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<h1 id="creating-a-custom-image-classifier-using-turicreate-to-detect-smoke-and-fire">Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire</h1>
<p><em>For setting up Kaggle with Google Colab, please refer to <a rel="noopener" target="_blank" href="/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab"> my previous post</a></em></p>
<h2 id="dataset">Dataset</h2>
<h3 id="mounting-google-drive">Mounting Google Drive</h3>
<div class="codehilite">
<pre><span></span><code><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">google.colab</span> <span class="kn">import</span> <span class="n">drive</span>
<span class="n">drive</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s1">'/content/drive'</span><span class="p">)</span>
</code></pre>
</div>
<h3 id="downloading-dataset-from-kaggle">Downloading Dataset from Kaggle</h3>
<div class="codehilite">
<pre><span></span><code><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'KAGGLE_CONFIG_DIR'</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"/content/drive/My Drive/"</span>
<span class="err">!</span><span class="n">kaggle</span> <span class="n">datasets</span> <span class="n">download</span> <span class="n">ashutosh69</span><span class="o">/</span><span class="n">fire</span><span class="o">-</span><span class="ow">and</span><span class="o">-</span><span class="n">smoke</span><span class="o">-</span><span class="n">dataset</span>
<span class="err">!</span><span class="n">unzip</span> <span class="s2">"fire-and-smoke-dataset.zip"</span>
</code></pre>
</div>
<h2 id="pre-processing">Pre-Processing</h2>
<div class="codehilite">
<pre><span></span><code><span class="nt">!mkdir</span><span class="na"> default smoke fire</span>
</code></pre>
</div>
<p>\</p>
<div class="codehilite">
<pre><span></span><code><span class="nt">!ls</span><span class="na"> data/data/img_data/train/default/*.jpg</span>
</code></pre>
</div>
<p>\</p>
<div class="codehilite">
<pre><span></span><code><span class="nt">img_1002.jpg</span><span class="na"> img_20.jpg img_519.jpg img_604.jpg img_80.jpg</span>
<span class="na">img_1003.jpg img_21.jpg img_51.jpg img_60.jpg img_8.jpg</span>
<span class="na">img_1007.jpg img_22.jpg img_520.jpg img_61.jpg img_900.jpg</span>
<span class="na">img_100.jpg img_23.jpg img_521.jpg 'img_62 (2).jpg' img_920.jpg</span>
<span class="na">img_1014.jpg img_24.jpg 'img_52 (2).jpg' img_62.jpg img_921.jpg</span>
<span class="na">img_1018.jpg img_29.jpg img_522.jpg 'img_63 (2).jpg' img_922.jpg</span>
<span class="na">img_101.jpg img_3000.jpg img_523.jpg img_63.jpg img_923.jpg</span>
<span class="na">img_1027.jpg img_335.jpg img_524.jpg img_66.jpg img_924.jpg</span>
<span class="na">img_102.jpg img_336.jpg img_52.jpg img_67.jpg img_925.jpg</span>
<span class="na">img_1042.jpg img_337.jpg img_530.jpg img_68.jpg img_926.jpg</span>
<span class="na">img_1043.jpg img_338.jpg img_531.jpg img_700.jpg img_927.jpg</span>
<span class="na">img_1046.jpg img_339.jpg 'img_53 (2).jpg' img_701.jpg img_928.jpg</span>
<span class="na">img_1052.jpg img_340.jpg img_532.jpg img_702.jpg img_929.jpg</span>
<span class="na">img_107.jpg img_341.jpg img_533.jpg img_703.jpg img_930.jpg</span>
<span class="na">img_108.jpg img_3.jpg img_537.jpg img_704.jpg img_931.jpg</span>
<span class="na">img_109.jpg img_400.jpg img_538.jpg img_705.jpg img_932.jpg</span>
<span class="na">img_10.jpg img_471.jpg img_539.jpg img_706.jpg img_933.jpg</span>
<span class="na">img_118.jpg img_472.jpg img_53.jpg img_707.jpg img_934.jpg</span>
<span class="na">img_12.jpg img_473.jpg img_540.jpg img_708.jpg img_935.jpg</span>
<span class="na">img_14.jpg img_488.jpg img_541.jpg img_709.jpg img_938.jpg</span>
<span class="na">img_15.jpg img_489.jpg 'img_54 (2).jpg' img_70.jpg img_958.jpg</span>
<span class="na">img_16.jpg img_490.jpg img_542.jpg img_710.jpg img_971.jpg</span>
<span class="na">img_17.jpg img_491.jpg img_543.jpg 'img_71 (2).jpg' img_972.jpg</span>
<span class="na">img_18.jpg img_492.jpg img_54.jpg img_71.jpg img_973.jpg</span>
<span class="na">img_19.jpg img_493.jpg 'img_55 (2).jpg' img_72.jpg img_974.jpg</span>
<span class="na">img_1.jpg img_494.jpg img_55.jpg img_73.jpg img_975.jpg</span>
<span class="na">img_200.jpg img_495.jpg img_56.jpg img_74.jpg img_980.jpg</span>
<span class="na">img_201.jpg img_496.jpg img_57.jpg img_75.jpg img_988.jpg</span>
<span class="na">img_202.jpg img_497.jpg img_58.jpg img_76.jpg img_9.jpg</span>
<span class="na">img_203.jpg img_4.jpg img_59.jpg img_77.jpg</span>
<span class="na">img_204.jpg img_501.jpg img_601.jpg img_78.jpg</span>
<span class="na">img_205.jpg img_502.jpg img_602.jpg img_79.jpg</span>
<span class="na">img_206.jpg img_50.jpg img_603.jpg img_7.jpg</span>
</code></pre>
</div>
<p>The image files are not actually JPEG, thus we first need to save them in the correct format for Turicreate</p>
<div class="codehilite">
<pre><span></span><code><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">import</span> <span class="nn">glob</span>
<span class="n">folders</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"default"</span><span class="p">,</span><span class="s2">"smoke"</span><span class="p">,</span><span class="s2">"fire"</span><span class="p">]</span>
<span class="k">for</span> <span class="n">folder</span> <span class="ow">in</span> <span class="n">folders</span><span class="p">:</span>
<span class="n">n</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s2">"./data/data/img_data/train/"</span> <span class="o">+</span> <span class="n">folder</span> <span class="o">+</span> <span class="s2">"/*.jpg"</span><span class="p">):</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
<span class="n">rgb_im</span> <span class="o">=</span> <span class="n">im</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s1">'RGB'</span><span class="p">)</span>
<span class="n">rgb_im</span><span class="o">.</span><span class="n">save</span><span class="p">((</span><span class="n">folder</span> <span class="o">+</span> <span class="s2">"/"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="o">+</span> <span class="s2">".jpg"</span><span class="p">),</span> <span class="n">quality</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="n">n</span> <span class="o">+=</span><span class="mi">1</span>
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s2">"./data/data/img_data/train/"</span> <span class="o">+</span> <span class="n">folder</span> <span class="o">+</span> <span class="s2">"/*.jpg"</span><span class="p">):</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
<span class="n">rgb_im</span> <span class="o">=</span> <span class="n">im</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s1">'RGB'</span><span class="p">)</span>
<span class="n">rgb_im</span><span class="o">.</span><span class="n">save</span><span class="p">((</span><span class="n">folder</span> <span class="o">+</span> <span class="s2">"/"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="o">+</span> <span class="s2">".jpg"</span><span class="p">),</span> <span class="n">quality</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="n">n</span> <span class="o">+=</span><span class="mi">1</span>
</code></pre>
</div>
<p>\</p>
<div class="codehilite">
<pre><span></span><code><span class="nt">!mkdir</span><span class="na"> train</span>
<span class="na">!mv default ./train</span>
<span class="na">!mv smoke ./train</span>
<span class="na">!mv fire ./train</span>
</code></pre>
</div>
<h2 id="making-the-image-classifier">Making the Image Classifier</h2>
<h3 id="making-an-sframe">Making an SFrame</h3>
<div class="codehilite">
<pre><span></span><code><span class="nt">!pip</span><span class="na"> install turicreate</span>
</code></pre>
</div>
<p>\</p>
<div class="codehilite">
<pre><span></span><code><span class="kn">import</span> <span class="nn">turicreate</span> <span class="k">as</span> <span class="nn">tc</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">tc</span><span class="o">.</span><span class="n">image_analysis</span><span class="o">.</span><span class="n">load_images</span><span class="p">(</span><span class="s2">"./train"</span><span class="p">,</span> <span class="n">with_path</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">data</span><span class="p">[</span><span class="s2">"label"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s2">"path"</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">path</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">path</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">data</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'fire-smoke.sframe'</span><span class="p">)</span>
</code></pre>
</div>
<p>\</p>
<div class="codehilite">
<pre><span></span><code><span class="nt">+-------------------------+------------------------+</span>
<span class="err">|</span><span class="w"> </span><span class="err">path</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">image</span><span class="w"> </span><span class="err">|</span>
<span class="nt">+-------------------------+------------------------+</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/1.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/10.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/100.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/101.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/102.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/103.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/104.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/105.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/106.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/107.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span>
<span class="nt">+-------------------------+------------------------+</span>
<span class="nt">[2028</span><span class="na"> rows x 2 columns]</span>
<span class="na">Note</span><span class="p">:</span><span class="err"> </span><span class="nc">Only</span><span class="err"> </span><span class="nc">the</span><span class="err"> </span><span class="nc">head</span><span class="err"> </span><span class="nc">of</span><span class="err"> </span><span class="nc">the</span><span class="err"> </span><span class="nc">SFrame</span><span class="err"> </span><span class="nc">is</span><span class="err"> </span><span class="nc">printed.</span>
<span class="nt">You</span><span class="na"> can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.</span>
<span class="na">+-------------------------+------------------------+---------+</span>
<span class="p">|</span><span class="na"> path </span><span class="p">|</span><span class="na"> image </span><span class="p">|</span><span class="na"> label </span><span class="p">|</span>
<span class="nt">+-------------------------+------------------------+---------+</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/1.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">default</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/10.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">default</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/100.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">default</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/101.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">default</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/102.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">default</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/103.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">default</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/104.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">default</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/105.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">default</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/106.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">default</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">./train/default/107.jpg</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">Height:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">Width:</span><span class="w"> </span><span class="err">224</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">default</span><span class="w"> </span><span class="err">|</span>
<span class="nt">+-------------------------+------------------------+---------+</span>
<span class="nt">[2028</span><span class="na"> rows x 3 columns]</span>
<span class="na">Note</span><span class="p">:</span><span class="err"> </span><span class="nc">Only</span><span class="err"> </span><span class="nc">the</span><span class="err"> </span><span class="nc">head</span><span class="err"> </span><span class="nc">of</span><span class="err"> </span><span class="nc">the</span><span class="err"> </span><span class="nc">SFrame</span><span class="err"> </span><span class="nc">is</span><span class="err"> </span><span class="nc">printed.</span>
<span class="nt">You</span><span class="na"> can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.</span>
</code></pre>
</div>
<h3 id="making-the-model">Making the Model</h3>
<div class="codehilite">
<pre><span></span><code><span class="kn">import</span> <span class="nn">turicreate</span> <span class="k">as</span> <span class="nn">tc</span>
<span class="c1"># Load the data</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">tc</span><span class="o">.</span><span class="n">SFrame</span><span class="p">(</span><span class="s1">'fire-smoke.sframe'</span><span class="p">)</span>
<span class="c1"># Make a train-test split</span>
<span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">random_split</span><span class="p">(</span><span class="mf">0.8</span><span class="p">)</span>
<span class="c1"># Create the model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">tc</span><span class="o">.</span><span class="n">image_classifier</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="s1">'label'</span><span class="p">)</span>
<span class="c1"># Save predictions to an SArray</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_data</span><span class="p">)</span>
<span class="c1"># Evaluate the model and print the results</span>
<span class="n">metrics</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_data</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">metrics</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span>
<span class="c1"># Save the model for later use in Turi Create</span>
<span class="n">model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'fire-smoke.model'</span><span class="p">)</span>
<span class="c1"># Export for use in Core ML</span>
<span class="n">model</span><span class="o">.</span><span class="n">export_coreml</span><span class="p">(</span><span class="s1">'fire-smoke.mlmodel'</span><span class="p">)</span>
</code></pre>
</div>
<p>\</p>
<div class="codehilite">
<pre><span></span><code><span class="nt">Performing</span><span class="na"> feature extraction on resized images...</span>
<span class="na">Completed 64/1633</span>
<span class="na">Completed 128/1633</span>
<span class="na">Completed 192/1633</span>
<span class="na">Completed 256/1633</span>
<span class="na">Completed 320/1633</span>
<span class="na">Completed 384/1633</span>
<span class="na">Completed 448/1633</span>
<span class="na">Completed 512/1633</span>
<span class="na">Completed 576/1633</span>
<span class="na">Completed 640/1633</span>
<span class="na">Completed 704/1633</span>
<span class="na">Completed 768/1633</span>
<span class="na">Completed 832/1633</span>
<span class="na">Completed 896/1633</span>
<span class="na">Completed 960/1633</span>
<span class="na">Completed 1024/1633</span>
<span class="na">Completed 1088/1633</span>
<span class="na">Completed 1152/1633</span>
<span class="na">Completed 1216/1633</span>
<span class="na">Completed 1280/1633</span>
<span class="na">Completed 1344/1633</span>
<span class="na">Completed 1408/1633</span>
<span class="na">Completed 1472/1633</span>
<span class="na">Completed 1536/1633</span>
<span class="na">Completed 1600/1633</span>
<span class="na">Completed 1633/1633</span>
<span class="na">PROGRESS</span><span class="p">:</span><span class="err"> </span><span class="nc">Creating</span><span class="err"> </span><span class="nc">a</span><span class="err"> </span><span class="nc">validation</span><span class="err"> </span><span class="nc">set</span><span class="err"> </span><span class="nc">from</span><span class="err"> </span><span class="nc">5</span><span class="err"> </span><span class="nc">percent</span><span class="err"> </span><span class="nc">of</span><span class="err"> </span><span class="nc">training</span><span class="err"> </span><span class="nc">data.</span><span class="err"> </span><span class="nc">This</span><span class="err"> </span><span class="nc">may</span><span class="err"> </span><span class="nc">take</span><span class="err"> </span><span class="nc">a</span><span class="err"> </span><span class="nc">while.</span>
<span class="w"> </span><span class="err">You</span><span class="w"> </span><span class="err">can</span><span class="w"> </span><span class="err">set</span><span class="w"> </span><span class="err">``validation_set=None``</span><span class="w"> </span><span class="err">to</span><span class="w"> </span><span class="err">disable</span><span class="w"> </span><span class="err">validation</span><span class="w"> </span><span class="err">tracking.</span>
<span class="nt">Logistic</span><span class="na"> regression</span><span class="p">:</span>
<span class="nt">--------------------------------------------------------</span>
<span class="nt">Number</span><span class="na"> of examples </span><span class="p">:</span><span class="err"> </span><span class="nc">1551</span>
<span class="nt">Number</span><span class="na"> of classes </span><span class="p">:</span><span class="err"> </span><span class="nc">3</span>
<span class="nt">Number</span><span class="na"> of feature columns </span><span class="p">:</span><span class="err"> </span><span class="nc">1</span>
<span class="nt">Number</span><span class="na"> of unpacked features </span><span class="p">:</span><span class="err"> </span><span class="nc">2048</span>
<span class="nt">Number</span><span class="na"> of coefficients </span><span class="p">:</span><span class="err"> </span><span class="nc">4098</span>
<span class="nt">Starting</span><span class="na"> L-BFGS</span>
<span class="na">--------------------------------------------------------</span>
<span class="na">+-----------+----------+-----------+--------------+-------------------+---------------------+</span>
<span class="p">|</span><span class="na"> Iteration </span><span class="p">|</span><span class="na"> Passes </span><span class="p">|</span><span class="na"> Step size </span><span class="p">|</span><span class="na"> Elapsed Time </span><span class="p">|</span><span class="na"> Training Accuracy </span><span class="p">|</span><span class="na"> Validation Accuracy </span><span class="p">|</span>
<span class="nt">+-----------+----------+-----------+--------------+-------------------+---------------------+</span>
<span class="err">|</span><span class="w"> </span><span class="err">0</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">6</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.018611</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.891830</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.553836</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.560976</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">1</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">10</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.390832</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">1.622383</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.744681</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.792683</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">2</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">11</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.488541</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">1.943987</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.733075</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.804878</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">3</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">14</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">2.442703</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">2.512545</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.727917</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.841463</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">4</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">15</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">2.442703</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">2.826964</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.861380</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.853659</span><span class="w"> </span><span class="err">|</span>
<span class="err">|</span><span class="w"> </span><span class="err">9</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">28</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">2.340435</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">5.492035</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.941328</span><span class="w"> </span><span class="err">|</span><span class="w"> </span><span class="err">0.975610</span><span class="w"> </span><span class="err">|</span>
<span class="nt">+-----------+----------+-----------+--------------+-------------------+---------------------+</span>
<span class="nt">Performing</span><span class="na"> feature extraction on resized images...</span>
<span class="na">Completed 64/395</span>
<span class="na">Completed 128/395</span>
<span class="na">Completed 192/395</span>
<span class="na">Completed 256/395</span>
<span class="na">Completed 320/395</span>
<span class="na">Completed 384/395</span>
<span class="na">Completed 395/395</span>
<span class="na">0.9316455696202531</span>
</code></pre>
</div>
<p>We just got an accuracy of 94% on Training Data and 97% on Validation Data!</p>
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