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futher transform your input dataset before training.</p> <p>Here's a quick example: let's say you have 10 folders, each containing 10,000 images from a different category, and you want to train a classifier that maps an image to its category.</p> <p>Your training data folder would look like this:</p> <div class="codehilite"><pre><span></span><code>training_data/ ...class_a/ ......a_image_1.jpg ......a_image_2.jpg ...class_b/ ......b_image_1.jpg ......b_image_2.jpg etc. </code></pre></div> <p>You may also have a validation data folder <code>validation_data/</code> structured in the same way.</p> <p>You could simply do:</p> <div class="codehilite"><pre><span></span><code><span class="kn">import</span> <span class="nn">keras</span> <span class="n">train_ds</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">image_dataset_from_directory</span><span class="p">(</span> <span class="n">directory</span><span class="o">=</span><span class="s1">'training_data/'</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="s1">'inferred'</span><span class="p">,</span> <span class="n">label_mode</span><span class="o">=</span><span class="s1">'categorical'</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">image_size</span><span class="o">=</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">))</span> <span class="n">validation_ds</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">image_dataset_from_directory</span><span class="p">(</span> <span class="n">directory</span><span class="o">=</span><span class="s1">'validation_data/'</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="s1">'inferred'</span><span class="p">,</span> <span class="n">label_mode</span><span class="o">=</span><span class="s1">'categorical'</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">image_size</span><span class="o">=</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</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">applications</span><span class="o">.</span><span class="n">Xception</span><span class="p">(</span> <span class="n">weights</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="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">classes</span><span class="o">=</span><span class="mi">10</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">optimizer</span><span class="o">=</span><span class="s1">'rmsprop'</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'categorical_crossentropy'</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_ds</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="n">validation_ds</span><span class="p">)</span> </code></pre></div> <h2 id="available-dataset-loading-utilities">Available dataset loading utilities</h2> <h3 id="image-data-loading"><a href="/api/data_loading/image/">Image data loading</a></h3> <ul> <li><a href="/api/data_loading/image/#image_dataset_from_directory-function">image_dataset_from_directory function</a></li> <li><a href="/api/data_loading/image/#load_img-function">load_img function</a></li> <li><a href="/api/data_loading/image/#img_to_array-function">img_to_array function</a></li> <li><a href="/api/data_loading/image/#save_img-function">save_img function</a></li> <li><a href="/api/data_loading/image/#array_to_img-function">array_to_img function</a></li> </ul> <h3 id="timeseries-data-loading"><a href="/api/data_loading/timeseries/">Timeseries data loading</a></h3> <ul> <li><a href="/api/data_loading/timeseries/#timeseries_dataset_from_array-function">timeseries_dataset_from_array function</a></li> <li><a href="/api/data_loading/timeseries/#pad_sequences-function">pad_sequences function</a></li> </ul> <h3 id="text-data-loading"><a href="/api/data_loading/text/">Text data loading</a></h3> <ul> <li><a href="/api/data_loading/text/#text_dataset_from_directory-function">text_dataset_from_directory function</a></li> </ul> <h3 id="audio-data-loading"><a href="/api/data_loading/audio/">Audio data loading</a></h3> <ul> <li><a href="/api/data_loading/audio/#audio_dataset_from_directory-function">audio_dataset_from_directory function</a></li> </ul> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#data-loading'>Data loading</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#available-dataset-loading-utilities'>Available dataset loading utilities</a> </div> <div class='k-outline-depth-3'> <a href='#image-data-loading'>Image data loading</a> </div> <div class='k-outline-depth-3'> <a href='#timeseries-data-loading'>Timeseries data loading</a> </div> <div class='k-outline-depth-3'> <a href='#text-data-loading'>Text data loading</a> </div> <div class='k-outline-depth-3'> <a href='#audio-data-loading'>Audio data loading</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>

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