AutoModel - AutoKeras

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Image Regression </span> </a> </li> <li class="md-nav__item"> <a href="../tutorial/text_classification/" class="md-nav__link"> <span class="md-ellipsis"> Text Classification </span> </a> </li> <li class="md-nav__item"> <a href="../tutorial/text_regression/" class="md-nav__link"> <span class="md-ellipsis"> Text Regression </span> </a> </li> <li class="md-nav__item"> <a href="../tutorial/multi/" class="md-nav__link"> <span class="md-ellipsis"> Multi-Modal and Multi-Task </span> </a> </li> <li class="md-nav__item"> <a href="../tutorial/customized/" class="md-nav__link"> <span class="md-ellipsis"> Customized Model </span> </a> </li> <li class="md-nav__item"> <a href="../tutorial/export/" class="md-nav__link"> <span class="md-ellipsis"> Export Model </span> </a> </li> <li class="md-nav__item"> <a href="../tutorial/load/" class="md-nav__link"> <span class="md-ellipsis"> Load Data from Disk </span> </a> </li> <li class="md-nav__item"> <a href="../tutorial/faq/" class="md-nav__link"> <span 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</span> </a> </li> <li class="md-nav__item"> <a href="../contributing/" class="md-nav__link"> <span class="md-ellipsis"> Contributing Guide </span> </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_7" checked> <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0"> <span class="md-ellipsis"> Documentation </span> <span class="md-nav__icon md-icon"></span> </label> <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="true"> <label class="md-nav__title" for="__nav_7"> <span class="md-nav__icon md-icon"></span> Documentation </label> <ul class="md-nav__list" data-md-scrollfix> <li class="md-nav__item"> <a href="../image_classifier/" class="md-nav__link"> <span class="md-ellipsis"> ImageClassifier </span> </a> </li> <li class="md-nav__item"> <a href="../image_regressor/" class="md-nav__link"> <span class="md-ellipsis"> ImageRegressor </span> </a> </li> <li class="md-nav__item"> <a href="../text_classifier/" class="md-nav__link"> <span class="md-ellipsis"> TextClassifier </span> </a> </li> <li class="md-nav__item"> <a href="../text_regressor/" class="md-nav__link"> <span class="md-ellipsis"> TextRegressor </span> </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"> <span class="md-ellipsis"> AutoModel </span> <span class="md-nav__icon md-icon"></span> </label> <a href="./" class="md-nav__link md-nav__link--active"> <span class="md-ellipsis"> AutoModel </span> </a> <nav class="md-nav md-nav--secondary" aria-label="Table of contents"> <label class="md-nav__title" for="__toc"> <span class="md-nav__icon md-icon"></span> Table of contents </label> <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix> <li class="md-nav__item"> <a href="#automodel" class="md-nav__link"> <span class="md-ellipsis"> AutoModel </span> </a> </li> <li class="md-nav__item"> <a href="#example" class="md-nav__link"> <span class="md-ellipsis"> Example </span> </a> <nav class="md-nav" aria-label="Example"> <ul class="md-nav__list"> <li class="md-nav__item"> <a href="#fit" class="md-nav__link"> <span class="md-ellipsis"> fit </span> </a> </li> <li class="md-nav__item"> <a href="#predict" class="md-nav__link"> <span class="md-ellipsis"> predict </span> </a> </li> <li class="md-nav__item"> <a href="#evaluate" class="md-nav__link"> <span class="md-ellipsis"> evaluate </span> </a> </li> <li class="md-nav__item"> <a href="#export_model" class="md-nav__link"> <span class="md-ellipsis"> export_model </span> </a> </li> </ul> </nav> </li> </ul> </nav> </li> <li class="md-nav__item"> <a href="../base/" class="md-nav__link"> <span class="md-ellipsis"> Base Class </span> </a> </li> <li class="md-nav__item"> <a href="../node/" class="md-nav__link"> <span class="md-ellipsis"> Node </span> </a> </li> <li class="md-nav__item"> <a href="../block/" class="md-nav__link"> <span class="md-ellipsis"> Block </span> </a> </li> <li class="md-nav__item"> <a href="../utils/" class="md-nav__link"> <span class="md-ellipsis"> Utils </span> </a> </li> </ul> </nav> </li> <li class="md-nav__item"> <a href="../benchmarks/" class="md-nav__link"> <span class="md-ellipsis"> Benchmarks </span> </a> </li> <li class="md-nav__item"> <a href="../about/" class="md-nav__link"> <span class="md-ellipsis"> About </span> </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"> <span class="md-nav__icon md-icon"></span> Table of contents </label> <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix> <li class="md-nav__item"> <a href="#automodel" class="md-nav__link"> <span class="md-ellipsis"> AutoModel </span> </a> </li> <li class="md-nav__item"> <a href="#example" class="md-nav__link"> <span class="md-ellipsis"> Example </span> </a> <nav class="md-nav" aria-label="Example"> <ul class="md-nav__list"> <li class="md-nav__item"> <a href="#fit" class="md-nav__link"> <span class="md-ellipsis"> fit </span> </a> </li> <li class="md-nav__item"> <a href="#predict" class="md-nav__link"> <span class="md-ellipsis"> predict </span> </a> </li> <li class="md-nav__item"> <a href="#evaluate" class="md-nav__link"> <span class="md-ellipsis"> evaluate </span> </a> </li> <li class="md-nav__item"> <a href="#export_model" class="md-nav__link"> <span class="md-ellipsis"> export_model </span> </a> </li> </ul> </nav> </li> </ul> </nav> </div> </div> </div> <div class="md-content" data-md-component="content"> <article class="md-content__inner md-typeset"> <p><span style="float:right;"><a href="https://github.com/keras-team/autokeras/blob/master/autokeras/auto_model.py#L56">[source]</a></span></p> <h3 id="automodel">AutoModel</h3> <div class="highlight"><pre><span></span><code><span class="n">autokeras</span><span class="o">.</span><span class="n">AutoModel</span><span class="p">(</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">project_name</span><span class="o">=</span><span class="s2">"auto_model"</span><span class="p">,</span> <span class="n">max_trials</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">directory</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">objective</span><span class="o">=</span><span class="s2">"val_loss"</span><span class="p">,</span> <span class="n">tuner</span><span class="o">=</span><span class="s2">"greedy"</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_model_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span> </code></pre></div> <p>A Model defined by inputs and outputs. AutoModel combines a HyperModel and a Tuner to tune the HyperModel. The user can use it in a similar way to a Keras model since it also has <code>fit()</code> and <code>predict()</code> methods.</p> <p>The AutoModel has two use cases. In the first case, the user only specifies the input nodes and output heads of the AutoModel. The AutoModel infers the rest part of the model. In the second case, user can specify the high-level architecture of the AutoModel by connecting the Blocks with the functional API, which is the same as the Keras <a href="https://www.tensorflow.org/guide/keras/functional">functional API</a>.</p> <p><strong>Exampl</strong></p> <h1 id="example">Example</h1> <p><div class="highlight"><pre><span></span><code> <span class="c1"># The user only specifies the input nodes and output heads.</span> <span class="kn">import</span> <span class="nn">autokeras</span> <span class="k">as</span> <span class="nn">ak</span> <span class="n">ak</span><span class="o">.</span><span class="n">AutoModel</span><span class="p">(</span> <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">ak</span><span class="o">.</span><span class="n">ImageInput</span><span class="p">(),</span> <span class="n">ak</span><span class="o">.</span><span class="n">TextInput</span><span class="p">()],</span> <span class="n">outputs</span><span class="o">=</span><span class="p">[</span><span class="n">ak</span><span class="o">.</span><span class="n">ClassificationHead</span><span class="p">(),</span> <span class="n">ak</span><span class="o">.</span><span class="n">RegressionHead</span><span class="p">()]</span> <span class="p">)</span> </code></pre></div> <div class="highlight"><pre><span></span><code> <span class="c1"># The user specifies the high-level architecture.</span> <span class="kn">import</span> <span class="nn">autokeras</span> <span class="k">as</span> <span class="nn">ak</span> <span class="n">image_input</span> <span class="o">=</span> <span class="n">ak</span><span class="o">.</span><span class="n">ImageInput</span><span class="p">()</span> <span class="n">image_output</span> <span class="o">=</span> <span class="n">ak</span><span class="o">.</span><span class="n">ImageBlock</span><span class="p">()(</span><span class="n">image_input</span><span class="p">)</span> <span class="n">text_input</span> <span class="o">=</span> <span class="n">ak</span><span class="o">.</span><span class="n">TextInput</span><span class="p">()</span> <span class="n">text_output</span> <span class="o">=</span> <span class="n">ak</span><span class="o">.</span><span class="n">TextBlock</span><span class="p">()(</span><span class="n">text_input</span><span class="p">)</span> <span class="n">output</span> <span class="o">=</span> <span class="n">ak</span><span class="o">.</span><span class="n">Merge</span><span class="p">()([</span><span class="n">image_output</span><span class="p">,</span> <span class="n">text_output</span><span class="p">])</span> <span class="n">classification_output</span> <span class="o">=</span> <span class="n">ak</span><span class="o">.</span><span class="n">ClassificationHead</span><span class="p">()(</span><span class="n">output</span><span class="p">)</span> <span class="n">regression_output</span> <span class="o">=</span> <span class="n">ak</span><span class="o">.</span><span class="n">RegressionHead</span><span class="p">()(</span><span class="n">output</span><span class="p">)</span> <span class="n">ak</span><span class="o">.</span><span class="n">AutoModel</span><span class="p">(</span> <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">image_input</span><span class="p">,</span> <span class="n">text_input</span><span class="p">],</span> <span class="n">outputs</span><span class="o">=</span><span class="p">[</span><span class="n">classification_output</span><span class="p">,</span> <span class="n">regression_output</span><span class="p">]</span> <span class="p">)</span> </code></pre></div></p> <p><strong>Arguments</strong></p> <ul> <li><strong>inputs</strong> <code>autokeras.Input | List[autokeras.Input]</code>: A list of Node instances. The input node(s) of the AutoModel.</li> <li><strong>outputs</strong> <code>autokeras.Head | autokeras.Node | list</code>: A list of Node or Head instances. The output node(s) or head(s) of the AutoModel.</li> <li><strong>project_name</strong> <code>str</code>: String. The name of the AutoModel. Defaults to 'auto_model'.</li> <li><strong>max_trials</strong> <code>int</code>: Int. The maximum number of different Keras Models to try. The search may finish before reaching the max_trials. Defaults to 100.</li> <li><strong>directory</strong> <code>str | pathlib.Path | None</code>: String. The path to a directory for storing the search outputs. Defaults to None, which would create a folder with the name of the AutoModel in the current directory.</li> <li><strong>objective</strong> <code>str</code>: String. Name of model metric to minimize or maximize, e.g. 'val_accuracy'. Defaults to 'val_loss'.</li> <li><strong>tuner</strong> <code>str | Type[autokeras.engine.tuner.AutoTuner]</code>: String or subclass of AutoTuner. If string, it should be one of 'greedy', 'bayesian', 'hyperband' or 'random'. It can also be a subclass of AutoTuner. Defaults to 'greedy'.</li> <li><strong>overwrite</strong> <code>bool</code>: Boolean. Defaults to <code>False</code>. If <code>False</code>, reloads an existing project of the same name if one is found. Otherwise, overwrites the project.</li> <li><strong>seed</strong> <code>int | None</code>: Int. Random seed.</li> <li><strong>max_model_size</strong> <code>int | None</code>: Int. Maximum number of scalars in the parameters of a model. Models larger than this are rejected.</li> <li><strong>**kwargs</strong>: Any arguments supported by keras_tuner.Tuner.</li> </ul> <hr /> <p><span style="float:right;"><a href="https://github.com/keras-team/autokeras/blob/master/autokeras/auto_model.py#L218">[source]</a></span></p> <h3 id="fit">fit</h3> <div class="highlight"><pre><span></span><code><span class="n">AutoModel</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span> <span class="n">x</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">validation_split</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span> </code></pre></div> <p>Search for the best model and hyperparameters for the AutoModel.</p> <p>It will search for the best model based on the performances on validation data.</p> <p><strong>Arguments</strong></p> <ul> <li><strong>x</strong>: numpy.ndarray or tensorflow.Dataset. Training data x.</li> <li><strong>y</strong>: numpy.ndarray or tensorflow.Dataset. Training data y.</li> <li><strong>batch_size</strong>: Int. Number of samples per gradient update. Defaults to 32.</li> <li><strong>epochs</strong>: Int. The number of epochs to train each model during the search. If unspecified, by default we train for a maximum of 1000 epochs, but we stop training if the validation loss stops improving for 10 epochs (unless you specified an EarlyStopping callback as part of the callbacks argument, in which case the EarlyStopping callback you specified will determine early stopping).</li> <li><strong>callbacks</strong>: List of Keras callbacks to apply during training and validation.</li> <li><strong>validation_split</strong>: Float between 0 and 1. Defaults to 0.2. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the <code>x</code> and <code>y</code> data provided, before shuffling. This argument is not supported when <code>x</code> is a dataset. The best model found would be fit on the entire dataset including the validation data.</li> <li><strong>validation_data</strong>: Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. <code>validation_data</code> will override <code>validation_split</code>. The type of the validation data should be the same as the training data. The best model found would be fit on the training dataset without the validation data.</li> <li><strong>verbose</strong>: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment). Controls the verbosity of both KerasTuner search and <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit">keras.Model.fit</a></li> <li><strong>**kwargs</strong>: Any arguments supported by <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit">keras.Model.fit</a>.</li> </ul> <p><strong>Returns</strong></p> <p>history: A Keras History object corresponding to the best model. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).</p> <hr /> <p><span style="float:right;"><a href="https://github.com/keras-team/autokeras/blob/master/autokeras/auto_model.py#L448">[source]</a></span></p> <h3 id="predict">predict</h3> <div class="highlight"><pre><span></span><code><span class="n">AutoModel</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> </code></pre></div> <p>Predict the output for a given testing data.</p> <p><strong>Arguments</strong></p> <ul> <li><strong>x</strong>: Any allowed types according to the input node. Testing data.</li> <li><strong>batch_size</strong>: Number of samples per batch. If unspecified, batch_size will default to 32.</li> <li><strong>verbose</strong>: Verbosity mode. 0 = silent, 1 = progress bar. Controls the verbosity of <a href="https://tensorflow.org/api_docs/python/tf/keras/Model#predict">keras.Model.predict</a></li> <li><strong>**kwargs</strong>: Any arguments supported by keras.Model.predict.</li> </ul> <p><strong>Returns</strong></p> <p>A list of numpy.ndarray objects or a single numpy.ndarray. The predicted results.</p> <hr /> <p><span style="float:right;"><a href="https://github.com/keras-team/autokeras/blob/master/autokeras/auto_model.py#L482">[source]</a></span></p> <h3 id="evaluate">evaluate</h3> <div class="highlight"><pre><span></span><code><span class="n">AutoModel</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> </code></pre></div> <p>Evaluate the best model for the given data.</p> <p><strong>Arguments</strong></p> <ul> <li><strong>x</strong>: Any allowed types according to the input node. Testing data.</li> <li><strong>y</strong>: Any allowed types according to the head. Testing targets. Defaults to None.</li> <li><strong>batch_size</strong>: Number of samples per batch. If unspecified, batch_size will default to 32.</li> <li><strong>verbose</strong>: Verbosity mode. 0 = silent, 1 = progress bar. Controls the verbosity of <a href="http://tensorflow.org/api_docs/python/tf/keras/Model#evaluate">keras.Model.evaluate</a></li> <li><strong>**kwargs</strong>: Any arguments supported by keras.Model.evaluate.</li> </ul> <p><strong>Returns</strong></p> <p>Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.</p> <hr /> <p><span style="float:right;"><a href="https://github.com/keras-team/autokeras/blob/master/autokeras/auto_model.py#L521">[source]</a></span></p> <h3 id="export_model">export_model</h3> <div class="highlight"><pre><span></span><code><span class="n">AutoModel</span><span class="o">.</span><span class="n">export_model</span><span class="p">()</span> </code></pre></div> <p>Export the best Keras Model.</p> <p><strong>Returns</strong></p> <p>keras.Model instance. The best model found during the search, loaded with trained weights.</p> <hr /> </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>

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