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<h1>
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Logging the training progress in a CSV
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</h1>
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<ol class="breadcrumb">
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<li>
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<time class="published" datetime="2018-12-04T21:33:00+01:00">
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04 décembre 2018
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</time>
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</li>
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<li>DL</li>
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<li>basics</li>
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</ol>
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</header>
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<div class='article_content'>
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<p>Let's see how we can log the progress and various metrics during the training process to a csv file.
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Let's first import some libraries</p>
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<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">keras</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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</pre></div>
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<p>In this example, we will be using the fashion MNIST dataset to do some basic computer vision, where we will train a Keras neural network to classify items of clothing.</p>
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<p>In order to import the data we will be using the built in function in Keras : </p>
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<div class="highlight"><pre><span></span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fashion_mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
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</pre></div>
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<p>The model is a very simple neural network consisting in 2 fully connected layers. The model loss function is chosen in order to have a multiclass classifier : "sparse_categorical_crossentropy"</p>
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<p>Let's define a simple feedforward network.</p>
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<div class="highlight"><pre><span></span><span class="c1">##get and preprocess the data</span>
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<span class="n">fashion_mnist</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fashion_mnist</span>
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<span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">),</span> <span class="p">(</span><span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)</span> <span class="o">=</span> <span class="n">fashion_mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
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<span class="n">train_images</span> <span class="o">=</span> <span class="n">train_images</span> <span class="o">/</span> <span class="mf">255.0</span>
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<span class="n">test_images</span> <span class="o">=</span> <span class="n">test_images</span> <span class="o">/</span> <span class="mf">255.0</span>
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<span class="c1">## define the model </span>
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<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>
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<span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">28</span><span class="p">,</span><span class="mi">28</span><span class="p">)),</span>
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<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">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">),</span>
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<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">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"softmax"</span><span class="p">)</span>
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<span class="p">])</span>
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<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="s2">"adam"</span><span class="p">,</span>
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<span class="n">loss</span> <span class="o">=</span> <span class="s2">"sparse_categorical_crossentropy"</span><span class="p">,</span>
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<span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"accuracy"</span><span class="p">,</span><span class="s1">'mae'</span><span class="p">])</span>
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</pre></div>
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<p>In order to stream to a csv file the epoch results and metrics, we define a CSV logger. It is a callback located in </p>
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<div class="highlight"><pre><span></span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span>
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</pre></div>
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<p>Let's first import it </p>
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<div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="kn">import</span> <span class="n">CSVLogger</span>
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</pre></div>
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<p>We now need to define the callback by specifiying a file to be written to, the separator and whether to append to the file or erase it every time.</p>
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<p>The callback has to be added to the callbacks list in the fit method.</p>
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<div class="highlight"><pre><span></span><span class="n">csv_logger</span> <span class="o">=</span> <span class="n">CSVLogger</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="s2">"my_csv.csv"</span><span class="p">,</span> <span class="n">separator</span><span class="o">=</span><span class="s1">';'</span><span class="p">,</span> <span class="n">append</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</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">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">csv_logger</span><span class="p">])</span>
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</pre></div>
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<div class="highlight"><pre><span></span><span class="n">Epoch</span> <span class="mi">1</span><span class="o">/</span><span class="mi">5</span>
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<span class="mi">60000</span><span class="o">/</span><span class="mi">60000</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">9</span><span class="n">s</span> <span class="mi">148</span><span class="n">us</span><span class="o">/</span><span class="n">step</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">5020</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">8234</span> <span class="o">-</span> <span class="n">mean_absolute_error</span><span class="p">:</span> <span class="mi">4</span><span class="p">.</span><span class="mi">4200</span>
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<span class="n">Epoch</span> <span class="mi">2</span><span class="o">/</span><span class="mi">5</span>
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<span class="mi">60000</span><span class="o">/</span><span class="mi">60000</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">8</span><span class="n">s</span> <span class="mi">138</span><span class="n">us</span><span class="o">/</span><span class="n">step</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">3765</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">8630</span> <span class="o">-</span> <span class="n">mean_absolute_error</span><span class="p">:</span> <span class="mi">4</span><span class="p">.</span><span class="mi">4200</span>
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<span class="n">Epoch</span> <span class="mi">3</span><span class="o">/</span><span class="mi">5</span>
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<span class="mi">60000</span><span class="o">/</span><span class="mi">60000</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">8</span><span class="n">s</span> <span class="mi">129</span><span class="n">us</span><span class="o">/</span><span class="n">step</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">3371</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">8789</span> <span class="o">-</span> <span class="n">mean_absolute_error</span><span class="p">:</span> <span class="mi">4</span><span class="p">.</span><span class="mi">4200</span>
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<span class="n">Epoch</span> <span class="mi">4</span><span class="o">/</span><span class="mi">5</span>
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<span class="mi">60000</span><span class="o">/</span><span class="mi">60000</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">8</span><span class="n">s</span> <span class="mi">133</span><span class="n">us</span><span class="o">/</span><span class="n">step</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">3129</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">8843</span> <span class="o">-</span> <span class="n">mean_absolute_error</span><span class="p">:</span> <span class="mi">4</span><span class="p">.</span><span class="mi">4200</span>
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<span class="n">Epoch</span> <span class="mi">5</span><span class="o">/</span><span class="mi">5</span>
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<span class="mi">60000</span><span class="o">/</span><span class="mi">60000</span> <span class="p">[</span><span class="o">==============================</span><span class="p">]</span> <span class="o">-</span> <span class="mi">9</span><span class="n">s</span> <span class="mi">151</span><span class="n">us</span><span class="o">/</span><span class="n">step</span> <span class="o">-</span> <span class="n">loss</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">2952</span> <span class="o">-</span> <span class="n">acc</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">8916</span> <span class="o">-</span> <span class="n">mean_absolute_error</span><span class="p">:</span> <span class="mi">4</span><span class="p">.</span><span class="mi">4200</span>
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<span class="o"><</span><span class="n">keras</span><span class="p">.</span><span class="n">callbacks</span><span class="p">.</span><span class="n">History</span> <span class="k">at</span> <span class="mi">0</span><span class="n">x1582adc6780</span><span class="o">></span>
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</pre></div>
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<p>The results are stored in the my_csv.csv file and contain the epoch results </p>
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<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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<span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"my_csv.csv"</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">";"</span><span class="p">)</span>
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</pre></div>
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<div>
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<style scoped>
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.dataframe tbody tr th:only-of-type {
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vertical-align: middle;
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}
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.dataframe tbody tr th {
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vertical-align: top;
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}
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.dataframe thead th {
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text-align: right;
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}
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</style>
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>epoch</th>
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<th>acc</th>
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<th>loss</th>
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<th>mean_absolute_error</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>0</th>
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<td>0</td>
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<td>0.823400</td>
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<td>0.502013</td>
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<td>4.42</td>
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</tr>
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<tr>
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<th>1</th>
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<td>1</td>
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<td>0.863050</td>
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<td>0.376516</td>
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<td>4.42</td>
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</tr>
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<tr>
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<th>2</th>
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<td>2</td>
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<td>0.878867</td>
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<td>0.337097</td>
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<td>4.42</td>
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</tr>
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<tr>
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<th>3</th>
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<td>3</td>
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<td>0.884317</td>
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<td>0.312893</td>
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<td>4.42</td>
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</tr>
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<tr>
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<th>4</th>
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<td>4</td>
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<td>0.891583</td>
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<td>0.295157</td>
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<td>4.42</td>
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</tr>
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</tbody>
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</table>
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</div>
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</div>
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<p>Everything on this site is avaliable on GitHub. Head on over and <a href='https://github.com/redoules/redoules.github.io/issues/new'>submit an issue.</a> You can also message me directly by <a href='mailto:guillaume.redoules@gadz.org'>email</a>.</p>
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<center>This project contains 119 pages and is available on <a href="https://github.com/redoules/redoules.github.io">GitHub</a>.
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Copyright © Guillaume Redoulès,
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