InceptionV3

<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1"> <meta name="description" content="Keras documentation"> <meta name="author" content="Keras Team"> <link rel="shortcut icon" href="https://keras.io/img/favicon.ico"> <link rel="canonical" href="https://keras.io/api/applications/inceptionv3/" />  <meta property="og:title" content="Keras documentation: InceptionV3"> <meta property="og:image" content="https://keras.io/img/logo-k-keras-wb.png"> <meta name="twitter:title" content="Keras documentation: InceptionV3"> <meta name="twitter:image" content="https://keras.io/img/k-keras-social.png"> <meta name="twitter:card" content="summary"> <title>InceptionV3</title>  <link href="/css/bootstrap.min.css" rel="stylesheet">  <link href="https://fonts.googleapis.com/css2?family=Open+Sans:wght@400;600;700;800&display=swap" rel="stylesheet">  <link href="/css/docs.css" rel="stylesheet"> <link href="/css/monokai.css" rel="stylesheet">  <script>(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-5DNGF4N'); </script> <script> (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) })(window,document,'script','https://www.google-analytics.com/analytics.js','ga'); ga('create', 'UA-175165319-128', 'auto'); ga('send', 'pageview'); </script>  <script async defer src="https://buttons.github.io/buttons.js"></script> </head> <body>  <noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-5DNGF4N" height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>  <div class='k-page'> <div class="k-nav" id="nav-menu"> <a href='/'><img src='/img/logo-small.png' class='logo-small' /></a> <div class="nav flex-column nav-pills" role="tablist" aria-orientation="vertical"> <a class="nav-link" href="/about/" role="tab" aria-selected="">About Keras</a> <a class="nav-link" href="/getting_started/" role="tab" aria-selected="">Getting started</a> <a class="nav-link" href="/guides/" role="tab" aria-selected="">Developer guides</a> <a class="nav-link" href="/examples/" role="tab" aria-selected="">Code examples</a> <a class="nav-link active" href="/api/" role="tab" aria-selected="">Keras 3 API documentation</a> <a class="nav-sublink" href="/api/models/">Models API</a> <a class="nav-sublink" href="/api/layers/">Layers API</a> <a class="nav-sublink" href="/api/callbacks/">Callbacks API</a> <a class="nav-sublink" href="/api/ops/">Ops API</a> <a class="nav-sublink" href="/api/optimizers/">Optimizers</a> <a class="nav-sublink" href="/api/metrics/">Metrics</a> <a class="nav-sublink" href="/api/losses/">Losses</a> <a class="nav-sublink" href="/api/data_loading/">Data loading</a> <a class="nav-sublink" href="/api/datasets/">Built-in small datasets</a> <a class="nav-sublink active" href="/api/applications/">Keras Applications</a> <a class="nav-sublink2" href="/api/applications/xception/">Xception</a> <a class="nav-sublink2" href="/api/applications/efficientnet/">EfficientNet B0 to B7</a> <a class="nav-sublink2" href="/api/applications/efficientnet_v2/">EfficientNetV2 B0 to B3 and S, M, L</a> <a class="nav-sublink2" href="/api/applications/convnext/">ConvNeXt Tiny, Small, Base, Large, XLarge</a> <a class="nav-sublink2" href="/api/applications/vgg/">VGG16 and VGG19</a> <a class="nav-sublink2" href="/api/applications/resnet/">ResNet and ResNetV2</a> <a class="nav-sublink2" href="/api/applications/mobilenet/">MobileNet, MobileNetV2, and MobileNetV3</a> <a class="nav-sublink2" href="/api/applications/densenet/">DenseNet</a> <a class="nav-sublink2" href="/api/applications/nasnet/">NasNetLarge and NasNetMobile</a> <a class="nav-sublink2 active" href="/api/applications/inceptionv3/">InceptionV3</a> <a class="nav-sublink2" href="/api/applications/inceptionresnetv2/">InceptionResNetV2</a> <a class="nav-sublink" href="/api/mixed_precision/">Mixed precision</a> <a class="nav-sublink" href="/api/distribution/">Multi-device distribution</a> <a class="nav-sublink" href="/api/random/">RNG API</a> <a class="nav-sublink" href="/api/utils/">Utilities</a> <a class="nav-link" href="/2.18/api/" role="tab" aria-selected="">Keras 2 API documentation</a> <a class="nav-link" href="/keras_tuner/" role="tab" aria-selected="">KerasTuner: Hyperparam Tuning</a> <a class="nav-link" href="/keras_hub/" role="tab" aria-selected="">KerasHub: Pretrained Models</a> </div> </div> <div class='k-main'> <div class='k-main-top'> <script> function displayDropdownMenu() { e = document.getElementById("nav-menu"); if (e.style.display == "block") { e.style.display = "none"; } else { e.style.display = "block"; document.getElementById("dropdown-nav").style.display = "block"; } } function resetMobileUI() { if (window.innerWidth <= 840) { document.getElementById("nav-menu").style.display = "none"; document.getElementById("dropdown-nav").style.display = "block"; } else { document.getElementById("nav-menu").style.display = "block"; document.getElementById("dropdown-nav").style.display = "none"; } var navmenu = document.getElementById("nav-menu"); var menuheight = navmenu.clientHeight; var kmain = document.getElementById("k-main-id"); kmain.style.minHeight = (menuheight + 100) + 'px'; } window.onresize = resetMobileUI; window.addEventListener("load", (event) => { resetMobileUI() }); </script> <div id='dropdown-nav' onclick="displayDropdownMenu();"> <svg viewBox="-20 -20 120 120" width="60" height="60"> <rect width="100" height="20"></rect> <rect y="30" width="100" height="20"></rect> <rect y="60" width="100" height="20"></rect> </svg> </div> <form class="bd-search d-flex align-items-center k-search-form" id="search-form"> <input type="search" class="k-search-input" id="search-input" placeholder="Search Keras documentation..." aria-label="Search Keras documentation..." autocomplete="off"> <button class="k-search-btn"> <svg width="13" height="13" viewBox="0 0 13 13"><title>search</title><path d="m4.8495 7.8226c0.82666 0 1.5262-0.29146 2.0985-0.87438 0.57232-0.58292 0.86378-1.2877 0.87438-2.1144 0.010599-0.82666-0.28086-1.5262-0.87438-2.0985-0.59352-0.57232-1.293-0.86378-2.0985-0.87438-0.8055-0.010599-1.5103 0.28086-2.1144 0.87438-0.60414 0.59352-0.8956 1.293-0.87438 2.0985 0.021197 0.8055 0.31266 1.5103 0.87438 2.1144 0.56172 0.60414 1.2665 0.8956 2.1144 0.87438zm4.4695 0.2115 3.681 3.6819-1.259 1.284-3.6817-3.7 0.0019784-0.69479-0.090043-0.098846c-0.87973 0.76087-1.92 1.1413-3.1207 1.1413-1.3553 0-2.5025-0.46363-3.4417-1.3909s-1.4088-2.0686-1.4088-3.4239c0-1.3553 0.4696-2.4966 1.4088-3.4239 0.9392-0.92727 2.0864-1.3969 3.4417-1.4088 1.3553-0.011889 2.4906 0.45771 3.406 1.4088 0.9154 0.95107 1.379 2.0924 1.3909 3.4239 0 1.2126-0.38043 2.2588-1.1413 3.1385l0.098834 0.090049z"></path></svg> </button> </form> <script> var form = document.getElementById('search-form'); form.onsubmit = function(e) { e.preventDefault(); var query = document.getElementById('search-input').value; window.location.href = '/search.html?query=' + query; return False } </script> </div> <div class='k-main-inner' id='k-main-id'> <div class='k-location-slug'> <span class="k-location-slug-pointer">►</span> <a href='/api/'>Keras 3 API documentation</a> / <a href='/api/applications/'>Keras Applications</a> / InceptionV3 </div> <div class='k-content'> <h1 id="inceptionv3">InceptionV3</h1> <p><span style="float:right;"><a href="https://github.com/keras-team/keras/tree/v3.8.0/keras/src/applications/inception_v3.py#L19">[source]</a></span></p> <h3 id="inceptionv3-function"><code>InceptionV3</code> function</h3> <div class="codehilite"><pre><span></span><code><span class="n">keras</span><span class="o">.</span><span class="n">applications</span><span class="o">.</span><span class="n">InceptionV3</span><span class="p">(</span> <span class="n">include_top</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="s2">"imagenet"</span><span class="p">,</span> <span class="n">input_tensor</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pooling</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">classifier_activation</span><span class="o">=</span><span class="s2">"softmax"</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"inception_v3"</span><span class="p">,</span> <span class="p">)</span> </code></pre></div> <p>Instantiates the Inception v3 architecture.</p> <p><strong>Reference</strong></p> <ul> <li><a href="http://arxiv.org/abs/1512.00567">Rethinking the Inception Architecture for Computer Vision</a> (CVPR 2016)</li> </ul> <p>This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.</p> <p>For image classification use cases, see <a href="https://keras.io/api/applications/#usage-examples-for-image-classification-models">this page for detailed examples</a>.</p> <p>For transfer learning use cases, make sure to read the <a href="https://keras.io/guides/transfer_learning/">guide to transfer learning & fine-tuning</a>.</p> <p>Note: each Keras Application expects a specific kind of input preprocessing. For <code>InceptionV3</code>, call <code>keras.applications.inception_v3.preprocess_input</code> on your inputs before passing them to the model. <code>inception_v3.preprocess_input</code> will scale input pixels between -1 and 1.</p> <p><strong>Arguments</strong></p> <ul> <li><strong>include_top</strong>: Boolean, whether to include the fully-connected layer at the top, as the last layer of the network. Defaults to <code>True</code>.</li> <li><strong>weights</strong>: One of <code>None</code> (random initialization), <code>imagenet</code> (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to <code>"imagenet"</code>.</li> <li><strong>input_tensor</strong>: Optional Keras tensor (i.e. output of <code>layers.Input()</code>) to use as image input for the model. <code>input_tensor</code> is useful for sharing inputs between multiple different networks. Defaults to <code>None</code>.</li> <li><strong>input_shape</strong>: Optional shape tuple, only to be specified if <code>include_top</code> is False (otherwise the input shape has to be <code>(299, 299, 3)</code> (with <code>channels_last</code> data format) or <code>(3, 299, 299)</code> (with <code>channels_first</code> data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g. <code>(150, 150, 3)</code> would be one valid value. <code>input_shape</code> will be ignored if the <code>input_tensor</code> is provided.</li> <li><strong>pooling</strong>: Optional pooling mode for feature extraction when <code>include_top</code> is <code>False</code>.<ul> <li><code>None</code> (default) means that the output of the model will be the 4D tensor output of the last convolutional block.</li> <li><code>avg</code> means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor.</li> <li><code>max</code> means that global max pooling will be applied.</li> </ul> </li> <li><strong>classes</strong>: optional number of classes to classify images into, only to be specified if <code>include_top</code> is <code>True</code>, and if no <code>weights</code> argument is specified. Defaults to 1000.</li> <li><strong>classifier_activation</strong>: A <code>str</code> or callable. The activation function to use on the "top" layer. Ignored unless <code>include_top=True</code>. Set <code>classifier_activation=None</code> to return the logits of the "top" layer. When loading pretrained weights, <code>classifier_activation</code> can only be <code>None</code> or <code>"softmax"</code>.</li> <li><strong>name</strong>: The name of the model (string).</li> </ul> <p><strong>Returns</strong></p> <p>A model instance.</p> <hr /> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#inceptionv3'>InceptionV3</a> </div> <div class='k-outline-depth-3'> <a href='#inceptionv3-function'><code>InceptionV3</code> function</a> </div> </div> </div> </div> </div> </body> <footer style="float: left; width: 100%; padding: 1em; border-top: solid 1px #bbb;"> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>

CINXE.COM

InceptionV3