, height, channels, batch), which is Flux.jl's convention.

Explanations

We can now select an analyzer of our choice and call analyze to get an Explanation:

analyzer = InputTimesGradient(model)
+expl = analyze(input, analyzer);

The return value expl is of type Explanation and bundles the following data:

  • expl.val: numerical output of the analyzer, e.g. an attribution or gradient
  • expl.output: model output for the given analyzer input
  • expl.output_selection: index of the output used for the explanation
  • expl.analyzer: symbol corresponding the used analyzer, e.g. :Gradient or :LRP
  • expl.heatmap: symbol indicating a preset heatmapping style, e.g. :attibution, :sensitivity or :cam
  • expl.extras: optional named tuple that can be used by analyzers to return additional information.

We used InputTimesGradient, so expl.analyzer is :InputTimesGradient.

expl.analyzer
:InputTimesGradient

By default, the explanation is computed for the maximally activated output neuron. Since our digit is a 9 and Julia's indexing is 1-based, the output neuron at index 10 of our trained model is maximally activated.

Finally, we obtain the result of the analyzer in form of an array.

expl.val
28×28×1×1 Array{Float32, 4}:
+[:, :, 1, 1] =
+ -0.0   0.0   0.0  -0.0  -0.0  -0.0  -0.0  …  -0.0       0.0         0.0
+ -0.0   0.0   0.0  -0.0  -0.0  -0.0  -0.0     -0.0       0.0         0.0
+ -0.0   0.0   0.0  -0.0  -0.0  -0.0  -0.0     -0.0       0.0         0.0
+  0.0   0.0   0.0  -0.0  -0.0  -0.0  -0.0      0.0       0.0         0.0
+  0.0   0.0  -0.0  -0.0  -0.0   0.0   0.0      0.0       0.0         0.0
+ -0.0   0.0   0.0  -0.0   0.0  -0.0  -0.0  …   0.0       0.0         0.0
+ -0.0   0.0   0.0   0.0   0.0   0.0   0.0      0.0       0.0         0.0
+ -0.0   0.0   0.0   0.0  -0.0   0.0  -0.0      0.0       0.0        -0.0
+ -0.0   0.0   0.0   0.0  -0.0  -0.0  -0.0      0.0       0.0        -0.0
+ -0.0   0.0   0.0   0.0  -0.0   0.0  -0.0      0.385115  0.0714216   0.0
+  ⋮                             ⋮          ⋱   ⋮                    
+  0.0  -0.0  -0.0   0.0   0.0   0.0  -0.0     -0.0       0.0         0.0
+ -0.0  -0.0   0.0   0.0   0.0   0.0  -0.0  …  -0.0       0.0         0.0
+ -0.0   0.0   0.0   0.0   0.0   0.0  -0.0      0.0       0.0         0.0
+ -0.0   0.0   0.0   0.0  -0.0   0.0  -0.0      0.0       0.0         0.0
+ -0.0   0.0   0.0  -0.0  -0.0   0.0  -0.0      0.0       0.0         0.0
+  0.0   0.0  -0.0  -0.0  -0.0   0.0  -0.0      0.0       0.0         0.0
+  0.0   0.0   0.0  -0.0  -0.0   0.0   0.0  …   0.0       0.0         0.0
+  0.0   0.0  -0.0  -0.0   0.0   0.0   0.0      0.0       0.0         0.0
+  0.0   0.0   0.0   0.0   0.0   0.0  -0.0      0.0       0.0         0.0

Heatmapping basics

Since the array expl.val is not very informative at first sight, we can visualize Explanations by computing a heatmap using either VisionHeatmaps.jl or TextHeatmaps.jl.

using VisionHeatmaps
+
+heatmap(expl)

If we are only interested in the heatmap, we can combine analysis and heatmapping into a single function call:

heatmap(input, analyzer)

For a more detailed explanation of the heatmap function, refer to the heatmapping section.

List of analyzers

Neuron selection

By passing an additional index to our call to analyze, we can compute an explanation with respect to a specific output neuron. Let's see why the output wasn't interpreted as a 4 (output neuron at index 5)

expl = analyze(input, analyzer, 5)
+heatmap(expl)

This heatmap shows us that the "upper loop" of the hand-drawn 9 has negative relevance with respect to the output neuron corresponding to digit 4!

Note

The output neuron can also be specified when calling heatmap:

heatmap(input, analyzer, 5)

Analyzing batches

ExplainableAI also supports explanations of input batches:

batchsize = 20
+xs, _ = MNIST(Float32, :test)[1:batchsize]
+batch = reshape(xs, 28, 28, 1, :) # reshape to WHCN format
+expl = analyze(batch, analyzer);

This will return a single Explanation expl for the entire batch. Calling heatmap on expl will detect the batch dimension and return a vector of heatmaps.

heatmap(expl)
(a vector displayed as a row to save space)

For more information on heatmapping batches, refer to the heatmapping documentation.

This page was generated using Literate.jl.

diff --git a/v0.8.1/generated/heatmapping.ipynb b/v0.8.1/generated/heatmapping.ipynb new file mode 100644 index 0000000..6863d9c --- /dev/null +++ b/v0.8.1/generated/heatmapping.ipynb @@ -0,0 +1,695 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Heatmapping\n", + "Since numerical explanations are not very informative at first sight,\n", + "we can visualize them by computing a `heatmap`, using either\n", + "[VisionHeatmaps.jl](https://julia-xai.github.io/XAIDocs/VisionHeatmaps/stable/) or\n", + "[TextHeatmaps.jl](https://julia-xai.github.io/XAIDocs/TextHeatmaps/stable/).\n", + "\n", + "This page showcases different options and preset for heatmapping,\n", + "building on the basics shown in the *Getting started* section.\n", + "\n", + "We start out by loading the same pre-trained LeNet5 model and MNIST input data:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "Chain(\n Conv((5, 5), 1 => 6, relu), \u001b[90m# 156 parameters\u001b[39m\n MaxPool((2, 2)),\n Conv((5, 5), 6 => 16, relu), \u001b[90m# 2_416 parameters\u001b[39m\n MaxPool((2, 2)),\n Flux.flatten,\n Dense(256 => 120, relu), \u001b[90m# 30_840 parameters\u001b[39m\n Dense(120 => 84, relu), \u001b[90m# 10_164 parameters\u001b[39m\n Dense(84 => 10), \u001b[90m# 850 parameters\u001b[39m\n) \u001b[90m # Total: 10 arrays, \u001b[39m44_426 parameters, 174.867 KiB." + }, + "metadata": {}, + "execution_count": 1 + } + ], + "cell_type": "code", + "source": [ + "using ExplainableAI\n", + "using VisionHeatmaps\n", + "using Flux\n", + "\n", + "using BSON # hide\n", + "model = BSON.load(\"../model.bson\", @__MODULE__)[:model] # hide\n", + "model" + ], + "metadata": {}, + "execution_count": 1 + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 reinterpret(reshape, ColorTypes.Gray{Float32}, ::Matrix{Float32}) with eltype ColorTypes.Gray{Float32}:\n Gray{Float32}(0.0) Gray{Float32}(0.0) … Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) … Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n ⋮ ⋱ \n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) … Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) … Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)\n Gray{Float32}(0.0) Gray{Float32}(0.0) Gray{Float32}(0.0)", + "image/png": 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"execution_count": 2 + } + ], + "cell_type": "code", + "source": [ + "using MLDatasets\n", + "using ImageCore, ImageIO, ImageShow\n", + "\n", + "index = 10\n", + "x, y = MNIST(Float32, :test)[10]\n", + "input = reshape(x, 28, 28, 1, :)\n", + "\n", + "img = convert2image(MNIST, x)" + ], + "metadata": {}, + "execution_count": 2 + }, + { + "cell_type": "markdown", + "source": [ + "## Automatic heatmap presets\n", + "The function `heatmap` automatically applies common presets for each method.\n", + "\n", + "Since `InputTimesGradient` computes attributions,\n", + "heatmaps are shown in a blue-white-red color scheme.\n", + "Gradient methods however are typically shown in grayscale:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.461087,0.459224,0.457032) … RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.468989,0.467139,0.464901) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.479787,0.477956,0.475654) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.459915,0.45805,0.455864) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.445069,0.443181,0.441081) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.445034,0.443146,0.441047) … RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.457174,0.455305,0.453135) RGB{Float64}(0.463812,0.461953,0.459745)\n RGB{Float64}(0.466269,0.464414,0.462192) RGB{Float64}(0.463755,0.461896,0.459688)\n RGB{Float64}(0.470723,0.468876,0.466628) RGB{Float64}(0.452716,0.45084,0.448697)\n RGB{Float64}(0.485669,0.483847,0.481511) RGB{Float64}(0.450914,0.449035,0.446902)\n ⋮ ⋱ \n RGB{Float64}(0.46542,0.463564,0.461346) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.481108,0.479278,0.476969) … RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.456239,0.454368,0.452204) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.443836,0.441947,0.439855) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.435607,0.433706,0.431661) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.452624,0.450747,0.448604) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.451326,0.449448,0.447312) … RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.467146,0.465293,0.463065) RGB{Float64}(0.464131,0.462273,0.460063)\n RGB{Float64}(0.464131,0.462273,0.460063) RGB{Float64}(0.464131,0.462273,0.460063)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ], + "cell_type": "code", + "source": [ + "analyzer = Gradient(model)\n", + "heatmap(input, analyzer)" + ], + "metadata": {}, + "execution_count": 3 + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n ⋮ ⋱ \n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "cell_type": "code", + "source": [ + "analyzer = InputTimesGradient(model)\n", + "heatmap(input, analyzer)" + ], + "metadata": {}, + "execution_count": 4 + }, + { + "cell_type": "markdown", + "source": [ + "## Custom heatmap settings\n", + "### Color schemes\n", + "We can partially or fully override presets by passing keyword arguments to `heatmap`.\n", + "For example, we can use a custom color scheme from ColorSchemes.jl using the keyword argument `colorscheme`:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.498,1.0,0.498) … RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) … RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n ⋮ ⋱ \n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) … RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) … RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)\n RGB{Float64}(0.498,1.0,0.498) RGB{Float64}(0.498,1.0,0.498)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "cell_type": "code", + "source": [ + "using ColorSchemes\n", + "\n", + "expl = analyze(input, analyzer)\n", + "heatmap(expl; colorscheme=:jet)" + ], + "metadata": {}, + "execution_count": 5 + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.732796,0.214332,0.332053) … RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) … RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n ⋮ ⋱ \n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) … RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) … RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "cell_type": "code", + "source": [ + "heatmap(expl; colorscheme=:inferno)" + ], + "metadata": {}, + "execution_count": 6 + }, + { + "cell_type": "markdown", + "source": [ + "Refer to the [ColorSchemes.jl catalogue](https://juliagraphics.github.io/ColorSchemes.jl/stable/basics/)\n", + "for a gallery of available color schemes." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Color channel reduction\n", + "Explanations have the same dimensionality as the inputs to the classifier.\n", + "For images with multiple color channels,\n", + "this means that the explanation also has a \"color channel\" dimension.\n", + "\n", + "The keyword argument `reduce` can be used to reduce this dimension\n", + "to a single scalar value for each pixel.\n", + "The following presets are available:\n", + "- `:sum`: sum up color channels (default setting)\n", + "- `:norm`: compute 2-norm over the color channels\n", + "- `:maxabs`: compute `maximum(abs, x)` over the color channels" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n ⋮ ⋱ \n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)", + "image/png": 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+ "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "cell_type": "code", + "source": [ + "heatmap(expl; reduce=:sum)" + ], + "metadata": {}, + "execution_count": 7 + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n ⋮ ⋱ \n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)", + "image/png": 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RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)", + "image/png": 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+ "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "cell_type": "code", + "source": [ + "heatmap(expl; reduce=:maxabs)" + ], + "metadata": {}, + "execution_count": 9 + }, + { + "cell_type": "markdown", + "source": [ + "In this example, the heatmaps look identical.\n", + "Since MNIST only has a single color channel, there is no need for color channel reduction." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### Mapping explanations onto the color scheme\n", + "To map a color-channel-reduced explanation onto a color scheme,\n", + "we first need to normalize all values to the range $[0, 1]$.\n", + "\n", + "For this purpose, two presets are available through the `rangescale` keyword argument:\n", + "- `:extrema`: normalize to the minimum and maximum value of the explanation\n", + "- `:centered`: normalize to the maximum absolute value of the explanation.\n", + " Values of zero will be mapped to the center of the color scheme.\n", + "\n", + "Depending on the color scheme, one of these presets may be more suitable than the other.\n", + "The default color scheme for `InputTimesGradient`, `seismic`, is centered around zero,\n", + "making `:centered` a good choice:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n ⋮ ⋱ \n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)", + "image/png": 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RGB{Float64}(0.54288,0.54288,1.0) RGB{Float64}(0.54288,0.54288,1.0)\n ⋮ ⋱ \n RGB{Float64}(0.54288,0.54288,1.0) RGB{Float64}(0.54288,0.54288,1.0)\n RGB{Float64}(0.54288,0.54288,1.0) … RGB{Float64}(0.54288,0.54288,1.0)\n RGB{Float64}(0.54288,0.54288,1.0) RGB{Float64}(0.54288,0.54288,1.0)\n RGB{Float64}(0.54288,0.54288,1.0) RGB{Float64}(0.54288,0.54288,1.0)\n RGB{Float64}(0.54288,0.54288,1.0) RGB{Float64}(0.54288,0.54288,1.0)\n RGB{Float64}(0.54288,0.54288,1.0) RGB{Float64}(0.54288,0.54288,1.0)\n RGB{Float64}(0.54288,0.54288,1.0) … RGB{Float64}(0.54288,0.54288,1.0)\n RGB{Float64}(0.54288,0.54288,1.0) RGB{Float64}(0.54288,0.54288,1.0)\n RGB{Float64}(0.54288,0.54288,1.0) RGB{Float64}(0.54288,0.54288,1.0)", + "image/png": 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+ "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ], + "cell_type": "code", + "source": [ + "heatmap(expl; rangescale=:extrema)" + ], + "metadata": {}, + "execution_count": 11 + }, + { + "cell_type": "markdown", + "source": [ + "However, for the `inferno` color scheme, which is not centered around zero,\n", + "`:extrema` leads to a heatmap with higher contrast." + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.732796,0.214332,0.332053) … RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) … RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n ⋮ ⋱ \n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) … RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) … RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)\n RGB{Float64}(0.732796,0.214332,0.332053) RGB{Float64}(0.732796,0.214332,0.332053)", + "image/png": 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+ "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ], + "cell_type": "code", + "source": [ + "heatmap(expl; rangescale=:centered, colorscheme=:inferno)" + ], + "metadata": {}, + "execution_count": 12 + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.555627,0.139927,0.411201) … RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) … RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n ⋮ ⋱ \n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) … RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) … RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)\n RGB{Float64}(0.555627,0.139927,0.411201) RGB{Float64}(0.555627,0.139927,0.411201)", + "image/png": 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+ "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ], + "cell_type": "code", + "source": [ + "heatmap(expl; rangescale=:extrema, colorscheme=:inferno)" + ], + "metadata": {}, + "execution_count": 13 + }, + { + "cell_type": "markdown", + "source": [ + "For the full list of `heatmap` keyword arguments, refer to the `heatmap` documentation." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## Heatmap overlays\n", + "Heatmaps can be overlaid onto the input image using the `heatmap_overlay` function\n", + "from VisionHeatmaps.jl.\n", + "This can be useful for visualizing the relevance of specific regions of the input:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.6,0.6,0.6) … RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) … RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n ⋮ ⋱ \n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) … RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) … RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)\n RGB{Float64}(0.6,0.6,0.6) RGB{Float64}(0.6,0.6,0.6)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ], + "cell_type": "code", + "source": [ + "heatmap_overlay(expl, img)" + ], + "metadata": {}, + "execution_count": 14 + }, + { + "cell_type": "markdown", + "source": [ + "The alpha value of the heatmap can be adjusted using the `alpha` keyword argument:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.3,0.3,0.3) … RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) … RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n ⋮ ⋱ \n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) … RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) … RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)\n RGB{Float64}(0.3,0.3,0.3) RGB{Float64}(0.3,0.3,0.3)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ], + "cell_type": "code", + "source": [ + "heatmap_overlay(expl, img; alpha=0.3)" + ], + "metadata": {}, + "execution_count": 15 + }, + { + "cell_type": "markdown", + "source": [ + "All previously discussed keyword arguments for `heatmap` can also be used with `heatmap_overlay`:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "28×28 Array{RGB{Float64},2} with eltype ColorTypes.RGB{Float64}:\n RGB{Float64}(0.388939,0.0979491,0.287841) … RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) … RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n ⋮ ⋱ \n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) … RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) … RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)\n RGB{Float64}(0.388939,0.0979491,0.287841) RGB{Float64}(0.388939,0.0979491,0.287841)", + "image/png": "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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ], + "cell_type": "code", + "source": [ + "heatmap_overlay(expl, img; alpha=0.7, colorscheme=:inferno, rangescale=:extrema)" + ], + "metadata": {}, + "execution_count": 16 + }, + { + "cell_type": "markdown", + "source": [ + "## Heatmapping batches\n", + "Heatmapping also works with input batches.\n", + "Let's demonstrate this by using a batch of 25 images from the MNIST dataset:" + ], + "metadata": {} + }, + { + "outputs": [], + "cell_type": "code", + "source": [ + "xs, ys = MNIST(Float32, :test)[1:25]\n", + "batch = reshape(xs, 28, 28, 1, :); # reshape to WHCN format" + ], + "metadata": {}, + "execution_count": 17 + }, + { + "cell_type": "markdown", + "source": [ + "The `heatmap` function automatically recognizes\n", + "that the explanation is batched and returns a `Vector` of images:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "25-element Vector{Matrix{ColorTypes.RGB{Float64}}}:\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) … RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0); RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) … RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0); … ; RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) … RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0); RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) … RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0)]\n [RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) … RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093); RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) … RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093); … ; RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) … RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093); RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) … RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) … RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0); RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) … RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0); … ; RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) … RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0); RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) … RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0) RGB{Float64}(0.9999998474121093,0.9999998474121093,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n ⋮\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) … RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093); RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) … RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093); … ; RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) … RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093); RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) … RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093) RGB{Float64}(1.0,0.9999998474121093,0.9999998474121093)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]\n [RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); … ; RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0); RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)]", + "text/html": [ + "
(a vector displayed as a row to save space)
" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], + "cell_type": "code", + "source": [ + "heatmaps = heatmap(batch, analyzer)" + ], + "metadata": {}, + "execution_count": 18 + }, + { + "cell_type": "markdown", + "source": [ + "Image.jl's `mosaic` function can used to display them in a grid:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "140×140 MosaicViews.MosaicView{ColorTypes.RGB{Float64}, 4, Base.ReshapedArray{ColorTypes.RGB{Float64}, 4, PaddedViews.PaddedView{ColorTypes.RGB{Float64}, 3, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}, Base.OneTo{Int64}}, StackViews.StackView{ColorTypes.RGB{Float64}, 3, 3, Vector{OffsetArrays.OffsetMatrix{ColorTypes.RGB{Float64}, PaddedViews.PaddedView{ColorTypes.RGB{Float64}, 2, Tuple{Base.IdentityUnitRange{UnitRange{Int64}}, Base.IdentityUnitRange{UnitRange{Int64}}}, Matrix{ColorTypes.RGB{Float64}}}}}}}, Tuple{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}, Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}}}}:\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n ⋮ ⋱ \n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)", + "image/png": 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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ], + "cell_type": "code", + "source": [ + "mosaic(heatmaps; nrow=5)" + ], + "metadata": {}, + "execution_count": 19 + }, + { + "cell_type": "markdown", + "source": [ + "When heatmapping batches, the mapping to the color scheme is applied per sample.\n", + "For example, `rangescale=:extrema` will normalize each heatmap\n", + "to the minimum and maximum value of each sample in the batch.\n", + "This ensures that heatmaps don't depend on other samples in the batch.\n", + "\n", + "If this bevahior is not desired,\n", + "`heatmap` can be called with the keyword-argument `process_batch=true`:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "140×140 MosaicViews.MosaicView{ColorTypes.RGB{Float64}, 4, Base.ReshapedArray{ColorTypes.RGB{Float64}, 4, PaddedViews.PaddedView{ColorTypes.RGB{Float64}, 3, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}, Base.OneTo{Int64}}, StackViews.StackView{ColorTypes.RGB{Float64}, 3, 3, Vector{OffsetArrays.OffsetMatrix{ColorTypes.RGB{Float64}, PaddedViews.PaddedView{ColorTypes.RGB{Float64}, 2, Tuple{Base.IdentityUnitRange{UnitRange{Int64}}, Base.IdentityUnitRange{UnitRange{Int64}}}, Matrix{ColorTypes.RGB{Float64}}}}}}}, Tuple{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}, Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}}}}:\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n ⋮ ⋱ \n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)", + "image/png": 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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], + "cell_type": "code", + "source": [ + "expl = analyze(batch, analyzer)\n", + "heatmaps = heatmap(expl; process_batch=true)\n", + "mosaic(heatmaps; nrow=5)" + ], + "metadata": {}, + "execution_count": 20 + }, + { + "cell_type": "markdown", + "source": [ + "This can be useful when comparing heatmaps for fixed output neurons:" + ], + "metadata": {} + }, + { + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "140×140 MosaicViews.MosaicView{ColorTypes.RGB{Float64}, 4, Base.ReshapedArray{ColorTypes.RGB{Float64}, 4, PaddedViews.PaddedView{ColorTypes.RGB{Float64}, 3, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}, Base.OneTo{Int64}}, StackViews.StackView{ColorTypes.RGB{Float64}, 3, 3, Vector{OffsetArrays.OffsetMatrix{ColorTypes.RGB{Float64}, PaddedViews.PaddedView{ColorTypes.RGB{Float64}, 2, Tuple{Base.IdentityUnitRange{UnitRange{Int64}}, Base.IdentityUnitRange{UnitRange{Int64}}}, Matrix{ColorTypes.RGB{Float64}}}}}}}, Tuple{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}, Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}}}}:\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n ⋮ ⋱ \n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) … RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)\n RGB{Float64}(1.0,1.0,1.0) RGB{Float64}(1.0,1.0,1.0)", + "image/png": 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", + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ], + "cell_type": "code", + "source": [ + "expl = analyze(batch, analyzer, 7) # explain digit \"6\"\n", + "heatmaps = heatmap(expl; process_batch=true)\n", + "mosaic(heatmaps; nrow=5)" + ], + "metadata": {}, + "execution_count": 21 + }, + { + "cell_type": "markdown", + "source": [ + "## Processing heatmaps\n", + "Heatmapping makes use of the\n", + "[Julia-based image processing ecosystem Images.jl](https://github.com/JuliaImages).\n", + "\n", + "If you want to further process heatmaps, you may benefit from reading about some\n", + "[fundamental conventions](https://juliaimages.org/latest/tutorials/quickstart/)\n", + "that the ecosystem utilizes that are different from how images are typically represented\n", + "in OpenCV, MATLAB, ImageJ or Python." + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "## Saving heatmaps\n", + "Since heatmaps are regular Images.jl images, they can be saved as such:\n", + "```julia\n", + "using FileIO\n", + "\n", + "img = heatmap(input, analyzer)\n", + "save(\"heatmap.png\", img)\n", + "```" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "---\n", + "\n", + "*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*" + ], + "metadata": {} + } + ], + "nbformat_minor": 3, + "metadata": { + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.4" + }, + "kernelspec": { + "name": "julia-1.10", + "display_name": "Julia 1.10.4", + "language": "julia" + } + }, + "nbformat": 4 +} diff --git a/v0.8.1/generated/heatmapping.jl b/v0.8.1/generated/heatmapping.jl new file mode 100644 index 0000000..03b04f4 --- /dev/null +++ b/v0.8.1/generated/heatmapping.jl @@ -0,0 +1,66 @@ +using ExplainableAI +using VisionHeatmaps +using Flux + +using BSON # hide +model = BSON.load("../model.bson", @__MODULE__)[:model] # hide +model + +using MLDatasets +using ImageCore, ImageIO, ImageShow + +index = 10 +x, y = MNIST(Float32, :test)[10] +input = reshape(x, 28, 28, 1, :) + +img = convert2image(MNIST, x) + +analyzer = Gradient(model) +heatmap(input, analyzer) + +analyzer = InputTimesGradient(model) +heatmap(input, analyzer) + +using ColorSchemes + +expl = analyze(input, analyzer) +heatmap(expl; colorscheme=:jet) + +heatmap(expl; colorscheme=:inferno) + +heatmap(expl; reduce=:sum) + +heatmap(expl; reduce=:norm) + +heatmap(expl; reduce=:maxabs) + +heatmap(expl; rangescale=:centered) + +heatmap(expl; rangescale=:extrema) + +heatmap(expl; rangescale=:centered, colorscheme=:inferno) + +heatmap(expl; rangescale=:extrema, colorscheme=:inferno) + +heatmap_overlay(expl, img) + +heatmap_overlay(expl, img; alpha=0.3) + +heatmap_overlay(expl, img; alpha=0.7, colorscheme=:inferno, rangescale=:extrema) + +xs, ys = MNIST(Float32, :test)[1:25] +batch = reshape(xs, 28, 28, 1, :); # reshape to WHCN format + +heatmaps = heatmap(batch, analyzer) + +mosaic(heatmaps; nrow=5) + +expl = analyze(batch, analyzer) +heatmaps = heatmap(expl; process_batch=true) +mosaic(heatmaps; nrow=5) + +expl = analyze(batch, analyzer, 7) # explain digit "6" +heatmaps = heatmap(expl; process_batch=true) +mosaic(heatmaps; nrow=5) + +# This file was generated using Literate.jl, https://github.com/fredrikekre/Literate.jl diff --git a/v0.8.1/generated/heatmapping/8a7b2f4e.png b/v0.8.1/generated/heatmapping/8a7b2f4e.png new file mode 100644 index 0000000..41fcc7a Binary files /dev/null and b/v0.8.1/generated/heatmapping/8a7b2f4e.png differ diff --git a/v0.8.1/generated/heatmapping/ca3a1608.png b/v0.8.1/generated/heatmapping/ca3a1608.png new file mode 100644 index 0000000..2aa388d Binary files /dev/null and b/v0.8.1/generated/heatmapping/ca3a1608.png differ diff --git a/v0.8.1/generated/heatmapping/ce7ad9ab.png b/v0.8.1/generated/heatmapping/ce7ad9ab.png new file mode 100644 index 0000000..af1bd37 Binary files /dev/null and b/v0.8.1/generated/heatmapping/ce7ad9ab.png differ diff --git a/v0.8.1/generated/heatmapping/index.html b/v0.8.1/generated/heatmapping/index.html new file mode 100644 index 0000000..f91c57a --- /dev/null +++ b/v0.8.1/generated/heatmapping/index.html @@ -0,0 +1,35 @@ + +Heatmapping · ExplainableAI.jl
GitHub

Heatmapping

Since numerical explanations are not very informative at first sight, we can visualize them by computing a heatmap, using either VisionHeatmaps.jl or TextHeatmaps.jl.

This page showcases different options and preset for heatmapping, building on the basics shown in the Getting started section.

We start out by loading the same pre-trained LeNet5 model and MNIST input data:

using ExplainableAI
+using VisionHeatmaps
+using Flux
+
+model
Chain(
+  Conv((5, 5), 1 => 6, relu),           # 156 parameters
+  MaxPool((2, 2)),
+  Conv((5, 5), 6 => 16, relu),          # 2_416 parameters
+  MaxPool((2, 2)),
+  Flux.flatten,
+  Dense(256 => 120, relu),              # 30_840 parameters
+  Dense(120 => 84, relu),               # 10_164 parameters
+  Dense(84 => 10),                      # 850 parameters
+)                   # Total: 10 arrays, 44_426 parameters, 174.867 KiB.
using MLDatasets
+using ImageCore, ImageIO, ImageShow
+
+index = 10
+x, y = MNIST(Float32, :test)[10]
+input = reshape(x, 28, 28, 1, :)
+
+img = convert2image(MNIST, x)

Automatic heatmap presets

The function heatmap automatically applies common presets for each method.

Since InputTimesGradient computes attributions, heatmaps are shown in a blue-white-red color scheme. Gradient methods however are typically shown in grayscale:

analyzer = Gradient(model)
+heatmap(input, analyzer)
analyzer = InputTimesGradient(model)
+heatmap(input, analyzer)

Custom heatmap settings

Color schemes

We can partially or fully override presets by passing keyword arguments to heatmap. For example, we can use a custom color scheme from ColorSchemes.jl using the keyword argument colorscheme:

using ColorSchemes
+
+expl = analyze(input, analyzer)
+heatmap(expl; colorscheme=:jet)
heatmap(expl; colorscheme=:inferno)

Refer to the ColorSchemes.jl catalogue for a gallery of available color schemes.

Color channel reduction

Explanations have the same dimensionality as the inputs to the classifier. For images with multiple color channels, this means that the explanation also has a "color channel" dimension.

The keyword argument reduce can be used to reduce this dimension to a single scalar value for each pixel. The following presets are available:

  • :sum: sum up color channels (default setting)
  • :norm: compute 2-norm over the color channels
  • :maxabs: compute maximum(abs, x) over the color channels
heatmap(expl; reduce=:sum)
heatmap(expl; reduce=:norm)
heatmap(expl; reduce=:maxabs)

In this example, the heatmaps look identical. Since MNIST only has a single color channel, there is no need for color channel reduction.

Mapping explanations onto the color scheme

To map a color-channel-reduced explanation onto a color scheme, we first need to normalize all values to the range $[0, 1]$.

For this purpose, two presets are available through the rangescale keyword argument:

  • :extrema: normalize to the minimum and maximum value of the explanation
  • :centered: normalize to the maximum absolute value of the explanation. Values of zero will be mapped to the center of the color scheme.

Depending on the color scheme, one of these presets may be more suitable than the other. The default color scheme for InputTimesGradient, seismic, is centered around zero, making :centered a good choice:

heatmap(expl; rangescale=:centered)
heatmap(expl; rangescale=:extrema)

However, for the inferno color scheme, which is not centered around zero, :extrema leads to a heatmap with higher contrast.

heatmap(expl; rangescale=:centered, colorscheme=:inferno)
heatmap(expl; rangescale=:extrema, colorscheme=:inferno)

For the full list of heatmap keyword arguments, refer to the heatmap documentation.

Heatmap overlays

Heatmaps can be overlaid onto the input image using the heatmap_overlay function from VisionHeatmaps.jl. This can be useful for visualizing the relevance of specific regions of the input:

heatmap_overlay(expl, img)

The alpha value of the heatmap can be adjusted using the alpha keyword argument:

heatmap_overlay(expl, img; alpha=0.3)

All previously discussed keyword arguments for heatmap can also be used with heatmap_overlay:

heatmap_overlay(expl, img; alpha=0.7, colorscheme=:inferno, rangescale=:extrema)

Heatmapping batches

Heatmapping also works with input batches. Let's demonstrate this by using a batch of 25 images from the MNIST dataset:

xs, ys = MNIST(Float32, :test)[1:25]
+batch = reshape(xs, 28, 28, 1, :); # reshape to WHCN format

The heatmap function automatically recognizes that the explanation is batched and returns a Vector of images:

heatmaps = heatmap(batch, analyzer)
(a vector displayed as a row to save space)

Image.jl's mosaic function can used to display them in a grid:

mosaic(heatmaps; nrow=5)
Example block output

When heatmapping batches, the mapping to the color scheme is applied per sample. For example, rangescale=:extrema will normalize each heatmap to the minimum and maximum value of each sample in the batch. This ensures that heatmaps don't depend on other samples in the batch.

If this bevahior is not desired, heatmap can be called with the keyword-argument process_batch=true:

expl = analyze(batch, analyzer)
+heatmaps = heatmap(expl; process_batch=true)
+mosaic(heatmaps; nrow=5)
Example block output

This can be useful when comparing heatmaps for fixed output neurons:

expl = analyze(batch, analyzer, 7) # explain digit "6"
+heatmaps = heatmap(expl; process_batch=true)
+mosaic(heatmaps; nrow=5)
Example block output
Output type consistency

To obtain a singleton Vector containing a single heatmap for non-batched inputs, use the heatmap keyword argument unpack_singleton=false.

Processing heatmaps

Heatmapping makes use of the Julia-based image processing ecosystem Images.jl.

If you want to further process heatmaps, you may benefit from reading about some fundamental conventions that the ecosystem utilizes that are different from how images are typically represented in OpenCV, MATLAB, ImageJ or Python.

Saving heatmaps

Since heatmaps are regular Images.jl images, they can be saved as such:

using FileIO
+
+img = heatmap(input, analyzer)
+save("heatmap.png", img)

This page was generated using Literate.jl.

diff --git a/v0.8.1/index.html b/v0.8.1/index.html new file mode 100644 index 0000000..eb4c8b5 --- /dev/null +++ b/v0.8.1/index.html @@ -0,0 +1,2 @@ + +Home · ExplainableAI.jl
GitHub

ExplainableAI.jl

Explainable AI methods in Julia.

Note

This package is part of a wider Julia XAI ecosystem. For an introduction to this ecosystem, please refer to the Getting started guide.

Installation

To install this package and its dependencies, open the Julia REPL and run 

julia> ]add ExplainableAI

Manual

API reference

diff --git a/v0.8.1/literate/augmentations.jl b/v0.8.1/literate/augmentations.jl new file mode 100644 index 0000000..0b7438b --- /dev/null +++ b/v0.8.1/literate/augmentations.jl @@ -0,0 +1,84 @@ +# # [Analyzer augmentations](@id docs-augmentations) +# All analyzers implemented in ExplainableAI.jl can be augmented by two types of augmentations: +# [`NoiseAugmentation`](@ref)s and [`InterpolationAugmentation`](@ref)s. +# These augmentations are wrappers around analyzers +# that modify the input before passing it to the analyzer. +# +# We build on the basics shown in the [*Getting started*](@ref docs-getting-started) section +# and start out by loading the same pre-trained LeNet5 model and MNIST input data: +using ExplainableAI +using VisionHeatmaps +using Flux + +using BSON # hide +model = BSON.load("../model.bson", @__MODULE__)[:model] # hide +model +#- +using MLDatasets +using ImageCore, ImageIO, ImageShow + +index = 10 +x, y = MNIST(Float32, :test)[10] +input = reshape(x, 28, 28, 1, :) + +convert2image(MNIST, x) + +# ## Noise augmentation +# The [`NoiseAugmentation`](@ref) wrapper computes explanations averaged over noisy inputs. +# Let's demonstrate this on the [`Gradient`](@ref) analyzer. +# First, we compute the heatmap of an explanation without augmentation: +analyzer = Gradient(model) +heatmap(input, analyzer) + +# Now we wrap the analyzer in a [`NoiseAugmentation`](@ref) with 10 samples of noise. +# By default, the noise is sampled from a Gaussian distribution +# with mean 0 and standard deviation 1. +analyzer = NoiseAugmentation(Gradient(model), 50) +heatmap(input, analyzer) + +# Note that a higher sample size is desired, as it will lead to a smoother heatmap. +# However, this comes at the cost of a longer computation time. +# +# We can also set the standard deviation of the Gaussian distribution: +analyzer = NoiseAugmentation(Gradient(model), 50, 0.1) +heatmap(input, analyzer) + +# When used with a `Gradient` analyzer, this is equivalent to [`SmoothGrad`](@ref): +analyzer = SmoothGrad(model, 50) +heatmap(input, analyzer) + +# We can also use any distribution from +# [Distributions.jl](https://github.com/JuliaStats/Distributions.jl), +# for example Poisson noise with rate $\lambda=0.5$: +using Distributions + +analyzer = NoiseAugmentation(Gradient(model), 50, Poisson(0.5)) +heatmap(input, analyzer) + +# Is is also possible to define your own distributions or mixture distributions. +# +# `NoiseAugmentation` can be combined with any analyzer type from the Julia-XAI ecosystem, +# for example `LRP` from [RelevancePropagation.jl](https://github.com/Julia-XAI/RelevancePropagation.jl). + +# ## Integration augmentation +# The [`InterpolationAugmentation`](@ref) wrapper computes explanations +# averaged over `n` steps of linear interpolation between the input and a reference input, +# which is set to `zero(input)` by default: +analyzer = InterpolationAugmentation(Gradient(model), 50) +heatmap(input, analyzer) + +# When used with a `Gradient` analyzer, this is equivalent to [`IntegratedGradients`](@ref): +analyzer = IntegratedGradients(model, 50) +heatmap(input, analyzer) + +# To select a different reference input, pass it to the `analyze` function +# using the keyword argument `input_ref`. +# Note that this is an arbitrary example for the sake of demonstration. +matrix_of_ones = ones(Float32, size(input)) + +analyzer = InterpolationAugmentation(Gradient(model), 50) +expl = analyzer(input; input_ref=matrix_of_ones) +heatmap(expl) + +# Once again, `InterpolationAugmentation` can be combined with any analyzer type from the Julia-XAI ecosystem, +# for example `LRP` from [RelevancePropagation.jl](https://github.com/Julia-XAI/RelevancePropagation.jl). diff --git a/v0.8.1/literate/example.jl b/v0.8.1/literate/example.jl new file mode 100644 index 0000000..7efa543 --- /dev/null +++ b/v0.8.1/literate/example.jl @@ -0,0 +1,116 @@ +# # [Getting started](@id docs-getting-started) + +#md # !!! note +#md # This package is part of a wider [Julia XAI ecosystem](https://github.com/Julia-XAI). +#md # For an introduction to this ecosystem, please refer to the +#md # [Getting started guide](https://julia-xai.github.io/XAIDocs/). + +# For this first example, we already have loaded a pre-trained LeNet5 model +# to look at explanations on the MNIST dataset. +using ExplainableAI +using Flux + +using BSON # hide +model = BSON.load("../model.bson", @__MODULE__)[:model] # hide +model + +#md # !!! note "Supported models" +#md # +#md # ExplainableAI.jl can be used on any differentiable classifier. + +# ## Preparing the input data +# We use MLDatasets to load a single image from the MNIST dataset: +using MLDatasets +using ImageCore, ImageIO, ImageShow + +index = 10 +x, y = MNIST(Float32, :test)[10] + +convert2image(MNIST, x) + +# By convention in Flux.jl, this input needs to be resized to WHCN format +# by adding a color channel and batch dimensions. +input = reshape(x, 28, 28, 1, :); + +#md # !!! note "Input format" +#md # +#md # For any explanation of a model, ExplainableAI.jl assumes the batch dimension +#md # to come last in the input. +#md # +#md # For the purpose of heatmapping, the input is assumed to be in WHCN order +#md # (width, height, channels, batch), which is Flux.jl's convention. + +# ## Explanations +# We can now select an analyzer of our choice and call [`analyze`](@ref) +# to get an [`Explanation`](@ref): +analyzer = InputTimesGradient(model) +expl = analyze(input, analyzer); + +# The return value `expl` is of type [`Explanation`](@ref) and bundles the following data: +# * `expl.val`: numerical output of the analyzer, e.g. an attribution or gradient +# * `expl.output`: model output for the given analyzer input +# * `expl.output_selection`: index of the output used for the explanation +# * `expl.analyzer`: symbol corresponding the used analyzer, e.g. `:Gradient` or `:LRP` +# * `expl.heatmap`: symbol indicating a preset heatmapping style, +# e.g. `:attibution`, `:sensitivity` or `:cam` +# * `expl.extras`: optional named tuple that can be used by analyzers +# to return additional information. +# +# We used `InputTimesGradient`, so `expl.analyzer` is `:InputTimesGradient`. +expl.analyzer + +# By default, the explanation is computed for the maximally activated output neuron. +# Since our digit is a 9 and Julia's indexing is 1-based, +# the output neuron at index `10` of our trained model is maximally activated. + +# Finally, we obtain the result of the analyzer in form of an array. +expl.val + +# ## Heatmapping basics +# Since the array `expl.val` is not very informative at first sight, +# we can visualize `Explanation`s by computing a `heatmap` using either +# [VisionHeatmaps.jl](https://julia-xai.github.io/XAIDocs/VisionHeatmaps/stable/) or +# [TextHeatmaps.jl](https://julia-xai.github.io/XAIDocs/TextHeatmaps/stable/). +using VisionHeatmaps + +heatmap(expl) + +# If we are only interested in the heatmap, we can combine analysis and heatmapping +# into a single function call: +heatmap(input, analyzer) + +# For a more detailed explanation of the `heatmap` function, +# refer to the [heatmapping section](@ref docs-heatmapping). + +# ## [List of analyzers](@id docs-analyzers-list) + +# ## Neuron selection +# By passing an additional index to our call to [`analyze`](@ref), +# we can compute an explanation with respect to a specific output neuron. +# Let's see why the output wasn't interpreted as a 4 (output neuron at index 5) +expl = analyze(input, analyzer, 5) +heatmap(expl) + +# This heatmap shows us that the "upper loop" of the hand-drawn 9 has negative relevance +# with respect to the output neuron corresponding to digit 4! + +#md # !!! note +#md # +#md # The output neuron can also be specified when calling `heatmap`: +#md # ```julia +#md # heatmap(input, analyzer, 5) +#md # ``` + +# ## Analyzing batches +# ExplainableAI also supports explanations of input batches: +batchsize = 20 +xs, _ = MNIST(Float32, :test)[1:batchsize] +batch = reshape(xs, 28, 28, 1, :) # reshape to WHCN format +expl = analyze(batch, analyzer); + +# This will return a single `Explanation` `expl` for the entire batch. +# Calling `heatmap` on `expl` will detect the batch dimension and return a vector of heatmaps. +heatmap(expl) + +# For more information on heatmapping batches, +# refer to the [heatmapping documentation](@ref docs-heatmapping-batches). diff --git a/v0.8.1/literate/heatmapping.jl b/v0.8.1/literate/heatmapping.jl new file mode 100644 index 0000000..9289dff --- /dev/null +++ b/v0.8.1/literate/heatmapping.jl @@ -0,0 +1,160 @@ +# # [Heatmapping](@id docs-heatmapping) +# Since numerical explanations are not very informative at first sight, +# we can visualize them by computing a `heatmap`, using either +# [VisionHeatmaps.jl](https://julia-xai.github.io/XAIDocs/VisionHeatmaps/stable/) or +# [TextHeatmaps.jl](https://julia-xai.github.io/XAIDocs/TextHeatmaps/stable/). +# +# This page showcases different options and preset for heatmapping, +# building on the basics shown in the [*Getting started*](@ref docs-getting-started) section. +# +# We start out by loading the same pre-trained LeNet5 model and MNIST input data: +using ExplainableAI +using VisionHeatmaps +using Flux + +using BSON # hide +model = BSON.load("../model.bson", @__MODULE__)[:model] # hide +model +#- +using MLDatasets +using ImageCore, ImageIO, ImageShow + +index = 10 +x, y = MNIST(Float32, :test)[10] +input = reshape(x, 28, 28, 1, :) + +img = convert2image(MNIST, x) + +# ## Automatic heatmap presets +# The function `heatmap` automatically applies common presets for each method. +# +# Since [`InputTimesGradient`](@ref) computes attributions, +# heatmaps are shown in a blue-white-red color scheme. +# Gradient methods however are typically shown in grayscale: +analyzer = Gradient(model) +heatmap(input, analyzer) +#- +analyzer = InputTimesGradient(model) +heatmap(input, analyzer) + +# ## Custom heatmap settings +# ### Color schemes +# We can partially or fully override presets by passing keyword arguments to `heatmap`. +# For example, we can use a custom color scheme from ColorSchemes.jl using the keyword argument `colorscheme`: +using ColorSchemes + +expl = analyze(input, analyzer) +heatmap(expl; colorscheme=:jet) +#- +heatmap(expl; colorscheme=:inferno) + +# Refer to the [ColorSchemes.jl catalogue](https://juliagraphics.github.io/ColorSchemes.jl/stable/basics/) +# for a gallery of available color schemes. + +# ### [Color channel reduction](@id docs-heatmap-reduce) +# Explanations have the same dimensionality as the inputs to the classifier. +# For images with multiple color channels, +# this means that the explanation also has a "color channel" dimension. +# +# The keyword argument `reduce` can be used to reduce this dimension +# to a single scalar value for each pixel. +# The following presets are available: +# - `:sum`: sum up color channels (default setting) +# - `:norm`: compute 2-norm over the color channels +# - `:maxabs`: compute `maximum(abs, x)` over the color channels + +heatmap(expl; reduce=:sum) +#- +heatmap(expl; reduce=:norm) +#- +heatmap(expl; reduce=:maxabs) + +# In this example, the heatmaps look identical. +# Since MNIST only has a single color channel, there is no need for color channel reduction. + +# ### [Mapping explanations onto the color scheme](@id docs-heatmap-rangescale) +# To map a [color-channel-reduced](@ref docs-heatmap-reduce) explanation onto a color scheme, +# we first need to normalize all values to the range $[0, 1]$. +# +# For this purpose, two presets are available through the `rangescale` keyword argument: +# - `:extrema`: normalize to the minimum and maximum value of the explanation +# - `:centered`: normalize to the maximum absolute value of the explanation. +# Values of zero will be mapped to the center of the color scheme. +# +# Depending on the color scheme, one of these presets may be more suitable than the other. +# The default color scheme for `InputTimesGradient`, `seismic`, is centered around zero, +# making `:centered` a good choice: +heatmap(expl; rangescale=:centered) +#- +heatmap(expl; rangescale=:extrema) +# However, for the `inferno` color scheme, which is not centered around zero, +# `:extrema` leads to a heatmap with higher contrast. +heatmap(expl; rangescale=:centered, colorscheme=:inferno) +#- +heatmap(expl; rangescale=:extrema, colorscheme=:inferno) + +# For the full list of `heatmap` keyword arguments, refer to the `heatmap` documentation. + +# ## [Heatmap overlays](@id overlay) +# Heatmaps can be overlaid onto the input image using the `heatmap_overlay` function +# from VisionHeatmaps.jl. +# This can be useful for visualizing the relevance of specific regions of the input: +heatmap_overlay(expl, img) + +# The alpha value of the heatmap can be adjusted using the `alpha` keyword argument: +heatmap_overlay(expl, img; alpha=0.3) + +# All previously discussed keyword arguments for `heatmap` can also be used with `heatmap_overlay`: +heatmap_overlay(expl, img; alpha=0.7, colorscheme=:inferno, rangescale=:extrema) + +# ## [Heatmapping batches](@id docs-heatmapping-batches) +# Heatmapping also works with input batches. +# Let's demonstrate this by using a batch of 25 images from the MNIST dataset: +xs, ys = MNIST(Float32, :test)[1:25] +batch = reshape(xs, 28, 28, 1, :); # reshape to WHCN format + +# The `heatmap` function automatically recognizes +# that the explanation is batched and returns a `Vector` of images: +heatmaps = heatmap(batch, analyzer) + +# Image.jl's `mosaic` function can used to display them in a grid: +mosaic(heatmaps; nrow=5) + +# When heatmapping batches, the mapping to the color scheme is applied per sample. +# For example, `rangescale=:extrema` will normalize each heatmap +# to the minimum and maximum value of each sample in the batch. +# This ensures that heatmaps don't depend on other samples in the batch. +# +# If this bevahior is not desired, +# `heatmap` can be called with the keyword-argument `process_batch=true`: +expl = analyze(batch, analyzer) +heatmaps = heatmap(expl; process_batch=true) +mosaic(heatmaps; nrow=5) + +# This can be useful when comparing heatmaps for fixed output neurons: +expl = analyze(batch, analyzer, 7) # explain digit "6" +heatmaps = heatmap(expl; process_batch=true) +mosaic(heatmaps; nrow=5) + +#md # !!! note "Output type consistency" +#md # +#md # To obtain a singleton `Vector` containing a single heatmap for non-batched inputs, +#md # use the `heatmap` keyword argument `unpack_singleton=false`. + +# ## Processing heatmaps +# Heatmapping makes use of the +# [Julia-based image processing ecosystem Images.jl](https://github.com/JuliaImages). +# +# If you want to further process heatmaps, you may benefit from reading about some +# [fundamental conventions](https://juliaimages.org/latest/tutorials/quickstart/) +# that the ecosystem utilizes that are different from how images are typically represented +# in OpenCV, MATLAB, ImageJ or Python. + +# ## Saving heatmaps +# Since heatmaps are regular Images.jl images, they can be saved as such: +# ```julia +# using FileIO +# +# img = heatmap(input, analyzer) +# save("heatmap.png", img) +# ``` diff --git a/v0.8.1/model.bson b/v0.8.1/model.bson new file mode 100644 index 0000000..151a832 Binary files /dev/null and b/v0.8.1/model.bson differ diff --git a/v0.8.1/objects.inv b/v0.8.1/objects.inv new file mode 100644 index 0000000..56cfcfb --- /dev/null +++ b/v0.8.1/objects.inv @@ -0,0 +1,7 @@ +# Sphinx inventory version 2 +# Project: ExplainableAI.jl +# Version: 0.8.1 +# The remainder of this file is compressed using zlib. +xUn0+W575(bu4=ֲ$H*]R*9Jܙ}pf٨ +O%8_#ZXG#1 yw}jK `Lw:Ñp@[QyVn% Y2OI=ZT%g, &ar/_}ה"HBvP{hGOCnZtjӡyhJ8/d{cOZeAt>BX@RB2bxaOq( F+!T؏(I,6w2"`屶1si<T s)cU*Q!Nu%Xђ %e,.*jď*wm,P9v"@;|,] wChRKm#2LBwI)Υ_ |f}"yDB}Ҿ.A"}_7_램 eکxj%d azG {OLC]&tE + rBʙ!:A/~T|P=[* O|гwBȾU|/G!^h-9=0V!Yeąd/4QܪNv \ No newline at end of file diff --git a/v0.8.1/search_index.js b/v0.8.1/search_index.js new file mode 100644 index 0000000..6cbedfb --- /dev/null +++ b/v0.8.1/search_index.js @@ -0,0 +1,3 @@ +var documenterSearchIndex = {"docs": +[{"location":"api/#Basic-API","page":"API Reference","title":"Basic API","text":"","category":"section"},{"location":"api/","page":"API Reference","title":"API Reference","text":"All methods in ExplainableAI.jl work by calling analyze on an input and an analyzer:","category":"page"},{"location":"api/","page":"API Reference","title":"API Reference","text":"analyze\nExplanation","category":"page"},{"location":"api/#XAIBase.analyze","page":"API Reference","title":"XAIBase.analyze","text":"analyze(input, method)\nanalyze(input, method, output_selection)\n\nApply the analyzer method for the given input, returning an Explanation. If output_selection is specified, the explanation will be calculated for that output. Otherwise, the output with the highest activation is automatically chosen.\n\nSee also Explanation.\n\nKeyword arguments\n\nadd_batch_dim: add batch dimension to the input without allocating. Default is false.\n\n\n\n\n\n","category":"function"},{"location":"api/#XAIBase.Explanation","page":"API Reference","title":"XAIBase.Explanation","text":"Explanation(val, output, output_selection, analyzer, heatmap, extras)\n\nReturn type of analyzers when calling analyze.\n\nFields\n\nval: numerical output of the analyzer, e.g. an attribution or gradient\noutput: model output for the given analyzer input\noutput_selection: index of the output used for the explanation\nanalyzer: symbol corresponding the used analyzer, e.g. :Gradient or :LRP\nheatmap: symbol indicating a preset heatmapping style, e.g. :attribution, :sensitivity or :cam\nextras: optional named tuple that can be used by analyzers to return additional information.\n\n\n\n\n\n","category":"type"},{"location":"api/","page":"API Reference","title":"API Reference","text":"For heatmapping functionality, take a look at either VisionHeatmaps.jl or TextHeatmaps.jl. Both provide heatmap methods for visualizing explanations, either for images or text, respectively.","category":"page"},{"location":"api/#Analyzers","page":"API Reference","title":"Analyzers","text":"","category":"section"},{"location":"api/","page":"API Reference","title":"API Reference","text":"Gradient\nInputTimesGradient\nSmoothGrad\nIntegratedGradients\nGradCAM","category":"page"},{"location":"api/#ExplainableAI.Gradient","page":"API Reference","title":"ExplainableAI.Gradient","text":"Gradient(model)\n\nAnalyze model by calculating the gradient of a neuron activation with respect to the input.\n\n\n\n\n\n","category":"type"},{"location":"api/#ExplainableAI.InputTimesGradient","page":"API Reference","title":"ExplainableAI.InputTimesGradient","text":"InputTimesGradient(model)\n\nAnalyze model by calculating the gradient of a neuron activation with respect to the input. 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GradCAM provides a visual explanation of the regions with significant neuron importance for the model's classification decision.\n\nParameters\n\nfeature_layers: The layers of a convolutional neural network (CNN) responsible for extracting feature maps.\nadaptation_layers: The layers of the CNN used for adaptation and classification.\n\nNote\n\nFlux is not required for GradCAM. 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This input augmentation is then averaged to return an Explanation.\n\n\n\n\n\n","category":"type"},{"location":"api/#ExplainableAI.InterpolationAugmentation","page":"API Reference","title":"ExplainableAI.InterpolationAugmentation","text":"InterpolationAugmentation(model, [n=50])\n\nA wrapper around analyzers that augments the input with n steps of linear interpolation between the input and a reference input (typically zero(input)). The gradients w.r.t. this augmented input are then averaged and multiplied with the difference between the input and the reference input.\n\n\n\n\n\n","category":"type"},{"location":"api/#Index","page":"API Reference","title":"Index","text":"","category":"section"},{"location":"api/","page":"API Reference","title":"API Reference","text":"","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"EditURL = \"../literate/heatmapping.jl\"","category":"page"},{"location":"generated/heatmapping/#docs-heatmapping","page":"Heatmapping","title":"Heatmapping","text":"","category":"section"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"Since numerical explanations are not very informative at first sight, we can visualize them by computing a heatmap, using either VisionHeatmaps.jl or TextHeatmaps.jl.","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"This page showcases different options and preset for heatmapping, building on the basics shown in the Getting started section.","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"We start out by loading the same pre-trained LeNet5 model and MNIST input data:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"using ExplainableAI\nusing VisionHeatmaps\nusing Flux\n\nusing BSON # hide\nmodel = BSON.load(\"../model.bson\", @__MODULE__)[:model] # hide\nmodel","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"using MLDatasets\nusing ImageCore, ImageIO, ImageShow\n\nindex = 10\nx, y = MNIST(Float32, :test)[10]\ninput = reshape(x, 28, 28, 1, :)\n\nimg = convert2image(MNIST, x)","category":"page"},{"location":"generated/heatmapping/#Automatic-heatmap-presets","page":"Heatmapping","title":"Automatic heatmap presets","text":"","category":"section"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"The function heatmap automatically applies common presets for each method.","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"Since InputTimesGradient computes attributions, heatmaps are shown in a blue-white-red color scheme. Gradient methods however are typically shown in grayscale:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"analyzer = Gradient(model)\nheatmap(input, analyzer)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"analyzer = InputTimesGradient(model)\nheatmap(input, analyzer)","category":"page"},{"location":"generated/heatmapping/#Custom-heatmap-settings","page":"Heatmapping","title":"Custom heatmap settings","text":"","category":"section"},{"location":"generated/heatmapping/#Color-schemes","page":"Heatmapping","title":"Color schemes","text":"","category":"section"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"We can partially or fully override presets by passing keyword arguments to heatmap. For example, we can use a custom color scheme from ColorSchemes.jl using the keyword argument colorscheme:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"using ColorSchemes\n\nexpl = analyze(input, analyzer)\nheatmap(expl; colorscheme=:jet)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmap(expl; colorscheme=:inferno)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"Refer to the ColorSchemes.jl catalogue for a gallery of available color schemes.","category":"page"},{"location":"generated/heatmapping/#docs-heatmap-reduce","page":"Heatmapping","title":"Color channel reduction","text":"","category":"section"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"Explanations have the same dimensionality as the inputs to the classifier. For images with multiple color channels, this means that the explanation also has a \"color channel\" dimension.","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"The keyword argument reduce can be used to reduce this dimension to a single scalar value for each pixel. The following presets are available:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":":sum: sum up color channels (default setting)\n:norm: compute 2-norm over the color channels\n:maxabs: compute maximum(abs, x) over the color channels","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmap(expl; reduce=:sum)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmap(expl; reduce=:norm)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmap(expl; reduce=:maxabs)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"In this example, the heatmaps look identical. Since MNIST only has a single color channel, there is no need for color channel reduction.","category":"page"},{"location":"generated/heatmapping/#docs-heatmap-rangescale","page":"Heatmapping","title":"Mapping explanations onto the color scheme","text":"","category":"section"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"To map a color-channel-reduced explanation onto a color scheme, we first need to normalize all values to the range 0 1.","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"For this purpose, two presets are available through the rangescale keyword argument:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":":extrema: normalize to the minimum and maximum value of the explanation\n:centered: normalize to the maximum absolute value of the explanation. Values of zero will be mapped to the center of the color scheme.","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"Depending on the color scheme, one of these presets may be more suitable than the other. The default color scheme for InputTimesGradient, seismic, is centered around zero, making :centered a good choice:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmap(expl; rangescale=:centered)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmap(expl; rangescale=:extrema)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"However, for the inferno color scheme, which is not centered around zero, :extrema leads to a heatmap with higher contrast.","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmap(expl; rangescale=:centered, colorscheme=:inferno)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmap(expl; rangescale=:extrema, colorscheme=:inferno)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"For the full list of heatmap keyword arguments, refer to the heatmap documentation.","category":"page"},{"location":"generated/heatmapping/#overlay","page":"Heatmapping","title":"Heatmap overlays","text":"","category":"section"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"Heatmaps can be overlaid onto the input image using the heatmap_overlay function from VisionHeatmaps.jl. This can be useful for visualizing the relevance of specific regions of the input:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmap_overlay(expl, img)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"The alpha value of the heatmap can be adjusted using the alpha keyword argument:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmap_overlay(expl, img; alpha=0.3)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"All previously discussed keyword arguments for heatmap can also be used with heatmap_overlay:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmap_overlay(expl, img; alpha=0.7, colorscheme=:inferno, rangescale=:extrema)","category":"page"},{"location":"generated/heatmapping/#docs-heatmapping-batches","page":"Heatmapping","title":"Heatmapping batches","text":"","category":"section"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"Heatmapping also works with input batches. Let's demonstrate this by using a batch of 25 images from the MNIST dataset:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"xs, ys = MNIST(Float32, :test)[1:25]\nbatch = reshape(xs, 28, 28, 1, :); # reshape to WHCN format\nnothing #hide","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"The heatmap function automatically recognizes that the explanation is batched and returns a Vector of images:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"heatmaps = heatmap(batch, analyzer)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"Image.jl's mosaic function can used to display them in a grid:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"mosaic(heatmaps; nrow=5)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"When heatmapping batches, the mapping to the color scheme is applied per sample. For example, rangescale=:extrema will normalize each heatmap to the minimum and maximum value of each sample in the batch. This ensures that heatmaps don't depend on other samples in the batch.","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"If this bevahior is not desired, heatmap can be called with the keyword-argument process_batch=true:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"expl = analyze(batch, analyzer)\nheatmaps = heatmap(expl; process_batch=true)\nmosaic(heatmaps; nrow=5)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"This can be useful when comparing heatmaps for fixed output neurons:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"expl = analyze(batch, analyzer, 7) # explain digit \"6\"\nheatmaps = heatmap(expl; process_batch=true)\nmosaic(heatmaps; nrow=5)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"note: Output type consistency\nTo obtain a singleton Vector containing a single heatmap for non-batched inputs, use the heatmap keyword argument unpack_singleton=false.","category":"page"},{"location":"generated/heatmapping/#Processing-heatmaps","page":"Heatmapping","title":"Processing heatmaps","text":"","category":"section"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"Heatmapping makes use of the Julia-based image processing ecosystem Images.jl.","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"If you want to further process heatmaps, you may benefit from reading about some fundamental conventions that the ecosystem utilizes that are different from how images are typically represented in OpenCV, MATLAB, ImageJ or Python.","category":"page"},{"location":"generated/heatmapping/#Saving-heatmaps","page":"Heatmapping","title":"Saving heatmaps","text":"","category":"section"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"Since heatmaps are regular Images.jl images, they can be saved as such:","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"using FileIO\n\nimg = heatmap(input, analyzer)\nsave(\"heatmap.png\", img)","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"","category":"page"},{"location":"generated/heatmapping/","page":"Heatmapping","title":"Heatmapping","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"EditURL = \"../literate/augmentations.jl\"","category":"page"},{"location":"generated/augmentations/#docs-augmentations","page":"Input augmentations","title":"Analyzer augmentations","text":"","category":"section"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"All analyzers implemented in ExplainableAI.jl can be augmented by two types of augmentations: NoiseAugmentations and InterpolationAugmentations. These augmentations are wrappers around analyzers that modify the input before passing it to the analyzer.","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"We build on the basics shown in the Getting started section and start out by loading the same pre-trained LeNet5 model and MNIST input data:","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"using ExplainableAI\nusing VisionHeatmaps\nusing Flux\n\nusing BSON # hide\nmodel = BSON.load(\"../model.bson\", @__MODULE__)[:model] # hide\nmodel","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"using MLDatasets\nusing ImageCore, ImageIO, ImageShow\n\nindex = 10\nx, y = MNIST(Float32, :test)[10]\ninput = reshape(x, 28, 28, 1, :)\n\nconvert2image(MNIST, x)","category":"page"},{"location":"generated/augmentations/#Noise-augmentation","page":"Input augmentations","title":"Noise augmentation","text":"","category":"section"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"The NoiseAugmentation wrapper computes explanations averaged over noisy inputs. Let's demonstrate this on the Gradient analyzer. First, we compute the heatmap of an explanation without augmentation:","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"analyzer = Gradient(model)\nheatmap(input, analyzer)","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"Now we wrap the analyzer in a NoiseAugmentation with 10 samples of noise. By default, the noise is sampled from a Gaussian distribution with mean 0 and standard deviation 1.","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"analyzer = NoiseAugmentation(Gradient(model), 50)\nheatmap(input, analyzer)","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"Note that a higher sample size is desired, as it will lead to a smoother heatmap. However, this comes at the cost of a longer computation time.","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"We can also set the standard deviation of the Gaussian distribution:","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"analyzer = NoiseAugmentation(Gradient(model), 50, 0.1)\nheatmap(input, analyzer)","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"When used with a Gradient analyzer, this is equivalent to SmoothGrad:","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"analyzer = SmoothGrad(model, 50)\nheatmap(input, analyzer)","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"We can also use any distribution from Distributions.jl, for example Poisson noise with rate lambda=05:","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"using Distributions\n\nanalyzer = NoiseAugmentation(Gradient(model), 50, Poisson(0.5))\nheatmap(input, analyzer)","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"Is is also possible to define your own distributions or mixture distributions.","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"NoiseAugmentation can be combined with any analyzer type from the Julia-XAI ecosystem, for example LRP from RelevancePropagation.jl.","category":"page"},{"location":"generated/augmentations/#Integration-augmentation","page":"Input augmentations","title":"Integration augmentation","text":"","category":"section"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"The InterpolationAugmentation wrapper computes explanations averaged over n steps of linear interpolation between the input and a reference input, which is set to zero(input) by default:","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"analyzer = InterpolationAugmentation(Gradient(model), 50)\nheatmap(input, analyzer)","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"When used with a Gradient analyzer, this is equivalent to IntegratedGradients:","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"analyzer = IntegratedGradients(model, 50)\nheatmap(input, analyzer)","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"To select a different reference input, pass it to the analyze function using the keyword argument input_ref. Note that this is an arbitrary example for the sake of demonstration.","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"matrix_of_ones = ones(Float32, size(input))\n\nanalyzer = InterpolationAugmentation(Gradient(model), 50)\nexpl = analyzer(input; input_ref=matrix_of_ones)\nheatmap(expl)","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"Once again, InterpolationAugmentation can be combined with any analyzer type from the Julia-XAI ecosystem, for example LRP from RelevancePropagation.jl.","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"","category":"page"},{"location":"generated/augmentations/","page":"Input augmentations","title":"Input augmentations","text":"This page was generated using Literate.jl.","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"EditURL = \"../literate/example.jl\"","category":"page"},{"location":"generated/example/#docs-getting-started","page":"Getting started","title":"Getting started","text":"","category":"section"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"note: Note\nThis package is part of a wider Julia XAI ecosystem. For an introduction to this ecosystem, please refer to the Getting started guide.","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"For this first example, we already have loaded a pre-trained LeNet5 model to look at explanations on the MNIST dataset.","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"using ExplainableAI\nusing Flux\n\nusing BSON # hide\nmodel = BSON.load(\"../model.bson\", @__MODULE__)[:model] # hide\nmodel","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"note: Supported models\nExplainableAI.jl can be used on any differentiable classifier.","category":"page"},{"location":"generated/example/#Preparing-the-input-data","page":"Getting started","title":"Preparing the input data","text":"","category":"section"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"We use MLDatasets to load a single image from the MNIST dataset:","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"using MLDatasets\nusing ImageCore, ImageIO, ImageShow\n\nindex = 10\nx, y = MNIST(Float32, :test)[10]\n\nconvert2image(MNIST, x)","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"By convention in Flux.jl, this input needs to be resized to WHCN format by adding a color channel and batch dimensions.","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"input = reshape(x, 28, 28, 1, :);\nnothing #hide","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"note: Input format\nFor any explanation of a model, ExplainableAI.jl assumes the batch dimension to come last in the input.For the purpose of heatmapping, the input is assumed to be in WHCN order (width, height, channels, batch), which is Flux.jl's convention.","category":"page"},{"location":"generated/example/#Explanations","page":"Getting started","title":"Explanations","text":"","category":"section"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"We can now select an analyzer of our choice and call analyze to get an Explanation:","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"analyzer = InputTimesGradient(model)\nexpl = analyze(input, analyzer);\nnothing #hide","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"The return value expl is of type Explanation and bundles the following data:","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"expl.val: numerical output of the analyzer, e.g. an attribution or gradient\nexpl.output: model output for the given analyzer input\nexpl.output_selection: index of the output used for the explanation\nexpl.analyzer: symbol corresponding the used analyzer, e.g. :Gradient or :LRP\nexpl.heatmap: symbol indicating a preset heatmapping style, e.g. :attibution, :sensitivity or :cam\nexpl.extras: optional named tuple that can be used by analyzers to return additional information.","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"We used InputTimesGradient, so expl.analyzer is :InputTimesGradient.","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"expl.analyzer","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"By default, the explanation is computed for the maximally activated output neuron. Since our digit is a 9 and Julia's indexing is 1-based, the output neuron at index 10 of our trained model is maximally activated.","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"Finally, we obtain the result of the analyzer in form of an array.","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"expl.val","category":"page"},{"location":"generated/example/#Heatmapping-basics","page":"Getting started","title":"Heatmapping basics","text":"","category":"section"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"Since the array expl.val is not very informative at first sight, we can visualize Explanations by computing a heatmap using either VisionHeatmaps.jl or TextHeatmaps.jl.","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"using VisionHeatmaps\n\nheatmap(expl)","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"If we are only interested in the heatmap, we can combine analysis and heatmapping into a single function call:","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"heatmap(input, analyzer)","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"For a more detailed explanation of the heatmap function, refer to the heatmapping section.","category":"page"},{"location":"generated/example/#docs-analyzers-list","page":"Getting started","title":"List of analyzers","text":"","category":"section"},{"location":"generated/example/#Neuron-selection","page":"Getting started","title":"Neuron selection","text":"","category":"section"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"By passing an additional index to our call to analyze, we can compute an explanation with respect to a specific output neuron. Let's see why the output wasn't interpreted as a 4 (output neuron at index 5)","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"expl = analyze(input, analyzer, 5)\nheatmap(expl)","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"This heatmap shows us that the \"upper loop\" of the hand-drawn 9 has negative relevance with respect to the output neuron corresponding to digit 4!","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"note: Note\nThe output neuron can also be specified when calling heatmap:heatmap(input, analyzer, 5)","category":"page"},{"location":"generated/example/#Analyzing-batches","page":"Getting started","title":"Analyzing batches","text":"","category":"section"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"ExplainableAI also supports explanations of input batches:","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"batchsize = 20\nxs, _ = MNIST(Float32, :test)[1:batchsize]\nbatch = reshape(xs, 28, 28, 1, :) # reshape to WHCN format\nexpl = analyze(batch, analyzer);\nnothing #hide","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"This will return a single Explanation expl for the entire batch. Calling heatmap on expl will detect the batch dimension and return a vector of heatmaps.","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"heatmap(expl)","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"For more information on heatmapping batches, refer to the heatmapping documentation.","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"","category":"page"},{"location":"generated/example/","page":"Getting started","title":"Getting started","text":"This page was generated using Literate.jl.","category":"page"},{"location":"","page":"Home","title":"Home","text":"CurrentModule = ExplainableAI","category":"page"},{"location":"#ExplainableAI.jl","page":"Home","title":"ExplainableAI.jl","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"Explainable AI methods in Julia.","category":"page"},{"location":"","page":"Home","title":"Home","text":"note: Note\nThis package is part of a wider Julia XAI ecosystem. For an introduction to this ecosystem, please refer to the Getting started guide.","category":"page"},{"location":"#Installation","page":"Home","title":"Installation","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"To install this package and its dependencies, open the Julia REPL and run ","category":"page"},{"location":"","page":"Home","title":"Home","text":"julia> ]add ExplainableAI","category":"page"},{"location":"#Manual","page":"Home","title":"Manual","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"Pages = [\n \"generated/example.md\",\n \"generated/heatmapping.md\",\n \"generated/augmentations.md\",\n]\nDepth = 3","category":"page"},{"location":"#API-reference","page":"Home","title":"API reference","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"Pages = [\"api.md\"]\nDepth = 2","category":"page"}] +} diff --git a/v0.8.1/siteinfo.js b/v0.8.1/siteinfo.js new file mode 100644 index 0000000..a6be1ae --- /dev/null +++ b/v0.8.1/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "v0.8.1"; diff --git a/versions.js b/versions.js index d3286ce..2c26679 100644 --- a/versions.js +++ b/versions.js @@ -9,5 +9,5 @@ var DOC_VERSIONS = [ "v0.2", "dev", ]; -var DOCUMENTER_NEWEST = "v0.8.0"; +var DOCUMENTER_NEWEST = "v0.8.1"; var DOCUMENTER_STABLE = "stable";