Image Regression - AutoKeras

<!doctype html> <html lang="en" class="no-js"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width,initial-scale=1"> <meta name="description" content="Documentation for AutoKeras."> <link rel="canonical" href="http://autokeras.com/tutorial/image_regression/"> <link rel="prev" href="../image_classification/"> <link rel="next" href="../text_classification/"> <link rel="icon" href="/img/favicon.png"> <meta name="generator" content="mkdocs-1.5.3, mkdocs-material-9.5.14"> <title>Image Regression - AutoKeras</title> <link rel="stylesheet" href="../../assets/stylesheets/main.10ba22f1.min.css"> <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,300i,400,400i,700,700i%7CRoboto+Mono:400,400i,700,700i&display=fallback"> <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style> <link rel="stylesheet" href="../../stylesheets/extra.css"> <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script> <script id="__analytics">function __md_analytics(){function n(){dataLayer.push(arguments)}window.dataLayer=window.dataLayer||[],n("js",new Date),n("config","G-GTF9QP8DFD"),document.addEventListener("DOMContentLoaded",function(){document.forms.search&&document.forms.search.query.addEventListener("blur",function(){this.value&&n("event","search",{search_term:this.value})}),document$.subscribe(function(){var a=document.forms.feedback;if(void 0!==a)for(var e of a.querySelectorAll("[type=submit]"))e.addEventListener("click",function(e){e.preventDefault();var t=document.location.pathname,e=this.getAttribute("data-md-value");n("event","feedback",{page:t,data:e}),a.firstElementChild.disabled=!0;e=a.querySelector(".md-feedback__note [data-md-value='"+e+"']");e&&(e.hidden=!1)}),a.hidden=!1}),location$.subscribe(function(e){n("config","G-GTF9QP8DFD",{page_path:e.pathname})})});var e=document.createElement("script");e.async=!0,e.src="https://www.googletagmanager.com/gtag/js?id=G-GTF9QP8DFD",document.getElementById("__analytics").insertAdjacentElement("afterEnd",e)}</script> <script>"undefined"!=typeof __md_analytics&&__md_analytics()</script> </head> <body dir="ltr"> <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off"> <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off"> <label class="md-overlay" for="__drawer"></label> <div data-md-component="skip"> <a href="#a-simple-example" class="md-skip"> Skip to content </a> </div> <div data-md-component="announce"> </div> <header class="md-header md-header--shadow" data-md-component="header"> <nav class="md-header__inner md-grid" aria-label="Header"> <a href="../.." title="AutoKeras" class="md-header__button md-logo" aria-label="AutoKeras" data-md-component="logo"> <img src="/img/logo_white.svg" alt="logo"> </a> <label class="md-header__button md-icon" for="__drawer"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg> </label> <div class="md-header__title" data-md-component="header-title"> <div class="md-header__ellipsis"> <div class="md-header__topic"> AutoKeras </div> <div class="md-header__topic" data-md-component="header-topic"> Image Regression </div> </div> </div> <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script> <label class="md-header__button md-icon" for="__search"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg> </label> <div class="md-search" data-md-component="search" role="dialog"> <label class="md-search__overlay" for="__search"></label> <div class="md-search__inner" role="search"> <form class="md-search__form" name="search"> <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required> <label class="md-search__icon md-icon" for="__search"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg> </label> <nav class="md-search__options" aria-label="Search"> <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg> </button> </nav> </form> <div class="md-search__output"> <div class="md-search__scrollwrap" data-md-scrollfix> <div class="md-search-result" data-md-component="search-result"> <div class="md-search-result__meta"> Initializing search </div> <ol class="md-search-result__list" role="presentation"></ol> </div> </div> </div> </div> </div> <div class="md-header__source"> <a href="https://github.com/keras-team/autokeras" title="Go to repository" class="md-source" data-md-component="source"> <div class="md-source__icon md-icon"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg> </div> <div class="md-source__repository"> GitHub </div> </a> </div> </nav> </header> <div class="md-container" data-md-component="container"> <main class="md-main" data-md-component="main"> <div class="md-main__inner md-grid"> <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" > <div class="md-sidebar__scrollwrap"> <div class="md-sidebar__inner"> <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0"> <label class="md-nav__title" for="__drawer"> <a href="../.." title="AutoKeras" class="md-nav__button md-logo" aria-label="AutoKeras" data-md-component="logo"> <img src="/img/logo_white.svg" alt="logo"> </a> AutoKeras </label> <div class="md-nav__source"> <a href="https://github.com/keras-team/autokeras" title="Go to repository" class="md-source" data-md-component="source"> <div class="md-source__icon md-icon"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg> </div> <div class="md-source__repository"> GitHub </div> </a> </div> <ul class="md-nav__list" data-md-scrollfix> <li class="md-nav__item"> <a href="../.." class="md-nav__link"> Home </a> </li> <li class="md-nav__item"> <a href="../../install/" class="md-nav__link"> Installation </a> </li> <li class="md-nav__item md-nav__item--active md-nav__item--nested"> <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked> <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0"> Tutorials </label> <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true"> <label class="md-nav__title" for="__nav_3"> Tutorials </label> <ul class="md-nav__list" data-md-scrollfix> <li class="md-nav__item"> <a href="../overview/" class="md-nav__link"> Overview </a> </li> <li class="md-nav__item"> <a href="../image_classification/" class="md-nav__link"> Image Classification </a> </li> <li class="md-nav__item md-nav__item--active"> <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc"> <label class="md-nav__link md-nav__link--active" for="__toc"> Image Regression </label> <a href="./" class="md-nav__link md-nav__link--active"> Image Regression </a> <nav class="md-nav md-nav--secondary" aria-label="Table of contents"> <label class="md-nav__title" for="__toc"> Table of contents </label> <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix> <li class="md-nav__item"> <a href="#a-simple-example" class="md-nav__link"> A Simple Example </a> </li> <li class="md-nav__item"> <a href="#validation-data" class="md-nav__link"> Validation Data </a> </li> <li class="md-nav__item"> <a href="#customized-search-space" class="md-nav__link"> Customized Search Space </a> </li> <li class="md-nav__item"> <a href="#data-format" class="md-nav__link"> Data Format </a> </li> <li class="md-nav__item"> <a href="#reference" class="md-nav__link"> Reference </a> </li> </ul> </nav> </li> <li class="md-nav__item"> <a href="../text_classification/" class="md-nav__link"> Text Classification </a> </li> <li class="md-nav__item"> <a href="../text_regression/" class="md-nav__link"> Text Regression </a> </li> <li class="md-nav__item"> <a href="../multi/" class="md-nav__link"> Multi-Modal and Multi-Task </a> </li> <li class="md-nav__item"> <a href="../customized/" class="md-nav__link"> Customized Model </a> </li> <li class="md-nav__item"> <a href="../export/" class="md-nav__link"> Export Model </a> </li> <li class="md-nav__item"> <a href="../load/" class="md-nav__link"> Load Data from Disk </a> </li> <li class="md-nav__item"> <a href="../faq/" class="md-nav__link"> FAQ </a> </li> </ul> </nav> </li> <li class="md-nav__item md-nav__item--nested"> <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" > <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0"> Extensions </label> <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false"> <label class="md-nav__title" for="__nav_4"> Extensions </label> <ul class="md-nav__list" data-md-scrollfix> <li class="md-nav__item"> <a href="../../extensions/tf_cloud/" class="md-nav__link"> TensorFlow Cloud </a> </li> <li class="md-nav__item"> <a href="../../extensions/trains/" class="md-nav__link"> TRAINS </a> </li> </ul> </nav> </li> <li class="md-nav__item"> <a href="../../docker/" class="md-nav__link"> Docker </a> </li> <li class="md-nav__item"> <a href="../../contributing/" class="md-nav__link"> Contributing Guide </a> </li> <li class="md-nav__item md-nav__item--nested"> <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" > <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0"> Documentation </label> <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false"> <label class="md-nav__title" for="__nav_7"> Documentation </label> <ul class="md-nav__list" data-md-scrollfix> <li class="md-nav__item"> <a href="../../image_classifier/" class="md-nav__link"> ImageClassifier </a> </li> <li class="md-nav__item"> <a href="../../image_regressor/" class="md-nav__link"> ImageRegressor </a> </li> <li class="md-nav__item"> <a href="../../text_classifier/" class="md-nav__link"> TextClassifier </a> </li> <li class="md-nav__item"> <a href="../../text_regressor/" class="md-nav__link"> TextRegressor </a> </li> <li class="md-nav__item"> <a href="../../auto_model/" class="md-nav__link"> AutoModel </a> </li> <li class="md-nav__item"> <a href="../../base/" class="md-nav__link"> Base Class </a> </li> <li class="md-nav__item"> <a href="../../node/" class="md-nav__link"> Node </a> </li> <li class="md-nav__item"> <a href="../../block/" class="md-nav__link"> Block </a> </li> <li class="md-nav__item"> <a href="../../utils/" class="md-nav__link"> Utils </a> </li> </ul> </nav> </li> <li class="md-nav__item"> <a href="../../benchmarks/" class="md-nav__link"> Benchmarks </a> </li> <li class="md-nav__item"> <a href="../../about/" class="md-nav__link"> About </a> </li> </ul> </nav> </div> </div> </div> <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" > <div class="md-sidebar__scrollwrap"> <div class="md-sidebar__inner"> <nav class="md-nav md-nav--secondary" aria-label="Table of contents"> <label class="md-nav__title" for="__toc"> Table of contents </label> <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix> <li class="md-nav__item"> <a href="#a-simple-example" class="md-nav__link"> A Simple Example </a> </li> <li class="md-nav__item"> <a href="#validation-data" class="md-nav__link"> Validation Data </a> </li> <li class="md-nav__item"> <a href="#customized-search-space" class="md-nav__link"> Customized Search Space </a> </li> <li class="md-nav__item"> <a href="#data-format" class="md-nav__link"> Data Format </a> </li> <li class="md-nav__item"> <a href="#reference" class="md-nav__link"> Reference </a> </li> </ul> </nav> </div> </div> </div> <div class="md-content" data-md-component="content"> <article class="md-content__inner md-typeset"> <h1>Image Regression</h1> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7a5 5 0 0 0-5 5 5 5 0 0 0 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1M8 13h8v-2H8v2m9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1 0 1.71-1.39 3.1-3.1 3.1h-4V17h4a5 5 0 0 0 5-5 5 5 0 0 0-5-5Z"/></svg> <a href="https://colab.research.google.com/github/keras-team/autokeras/blob/master/docs/ipynb/image_regression.ipynb">View in Colab</a>    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path d="M8 0c4.42 0 8 3.58 8 8a8.013 8.013 0 0 1-5.45 7.59c-.4.08-.55-.17-.55-.38 0-.27.01-1.13.01-2.2 0-.75-.25-1.23-.54-1.48 1.78-.2 3.65-.88 3.65-3.95 0-.88-.31-1.59-.82-2.15.08-.2.36-1.02-.08-2.12 0 0-.67-.22-2.2.82-.64-.18-1.32-.27-2-.27-.68 0-1.36.09-2 .27-1.53-1.03-2.2-.82-2.2-.82-.44 1.1-.16 1.92-.08 2.12-.51.56-.82 1.28-.82 2.15 0 3.06 1.86 3.75 3.64 3.95-.23.2-.44.55-.51 1.07-.46.21-1.61.55-2.33-.66-.15-.24-.6-.83-1.23-.82-.67.01-.27.38.01.53.34.19.73.9.82 1.13.16.45.68 1.31 2.69.94 0 .67.01 1.3.01 1.49 0 .21-.15.45-.55.38A7.995 7.995 0 0 1 0 8c0-4.42 3.58-8 8-8Z"/></svg> <a href="https://github.com/keras-team/autokeras/blob/master/docs/py/image_regression.py">GitHub source</a> <div class="highlight"><pre><code>!pip install autokeras </code></pre></div> <div class="highlight"><pre><code>import tensorflow as tf from keras.datasets import mnist import autokeras as ak </code></pre></div> To make this tutorial easy to follow, we just treat MNIST dataset as a regression dataset. It means we will treat prediction targets of MNIST dataset, which are integers ranging from 0 to 9 as numerical values, so that they can be directly used as the regression targets. <h2 id="a-simple-example">A Simple Example</h2> The first step is to prepare your data. Here we use the MNIST dataset as an example <div class="highlight"><pre><code>(x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train[:100] y_train = y_train[:100] x_test = x_test[:100] y_test = y_test[:100] print(x_train.shape) # (60000, 28, 28) print(y_train.shape) # (60000,) print(y_train[:3]) # array([7, 2, 1], dtype=uint8) </code></pre></div> The second step is to run the ImageRegressor. It is recommended have more trials for more complicated datasets. This is just a quick demo of MNIST, so we set max_trials to 1. For the same reason, we set epochs to 1. You can also leave the epochs unspecified for an adaptive number of epochs. <div class="highlight"><pre><code># Initialize the image regressor. reg = ak.ImageRegressor(overwrite=True, max_trials=1) # Feed the image regressor with training data. reg.fit(x_train, y_train, epochs=1) # Predict with the best model. predicted_y = reg.predict(x_test) print(predicted_y) # Evaluate the best model with testing data. print(reg.evaluate(x_test, y_test)) </code></pre></div> <h2 id="validation-data">Validation Data</h2> By default, AutoKeras use the last 20% of training data as validation data. As shown in the example below, you can use validation_split to specify the percentage. <div class="highlight"><pre><code>reg.fit( x_train, y_train, # Split the training data and use the last 15% as validation data. validation_split=0.15, epochs=1, ) </code></pre></div> You can also use your own validation set instead of splitting it from the training data with validation_data. <div class="highlight"><pre><code>split = 50000 x_val = x_train[split:] y_val = y_train[split:] x_train = x_train[:split] y_train = y_train[:split] reg.fit( x_train, y_train, # Use your own validation set. validation_data=(x_val, y_val), epochs=2, ) </code></pre></div> <h2 id="customized-search-space">Customized Search Space</h2> For advanced users, you may customize your search space by using AutoModel instead of ImageRegressor. You can configure the ImageBlock for some high-level configurations, e.g., block_type for the type of neural network to search, normalize for whether to do data normalization, augment for whether to do data augmentation. You can also do not specify these arguments, which would leave the different choices to be tuned automatically. See the following example for detail. <div class="highlight"><pre><code>input_node = ak.ImageInput() output_node = ak.ImageBlock( # Only search ResNet architectures. block_type="resnet", # Normalize the dataset. normalize=False, # Do not do data augmentation. augment=False, )(input_node) output_node = ak.RegressionHead()(output_node) reg = ak.AutoModel( inputs=input_node, outputs=output_node, overwrite=True, max_trials=1 ) reg.fit(x_train, y_train, epochs=1) </code></pre></div> The usage of AutoModel is similar to the functional API of Keras. Basically, you are building a graph, whose edges are blocks and the nodes are intermediate outputs of blocks. To add an edge from input_node to output_node with output_node = ak.<a href="[block_args]">some_block</a>(input_node). You can even also use more fine grained blocks to customize the search space even further. See the following example. <div class="highlight"><pre><code>input_node = ak.ImageInput() output_node = ak.Normalization()(input_node) output_node = ak.ImageAugmentation(horizontal_flip=False)(output_node) output_node = ak.ResNetBlock(version="v2")(output_node) output_node = ak.RegressionHead()(output_node) reg = ak.AutoModel( inputs=input_node, outputs=output_node, overwrite=True, max_trials=1 ) reg.fit(x_train, y_train, epochs=1) </code></pre></div> <h2 id="data-format">Data Format</h2> The AutoKeras ImageRegressor is quite flexible for the data format. For the image, it accepts data formats both with and without the channel dimension. The images in the MNIST dataset do not have the channel dimension. Each image is a matrix with shape (28, 28). AutoKeras also accepts images of three dimensions with the channel dimension at last, e.g., (32, 32, 3), (28, 28, 1). For the regression targets, it should be a vector of numerical values. AutoKeras accepts numpy.ndarray. We also support using tf.data.Dataset format for the training data. In this case, the images would have to be 3-dimentional. <div class="highlight"><pre><code>(x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train[:100] y_train = y_train[:100] x_test = x_test[:100] y_test = y_test[:100] # Reshape the images to have the channel dimension. x_train = x_train.reshape(x_train.shape + (1,)) x_test = x_test.reshape(x_test.shape + (1,)) y_train = y_train.reshape(y_train.shape + (1,)) y_test = y_test.reshape(y_test.shape + (1,)) print(x_train.shape) # (60000, 28, 28, 1) print(y_train.shape) # (60000, 10) train_set = tf.data.Dataset.from_tensor_slices(((x_train,), (y_train,))) test_set = tf.data.Dataset.from_tensor_slices(((x_test,), (y_test,))) reg = ak.ImageRegressor(overwrite=True, max_trials=1) # Feed the tensorflow Dataset to the regressor. reg.fit(train_set, epochs=1) # Predict with the best model. predicted_y = reg.predict(test_set) # Evaluate the best model with testing data. print(reg.evaluate(test_set)) </code></pre></div> <h2 id="reference">Reference</h2> <a href="/image_regressor">ImageRegressor</a>, <a href="/auto_model/#automodel-class">AutoModel</a>, <a href="/block/#imageblock-class">ImageBlock</a>, <a href="/block/#normalization-class">Normalization</a>, <a href="/block/#image-augmentation-class">ImageAugmentation</a>, <a href="/block/#resnetblock-class">ResNetBlock</a>, <a href="/node/#imageinput-class">ImageInput</a>, <a href="/block/#regressionhead-class">RegressionHead</a>. </article> </div> <script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script> </div> </main> <footer class="md-footer"> <div class="md-footer-meta md-typeset"> <div class="md-footer-meta__inner md-grid"> <div class="md-copyright"> Made with <a href="https://squidfunk.github.io/mkdocs-material/" target="_blank" rel="noopener"> Material for MkDocs </a> </div> </div> </div> </footer> </div> <div class="md-dialog" data-md-component="dialog"> <div class="md-dialog__inner md-typeset"></div> </div> <script id="__config" type="application/json">{"base": "../..", "features": [], "search": "../../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script> <script src="../../assets/javascripts/bundle.bd41221c.min.js"></script> <script src="https://unpkg.com/mermaid@8.4.4/dist/mermaid.min.js"></script> </body> </html>

CINXE.COM

Image Regression - AutoKeras