diff --git a/demos/homepage/Gemfile b/demos/homepage/Gemfile deleted file mode 100644 index 8b53357a7e..0000000000 --- a/demos/homepage/Gemfile +++ /dev/null @@ -1,26 +0,0 @@ -source "https://rubygems.org" - -# Hello! This is where you manage which Jekyll version is used to run. -# When you want to use a different version, change it below, save the -# file and run `bundle install`. Run Jekyll with `bundle exec`, like so: -# -# bundle exec jekyll serve -# -# This will help ensure the proper Jekyll version is running. -# Happy Jekylling! - -# This is the default theme for new Jekyll sites. You may change this to anything you like. -gem "minima", "~> 2.0" - -# If you want to use GitHub Pages, remove the "gem "jekyll"" above and -# uncomment the line below. To upgrade, run `bundle update github-pages`. -gem "github-pages", group: :jekyll_plugins - -# If you have any plugins, put them here! -group :jekyll_plugins do - gem "jekyll-feed", "~> 0.6" -end - -# Windows does not include zoneinfo files, so bundle the tzinfo-data gem -#gem 'tzinfo-data', platforms: [:mingw, :mswin, :x64_mingw, :jruby] - diff --git a/demos/homepage/Gemfile.lock b/demos/homepage/Gemfile.lock deleted file mode 100644 index 147c3924b8..0000000000 --- a/demos/homepage/Gemfile.lock +++ /dev/null @@ -1,205 +0,0 @@ -GEM - remote: https://rubygems.org/ - specs: - activesupport (4.2.8) - i18n (~> 0.7) - minitest (~> 5.1) - thread_safe (~> 0.3, >= 0.3.4) - tzinfo (~> 1.1) - addressable (2.5.1) - public_suffix (~> 2.0, >= 2.0.2) - coffee-script (2.4.1) - coffee-script-source - execjs - coffee-script-source (1.12.2) - colorator (1.1.0) - ethon (0.10.1) - ffi (>= 1.3.0) - execjs (2.7.0) - faraday (0.12.2) - multipart-post (>= 1.2, < 3) - ffi (1.9.18) - forwardable-extended (2.6.0) - gemoji (3.0.0) - github-pages (146) - activesupport (= 4.2.8) - github-pages-health-check (= 1.3.5) - jekyll (= 3.4.5) - jekyll-avatar (= 0.4.2) - jekyll-coffeescript (= 1.0.1) - jekyll-default-layout (= 0.1.4) - jekyll-feed (= 0.9.2) - jekyll-gist (= 1.4.0) - jekyll-github-metadata (= 2.5.1) - jekyll-mentions (= 1.2.0) - jekyll-optional-front-matter (= 0.2.0) - jekyll-paginate (= 1.1.0) - jekyll-readme-index (= 0.1.0) - jekyll-redirect-from (= 0.12.1) - jekyll-relative-links (= 0.4.1) - jekyll-sass-converter (= 1.5.0) - jekyll-seo-tag (= 2.2.3) - jekyll-sitemap (= 1.0.0) - jekyll-swiss (= 0.4.0) - jekyll-theme-architect (= 0.0.4) - jekyll-theme-cayman (= 0.0.4) - jekyll-theme-dinky (= 0.0.4) - jekyll-theme-hacker (= 0.0.4) - jekyll-theme-leap-day (= 0.0.4) - jekyll-theme-merlot (= 0.0.4) - jekyll-theme-midnight (= 0.0.4) - jekyll-theme-minimal (= 0.0.4) - jekyll-theme-modernist (= 0.0.4) - jekyll-theme-primer (= 0.3.1) - jekyll-theme-slate (= 0.0.4) - jekyll-theme-tactile (= 0.0.4) - jekyll-theme-time-machine (= 0.0.4) - jekyll-titles-from-headings (= 0.2.0) - jemoji (= 0.8.0) - kramdown (= 1.13.2) - liquid (= 3.0.6) - listen (= 3.0.6) - mercenary (~> 0.3) - minima (= 2.1.1) - rouge (= 1.11.1) - terminal-table (~> 1.4) - github-pages-health-check (1.3.5) - addressable (~> 2.3) - net-dns (~> 0.8) - octokit (~> 4.0) - public_suffix (~> 2.0) - typhoeus (~> 0.7) - html-pipeline (2.6.0) - activesupport (>= 2) - nokogiri (>= 1.4) - i18n (0.8.6) - jekyll (3.4.5) - addressable (~> 2.4) - colorator (~> 1.0) - jekyll-sass-converter (~> 1.0) - jekyll-watch (~> 1.1) - kramdown (~> 1.3) - liquid (~> 3.0) - mercenary (~> 0.3.3) - pathutil (~> 0.9) - rouge (~> 1.7) - safe_yaml (~> 1.0) - jekyll-avatar (0.4.2) - jekyll (~> 3.0) - jekyll-coffeescript (1.0.1) - coffee-script (~> 2.2) - jekyll-default-layout (0.1.4) - jekyll (~> 3.0) - jekyll-feed (0.9.2) - jekyll (~> 3.3) - jekyll-gist (1.4.0) - octokit (~> 4.2) - jekyll-github-metadata (2.5.1) - jekyll (~> 3.1) - octokit (~> 4.0, != 4.4.0) - jekyll-mentions (1.2.0) - activesupport (~> 4.0) - html-pipeline (~> 2.3) - jekyll (~> 3.0) - jekyll-optional-front-matter (0.2.0) - jekyll (~> 3.0) - jekyll-paginate (1.1.0) - jekyll-readme-index (0.1.0) - jekyll (~> 3.0) - jekyll-redirect-from (0.12.1) - jekyll (~> 3.3) - jekyll-relative-links (0.4.1) - jekyll (~> 3.3) - jekyll-sass-converter (1.5.0) - sass (~> 3.4) - jekyll-seo-tag (2.2.3) - jekyll (~> 3.3) - jekyll-sitemap (1.0.0) - jekyll (~> 3.3) - jekyll-swiss (0.4.0) - jekyll-theme-architect (0.0.4) - jekyll (~> 3.3) - jekyll-theme-cayman (0.0.4) - jekyll (~> 3.3) - jekyll-theme-dinky (0.0.4) - jekyll (~> 3.3) - jekyll-theme-hacker (0.0.4) - jekyll (~> 3.3) - jekyll-theme-leap-day (0.0.4) - jekyll (~> 3.3) - jekyll-theme-merlot (0.0.4) - jekyll (~> 3.3) - jekyll-theme-midnight (0.0.4) - jekyll (~> 3.3) - jekyll-theme-minimal (0.0.4) - jekyll (~> 3.3) - jekyll-theme-modernist (0.0.4) - jekyll (~> 3.3) - jekyll-theme-primer (0.3.1) - jekyll (~> 3.3) - jekyll-theme-slate (0.0.4) - jekyll (~> 3.3) - jekyll-theme-tactile (0.0.4) - jekyll (~> 3.3) - jekyll-theme-time-machine (0.0.4) - jekyll (~> 3.3) - jekyll-titles-from-headings (0.2.0) - jekyll (~> 3.3) - jekyll-watch (1.5.0) - listen (~> 3.0, < 3.1) - jemoji (0.8.0) - activesupport (~> 4.0) - gemoji (~> 3.0) - html-pipeline (~> 2.2) - jekyll (>= 3.0) - kramdown (1.13.2) - liquid (3.0.6) - listen (3.0.6) - rb-fsevent (>= 0.9.3) - rb-inotify (>= 0.9.7) - mercenary (0.3.6) - mini_portile2 (2.2.0) - minima (2.1.1) - jekyll (~> 3.3) - minitest (5.10.3) - multipart-post (2.0.0) - net-dns (0.8.0) - nokogiri (1.8.0) - mini_portile2 (~> 2.2.0) - octokit (4.7.0) - sawyer (~> 0.8.0, >= 0.5.3) - pathutil (0.14.0) - forwardable-extended (~> 2.6) - public_suffix (2.0.5) - rb-fsevent (0.10.2) - rb-inotify (0.9.10) - ffi (>= 0.5.0, < 2) - rouge (1.11.1) - safe_yaml (1.0.4) - sass (3.5.1) - sass-listen (~> 4.0.0) - sass-listen (4.0.0) - rb-fsevent (~> 0.9, >= 0.9.4) - rb-inotify (~> 0.9, >= 0.9.7) - sawyer (0.8.1) - addressable (>= 2.3.5, < 2.6) - faraday (~> 0.8, < 1.0) - terminal-table (1.8.0) - unicode-display_width (~> 1.1, >= 1.1.1) - thread_safe (0.3.6) - typhoeus (0.8.0) - ethon (>= 0.8.0) - tzinfo (1.2.3) - thread_safe (~> 0.1) - unicode-display_width (1.3.0) - -PLATFORMS - ruby - -DEPENDENCIES - github-pages - jekyll-feed (~> 0.6) - minima (~> 2.0) - -BUNDLED WITH - 1.15.3 diff --git a/demos/homepage/_config.yml b/demos/homepage/_config.yml deleted file mode 100644 index cb0c8718ed..0000000000 --- a/demos/homepage/_config.yml +++ /dev/null @@ -1,33 +0,0 @@ -# Welcome to Jekyll! -# -# This config file is meant for settings that affect your whole blog, values -# which you are expected to set up once and rarely edit after that. If you find -# yourself editing this file very often, consider using Jekyll's data files -# feature for the data you need to update frequently. -# -# For technical reasons, this file is *NOT* reloaded automatically when you use -# 'bundle exec jekyll serve'. If you change this file, please restart the server process. - -# Site settings -# These are used to personalize your new site. If you look in the HTML files, -# you will see them accessed via {{ site.title }}, {{ site.email }}, and so on. -# You can create any custom variable you would like, and they will be accessible -# in the templates via {{ site.myvariable }}. -title: deeplearn.js -baseurl: "" # the subpath of your site, e.g. /blog - -# Build settings -markdown: kramdown -theme: minima -gems: - - jekyll-feed - - jekyll-redirect-from -exclude: - - Gemfile - - Gemfile.lock - - node_modules/ - - demos/node_modules/ - - demos/bower_components/ - -# deployment -host: 0.0.0.0 diff --git a/demos/homepage/_includes/footer.html b/demos/homepage/_includes/footer.html deleted file mode 100644 index eca84845e6..0000000000 --- a/demos/homepage/_includes/footer.html +++ /dev/null @@ -1,32 +0,0 @@ - - - - diff --git a/demos/homepage/_includes/header.html b/demos/homepage/_includes/header.html deleted file mode 100644 index 852ea76eb2..0000000000 --- a/demos/homepage/_includes/header.html +++ /dev/null @@ -1,39 +0,0 @@ - -
- -
- - deeplearn.js - -
- - -
-
- -
- deeplearn.js - -
diff --git a/demos/homepage/_layouts/default.html b/demos/homepage/_layouts/default.html deleted file mode 100644 index 895f99b077..0000000000 --- a/demos/homepage/_layouts/default.html +++ /dev/null @@ -1,50 +0,0 @@ - - - - - - deeplearn.js - - - - - - - - - - - - - - - - - - - - - - -
- {% include header.html %} -
- {{ content }} - {% include footer.html %} -
-
- - diff --git a/demos/homepage/_layouts/page.html b/demos/homepage/_layouts/page.html deleted file mode 100644 index 8f69ea1b80..0000000000 --- a/demos/homepage/_layouts/page.html +++ /dev/null @@ -1,24 +0,0 @@ ---- -layout: default ---- -
-
-
-
- {{ content }} -
-
-
-
- {% assign pages_list = site.pages | sort: 'order' %} - -
-
diff --git a/demos/homepage/assets/style.css b/demos/homepage/assets/style.css deleted file mode 100644 index 20200fc79f..0000000000 --- a/demos/homepage/assets/style.css +++ /dev/null @@ -1,325 +0,0 @@ -html { - width: 100%; - height: 100%; -} -body { - font-family: "Roboto", "Helvetica", "Arial", sans-serif; - margin: 0; - width: 100%; - height: 100%; -} -head { - height: 0; - width: 0; -} -/* Typography */ -p { - font-family: 'Roboto', sans-serif; - line-height: 1.67em; - font-size: 16px; -} -.mdl-list__item a { - text-decoration: none; - color: rgba(0,0,0,0.64); - font-weight: 400; - letter-spacing: 0; -} -.mdl-list__item a:hover { - text-decoration: underline; - color: rgba(0,0,0,0.64); - font-weight: 400; - letter-spacing: 0; -} -.mdl-navigation__link{ - font-family: 'Roboto', sans-serif; -} -.mdl-layout__drawer { - font-family: 'Roboto', sans-serif; -} - -/* CPPN Demo */ -.banner { - background-size: cover; - background-position: center; - min-height: 250px; - padding-top: 64px; - padding-bottom: 32px; - position: relative; -} -.banner-cover { - position: relative; - background: -webkit-linear-gradient(#eeede2, #a3d5bc, #6ab8b5, #42737f); - background: -moz-linear-gradient(#eeede2, #a3d5bc, #6ab8b5, #42737f); - background: linear-gradient(#eeede2, #a3d5bc, #6ab8b5, #42737f); -} -.banner-text { - color:#fff; -} -.cppn-controls { - min-width: 300px; - max-width: 300px; - padding:24px 24px 60px 24px; - background-color: rgba(255,255,255,0.99); - position: relative; -} -.cppn-demo { - color:#fff; -} -#disabled-demo-overlay { - position: absolute; - width: 100%; - height: 100%; - background-color: rgba(62, 82, 90, .9); - top: 0; - left: 0; -} -#disabled-demo { - margin: auto; - position: absolute; - top: 20%; - left: 0; - right: 0; - padding: 32px; - bottom: 0; - font-size: 18px; - color: white; - font-weight: 400; - text-align: left; - line-height: 1.3325em; - z-index: 2; -} -#inference { - width: 100%; - height: calc(100% + 1px); - position: absolute; -} -.getmdl-select .mdl-icon-toggle__label{ - float:right; - margin-top:-30px; - color:rgba(0,0,0,0.4); -} -/* Introduction */ -.intro-text { - margin: 32px auto 32px auto; -} -.mdl-grid { - max-width: 1200px; -} -.mdl-mini-footer { - margin-top: 72px; -} -.mdl-card__actions { - padding-left: 12px; -} -.intro-headline { - line-height: 1.67em; - font-weight: 300; - color: #888; -} -/* Responsive behavior */ -.intro-text > .mdl-cell--8-col-tablet { - margin-top: 72px; -} -.banner >.mdl-cell--8-col-tablet { - margin: 36px; -} -.resource-tabs { - font-family: 'Roboto', sans-serif; -} - -a { - color: #346f91; - font-size: 16px; -} -a:visited { - color: #346f91; -} -a:hover { - color: #346f91; -} -.mdl-card__actions a:hover { - text-decoration: none; -} - -a.main-title { - color: white; - text-decoration: none; -} - -blockquote { - color: black; - padding: 8px 8px 8px 20px; - border-left-width: 2px; - font-style: normal; - border-left-style: solid; - border-color: #9e9e9e; - background-color: #eee; - margin-bottom: 32px; -} -blockquote:before { - content: none; -} -blockquote:after { - content: none; -} -ul { - margin-left: 16px; - list-style-type: disc; -} -ul ul { - list-style-type: disc; -} -ul ul ul { - list-style-type: disc; -} -ul ul ul ul { - list-style-type: disc; -} -.highlight .err { - color: inherit; - background-color: inherit; -} -table { - border-spacing: 20px; -} -.site-nav { - position: fixed; - float: inherit; -} -.site-nav ul { - margin-top: 32px; - border-left: solid 3px #C0EbF1; - padding-left: 20px; - line-height: 28px; - list-style-type: none; - line-height: 1.3em; - font-weight: normal; -} -.site-nav ul li { - padding-bottom: 20px; -} -.site-nav ul a { - color: #50797f; - font-weight: normal; -} -ul.index { - border-left: 2px solid #346f91; - margin-left: 16px; - margin-top: 16px; - padding-left: 16px; - list-style-type: none; -} -.deeplearn-shine { - color: #777; - font-weight: 500; -} - -.featured-demo div.mdl-card__title { - height: 352px; - color: white; - z-index: 1; - padding: 5px 12px; - font-weight: 400; - font-size: 48px; -} - -.featured-demo h1.mdl-card__title-text { - font-size: 36px; - margin: 0 0 2px 2px; - padding: 5px 12px; -} - -.featured-demo div.mdl-card__title:before { - background: none; -} - -.demo-card .mdl-card__title { - color: #fff; - height: 176px; -} - -.demo-card .mdl-card__title:before { - content: ''; - height: 176px; - position: absolute; - top: 0; - right: 0; - bottom: 0; - left: 0; - background: rgba(41, 73, 77, 0.73); -} - -div.mdl-card__title h1.mdl-card__title-text { - font-weight: 200; - font-size: 0.6em; - letter-spacing: 0.5px; - background-color: rgba(0, 0, 0, .54); -} - -#teachable-machine { - background: url('../demos/images/teachablemachine_preview.jpg') center / cover; -} - -#perf-rnn { - background: url('../demos/images/performance_rnn_preview.jpg') center / cover; -} - -#model-builder { - background: url('../demos/images/model-builder.png') center / cover; -} - -#webcam { - background: url('../demos/images/imagenet.png') center / cover; -} - -#nnart { - background: url('../demos/images/nn-art.png') center / cover; -} - -#benchmarks { - background: url('../demos/images/benchmark.png') center / cover; -} - -#playground { - background: url('../demos/images/playground_preview.png') center / cover; -} - -.demo-card .mdl-card__title-text { - color: white; - z-index: 1; - font-weight: 400; -} - -.mdl-typography--display-2, h1, h2, h3, h4 { - color: #414141; - font-weight: 300; -} -code { - background: none; -} -.highlighter-rouge .highlight { - background-color: #f5f5f5; - padding-bottom: 16px; - margin-bottom: 32px; -} - -h2 { - font-size: 40px; -} - -h3 { - font-size: 30px; -} -p.intro-body { - font-weight: 300; -} -.mdl-mini-footer__link-list a { - color: rgb(158, 158, 158); -} -.mdl-mini-footer__link-list a:visited { - color: rgb(158, 158, 158); -} - -#travis-badge { - display: none; -} diff --git a/demos/homepage/assets/support.js b/demos/homepage/assets/support.js deleted file mode 100644 index 77b9334efb..0000000000 --- a/demos/homepage/assets/support.js +++ /dev/null @@ -1,68 +0,0 @@ -/** - * @license - * Copyright 2017 Google Inc. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */ -function isWebGLEnabled() { - var canvas = document.createElement('canvas'); - - var attributes = { - alpha: false, - antialias: false, - premultipliedAlpha: false, - preserveDrawingBuffer: false, - depth: false, - stencil: false, - failIfMajorPerformanceCaveat: true - }; - return null != (canvas.getContext('webgl', attributes) || - canvas.getContext('experimental-webgl', attributes)); -} - -function buildAndShowDialog(title, content) { - var dialogContainer = document.createElement('div'); - dialogContainer.innerHTML = ` - -

${title}

-
-

${content}

-
- - `; - document.body.appendChild(dialogContainer); - var dialog = document.getElementById('dialog'); - dialog.style.width = '430px'; - dialogPolyfill.registerDialog(dialog); - dialog.showModal(); -} - -function inializePolymerPage() { - document.addEventListener('WebComponentsReady', function(event) { - if (!isWebGLEnabled()) { - const title = `Check if hardware acceleration is enabled.`; - const content = ` - Looks like your device is supported but settings aren't in place. - Please check if WebGL is enabled for your browser. - - See: How can I enable WebGL in my browser? - `; - buildAndShowDialog(title, content); - } else { - var bundleScript = document.createElement('script'); - bundleScript.src = 'bundle.js'; - document.head.appendChild(bundleScript); - } - }); -} -inializePolymerPage(); diff --git a/demos/homepage/homepage.ts b/demos/homepage/homepage.ts deleted file mode 100644 index 60971f821d..0000000000 --- a/demos/homepage/homepage.ts +++ /dev/null @@ -1,140 +0,0 @@ -/** - * @license - * Copyright 2017 Google Inc. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */ - -import {ENV} from 'deeplearn'; -import {ActivationFunction, CPPN} from '../nn-art/cppn'; - -const inferenceCanvas = - document.querySelector('#inference') as HTMLCanvasElement; - -if (ENV.get('WEBGL_VERSION') >= 1) { - startCPPN(); -} else { - document.getElementById('disabled-demo-overlay').style.display = ''; - inferenceCanvas.style.display = 'none'; -} - -function startCPPN() { - const DEFAULT_Z_SCALE = 1; - const NUM_NEURONS = 30; - const DEFAULT_NUM_LAYERS = 2; - const WEIGHTS_STDEV = 0.6; - - const cppn = new CPPN(inferenceCanvas); - - cppn.setActivationFunction('tanh'); - cppn.setNumLayers(DEFAULT_NUM_LAYERS); - cppn.setZ1Scale(convertZScale(DEFAULT_Z_SCALE)); - cppn.setZ2Scale(convertZScale(DEFAULT_Z_SCALE)); - cppn.generateWeights(NUM_NEURONS, WEIGHTS_STDEV); - cppn.start(); - - const currentActivationFnElement = - document.querySelector('#activation-fn') as HTMLInputElement; - document.querySelector('#activation-selector') - .addEventListener( - // tslint:disable-next-line:no-any - 'click', (event: any) => { - const activationFn = - (event.target as HTMLElement).getAttribute('data-val') as - ActivationFunction; - currentActivationFnElement.value = activationFn; - cppn.setActivationFunction(activationFn); - }); - - const layersSlider = - document.querySelector('#layers-slider') as HTMLInputElement; - const layersCountElement = - document.querySelector('#layers-count') as HTMLDivElement; - layersSlider.addEventListener('input', (event) => { - // tslint:disable-next-line:no-any - const numLayers = parseInt((event as any).target.value, 10); - layersCountElement.innerText = numLayers.toString(); - cppn.setNumLayers(numLayers); - }); - layersCountElement.innerText = DEFAULT_NUM_LAYERS.toString(); - - const z1Slider = document.querySelector('#z1-slider') as HTMLInputElement; - z1Slider.addEventListener('input', (event) => { - // tslint:disable-next-line:no-any - const z1Scale = parseInt((event as any).target.value, 10); - cppn.setZ1Scale(convertZScale(z1Scale)); - }); - - const z2Slider = document.querySelector('#z2-slider') as HTMLInputElement; - z2Slider.addEventListener('input', (event) => { - // tslint:disable-next-line:no-any - const z2Scale = parseInt((event as any).target.value, 10); - cppn.setZ2Scale(convertZScale(z2Scale)); - }); - - const randomizeButton = - document.querySelector('#random') as HTMLButtonElement; - randomizeButton.addEventListener('click', () => { - cppn.generateWeights(NUM_NEURONS, WEIGHTS_STDEV); - if (!playing) { - cppn.start(); - requestAnimationFrame(() => { - cppn.stopInferenceLoop(); - }); - } - }); - - let playing = true; - const toggleButton = document.querySelector('#toggle') as HTMLButtonElement; - toggleButton.addEventListener('click', () => { - playing = !playing; - if (playing) { - toggleButton.innerHTML = 'STOP'; - cppn.start(); - } else { - toggleButton.innerHTML = 'START'; - cppn.stopInferenceLoop(); - } - }); - - let canvasOnScreenLast = true; - let scrollEventScheduled = false; - const mainElement = document.querySelector('main') as HTMLElement; - mainElement.addEventListener('scroll', () => { - if (!scrollEventScheduled) { - window.requestAnimationFrame(() => { - const canvasOnScreen = isCanvasOnScreen(); - if (canvasOnScreen !== canvasOnScreenLast) { - if (canvasOnScreen) { - if (playing) { - cppn.start(); - } - } else { - cppn.stopInferenceLoop(); - } - canvasOnScreenLast = canvasOnScreen; - } - scrollEventScheduled = false; - }); - } - scrollEventScheduled = true; - }); - - function isCanvasOnScreen() { - return mainElement.scrollTop < inferenceCanvas.offsetHeight; - } - - function convertZScale(z: number): number { - return (103 - z); - } -} diff --git a/demos/homepage/index.md b/demos/homepage/index.md deleted file mode 100644 index e78b532d85..0000000000 --- a/demos/homepage/index.md +++ /dev/null @@ -1,306 +0,0 @@ ---- -layout: default ---- - - - - -
-
-
-

deeplearn.js is an open-source library that brings performant machine learning building blocks to the web, allowing you to train neural networks in a browser or run pre-trained models in inference mode.

-
-
-
-

We provide an API that closely mirrors - the TensorFlow eager API.

deeplearn.js - was originally developed by the Google Brain PAIR team to build powerful - interactive machine learning tools for the browser. You can use the library - for everything from education, to model understanding, to art projects.

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Examples

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Interactive Playground

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Play with deeplearn.js code in the browser with no installs
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Performance RNN

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Enjoy a real-time piano performance by a neural network
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Teachable Machine

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Explore machine learning, no coding required!
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Build a neural network in your browser, without code!
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Squeezenet running in the browser
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Test the library's performance
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- {% capture my_include %}{% include README.md %}{% endcapture %} - {{ my_include | markdownify }} -
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Acknowledgements

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- deeplearn.js was originally developed by - , , and - Charles Nicholson. -

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- We would like to acknowledge Chi Zeng, David Farhi, Mahima Pushkarna, - Lauren Hannah-Murphy, Minsuk (Brian) Kahng, James Wexler, Martin Wattenberg, - Fernanda Viégas, Greg Corrado, Jeff Dean for their tremendous help, and the - Google Brain team for providing support for the project. -

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- diff --git a/demos/homepage/preview.png b/demos/homepage/preview.png deleted file mode 100644 index 6c8d5610b1..0000000000 Binary files a/demos/homepage/preview.png and /dev/null differ diff --git a/package.json b/package.json index ab6bb36a34..db9c270187 100644 --- a/package.json +++ b/package.json @@ -1,11 +1,11 @@ { - "name": "deeplearn", + "name": "@tensorflow/tfjs-core", "version": "0.6.0-alpha7", "description": "Hardware-accelerated JavaScript library for machine intelligence", "private": false, "main": "dist/index.js", - "jsdelivr": "dist/deeplearn.min.js", - "unpkg": "dist/deeplearn.min.js", + "jsdelivr": "dist/tf-core.min.js", + "unpkg": "dist/tf-core.min.js", "types": "dist/index.d.ts", "repository": { "type": "git", diff --git a/scripts/build-npm.sh b/scripts/build-npm.sh index 807c39ac9b..4a93f7428f 100755 --- a/scripts/build-npm.sh +++ b/scripts/build-npm.sh @@ -19,7 +19,7 @@ set -e rimraf dist/ yarn tsc --sourceMap false -browserify --standalone dl src/index.ts -p [tsify] > dist/deeplearn.js -uglifyjs dist/deeplearn.js -c -m -o dist/deeplearn.min.js -echo "Stored standalone library at dist/deeplearn(.min).js" +browserify --standalone tfc src/index.ts -p [tsify] > dist/tf-core.js +uglifyjs dist/tf-core.js -c -m -o dist/tf-core.min.js +echo "Stored standalone library at dist/tf-core(.min).js" npm pack diff --git a/src/browser_util.ts b/src/browser_util.ts index b84058cbbc..26e5c9fbc3 100644 --- a/src/browser_util.ts +++ b/src/browser_util.ts @@ -21,7 +21,7 @@ export class BrowserUtil { * Returns a promise that resolve when a requestAnimationFrame has completed. * * This is simply a sugar method so that users can do the following: - * `await dl.nextFrame();` + * `await tf.nextFrame();` */ @doc({heading: 'Performance', subheading: 'Timing'}) static nextFrame(): Promise { diff --git a/src/environment.ts b/src/environment.ts index e6301624d5..b96c53b774 100644 --- a/src/environment.ts +++ b/src/environment.ts @@ -222,7 +222,7 @@ export class Environment { * * @param backendType The backend type. Currently supports `'webgl'|'cpu'`. * @param safeMode Defaults to false. In safe mode, you are forced to - * construct tensors and call math operations inside a `dl.tidy()` which + * construct tensors and call math operations inside a `tidy()` which * will automatically clean up intermediate tensors. */ @doc({heading: 'Environment'}) @@ -256,7 +256,7 @@ export class Environment { * - `unreliable`: `Optional` `boolean`: * - On WebGL, not present (always reliable). * - On CPU, true. Due to automatic garbage collection, these numbers - * represent undisposed tensors, i.e. not wrapped in `dl.tidy()`, or + * represent undisposed tensors, i.e. not wrapped in `tidy()`, or * lacking a call to `tensor.dispose()`. */ @doc({heading: 'Performance', subheading: 'Memory'}) diff --git a/src/gradients.ts b/src/gradients.ts index 847d779245..e26ee83601 100644 --- a/src/gradients.ts +++ b/src/gradients.ts @@ -53,21 +53,21 @@ export class Gradients { * // f(x) = x ^ 2 * const f = x => x.square(); * // f'(x) = 2x - * const g = dl.grad(f); + * const g = tf.grad(f); * - * const x = dl.tensor1d([2, 3]); + * const x = tf.tensor1d([2, 3]); * g(x).print(); * ``` * * ```js * // f(x) = x ^ 3 - * const f = x => x.pow(dl.scalar(3, 'int32')); + * const f = x => x.pow(tf.scalar(3, 'int32')); * // f'(x) = 3x ^ 2 - * const g = dl.grad(f); + * const g = tf.grad(f); * // f''(x) = 6x - * const gg = dl.grad(g); + * const gg = tf.grad(g); * - * const x = dl.tensor1d([2, 3]); + * const x = tf.tensor1d([2, 3]); * gg(x).print(); * ``` * @@ -111,10 +111,10 @@ export class Gradients { * // f(a, b) = a * b * const f = (a, b) => a.mul(b); * // df / da = b, df / db = a - * const g = dl.grads(f); + * const g = tf.grads(f); * - * const a = dl.tensor1d([2, 3]); - * const b = dl.tensor1d([-2, -3]); + * const a = tf.tensor1d([2, 3]); + * const b = tf.tensor1d([-2, -3]); * const [da, db] = g([a, b]); * console.log('da'); * da.print(); @@ -150,7 +150,7 @@ export class Gradients { } /** - * Like `dl.grad`, but also returns the value of `f()`. Useful when `f()` + * Like `grad`, but also returns the value of `f()`. Useful when `f()` * returns a metric you want to show. * * The result is a rich object with the following properties: @@ -161,9 +161,9 @@ export class Gradients { * // f(x) = x ^ 2 * const f = x => x.square(); * // f'(x) = 2x - * const g = dl.valueAndGrad(f); + * const g = tf.valueAndGrad(f); * - * const x = dl.tensor1d([2, 3]); + * const x = tf.tensor1d([2, 3]); * const {value, grad} = g(x); * * console.log('value'); @@ -206,10 +206,10 @@ export class Gradients { * // f(a, b) = a * b * const f = (a, b) => a.mul(b); * // df/da = b, df/db = a - * const g = dl.valueAndGrads(f); + * const g = tf.valueAndGrads(f); * - * const a = dl.tensor1d([2, 3]); - * const b = dl.tensor1d([-2, -3]); + * const a = tf.tensor1d([2, 3]); + * const b = tf.tensor1d([-2, -3]); * const {value, grads} = g([a, b]); * * const [da, db] = grads; @@ -257,14 +257,14 @@ export class Gradients { * defaults to all trainable variables. * * ```js - * const a = dl.variable(dl.tensor1d([3, 4])); - * const b = dl.variable(dl.tensor1d([5, 6])); - * const x = dl.tensor1d([1, 2]); + * const a = tf.variable(tf.tensor1d([3, 4])); + * const b = tf.variable(tf.tensor1d([5, 6])); + * const x = tf.tensor1d([1, 2]); * * // f(a, b) = a * x ^ 2 + b * x * const f = () => a.mul(x.square()).add(b.mul(x)).sum(); * // df/da = x ^ 2, df/db = x - * const {value, grads} = dl.variableGrads(f); + * const {value, grads} = tf.variableGrads(f); * * Object.keys(grads).forEach(varName => grads[varName].print()); * ``` @@ -332,13 +332,13 @@ export class Gradients { * respect to each input of `f` are computed using `f().gradFunc`. * * ```js - * const customOp = dl.customGrad(x => { + * const customOp = tf.customGrad(x => { * // Override gradient of our custom x ^ 2 op to be dy * abs(x); * return {value: x.square(), gradFunc: dy => [dy.mul(x.abs())]}; * }); * - * const x = dl.tensor1d([-1, -2, 3]); - * const dx = dl.grad(x => customOp(x)); + * const x = tf.tensor1d([-1, -2, 3]); + * const dx = tf.grad(x => customOp(x)); * * console.log(`f(x):`); * customOp(x).print(); diff --git a/src/ops/array_ops.ts b/src/ops/array_ops.ts index a6a37723df..6ce0130063 100644 --- a/src/ops/array_ops.ts +++ b/src/ops/array_ops.ts @@ -36,18 +36,18 @@ export class ArrayOps { * * ```js * // Pass an array of values to create a vector. - * dl.tensor([1, 2, 3, 4]).print(); + * tf.tensor([1, 2, 3, 4]).print(); * ``` * * ```js * // Pass a nested array of values to make a matrix or a higher * // dimensional tensor. - * dl.tensor([[1, 2], [3, 4]]).print(); + * tf.tensor([[1, 2], [3, 4]]).print(); * ``` * * ```js * // Pass a flat array and specify a shape yourself. - * dl.tensor([1, 2, 3, 4], [2, 2]).print(); + * tf.tensor([1, 2, 3, 4], [2, 2]).print(); * ``` * * @param values The values of the tensor. Can be nested array of numbers, @@ -80,12 +80,11 @@ export class ArrayOps { /** * Creates rank-0 `Tensor` (scalar) with the provided value and dtype. * - * This method is mainly for self documentation and TypeScript typings as the - * same functionality can be achieved with `tensor`. In general, we recommend - * using this method as it makes code more readable. + * The same functionality can be achieved with `tensor`, but in general + * we recommend using `scalar` as it makes the code more readable. * * ```js - * dl.scalar(3.14).print(); + * tf.scalar(3.14).print(); * ``` * * @param value The value of the scalar. @@ -104,12 +103,11 @@ export class ArrayOps { /** * Creates rank-1 `Tensor` with the provided values, shape and dtype. * - * This method is mainly for self documentation and TypeScript typings as the - * same functionality can be achieved with `tensor`. In general, we recommend - * using this method as it makes code more readable. + * The same functionality can be achieved with `tensor`, but in general + * we recommend using `tensor1d` as it makes the code more readable. * * ```js - * dl.tensor1d([1, 2, 3]).print(); + * tf.tensor1d([1, 2, 3]).print(); * ``` * * @param values The values of the tensor. Can be array of numbers, @@ -129,17 +127,16 @@ export class ArrayOps { /** * Creates rank-2 `Tensor` with the provided values, shape and dtype. * - * This method is mainly for self documentation and TypeScript typings as the - * same functionality can be achieved with `tensor`. In general, we recommend - * using this method as it makes code more readable. + * The same functionality can be achieved with `tensor`, but in general + * we recommend using `tensor2d` as it makes the code more readable. * * ```js * // Pass a nested array. - * dl.tensor2d([[1, 2], [3, 4]]).print(); + * tf.tensor2d([[1, 2], [3, 4]]).print(); * ``` * ```js * // Pass a flat array and specify a shape. - * dl.tensor2d([1, 2, 3, 4], [2, 2]).print(); + * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(); * ``` * * @param values The values of the tensor. Can be nested array of numbers, @@ -165,17 +162,16 @@ export class ArrayOps { /** * Creates rank-3 `Tensor` with the provided values, shape and dtype. * - * This method is mainly for self documentation and TypeScript typings as - * the same functionality can be achieved with `tensor`. In general, we - * recommend using this method as it makes code more readable. + * The same functionality can be achieved with `tensor`, but in general + * we recommend using `tensor3d` as it makes the code more readable. * * ```js * // Pass a nested array. - * dl.tensor3d([[[1], [2]], [[3], [4]]]).print(); + * tf.tensor3d([[[1], [2]], [[3], [4]]]).print(); * ``` * ```js * // Pass a flat array and specify a shape. - * dl.tensor3d([1, 2, 3, 4], [2, 2, 1]).print(); + * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print(); * ``` * * @param values The values of the tensor. Can be nested array of numbers, @@ -200,13 +196,17 @@ export class ArrayOps { /** * Creates rank-4 `Tensor` with the provided values, shape and dtype. + * + * The same functionality can be achieved with `tensor`, but in general + * we recommend using `tensor4d` as it makes the code more readable. + * * ```js * // Pass a nested array. - * dl.tensor4d([[[[1], [2]], [[3], [4]]]]).print(); + * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print(); * ``` * ```js * // Pass a flat array and specify a shape. - * dl.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print(); + * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print(); * ``` * * @param values The values of the tensor. Can be nested array of numbers, @@ -233,7 +233,7 @@ export class ArrayOps { * Creates a `Tensor` with all elements set to 1. * * ```js - * dl.ones([2, 2]).print(); + * tf.ones([2, 2]).print(); * ``` * * @param shape An array of integers defining the output tensor shape. @@ -252,7 +252,7 @@ export class ArrayOps { * Creates a `Tensor` with all elements set to 0. * * ```js - * dl.zeros([2, 2]).print(); + * tf.zeros([2, 2]).print(); * ``` * * @param shape An array of integers defining the output tensor shape. @@ -271,7 +271,7 @@ export class ArrayOps { * Creates a `Tensor` filled with a scalar value. * * ```js - * dl.fill([2, 2], 4).print(); + * tf.fill([2, 2], 4).print(); * ``` * * @param shape An array of integers defining the output tensor shape. @@ -295,8 +295,8 @@ export class ArrayOps { * given tensor. * * ```js - * const x = dl.tensor([1, 2]); - * dl.onesLike(x).print(); + * const x = tf.tensor([1, 2]); + * tf.onesLike(x).print(); * ``` * @param x A tensor. */ @@ -311,8 +311,8 @@ export class ArrayOps { * given tensor. * * ```js - * const x = dl.tensor([1, 2]); - * dl.zerosLike(x).print(); + * const x = tf.tensor([1, 2]); + * tf.zerosLike(x).print(); * ``` * * @param x The tensor of required shape. @@ -328,7 +328,7 @@ export class ArrayOps { * tensor. * * ```js - * const x = dl.tensor([1, 2]); + * const x = tf.tensor([1, 2]); * x.clone().print(); * ``` * @@ -344,7 +344,7 @@ export class ArrayOps { * Creates a `Tensor` with values sampled from a normal distribution. * * ```js - * dl.randomNormal([2, 2]).print(); + * tf.randomNormal([2, 2]).print(); * ``` * * @param shape An array of integers defining the output tensor shape. @@ -375,7 +375,7 @@ export class ArrayOps { * distribution. * * ```js - * dl.truncatedNormal([2, 2]).print(); + * tf.truncatedNormal([2, 2]).print(); * ``` * * The generated values follow a normal distribution with specified mean and @@ -413,7 +413,7 @@ export class ArrayOps { * bound maxval is excluded. * * ```js - * dl.randomUniform([2, 2]).print(); + * tf.randomUniform([2, 2]).print(); * ``` * * @param shape An array of integers defining the output tensor shape. @@ -518,7 +518,7 @@ export class ArrayOps { * `offValue` (defaults to 0). * * ```js - * dl.oneHot(dl.tensor1d([0, 1]), 3).print(); + * tf.oneHot(tf.tensor1d([0, 1]), 3).print(); * ``` * * @param indices 1D Array of indices. @@ -550,7 +550,7 @@ export class ArrayOps { * image.data[2] = 200; * image.data[3] = 255; * - * dl.fromPixels(image).print(); + * tf.fromPixels(image).print(); * ``` * * @param pixels The input image to construct the tensor from. @@ -587,7 +587,7 @@ export class ArrayOps { * tensor. * * ```js - * const x = dl.tensor1d([1, 2, 3, 4]); + * const x = tf.tensor1d([1, 2, 3, 4]); * x.reshape([2, 2]).print(); * ``` * @@ -613,7 +613,7 @@ export class ArrayOps { * Removes dimensions of size 1 from the shape of a `Tensor`. * * ```js - * const x = dl.tensor([1, 2, 3, 4], [1, 1, 4]); + * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]); * x.squeeze().print(); * ``` * @@ -631,8 +631,8 @@ export class ArrayOps { * Casts a `Tensor` to a new dtype. * * ```js - * const x = dl.tensor1d([1.5, 2.5, 3]); - * dl.cast(x, 'int32').print(); + * const x = tf.tensor1d([1.5, 2.5, 3]); + * tf.cast(x, 'int32').print(); * ``` * @param x The input tensor to be casted. * @param dtype The dtype to cast the input tensor to. @@ -657,13 +657,13 @@ export class ArrayOps { * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`. * * ```js - * const a = dl.tensor1d([1, 2]); + * const a = tf.tensor1d([1, 2]); * * a.tile([2]).print(); // or a.tile([2]) * ``` * * ```js - * const a = dl.tensor2d([1, 2, 3, 4], [2, 2]); + * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * a.tile([1, 2]).print(); // or a.tile([1, 2]) * ``` @@ -734,15 +734,15 @@ export class ArrayOps { * Gather slices from tensor `x`'s axis `axis` according to `indices`. * * ```js - * const x = dl.tensor1d([1, 2, 3, 4]); - * const indices = dl.tensor1d([1, 3, 3]); + * const x = tf.tensor1d([1, 2, 3, 4]); + * const indices = tf.tensor1d([1, 3, 3]); * * x.gather(indices).print(); * ``` * * ```js - * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]); - * const indices = dl.tensor1d([1, 1, 0]); + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * const indices = tf.tensor1d([1, 1, 0]); * * x.gather(indices).print(); * ``` @@ -818,11 +818,10 @@ export class ArrayOps { /** * Pads a `Tensor` with a given value and paddings. * - * This operation currently only implements the `CONSTANT` mode from - * Tensorflow's `pad` operation. + * This operation currently only implements the `CONSTANT` mode. * * ```js - * const x = dl.tensor1d([1, 2, 3, 4]); + * const x = tf.tensor1d([1, 2, 3, 4]); * x.pad([[1, 2]]).print(); * ``` * @param x The tensor to pad. @@ -853,10 +852,10 @@ export class ArrayOps { * Stacks a list of rank-`R` `Tensor`s into one rank-`(R+1)` `Tensor`. * * ```js - * const a = dl.tensor1d([1, 2]); - * const b = dl.tensor1d([3, 4]); - * const c = dl.tensor1d([5, 6]); - * dl.stack([a, b, c]).print(); + * const a = tf.tensor1d([1, 2]); + * const b = tf.tensor1d([3, 4]); + * const c = tf.tensor1d([5, 6]); + * tf.stack([a, b, c]).print(); * ``` * * @param tensors A list of tensor objects with the same shape and dtype. @@ -892,7 +891,7 @@ export class ArrayOps { * into the tensor's shape. * * ```js - * const x = dl.tensor1d([1, 2, 3, 4]); + * const x = tf.tensor1d([1, 2, 3, 4]); * const axis = 1; * x.expandDims(axis).print(); * ``` @@ -914,7 +913,7 @@ export class ArrayOps { * Return an evenly spaced sequence of numbers over the given interval. * * ```js - * dl.linspace(0, 9, 10).print(); + * tf.linspace(0, 9, 10).print(); * ``` * @param start The start value of the sequence. * @param stop The end value of the sequence. @@ -948,7 +947,7 @@ export class ArrayOps { * supported. * * ```js - * dl.range(0, 9, 2).print(); + * tf.range(0, 9, 2).print(); * ``` * * @param start An integer start value @@ -1003,7 +1002,7 @@ export class ArrayOps { * * ```js * // Create a buffer and set values at particular indices. - * const buffer = dl.buffer([2, 2]); + * const buffer = tf.buffer([2, 2]); * buffer.set(3, 0, 0); * buffer.set(5, 1, 0); * @@ -1027,7 +1026,7 @@ export class ArrayOps { * * ```js * const verbose = true; - * dl.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose); + * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose); * ``` * @param x The tensor to be printed. * @param verbose Whether to print verbose information about the ` Tensor`, diff --git a/src/ops/binary_ops.ts b/src/ops/binary_ops.ts index 2a86523fc0..672f8aec32 100644 --- a/src/ops/binary_ops.ts +++ b/src/ops/binary_ops.ts @@ -32,18 +32,18 @@ export class BinaryOps { * asserts that `a` and `b` are the same shape (does not broadcast). * * ```js - * const a = dl.tensor1d([1, 2, 3, 4]); - * const b = dl.tensor1d([10, 20, 30, 40]); + * const a = tf.tensor1d([1, 2, 3, 4]); + * const b = tf.tensor1d([10, 20, 30, 40]); * - * a.add(b).print(); // or dl.add(a, b) + * a.add(b).print(); // or tf.add(a, b) * ``` * * ```js * // Broadcast add a with b. - * const a = dl.scalar(5); - * const b = dl.tensor1d([10, 20, 30, 40]); + * const a = tf.scalar(5); + * const b = tf.tensor1d([10, 20, 30, 40]); * - * a.add(b).print(); // or dl.add(a, b) + * a.add(b).print(); // or tf.add(a, b) * ``` * @param a The first `Tensor` to add. * @param b The second `Tensor` to add. Must have the same type as `a`. @@ -98,18 +98,18 @@ export class BinaryOps { * asserts that `a` and `b` are the same shape (does not broadcast). * * ```js - * const a = dl.tensor1d([10, 20, 30, 40]); - * const b = dl.tensor1d([1, 2, 3, 4]); + * const a = tf.tensor1d([10, 20, 30, 40]); + * const b = tf.tensor1d([1, 2, 3, 4]); * - * a.sub(b).print(); // or dl.sub(a, b) + * a.sub(b).print(); // or tf.sub(a, b) * ``` * * ```js * // Broadcast subtract a with b. - * const a = dl.tensor1d([10, 20, 30, 40]); - * const b = dl.scalar(5); + * const a = tf.tensor1d([10, 20, 30, 40]); + * const b = tf.scalar(5); * - * a.sub(b).print(); // or dl.sub(a, b) + * a.sub(b).print(); // or tf.sub(a, b) * ``` * @param a The first `Tensor` to subtract from. * @param b The second `Tensor` to be subtracted. Must have the same dtype as @@ -167,17 +167,17 @@ export class BinaryOps { * corresponding elements in x and y. * * ```js - * const a = dl.tensor([[2, 3], [4, 5]]) - * const b = dl.tensor([[1, 2], [3, 0]]).toInt(); + * const a = tf.tensor([[2, 3], [4, 5]]) + * const b = tf.tensor([[1, 2], [3, 0]]).toInt(); * - * a.pow(b).print(); // or dl.pow(a, b) + * a.pow(b).print(); // or tf.pow(a, b) * ``` * * ```js - * const a = dl.tensor([[1, 2], [3, 4]]) - * const b = dl.tensor(2).toInt(); + * const a = tf.tensor([[1, 2], [3, 4]]) + * const b = tf.tensor(2).toInt(); * - * a.pow(b).print(); // or dl.pow(a, b) + * a.pow(b).print(); // or tf.pow(a, b) * ``` * We also expose `powStrict` which has the same signature as this op and * asserts that `base` and `exp` are the same shape (does not broadcast). @@ -198,8 +198,8 @@ export class BinaryOps { } const derBase = () => { const expFloat = exp.toFloat(); - const dx = expFloat.mul( - base.toFloat().pow(expFloat.sub(scalar(1)))) as T; + const dx = + expFloat.mul(base.toFloat().pow(expFloat.sub(scalar(1)))) as T; return dy.mulStrict(dx) as T; }; return {base: derBase}; @@ -230,18 +230,18 @@ export class BinaryOps { * asserts that `a` and `b` are the same shape (does not broadcast). * * ```js - * const a = dl.tensor1d([1, 2, 3, 4]); - * const b = dl.tensor1d([2, 3, 4, 5]); + * const a = tf.tensor1d([1, 2, 3, 4]); + * const b = tf.tensor1d([2, 3, 4, 5]); * - * a.mul(b).print(); // or dl.mul(a, b) + * a.mul(b).print(); // or tf.mul(a, b) * ``` * * ```js * // Broadcast mul a with b. - * const a = dl.tensor1d([1, 2, 3, 4]); - * const b = dl.scalar(5); + * const a = tf.tensor1d([1, 2, 3, 4]); + * const b = tf.scalar(5); * - * a.mul(b).print(); // or dl.mul(a, b) + * a.mul(b).print(); // or tf.mul(a, b) * ``` * @param a The first tensor to multiply. * @param b The second tensor to multiply. Must have the same dtype as `a`. @@ -298,18 +298,18 @@ export class BinaryOps { * asserts that `a` and `b` are the same shape (does not broadcast). * * ```js - * const a = dl.tensor1d([1, 4, 9, 16]); - * const b = dl.tensor1d([1, 2, 3, 4]); + * const a = tf.tensor1d([1, 4, 9, 16]); + * const b = tf.tensor1d([1, 2, 3, 4]); * - * a.div(b).print(); // or dl.div(a, b) + * a.div(b).print(); // or tf.div(a, b) * ``` * * ```js * // Broadcast div a with b. - * const a = dl.tensor1d([2, 4, 6, 8]); - * const b = dl.scalar(2); + * const a = tf.tensor1d([2, 4, 6, 8]); + * const b = tf.scalar(2); * - * a.div(b).print(); // or dl.div(a, b) + * a.div(b).print(); // or tf.div(a, b) * ``` * * @param a The first tensor as the numerator. @@ -366,18 +366,18 @@ export class BinaryOps { * asserts that `a` and `b` are the same shape (does not broadcast). * * ```js - * const a = dl.tensor1d([1, 4, 3, 16]); - * const b = dl.tensor1d([1, 2, 9, 4]); + * const a = tf.tensor1d([1, 4, 3, 16]); + * const b = tf.tensor1d([1, 2, 9, 4]); * - * a.minimum(b).print(); // or dl.minimum(a, b) + * a.minimum(b).print(); // or tf.minimum(a, b) * ``` * * ```js * // Broadcast minimum a with b. - * const a = dl.tensor1d([2, 4, 6, 8]); - * const b = dl.scalar(5); + * const a = tf.tensor1d([2, 4, 6, 8]); + * const b = tf.scalar(5); * - * a.minimum(b).print(); // or dl.minimum(a, b) + * a.minimum(b).print(); // or tf.minimum(a, b) * ``` * * @param a The first tensor. @@ -418,18 +418,18 @@ export class BinaryOps { * asserts that `a` and `b` are the same shape (does not broadcast). * * ```js - * const a = dl.tensor1d([1, 4, 3, 16]); - * const b = dl.tensor1d([1, 2, 9, 4]); + * const a = tf.tensor1d([1, 4, 3, 16]); + * const b = tf.tensor1d([1, 2, 9, 4]); * - * a.maximum(b).print(); // or dl.maximum(a, b) + * a.maximum(b).print(); // or tf.maximum(a, b) * ``` * * ```js * // Broadcast maximum a with b. - * const a = dl.tensor1d([2, 4, 6, 8]); - * const b = dl.scalar(5); + * const a = tf.tensor1d([2, 4, 6, 8]); + * const b = tf.scalar(5); * - * a.maximum(b).print(); // or dl.maximum(a, b) + * a.maximum(b).print(); // or tf.maximum(a, b) * ``` * * @param a The first tensor. diff --git a/src/ops/concat.ts b/src/ops/concat.ts index e5e805808a..e5af04babc 100644 --- a/src/ops/concat.ts +++ b/src/ops/concat.ts @@ -122,23 +122,23 @@ export class ConcatOps { * dimensions except `axis`. * * ```js - * const a = dl.tensor1d([1, 2]); - * const b = dl.tensor1d([3, 4]); + * const a = tf.tensor1d([1, 2]); + * const b = tf.tensor1d([3, 4]); * a.concat(b).print(); // or a.concat(b) * ``` * * ```js - * const a = dl.tensor1d([1, 2]); - * const b = dl.tensor1d([3, 4]); - * const c = dl.tensor1d([5, 6]); - * dl.concat([a, b, c]).print(); + * const a = tf.tensor1d([1, 2]); + * const b = tf.tensor1d([3, 4]); + * const c = tf.tensor1d([5, 6]); + * tf.concat([a, b, c]).print(); * ``` * * ```js - * const a = dl.tensor2d([[1, 2], [10, 20]]); - * const b = dl.tensor2d([[3, 4], [30, 40]]); + * const a = tf.tensor2d([[1, 2], [10, 20]]); + * const b = tf.tensor2d([[3, 4], [30, 40]]); * const axis = 1; - * dl.concat([a, b], axis).print(); + * tf.concat([a, b], axis).print(); * ``` * @param tensors A list of tensors to concatenate. * @param axis The axis to concate along. Defaults to 0 (the first dim). diff --git a/src/ops/matmul.ts b/src/ops/matmul.ts index 92f2704ebc..b41cf56574 100644 --- a/src/ops/matmul.ts +++ b/src/ops/matmul.ts @@ -26,10 +26,10 @@ export class MatmulOps { * Computes the dot product of two matrices, A * B. These must be matrices. * * ```js - * const a = dl.tensor2d([1, 2], [1, 2]); - * const b = dl.tensor2d([1, 2, 3, 4], [2, 2]); + * const a = tf.tensor2d([1, 2], [1, 2]); + * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]); * - * a.matMul(b).print(); // or dl.matMul(a, b) + * a.matMul(b).print(); // or tf.matMul(a, b) * ``` * @param a First matrix in dot product operation. * @param b Second matrix in dot product operation. @@ -140,10 +140,10 @@ export class MatmulOps { * Computes the outer product of two vectors, v1 and v2. * * ```js - * const a = dl.tensor1d([1, 2, 3]); - * const b = dl.tensor1d([3, 4, 5]); + * const a = tf.tensor1d([1, 2, 3]); + * const b = tf.tensor1d([3, 4, 5]); * - * dl.outerProduct(a, b).print(); + * tf.outerProduct(a, b).print(); * ``` * @param v1 The first vector in the outer product operation. * @param v2 The second vector in the dot product operation. diff --git a/src/ops/norm.ts b/src/ops/norm.ts index 50685266b1..557f03e9f3 100644 --- a/src/ops/norm.ts +++ b/src/ops/norm.ts @@ -30,9 +30,9 @@ export class NormOps { * and matrix norms (Frobenius, 1-norm, and inf-norm). * * ```js - * const x = dl.tensor1d([1, 2, 3, 4]); + * const x = tf.tensor1d([1, 2, 3, 4]); * - * x.norm().print(); // or dl.norm(x) + * x.norm().print(); // or tf.norm(x) * ``` * * @param x The input array. diff --git a/src/ops/reduction_ops.ts b/src/ops/reduction_ops.ts index 561b36061f..cd4a5a3b9d 100644 --- a/src/ops/reduction_ops.ts +++ b/src/ops/reduction_ops.ts @@ -35,16 +35,16 @@ export class ReductionOps { * single element is returned. * * ```js - * const x = dl.tensor1d([1, 2, 3]); + * const x = tf.tensor1d([1, 2, 3]); * - * x.logSumExp().print(); // or dl.logSumExp(x) + * x.logSumExp().print(); // or tf.logSumExp(x) * ``` * * ```js - * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]); + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; - * x.logSumExp(axis).print(); // or dl.logSumExp(a, axis) + * x.logSumExp(axis).print(); // or tf.logSumExp(a, axis) * ``` * @param input The input tensor. * @param axis The dimension(s) to reduce. If null (the default), @@ -81,16 +81,16 @@ export class ReductionOps { * single element is returned. * * ```js - * const x = dl.tensor1d([1, 2, 3]); + * const x = tf.tensor1d([1, 2, 3]); * - * x.sum().print(); // or dl.logSumExp(x) + * x.sum().print(); // or tf.logSumExp(x) * ``` * * ```js - * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]); + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; - * x.sum(axis).print(); // or dl.sum(x, axis) + * x.sum(axis).print(); // or tf.sum(x, axis) * ``` * * @param x The input tensor to compute the sum over. @@ -147,16 +147,16 @@ export class ReductionOps { * a single element is returned. * * ```js - * const x = dl.tensor1d([1, 2, 3]); + * const x = tf.tensor1d([1, 2, 3]); * - * x.mean().print(); // or dl.logSumExp(a) + * x.mean().print(); // or tf.logSumExp(a) * ``` * * ```js - * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]); + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; - * x.mean(axis).print(); // or dl.mean(x, axis) + * x.mean(axis).print(); // or tf.mean(x, axis) * ``` * * @param x The input tensor. @@ -206,16 +206,16 @@ export class ReductionOps { * single element is returned. * * ```js - * const x = dl.tensor1d([1, 2, 3]); + * const x = tf.tensor1d([1, 2, 3]); * - * x.min().print(); // or dl.min(x) + * x.min().print(); // or tf.min(x) * ``` * * ```js - * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]); + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; - * x.min(axis).print(); // or dl.min(x, axis) + * x.min(axis).print(); // or tf.min(x, axis) * ``` * * @param x The input Tensor. @@ -252,16 +252,16 @@ export class ReductionOps { * a single element is returned. * * ```js - * const x = dl.tensor1d([1, 2, 3]); + * const x = tf.tensor1d([1, 2, 3]); * - * x.max().print(); // or dl.max(x) + * x.max().print(); // or tf.max(x) * ``` * * ```js - * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]); + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; - * x.max(axis).print(); // or dl.max(x, axis) + * x.max(axis).print(); // or tf.max(x, axis) * ``` * * @param x The input tensor. @@ -295,16 +295,16 @@ export class ReductionOps { * removed. * * ```js - * const x = dl.tensor1d([1, 2, 3]); + * const x = tf.tensor1d([1, 2, 3]); * - * x.argMin().print(); // or dl.argMin(x) + * x.argMin().print(); // or tf.argMin(x) * ``` * * ```js - * const x = dl.tensor2d([1, 2, 4, 3], [2, 2]); + * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]); * * const axis = 1; - * x.argMin(axis).print(); // or dl.argMin(x, axis) + * x.argMin(axis).print(); // or tf.argMin(x, axis) * ``` * * @param x The input tensor. @@ -331,16 +331,16 @@ export class ReductionOps { * removed. * * ```js - * const x = dl.tensor1d([1, 2, 3]); + * const x = tf.tensor1d([1, 2, 3]); * - * x.argMax().print(); // or dl.argMax(x) + * x.argMax().print(); // or tf.argMax(x) * ``` * * ```js - * const x = dl.tensor2d([1, 2, 4, 3], [2, 2]); + * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]); * * const axis = 1; - * x.argMax(axis).print(); // or dl.argMax(x, axis) + * x.argMax(axis).print(); // or tf.argMax(x, axis) * ``` * * @param x The input tensor. diff --git a/src/ops/reverse.ts b/src/ops/reverse.ts index 20bd76b4c0..ee413813da 100644 --- a/src/ops/reverse.ts +++ b/src/ops/reverse.ts @@ -75,13 +75,13 @@ export class ReverseOps { * Reverses a `Tensor` along a specified axis. * * ```js - * const x = dl.tensor1d([1, 2, 3, 4]); + * const x = tf.tensor1d([1, 2, 3, 4]); * * x.reverse().print(); * ``` * * ```js - * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]); + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; * x.reverse(axis).print(); diff --git a/src/ops/slice.ts b/src/ops/slice.ts index 4437a0d4a1..7e942000b4 100644 --- a/src/ops/slice.ts +++ b/src/ops/slice.ts @@ -79,19 +79,19 @@ export class SliceOps { * * Also available are stricter rank-specific methods with the same signature * as this method that assert that `x` is of the given rank: - * - `dl.slice1d` - * - `dl.slice2d` - * - `dl.slice3d` - * - `dl.slice4d` + * - `tf.slice1d` + * - `tf.slice2d` + * - `tf.slice3d` + * - `tf.slice4d` * * ```js - * const x = dl.tensor1d([1, 2, 3, 4]); + * const x = tf.tensor1d([1, 2, 3, 4]); * * x.slice([1], [2]).print(); * ``` * * ```js - * const x = dl.tensor2d([1, 2, 3, 4], [2, 2]); + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * x.slice([1, 0], [1, 2]).print(); * ``` diff --git a/src/ops/softmax.ts b/src/ops/softmax.ts index ea1cc84cd8..cd4c0b977a 100644 --- a/src/ops/softmax.ts +++ b/src/ops/softmax.ts @@ -29,15 +29,15 @@ export class SoftmaxOps { * Computes the softmax normalized vector given the logits. * * ```js - * const a = dl.tensor1d([1, 2, 3]); + * const a = tf.tensor1d([1, 2, 3]); * - * a.softmax().print(); // or dl.softmax(a) + * a.softmax().print(); // or tf.softmax(a) * ``` * * ```js - * const a = dl.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]); + * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]); * - * a.softmax().print(); // or dl.softmax(a) + * a.softmax().print(); // or tf.softmax(a) * ``` * * @param logits The logits array. diff --git a/src/ops/transpose.ts b/src/ops/transpose.ts index a725174075..d2a18f99ad 100644 --- a/src/ops/transpose.ts +++ b/src/ops/transpose.ts @@ -32,9 +32,9 @@ export class TransposeOps { * operation performs a regular matrix transpose on 2-D input `Tensor`s. * * ```js - * const a = dl.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]); + * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]); * - * a.transpose().print(); // or dl.transpose(a) + * a.transpose().print(); // or tf.transpose(a) * ``` * * @param x The tensor to transpose. diff --git a/src/ops/unary_ops.ts b/src/ops/unary_ops.ts index 01ef0d370e..aed5d3efb2 100644 --- a/src/ops/unary_ops.ts +++ b/src/ops/unary_ops.ts @@ -29,9 +29,9 @@ export class UnaryOps { * Computes `-1 * x` element-wise. * * ```js - * const x = dl.tensor2d([1, 2, -2, 0], [2, 2]); + * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]); * - * x.neg().print(); // or dl.neg(x) + * x.neg().print(); // or tf.neg(x) * ``` * * @param x The input tensor. @@ -49,9 +49,9 @@ export class UnaryOps { * Computes ceiling of input `Tensor` element-wise: `ceil(x)` * * ```js - * const x = dl.tensor1d([.6, 1.1, -3.3]); + * const x = tf.tensor1d([.6, 1.1, -3.3]); * - * x.ceil().print(); // or dl.ceil(x) + * x.ceil().print(); // or tf.ceil(x) * ``` * @param x The input Tensor. */ @@ -69,9 +69,9 @@ export class UnaryOps { * Computes floor of input `Tensor` element-wise: `floor(x)`. * * ```js - * const x = dl.tensor1d([.6, 1.1, -3.3]); + * const x = tf.tensor1d([.6, 1.1, -3.3]); * - * x.floor().print(); // or dl.floor(x) + * x.floor().print(); // or tf.floor(x) * ``` * @param x The input tensor. */ @@ -90,9 +90,9 @@ export class UnaryOps { * Computes exponential of the input `Tensor` element-wise. `e ^ x` * * ```js - * const x = dl.tensor1d([1, 2, -3]); + * const x = tf.tensor1d([1, 2, -3]); * - * x.exp().print(); // or dl.exp(x) + * x.exp().print(); // or tf.exp(x) * ``` * @param x The input tensor. */ @@ -111,9 +111,9 @@ export class UnaryOps { * Computes natural logarithm of the input `Tensor` element-wise: `ln(x)` * * ```js - * const x = dl.tensor1d([1, 2, Math.E]); + * const x = tf.tensor1d([1, 2, Math.E]); * - * x.log().print(); // or dl.log(x) + * x.log().print(); // or tf.log(x) * ``` * @param x The input tensor. */ @@ -131,9 +131,9 @@ export class UnaryOps { * element-wise: `ln(1 + x)` * * ```js - * const x = dl.tensor1d([1, 2, Math.E - 1]); + * const x = tf.tensor1d([1, 2, Math.E - 1]); * - * x.log1p().print(); // or dl.log1p(x) + * x.log1p().print(); // or tf.log1p(x) * ``` * @param x The input tensor. */ @@ -150,9 +150,9 @@ export class UnaryOps { * Computes square root of the input `Tensor` element-wise: `y = sqrt(x)` * * ```js - * const x = dl.tensor1d([1, 2, 4, -1]); + * const x = tf.tensor1d([1, 2, 4, -1]); * - * x.sqrt().print(); // or dl.sqrt(x) + * x.sqrt().print(); // or tf.sqrt(x) * ``` * @param x The input tensor. */ @@ -169,9 +169,9 @@ export class UnaryOps { * Computes square of `x` element-wise: `x ^ 2` * * ```js - * const x = dl.tensor1d([1, 2, Math.sqrt(2), -1]); + * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]); * - * x.square().print(); // or dl.square(x) + * x.square().print(); // or tf.square(x) * ``` * @param x The input Tensor. */ @@ -188,9 +188,9 @@ export class UnaryOps { * Computes absolute value element-wise: `abs(x)` * * ```js - * const x = dl.tensor1d([-1, 2, -3, 4]); + * const x = tf.tensor1d([-1, 2, -3, 4]); * - * x.abs().print(); // or dl.abs(x) + * x.abs().print(); // or tf.abs(x) * ``` * @param x The input `Tensor`. */ @@ -207,9 +207,9 @@ export class UnaryOps { * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)` * * ```js - * const x = dl.tensor1d([-1, 2, -3, 4]); + * const x = tf.tensor1d([-1, 2, -3, 4]); * - * x.clipByValue(-2, 3).print(); // or dl.clipByValue(x, -2, 3) + * x.clipByValue(-2, 3).print(); // or tf.clipByValue(x, -2, 3) * ``` * @param x The input tensor. * @param clipValueMin Lower-bound of range to be clipped to. @@ -241,9 +241,9 @@ export class UnaryOps { * Computes rectified linear element-wise: `max(x, 0)` * * ```js - * const x = dl.tensor1d([-1, 2, -3, 4]); + * const x = tf.tensor1d([-1, 2, -3, 4]); * - * x.relu().print(); // or dl.relu(x) + * x.relu().print(); // or tf.relu(x) * ``` * @param x The input tensor. */ @@ -261,9 +261,9 @@ export class UnaryOps { * Computes exponential linear element-wise, `x > 0 ? e ^ x - 1 : 0` * * ```js - * const x = dl.tensor1d([-1, 1, -3, 2]); + * const x = tf.tensor1d([-1, 1, -3, 2]); * - * x.elu().print(); // or dl.elu(x) + * x.elu().print(); // or tf.elu(x) * ``` * @param x The input tensor. */ @@ -282,9 +282,9 @@ export class UnaryOps { * `x < 0 ? scale * alpha * (exp(x) - 1) : x` * * ```js - * const x = dl.tensor1d([-1, 2, -3, 4]); + * const x = tf.tensor1d([-1, 2, -3, 4]); * - * x.selu().print(); // or dl.selu(x) + * x.selu().print(); // or tf.selu(x) * ``` * @param x The input tensor. */ @@ -317,9 +317,9 @@ export class UnaryOps { * http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf) * * ```js - * const x = dl.tensor1d([-1, 2, -3, 4]); + * const x = tf.tensor1d([-1, 2, -3, 4]); * - * x.leakyRelu(0.1).print(); // or dl.leakyRelu(x, 0.1) + * x.leakyRelu(0.1).print(); // or tf.leakyRelu(x, 0.1) * ``` * @param x The input tensor. * @param alpha The scaling factor for negative values, defaults to 0.2. @@ -340,10 +340,10 @@ export class UnaryOps { * `x < 0 ? alpha * x : f(x) = x` * * ```js - * const x = dl.tensor1d([-1, 2, -3, 4]); - * const alpha = dl.scalar(0.1); + * const x = tf.tensor1d([-1, 2, -3, 4]); + * const alpha = tf.scalar(0.1); * - * x.prelu(alpha).print(); // or dl.prelu(x, alpha) + * x.prelu(alpha).print(); // or tf.prelu(x, alpha) * ``` * @param x The input tensor. * @param alpha Scaling factor for negative values. @@ -361,9 +361,9 @@ export class UnaryOps { * Computes sigmoid element-wise, `1 / (1 + exp(-x))` * * ```js - * const x = dl.tensor1d([0, -1, 2, -3]); + * const x = tf.tensor1d([0, -1, 2, -3]); * - * x.sigmoid().print(); // or dl.sigmoid(x) + * x.sigmoid().print(); // or tf.sigmoid(x) * ``` * @param x The input tensor. */ @@ -382,9 +382,9 @@ export class UnaryOps { * Computes sin of the input Tensor element-wise: `sin(x)` * * ```js - * const x = dl.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); + * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); * - * x.sin().print(); // or dl.sin(x) + * x.sin().print(); // or tf.sin(x) * ``` * @param x The input tensor. */ @@ -401,9 +401,9 @@ export class UnaryOps { * Computes cos of the input `Tensor` element-wise: `cos(x)` * * ```js - * const x = dl.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); + * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); * - * x.cos().print(); // or dl.cos(x) + * x.cos().print(); // or tf.cos(x) * ``` * @param x The input tensor. */ @@ -420,9 +420,9 @@ export class UnaryOps { * Computes tan of the input `Tensor` element-wise, `tan(x)` * * ```js - * const x = dl.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); + * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); * - * x.tan().print(); // or dl.tan(x) + * x.tan().print(); // or tf.tan(x) * ``` * @param x The input tensor. */ @@ -439,9 +439,9 @@ export class UnaryOps { * Computes asin of the input `Tensor` element-wise: `asin(x)` * * ```js - * const x = dl.tensor1d([0, 1, -1, .7]); + * const x = tf.tensor1d([0, 1, -1, .7]); * - * x.asin().print(); // or dl.asin(x) + * x.asin().print(); // or tf.asin(x) * ``` * @param x The input tensor. */ @@ -461,9 +461,9 @@ export class UnaryOps { * Computes acos of the input `Tensor` element-wise: `acos(x)` * * ```js - * const x = dl.tensor1d([0, 1, -1, .7]); + * const x = tf.tensor1d([0, 1, -1, .7]); * - * x.acos().print(); // or dl.acos(x) + * x.acos().print(); // or tf.acos(x) * ``` * @param x The input tensor. */ @@ -484,9 +484,9 @@ export class UnaryOps { * Computes atan of the input `Tensor` element-wise: `atan(x)` * * ```js - * const x = dl.tensor1d([0, 1, -1, .7]); + * const x = tf.tensor1d([0, 1, -1, .7]); * - * x.atan().print(); // or dl.atan(x) + * x.atan().print(); // or tf.atan(x) * ``` * @param x The input tensor. */ @@ -503,9 +503,9 @@ export class UnaryOps { * Computes hyperbolic sin of the input `Tensor` element-wise: `sinh(x)` * * ```js - * const x = dl.tensor1d([0, 1, -1, .7]); + * const x = tf.tensor1d([0, 1, -1, .7]); * - * x.sinh().print(); // or dl.sinh(x) + * x.sinh().print(); // or tf.sinh(x) * ``` * @param x The input tensor. */ @@ -522,9 +522,9 @@ export class UnaryOps { * Computes hyperbolic cos of the input `Tensor` element-wise: `cosh(x)` * * ```js - * const x = dl.tensor1d([0, 1, -1, .7]); + * const x = tf.tensor1d([0, 1, -1, .7]); * - * x.cosh().print(); // or dl.cosh(x) + * x.cosh().print(); // or tf.cosh(x) * ``` * @param x The input tensor. */ @@ -541,9 +541,9 @@ export class UnaryOps { * Computes hyperbolic tangent of the input `Tensor` element-wise: `tanh(x)` * * ```js - * const x = dl.tensor1d([0, 1, -1, 70]); + * const x = tf.tensor1d([0, 1, -1, 70]); * - * x.tanh().print(); // or dl.tanh(x) + * x.tanh().print(); // or tf.tanh(x) * ``` * @param x The input tensor. */ @@ -562,9 +562,9 @@ export class UnaryOps { * Computes step of the input `Tensor` element-wise: `x > 0 ? 1 : alpha * x` * * ```js - * const x = dl.tensor1d([0, 2, -1, -3]); + * const x = tf.tensor1d([0, 2, -1, -3]); * - * x.step(.5).print(); // or dl.step(x, .5) + * x.step(.5).print(); // or tf.step(x, .5) * ``` * @param x The input tensor. * @param alpha The gradient when input is negative. diff --git a/src/optimizers/optimizer_constructors.ts b/src/optimizers/optimizer_constructors.ts index a77de1068a..9118f11ea1 100644 --- a/src/optimizers/optimizer_constructors.ts +++ b/src/optimizers/optimizer_constructors.ts @@ -31,19 +31,19 @@ export class OptimizerConstructors { * * ```js * // Fit a quadratic function by learning the coefficients a, b, c. - * const xs = dl.tensor1d([0, 1, 2, 3]); - * const ys = dl.tensor1d([1.1, 5.9, 16.8, 33.9]); + * const xs = tf.tensor1d([0, 1, 2, 3]); + * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]); * - * const a = dl.scalar(Math.random()).variable(); - * const b = dl.scalar(Math.random()).variable(); - * const c = dl.scalar(Math.random()).variable(); + * const a = tf.scalar(Math.random()).variable(); + * const b = tf.scalar(Math.random()).variable(); + * const c = tf.scalar(Math.random()).variable(); * * // y = a * x^2 + b * x + c. * const f = x => a.mul(x.square()).add(b.mul(x)).add(c); * const loss = (pred, label) => pred.sub(label).square().mean(); * * const learningRate = 0.01; - * const optimizer = dl.train.sgd(learningRate); + * const optimizer = tf.train.sgd(learningRate); * * // Train the model. * for (let i = 0; i < 10; i++) { diff --git a/src/tensor.ts b/src/tensor.ts index 08e54444a7..c445893c45 100644 --- a/src/tensor.ts +++ b/src/tensor.ts @@ -885,8 +885,8 @@ export class Variable extends Tensor { /** * Creates a new variable with the provided initial value. * ```js - * const x = dl.variable(dl.tensor([1, 2, 3])); - * x.assign(dl.tensor([4, 5, 6])); + * const x = tf.variable(tf.tensor([1, 2, 3])); + * x.assign(tf.tensor([4, 5, 6])); * * x.print(); * ``` diff --git a/src/tracking.ts b/src/tracking.ts index b53c912438..639b919b13 100644 --- a/src/tracking.ts +++ b/src/tracking.ts @@ -39,20 +39,20 @@ export class Tracking { * * ```js * // y = 2 ^ 2 + 1 - * const y = dl.tidy(() => { + * const y = tf.tidy(() => { * // a, b, and one will be cleaned up when the tidy ends. - * const one = dl.scalar(1); - * const a = dl.scalar(2); + * const one = tf.scalar(1); + * const a = tf.scalar(2); * const b = a.square(); * - * console.log('numTensors (in tidy): ' + dl.memory().numTensors); + * console.log('numTensors (in tidy): ' + tf.memory().numTensors); * * // The value returned inside the tidy function will return * // through the tidy, in this case to the variable y. * return b.add(one); * }); * - * console.log('numTensors (outside tidy): ' + dl.memory().numTensors); + * console.log('numTensors (outside tidy): ' + tf.memory().numTensors); * y.print(); * ``` * @@ -70,7 +70,7 @@ export class Tracking { if (fn == null) { // Called with only 1 argument. if (typeof nameOrFn !== 'function') { - throw new Error('Please provide a function to dl.tidy()'); + throw new Error('Please provide a function to tidy()'); } fn = nameOrFn; } else { @@ -78,12 +78,12 @@ export class Tracking { if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) { throw new Error( 'When calling with two arguments, the first argument ' + - 'to dl.tidy() must be a string'); + 'to tidy() must be a string'); } if (typeof fn !== 'function') { throw new Error( 'When calling with two arguments, the 2nd argument ' + - 'to dl.tidy() must be a function'); + 'to tidy() must be a function'); } name = nameOrFn as string; // TODO(nsthorat,smilkov): Do operation logging and performance @@ -119,22 +119,22 @@ export class Tracking { * * ```js * let b; - * const y = dl.tidy(() => { - * const one = dl.scalar(1); - * const a = dl.scalar(2); + * const y = tf.tidy(() => { + * const one = tf.scalar(1); + * const a = tf.scalar(2); * * // b will not be cleaned up by the tidy. a and one will be cleaned up * // when the tidy ends. - * b = dl.keep(a.square()); + * b = tf.keep(a.square()); * - * console.log('numTensors (in tidy): ' + dl.memory().numTensors); + * console.log('numTensors (in tidy): ' + tf.memory().numTensors); * * // The value returned inside the tidy function will return * // through the tidy, in this case to the variable y. * return b.add(one); * }); * - * console.log('numTensors (outside tidy): ' + dl.memory().numTensors); + * console.log('numTensors (outside tidy): ' + tf.memory().numTensors); * console.log('y:'); * y.print(); * console.log('b:'); @@ -161,8 +161,8 @@ export class Tracking { * - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels). * * ```js - * const x = dl.randomNormal([20, 20]); - * const time = await dl.time(() => x.matMul(x)); + * const x = tf.randomNormal([20, 20]); + * const time = await tf.time(() => x.matMul(x)); * * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`); * ```