| <!DOCTYPE html> |
| |
| <!--- |
| Licensed to the Apache Software Foundation (ASF) under one |
| or more contributor license agreements. See the NOTICE file |
| distributed with this work for additional information |
| regarding copyright ownership. The ASF licenses this file |
| to you 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. |
| --> |
| |
| <html lang=" en"><head> |
| <meta charset="utf-8"> |
| <meta http-equiv="X-UA-Compatible" content="IE=edge"> |
| <meta name="viewport" content="width=device-width, initial-scale=1"> |
| <link href="/versions/1.9.1/assets/img/mxnet-icon.png" rel="icon" type="image/png"><!-- Begin Jekyll SEO tag v2.6.1 --> |
| <title>Callback Function | Apache MXNet</title> |
| <meta name="generator" content="Jekyll v3.8.6" /> |
| <meta property="og:title" content="Callback Function" /> |
| <meta property="og:locale" content="en_US" /> |
| <meta name="description" content="A flexible and efficient library for deep learning." /> |
| <meta property="og:description" content="A flexible and efficient library for deep learning." /> |
| <link rel="canonical" href="https://mxnet.apache.org/versions/1.9.1/api/r/docs/tutorials/callback_function" /> |
| <meta property="og:url" content="https://mxnet.apache.org/versions/1.9.1/api/r/docs/tutorials/callback_function" /> |
| <meta property="og:site_name" content="Apache MXNet" /> |
| <script type="application/ld+json"> |
| {"description":"A flexible and efficient library for deep learning.","headline":"Callback Function","@type":"WebPage","url":"https://mxnet.apache.org/versions/1.9.1/api/r/docs/tutorials/callback_function","@context":"https://schema.org"}</script> |
| <!-- End Jekyll SEO tag --> |
| <link rel="stylesheet" href="/versions/1.9.1/assets/docsearch.min.css" /><link rel="stylesheet" href="/versions/1.9.1/assets/main.css"><link type="application/atom+xml" rel="alternate" href="https://mxnet.apache.org/versions/1.9.1/feed.xml" title="Apache MXNet" /><!-- Matomo --> |
| <script> |
| var _paq = window._paq = window._paq || []; |
| /* tracker methods like "setCustomDimension" should be called before "trackPageView" */ |
| /* We explicitly disable cookie tracking to avoid privacy issues */ |
| _paq.push(['disableCookies']); |
| _paq.push(['trackPageView']); |
| _paq.push(['enableLinkTracking']); |
| (function() { |
| var u="https://analytics.apache.org/"; |
| _paq.push(['setTrackerUrl', u+'matomo.php']); |
| _paq.push(['setSiteId', '23']); |
| var d=document, g=d.createElement('script'), s=d.getElementsByTagName('script')[0]; |
| g.async=true; g.src=u+'matomo.js'; s.parentNode.insertBefore(g,s); |
| })(); |
| </script> |
| <!-- End Matomo Code --> |
| |
| <script src="/versions/1.9.1/assets/js/jquery-3.3.1.min.js"></script> |
| <script src="/versions/1.9.1/assets/js/docsearch.min.js"></script><script src="/versions/1.9.1/assets/js/globalSearch.js" defer></script> |
| <script src="/versions/1.9.1/assets/js/clipboard.js" defer></script> |
| <script src="/versions/1.9.1/assets/js/copycode.js" defer></script></head> |
| <body><header class="site-header" role="banner"> |
| |
| <script> |
| $(document).ready(function () { |
| |
| // HEADER OPACITY LOGIC |
| |
| function opacity_header() { |
| var value = "rgba(4,140,204," + ($(window).scrollTop() / 300 + 0.4) + ")" |
| $('.site-header').css("background-color", value) |
| } |
| |
| $(window).scroll(function () { |
| opacity_header() |
| }) |
| opacity_header(); |
| |
| // MENU SELECTOR LOGIC |
| $('.page-link').each( function () { |
| if (window.location.href.includes(this.href)) { |
| $(this).addClass("page-current"); |
| } |
| }); |
| }) |
| </script> |
| <div class="wrapper"> |
| <a class="site-title" rel="author" href="/versions/1.9.1/"><img |
| src="/versions/1.9.1/assets/img/mxnet_logo.png" class="site-header-logo"></a> |
| <nav class="site-nav"> |
| <input type="checkbox" id="nav-trigger" class="nav-trigger"/> |
| <label for="nav-trigger"> |
| <span class="menu-icon"> |
| <svg viewBox="0 0 18 15" width="18px" height="15px"> |
| <path d="M18,1.484c0,0.82-0.665,1.484-1.484,1.484H1.484C0.665,2.969,0,2.304,0,1.484l0,0C0,0.665,0.665,0,1.484,0 h15.032C17.335,0,18,0.665,18,1.484L18,1.484z M18,7.516C18,8.335,17.335,9,16.516,9H1.484C0.665,9,0,8.335,0,7.516l0,0 c0-0.82,0.665-1.484,1.484-1.484h15.032C17.335,6.031,18,6.696,18,7.516L18,7.516z M18,13.516C18,14.335,17.335,15,16.516,15H1.484 C0.665,15,0,14.335,0,13.516l0,0c0-0.82,0.665-1.483,1.484-1.483h15.032C17.335,12.031,18,12.695,18,13.516L18,13.516z"/> |
| </svg> |
| </span> |
| </label> |
| <div class="gs-search-border"> |
| <div id="gs-search-icon"></div> |
| <form id="global-search-form"> |
| <input id="global-search" type="text" title="Search" placeholder="Search" /> |
| <div id="global-search-dropdown-container"> |
| <button class="gs-current-version btn" type="button" data-toggle="dropdown"> |
| <span id="gs-current-version-label">1.9.1</span> |
| <svg class="gs-dropdown-caret" viewBox="0 0 32 32" class="icon icon-caret-bottom" aria-hidden="true"> |
| <path class="dropdown-caret-path" d="M24 11.305l-7.997 11.39L8 11.305z"></path> |
| </svg> |
| </button> |
| <ul class="gs-opt-group gs-version-dropdown"> |
| |
| |
| <li class="gs-opt gs-versions">master</li> |
| |
| |
| |
| <li class="gs-opt gs-versions active">1.9.1</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.8.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.7.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.6.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.5.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.4.1</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.3.1</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.2.1</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.1.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.0.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">0.12.1</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">0.11.0</li> |
| |
| |
| </ul> |
| </div> |
| <span id="global-search-close">x</span> |
| </form> |
| </div> |
| <div class="trigger"> |
| <div id="global-search-mobile-border"> |
| <div id="gs-search-icon-mobile"></div> |
| <input id="global-search-mobile" placeholder="Search..." type="text"/> |
| <div id="global-search-dropdown-container-mobile"> |
| <button class="gs-current-version-mobile btn" type="button" data-toggle="dropdown"> |
| <svg class="gs-dropdown-caret" viewBox="0 0 32 32" class="icon icon-caret-bottom" aria-hidden="true"> |
| <path class="dropdown-caret-path" d="M24 11.305l-7.997 11.39L8 11.305z"></path> |
| </svg> |
| </button> |
| <ul class="gs-opt-group gs-version-dropdown-mobile"> |
| |
| |
| <li class="gs-opt gs-versions">master</li> |
| |
| |
| |
| <li class="gs-opt gs-versions active">1.9.1</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.8.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.7.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.6.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.5.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.4.1</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.3.1</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.2.1</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.1.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">1.0.0</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">0.12.1</li> |
| |
| |
| |
| <li class="gs-opt gs-versions">0.11.0</li> |
| |
| |
| </ul> |
| </div> |
| </div> |
| <a class="page-link" href="/versions/1.9.1/get_started">Get Started</a> |
| <a class="page-link" href="/versions/1.9.1/features">Features</a> |
| <a class="page-link" href="/versions/1.9.1/ecosystem">Ecosystem</a> |
| <a class="page-link" href="/versions/1.9.1/api">Docs & Tutorials</a> |
| <a class="page-link" href="/versions/1.9.1/trusted_by">Trusted By</a> |
| <a class="page-link" href="https://github.com/apache/mxnet">GitHub</a> |
| <div class="dropdown" style="min-width:100px"> |
| <span class="dropdown-header">Apache |
| <svg class="dropdown-caret" viewBox="0 0 32 32" class="icon icon-caret-bottom" aria-hidden="true"><path class="dropdown-caret-path" d="M24 11.305l-7.997 11.39L8 11.305z"></path></svg> |
| </span> |
| <div class="dropdown-content" style="min-width:250px"> |
| <a href="https://www.apache.org/foundation/">Apache Software Foundation</a> |
| <a href="https://www.apache.org/licenses/">License</a> |
| <a href="/versions/1.9.1/api/faq/security.html">Security</a> |
| <a href="https://privacy.apache.org/policies/privacy-policy-public.html">Privacy</a> |
| <a href="https://www.apache.org/events/current-event">Events</a> |
| <a href="https://www.apache.org/foundation/sponsorship.html">Sponsorship</a> |
| <a href="https://www.apache.org/foundation/thanks.html">Thanks</a> |
| </div> |
| </div> |
| <div class="dropdown"> |
| <span class="dropdown-header">1.9.1 |
| <svg class="dropdown-caret" viewBox="0 0 32 32" class="icon icon-caret-bottom" aria-hidden="true"><path class="dropdown-caret-path" d="M24 11.305l-7.997 11.39L8 11.305z"></path></svg> |
| </span> |
| <div class="dropdown-content"> |
| <a href="/">master</a> |
| <a class="dropdown-option-active" href="/versions/1.9.1/">1.9.1</a> |
| <a href="/versions/1.8.0/">1.8.0</a> |
| <a href="/versions/1.7.0/">1.7.0</a> |
| <a href="/versions/1.6.0/">1.6.0</a> |
| <a href="/versions/1.5.0/">1.5.0</a> |
| <a href="/versions/1.4.1/">1.4.1</a> |
| <a href="/versions/1.3.1/">1.3.1</a> |
| <a href="/versions/1.2.1/">1.2.1</a> |
| <a href="/versions/1.1.0/">1.1.0</a> |
| <a href="/versions/1.0.0/">1.0.0</a> |
| <a href="/versions/0.12.1/">0.12.1</a> |
| <a href="/versions/0.11.0/">0.11.0</a> |
| </div> |
| </div> |
| </div> |
| </nav> |
| </div> |
| </header> |
| <main class="page-content" aria-label="Content"> |
| <script> |
| |
| </script> |
| <article class="post"> |
| |
| <header class="post-header wrapper"> |
| <h1 class="post-title">Callback Function</h1> |
| <h3></h3></header> |
| |
| <div class="post-content"> |
| <div class="wrapper"> |
| <div class="row"> |
| <div class="col-3 docs-side-bar"> |
| |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| <div class="docs-card docs-side"> |
| <ul> |
| <div class="docs-action-btn"> |
| <a href="/versions/1.9.1/api/r"> <img src="/versions/1.9.1/assets/img/compass.svg" |
| class="docs-logo-docs">R Guide <span |
| class="span-accented">›</span></a> |
| </div> |
| <div class="docs-action-btn"> |
| <a href="/versions/1.9.1/api/r/docs/tutorials"> <img |
| src="/versions/1.9.1/assets/img/video-tutorial.svg" class="docs-logo-docs">R |
| Tutorials <span class="span-accented">›</span></a> |
| </div> |
| <div class="docs-action-btn"> |
| <a href="/versions/1.9.1/api/r/docs/api/R-package/build/mxnet-r-reference-manual.pdf"> <img src="/versions/1.9.1/assets/img/api.svg" |
| class="docs-logo-docs">R API Reference |
| <span class="span-accented">›</span></a> |
| </div> |
| |
| <!-- Let's show the list of tutorials --> |
| <br> |
| |
| <h3>Tutorials</h3> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| |
| |
| <li><a href="/versions/1.9.1/api/r/docs/tutorials/callback_function">Callback Function</a></li> |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| |
| <li><a href="/versions/1.9.1/api/r/docs/tutorials/char_rnn_model">Char RNN Model</a></li> |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| |
| <li><a href="/versions/1.9.1/api/r/docs/tutorials/classify_real_image_with_pretrained_model">Classify Images with a PreTrained Model</a></li> |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| |
| |
| <li><a href="/versions/1.9.1/api/r/docs/tutorials/custom_iterator">Custom Iterator Tutorial</a></li> |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| |
| <li><a href="/versions/1.9.1/api/r/docs/tutorials/custom_loss_function">Custom Loss Function</a></li> |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| |
| |
| <li><a href="/versions/1.9.1/api/r/docs/tutorials/five_minutes_neural_network">Five Minutes Neural Network</a></li> |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| |
| <li><a href="/versions/1.9.1/api/r/docs/tutorials/mnist_competition">MNIST Competition</a></li> |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| |
| |
| <li><a href="/versions/1.9.1/api/r/docs/tutorials/multi_dim_lstm">LSTM Time Series</a></li> |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| |
| <li><a href="/versions/1.9.1/api/r/docs/tutorials/ndarray">NDArray</a></li> |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| |
| <li><a href="/versions/1.9.1/api/r/docs/tutorials/symbol">NDArray</a></li> |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| |
| <!-- page-category --> |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| <!-- page --> |
| |
| </ul> |
| </div> |
| |
| |
| |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| |
| <!-- resource-p --> |
| <!-- page --> |
| </ul> |
| </div> |
| <div class="col-9"> |
| <!--- Licensed to the Apache Software Foundation (ASF) under one --> |
| |
| <!--- or more contributor license agreements. See the NOTICE file --> |
| |
| <!--- distributed with this work for additional information --> |
| |
| <!--- regarding copyright ownership. The ASF licenses this file --> |
| |
| <!--- to you 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. --> |
| |
| <h1 id="callback-function">Callback Function</h1> |
| |
| <p>This tutorial provides guidelines for using and writing callback functions, |
| which can very useful in model training.</p> |
| |
| <h2 id="model-training-example">Model Training Example</h2> |
| |
| <p>Let's begin with a small example. We can build and train a model with the following code:</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">library</span><span class="p">(</span><span class="n">mxnet</span><span class="p">)</span><span class="w"> |
| </span><span class="n">data</span><span class="p">(</span><span class="n">BostonHousing</span><span class="p">,</span><span class="w"> </span><span class="n">package</span><span class="o">=</span><span class="s2">"mlbench"</span><span class="p">)</span><span class="w"> |
| </span><span class="n">train.ind</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">seq</span><span class="p">(</span><span class="m">1</span><span class="p">,</span><span class="w"> </span><span class="m">506</span><span class="p">,</span><span class="w"> </span><span class="m">3</span><span class="p">)</span><span class="w"> |
| </span><span class="n">train.x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">data.matrix</span><span class="p">(</span><span class="n">BostonHousing</span><span class="p">[</span><span class="n">train.ind</span><span class="p">,</span><span class="w"> </span><span class="m">-14</span><span class="p">])</span><span class="w"> |
| </span><span class="n">train.y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">BostonHousing</span><span class="p">[</span><span class="n">train.ind</span><span class="p">,</span><span class="w"> </span><span class="m">14</span><span class="p">]</span><span class="w"> |
| </span><span class="n">test.x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">data.matrix</span><span class="p">(</span><span class="n">BostonHousing</span><span class="p">[</span><span class="o">-</span><span class="n">train.ind</span><span class="p">,</span><span class="w"> </span><span class="m">-14</span><span class="p">])</span><span class="w"> |
| </span><span class="n">test.y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">BostonHousing</span><span class="p">[</span><span class="o">-</span><span class="n">train.ind</span><span class="p">,</span><span class="w"> </span><span class="m">14</span><span class="p">]</span><span class="w"> |
| </span><span class="n">data</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.symbol.Variable</span><span class="p">(</span><span class="s2">"data"</span><span class="p">)</span><span class="w"> |
| </span><span class="n">fc1</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.symbol.FullyConnected</span><span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="w"> </span><span class="n">num_hidden</span><span class="o">=</span><span class="m">1</span><span class="p">)</span><span class="w"> |
| </span><span class="n">lro</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.symbol.LinearRegressionOutput</span><span class="p">(</span><span class="n">fc1</span><span class="p">)</span><span class="w"> |
| </span><span class="n">mx.set.seed</span><span class="p">(</span><span class="m">0</span><span class="p">)</span><span class="w"> |
| </span><span class="n">model</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.model.FeedForward.create</span><span class="p">(</span><span class="w"> |
| </span><span class="n">lro</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="o">=</span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">eval.data</span><span class="o">=</span><span class="nf">list</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test.x</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="o">=</span><span class="n">test.y</span><span class="p">),</span><span class="w"> |
| </span><span class="n">ctx</span><span class="o">=</span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> </span><span class="n">num.round</span><span class="o">=</span><span class="m">10</span><span class="p">,</span><span class="w"> </span><span class="n">array.batch.size</span><span class="o">=</span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="o">=</span><span class="m">2e-6</span><span class="p">,</span><span class="w"> </span><span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span><span class="w"> </span><span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.rmse</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## Auto detect layout of input matrix, use row major.. |
| ## Start training with 1 devices |
| ## [1] Train-rmse=16.063282524034 |
| ## [1] Validation-rmse=10.1766446093622 |
| ## [2] Train-rmse=12.2792375712573 |
| ## [2] Validation-rmse=12.4331776190813 |
| ## [3] Train-rmse=11.1984634005885 |
| ## [3] Validation-rmse=10.3303041888193 |
| ## [4] Train-rmse=10.2645236892904 |
| ## [4] Validation-rmse=8.42760407903415 |
| ## [5] Train-rmse=9.49711005504284 |
| ## [5] Validation-rmse=8.44557808483234 |
| ## [6] Train-rmse=9.07733734175182 |
| ## [6] Validation-rmse=8.33225500266177 |
| ## [7] Train-rmse=9.07884450847991 |
| ## [7] Validation-rmse=8.38827833418459 |
| ## [8] Train-rmse=9.10463850277417 |
| ## [8] Validation-rmse=8.37394452365264 |
| ## [9] Train-rmse=9.03977049028532 |
| ## [9] Validation-rmse=8.25927979725672 |
| ## [10] Train-rmse=8.96870685004475 |
| ## [10] Validation-rmse=8.19509291481822 |
| </code></pre></div> |
| <p>We also provide two optional parameters, <code>batch.end.callback</code> and <code>epoch.end.callback</code>, which can provide great flexibility in model training.</p> |
| |
| <h2 id="how-to-use-callback-functions">How to Use Callback Functions</h2> |
| |
| <p>This package provides two callback functions:</p> |
| |
| <ul> |
| <li><code>mx.callback.save.checkpoint</code> saves a checkpoint to files during each period iteration.</li> |
| </ul> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">model</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.model.FeedForward.create</span><span class="p">(</span><span class="w"> |
| </span><span class="n">lro</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="o">=</span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">eval.data</span><span class="o">=</span><span class="nf">list</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test.x</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="o">=</span><span class="n">test.y</span><span class="p">),</span><span class="w"> |
| </span><span class="n">ctx</span><span class="o">=</span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> </span><span class="n">num.round</span><span class="o">=</span><span class="m">10</span><span class="p">,</span><span class="w"> </span><span class="n">array.batch.size</span><span class="o">=</span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="o">=</span><span class="m">2e-6</span><span class="p">,</span><span class="w"> </span><span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span><span class="w"> </span><span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.rmse</span><span class="p">,</span><span class="w"> |
| </span><span class="n">epoch.end.callback</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.callback.save.checkpoint</span><span class="p">(</span><span class="s2">"boston"</span><span class="p">))</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## Auto detect layout of input matrix, use row major.. |
| ## Start training with 1 devices |
| ## [1] Train-rmse=19.1621424021617 |
| ## [1] Validation-rmse=20.721515592165 |
| ## Model checkpoint saved to boston-0001.params |
| ## [2] Train-rmse=13.5127391952367 |
| ## [2] Validation-rmse=14.1822123675007 |
| ## Model checkpoint saved to boston-0002.params |
| </code></pre></div> |
| <ul> |
| <li><code>mx.callback.log.train.metric</code> logs a training metric each period. You can use it either as a <code>batch.end.callback</code> or an |
| <code>epoch.end.callback</code>.</li> |
| </ul> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">model</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.model.FeedForward.create</span><span class="p">(</span><span class="w"> |
| </span><span class="n">lro</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="o">=</span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">eval.data</span><span class="o">=</span><span class="nf">list</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test.x</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="o">=</span><span class="n">test.y</span><span class="p">),</span><span class="w"> |
| </span><span class="n">ctx</span><span class="o">=</span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> </span><span class="n">num.round</span><span class="o">=</span><span class="m">10</span><span class="p">,</span><span class="w"> </span><span class="n">array.batch.size</span><span class="o">=</span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="o">=</span><span class="m">2e-6</span><span class="p">,</span><span class="w"> </span><span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span><span class="w"> </span><span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.rmse</span><span class="p">,</span><span class="w"> |
| </span><span class="n">batch.end.callback</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.callback.log.train.metric</span><span class="p">(</span><span class="m">5</span><span class="p">))</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## Auto detect layout of input matrix, use row major.. |
| ## Start training with 1 devices |
| ## Batch [5] Train-rmse=17.6514558545416 |
| ## [1] Train-rmse=15.2879610219001 |
| ## [1] Validation-rmse=12.3332062820921 |
| ## Batch [5] Train-rmse=11.939392828565 |
| ## [2] Train-rmse=11.4382242547217 |
| ## [2] Validation-rmse=9.91176550103181 |
| ............ |
| </code></pre></div> |
| <p>You also can save the training and evaluation errors for later use by passing a reference class:</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">logger</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.metric.logger</span><span class="o">$</span><span class="n">new</span><span class="p">()</span><span class="w"> |
| </span><span class="n">model</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.model.FeedForward.create</span><span class="p">(</span><span class="w"> |
| </span><span class="n">lro</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="o">=</span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">eval.data</span><span class="o">=</span><span class="nf">list</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test.x</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="o">=</span><span class="n">test.y</span><span class="p">),</span><span class="w"> |
| </span><span class="n">ctx</span><span class="o">=</span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> </span><span class="n">num.round</span><span class="o">=</span><span class="m">10</span><span class="p">,</span><span class="w"> </span><span class="n">array.batch.size</span><span class="o">=</span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="o">=</span><span class="m">2e-6</span><span class="p">,</span><span class="w"> </span><span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span><span class="w"> </span><span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.rmse</span><span class="p">,</span><span class="w"> |
| </span><span class="n">epoch.end.callback</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.callback.log.train.metric</span><span class="p">(</span><span class="m">5</span><span class="p">,</span><span class="w"> </span><span class="n">logger</span><span class="p">))</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## Auto detect layout of input matrix, use row major.. |
| ## Start training with 1 devices |
| ## [1] Train-rmse=19.1083228733256 |
| ## [1] Validation-rmse=12.7150687428974 |
| ## [2] Train-rmse=15.7684378116157 |
| ## [2] Validation-rmse=14.8105319420491 |
| ............ |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">logger</span><span class="o">$</span><span class="n">train</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## [1] 19.108323 15.768438 13.531470 11.386050 9.555477 9.351324 |
| </code></pre></div><div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">logger</span><span class="o">$</span><span class="n">eval</span><span class="p">)</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## [1] 12.715069 14.810532 15.840361 10.898733 9.349706 9.363087 |
| </code></pre></div> |
| <h2 id="how-to-write-your-own-callback-functions">How to Write Your Own Callback Functions</h2> |
| |
| <p>You can find the source code for the two callback functions on <a href="https://github.com/apache/mxnet/blob/v1.x/R-package/R/callback.R">GitHub</a> and use it as a template:</p> |
| |
| <p>Basically, all callback functions follow the following structure:</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">mx.callback.fun</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">()</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">function</span><span class="p">(</span><span class="n">iteration</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="n">env</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span></code></pre></div> |
| <p>The following <code>mx.callback.save.checkpoint</code> function is stateless. It gets the model from the environment and saves it:.</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">mx.callback.save.checkpoint</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span><span class="w"> </span><span class="n">period</span><span class="o">=</span><span class="m">1</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">function</span><span class="p">(</span><span class="n">iteration</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="n">env</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">iteration</span><span class="w"> </span><span class="o">%%</span><span class="w"> </span><span class="n">period</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="m">0</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="n">mx.model.save</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">model</span><span class="p">,</span><span class="w"> </span><span class="n">prefix</span><span class="p">,</span><span class="w"> </span><span class="n">iteration</span><span class="p">)</span><span class="w"> |
| </span><span class="n">cat</span><span class="p">(</span><span class="n">sprintf</span><span class="p">(</span><span class="s2">"Model checkpoint saved to %s-%04d.params\n"</span><span class="p">,</span><span class="w"> </span><span class="n">prefix</span><span class="p">,</span><span class="w"> </span><span class="n">iteration</span><span class="p">))</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="nf">return</span><span class="p">(</span><span class="kc">TRUE</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span></code></pre></div> |
| <p>The <code>mx.callback.log.train.metric</code> is a little more complex. It holds a reference class and updates it during the training |
| process:</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">mx.callback.log.train.metric</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">period</span><span class="p">,</span><span class="w"> </span><span class="n">logger</span><span class="o">=</span><span class="kc">NULL</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">function</span><span class="p">(</span><span class="n">iteration</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="n">env</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">nbatch</span><span class="w"> </span><span class="o">%%</span><span class="w"> </span><span class="n">period</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="o">&&</span><span class="w"> </span><span class="o">!</span><span class="nf">is.null</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">metric</span><span class="p">))</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="n">result</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">env</span><span class="o">$</span><span class="n">metric</span><span class="o">$</span><span class="n">get</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">train.metric</span><span class="p">)</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">nbatch</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="m">0</span><span class="p">)</span><span class="w"> |
| </span><span class="n">cat</span><span class="p">(</span><span class="n">paste0</span><span class="p">(</span><span class="s2">"Batch ["</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="s2">"] Train-"</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">name</span><span class="p">,</span><span class="w"> </span><span class="s2">"="</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">value</span><span class="p">,</span><span class="w"> </span><span class="s2">"\n"</span><span class="p">))</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="nf">is.null</span><span class="p">(</span><span class="n">logger</span><span class="p">))</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="nf">class</span><span class="p">(</span><span class="n">logger</span><span class="p">)</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="s2">"mx.metric.logger"</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="n">stop</span><span class="p">(</span><span class="s2">"Invalid mx.metric.logger."</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="n">logger</span><span class="o">$</span><span class="n">train</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="n">logger</span><span class="o">$</span><span class="n">train</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">value</span><span class="p">)</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="nf">is.null</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">eval.metric</span><span class="p">))</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="n">result</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">env</span><span class="o">$</span><span class="n">metric</span><span class="o">$</span><span class="n">get</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">eval.metric</span><span class="p">)</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">nbatch</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="m">0</span><span class="p">)</span><span class="w"> |
| </span><span class="n">cat</span><span class="p">(</span><span class="n">paste0</span><span class="p">(</span><span class="s2">"Batch ["</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="s2">"] Validation-"</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">name</span><span class="p">,</span><span class="w"> </span><span class="s2">"="</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">value</span><span class="p">,</span><span class="w"> </span><span class="s2">"\n"</span><span class="p">))</span><span class="w"> |
| </span><span class="n">logger</span><span class="o">$</span><span class="n">eval</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="n">logger</span><span class="o">$</span><span class="n">eval</span><span class="p">,</span><span class="w"> </span><span class="n">result</span><span class="o">$</span><span class="n">value</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="nf">return</span><span class="p">(</span><span class="kc">TRUE</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span></code></pre></div> |
| <p>Now you might be curious why both callback functions <code>return(TRUE)</code>.</p> |
| |
| <p>Can we <code>return(FALSE)</code>?</p> |
| |
| <p>Yes! You can stop the training early with <code>return(FALSE)</code>. See the following examples.</p> |
| <div class="highlight"><pre><code class="language-r" data-lang="r"><span class="w"> </span><span class="n">mx.callback.early.stop</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">eval.metric</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">function</span><span class="p">(</span><span class="n">iteration</span><span class="p">,</span><span class="w"> </span><span class="n">nbatch</span><span class="p">,</span><span class="w"> </span><span class="n">env</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="nf">is.null</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">metric</span><span class="p">))</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="o">!</span><span class="nf">is.null</span><span class="p">(</span><span class="n">eval.metric</span><span class="p">))</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="n">result</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">env</span><span class="o">$</span><span class="n">metric</span><span class="o">$</span><span class="n">get</span><span class="p">(</span><span class="n">env</span><span class="o">$</span><span class="n">eval.metric</span><span class="p">)</span><span class="w"> |
| </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">result</span><span class="o">$</span><span class="n">value</span><span class="w"> </span><span class="o"><</span><span class="w"> </span><span class="n">eval.metric</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> |
| </span><span class="nf">return</span><span class="p">(</span><span class="kc">FALSE</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="nf">return</span><span class="p">(</span><span class="kc">TRUE</span><span class="p">)</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="p">}</span><span class="w"> |
| </span><span class="n">model</span><span class="w"> </span><span class="o"><-</span><span class="w"> </span><span class="n">mx.model.FeedForward.create</span><span class="p">(</span><span class="w"> |
| </span><span class="n">lro</span><span class="p">,</span><span class="w"> </span><span class="n">X</span><span class="o">=</span><span class="n">train.x</span><span class="p">,</span><span class="w"> </span><span class="n">y</span><span class="o">=</span><span class="n">train.y</span><span class="p">,</span><span class="w"> |
| </span><span class="n">eval.data</span><span class="o">=</span><span class="nf">list</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test.x</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="o">=</span><span class="n">test.y</span><span class="p">),</span><span class="w"> |
| </span><span class="n">ctx</span><span class="o">=</span><span class="n">mx.cpu</span><span class="p">(),</span><span class="w"> </span><span class="n">num.round</span><span class="o">=</span><span class="m">10</span><span class="p">,</span><span class="w"> </span><span class="n">array.batch.size</span><span class="o">=</span><span class="m">20</span><span class="p">,</span><span class="w"> |
| </span><span class="n">learning.rate</span><span class="o">=</span><span class="m">2e-6</span><span class="p">,</span><span class="w"> </span><span class="n">momentum</span><span class="o">=</span><span class="m">0.9</span><span class="p">,</span><span class="w"> </span><span class="n">eval.metric</span><span class="o">=</span><span class="n">mx.metric.rmse</span><span class="p">,</span><span class="w"> |
| </span><span class="n">epoch.end.callback</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mx.callback.early.stop</span><span class="p">(</span><span class="m">10</span><span class="p">))</span><span class="w"> |
| </span></code></pre></div><div class="highlight"><pre><code class="language-" data-lang=""> ## Auto detect layout of input matrix, use row major.. |
| ## Start training with 1 devices |
| ## [1] Train-rmse=18.5897984387033 |
| ## [1] Validation-rmse=13.5555213820571 |
| ## [2] Train-rmse=12.5867564040256 |
| ## [2] Validation-rmse=9.76304967080928 |
| </code></pre></div> |
| <p>When the validation metric dips below the threshold we set, the training process stops.</p> |
| |
| <h2 id="next-steps">Next Steps</h2> |
| |
| <ul> |
| <li><a href="/api/r/docs/tutorials/five_minutes_neural_network">Neural Networks with MXNet in Five Minutes</a></li> |
| <li><a href="/api/r/docs/tutorials/classify_real_image_with_pretrained_model">Classify Real-World Images with a Pretrained Model</a></li> |
| <li><a href="/api/r/docs/tutorials/mnist_competition">Handwritten Digits Classification Competition</a></li> |
| <li><a href="/api/r/docs/tutorials/char_rnn_model">Character Language Model Using RNN</a></li> |
| </ul> |
| </div> |
| </div> |
| |
| </div> |
| </div> |
| |
| </article> |
| |
| </main><footer class="site-footer h-card"> |
| <div class="wrapper"> |
| <div class="row"> |
| <div class="col-4"> |
| <h4 class="footer-category-title">Resources</h4> |
| <ul class="contact-list"> |
| <li><a href="/versions/1.9.1/community/contribute#mxnet-dev-communications">Mailing lists</a></li> |
| <li><a href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li> |
| <li><a href="https://issues.apache.org/jira/projects/MXNET/issues">Jira Tracker</a></li> |
| <li><a href="https://github.com/apache/mxnet/labels/Roadmap">Github Roadmap</a></li> |
| <li><a href="https://medium.com/apache-mxnet">Blog</a></li> |
| <li><a href="https://discuss.mxnet.io">Forum</a></li> |
| <li><a href="/versions/1.9.1/community/contribute">Contribute</a></li> |
| </ul> |
| </div> |
| |
| <div class="col-4"><ul class="social-media-list"><li><a href="https://github.com/apache/mxnet"><svg class="svg-icon"><use xlink:href="/versions/1.9.1/assets/minima-social-icons.svg#github"></use></svg> <span class="username">apache/mxnet</span></a></li><li><a href="https://www.twitter.com/apachemxnet"><svg class="svg-icon"><use xlink:href="/versions/1.9.1/assets/minima-social-icons.svg#twitter"></use></svg> <span class="username">apachemxnet</span></a></li><li><a href="https://youtube.com/apachemxnet"><svg class="svg-icon"><use xlink:href="/versions/1.9.1/assets/minima-social-icons.svg#youtube"></use></svg> <span class="username">apachemxnet</span></a></li></ul> |
| </div> |
| |
| <div class="col-4 footer-text"> |
| <p>A flexible and efficient library for deep learning.</p> |
| </div> |
| </div> |
| </div> |
| </footer> |
| <footer class="site-footer2"> |
| <div class="wrapper"> |
| <div class="row"> |
| <div class="col-3"> |
| <img src="/versions/1.9.1/assets/img/asf_logo.svg" class="footer-logo col-2"> |
| </div> |
| <div class="footer-bottom-warning col-9"> |
| </p><p>"Copyright © 2017-2022, The Apache Software Foundation. Licensed under the Apache License, Version 2.0. Apache MXNet, MXNet, Apache, the Apache |
| feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the |
| Apache Software Foundation."</p> |
| </div> |
| </div> |
| </div> |
| </footer> |
| |
| |
| |
| |
| </body> |
| |
| </html> |
