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KerasTuner
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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> KerasTuner </div> <div class='k-content'> <h1 id="kerastuner">KerasTuner</h1> <p><a class="github-button" href="https://github.com/keras-team/keras-tuner" data-size="large" data-show-count="true" aria-label="Star keras-team/keras-tuner on GitHub">Star</a></p> <p>KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms.</p> <hr /> <h2 id="quick-links">Quick links</h2> <ul> <li><a href="/guides/keras_tuner/getting_started/">Getting started with KerasTuner</a></li> <li><a href="/guides/keras_tuner/">KerasTuner developer guides</a></li> <li><a href="/api/keras_tuner/">KerasTuner API reference</a></li> <li><a href="https://github.com/keras-team/keras-tuner">KerasTuner on GitHub</a></li> </ul> <hr /> <h2 id="installation">Installation</h2> <p>Install the latest release:</p> <div class="codehilite"><pre><span></span><code>pip install keras-tuner --upgrade </code></pre></div> <p>You can also check out other versions in our <a href="https://github.com/keras-team/keras-tuner">GitHub repository</a>.</p> <hr /> <h2 id="quick-introduction">Quick introduction</h2> <p>Import KerasTuner and TensorFlow:</p> <div class="codehilite"><pre><span></span><code><span class="kn">import</span> <span class="nn">keras_tuner</span> <span class="kn">import</span> <span class="nn">keras</span> </code></pre></div> <p>Write a function that creates and returns a Keras model. Use the <code>hp</code> argument to define the hyperparameters during model creation.</p> <div class="codehilite"><pre><span></span><code><span class="k">def</span> <span class="nf">build_model</span><span class="p">(</span><span class="n">hp</span><span class="p">):</span> <span class="n">model</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span> <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span> <span class="n">hp</span><span class="o">.</span><span class="n">Choice</span><span class="p">(</span><span class="s1">'units'</span><span class="p">,</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">]),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">'mse'</span><span class="p">)</span> <span class="k">return</span> <span class="n">model</span> </code></pre></div> <p>Initialize a tuner (here, <code>RandomSearch</code>). We use <code>objective</code> to specify the objective to select the best models, and we use <code>max_trials</code> to specify the number of different models to try.</p> <div class="codehilite"><pre><span></span><code><span class="n">tuner</span> <span class="o">=</span> <span class="n">keras_tuner</span><span class="o">.</span><span class="n">RandomSearch</span><span class="p">(</span> <span class="n">build_model</span><span class="p">,</span> <span class="n">objective</span><span class="o">=</span><span class="s1">'val_loss'</span><span class="p">,</span> <span class="n">max_trials</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> </code></pre></div> <p>Start the search and get the best model:</p> <div class="codehilite"><pre><span></span><code><span class="n">tuner</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">))</span> <span class="n">best_model</span> <span class="o">=</span> <span class="n">tuner</span><span class="o">.</span><span class="n">get_best_models</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> </code></pre></div> <p>To learn more about KerasTuner, check out <a href="https://keras.io/guides/keras_tuner/getting_started/">this starter guide</a>.</p> <hr /> <h2 id="citing-kerastuner">Citing KerasTuner</h2> <p>If KerasTuner helps your research, we appreciate your citations. Here is the BibTeX entry:</p> <div class="codehilite"><pre><span></span><code><span class="nc">@misc</span><span class="p">{</span><span class="nl">omalley2019kerastuner</span><span class="p">,</span> <span class="w"> </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{KerasTuner}</span><span class="p">,</span> <span class="w"> </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{O'Malley, Tom and Bursztein, Elie and Long, James and Chollet, Fran\c{c}ois and Jin, Haifeng and Invernizzi, Luca and others}</span><span class="p">,</span> <span class="w"> </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="m">2019</span><span class="p">,</span> <span class="w"> </span><span class="na">howpublished</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{\url{https://github.com/keras-team/keras-tuner}}</span> <span class="p">}</span> </code></pre></div> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#kerastuner'>KerasTuner</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#quick-links'>Quick links</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#installation'>Installation</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#quick-introduction'>Quick introduction</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#citing-kerastuner'>Citing KerasTuner</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>