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Code Issues 3 Releases Wiki Activity

added article CSVlogger

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added an article on CSVlogger for keras
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redoules 2018-12-04 21:46:31 +01:00
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<h1>
Logging the training progress in a CSV
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<time class="published" datetime="2018-12-04T21:33:00+01:00">
04 décembre 2018
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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.
Let's first import some libraries</p>
<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">keras</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
</pre></div>
<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>
<p>In order to import the data we will be using the built in function in Keras : </p>
<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>
</pre></div>
<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>
<p>Let's define a simple feedforward network.</p>
<div class="highlight"><pre><span></span><span class="c1">##get and preprocess the data</span>
<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>
<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>
<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>
<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>
<span class="c1">## define the model </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">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>
<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">&quot;relu&quot;</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">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;softmax&quot;</span><span class="p">)</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="s2">&quot;adam&quot;</span><span class="p">,</span>
<span class="n">loss</span> <span class="o">=</span> <span class="s2">&quot;sparse_categorical_crossentropy&quot;</span><span class="p">,</span>
<span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span><span class="s1">&#39;mae&#39;</span><span class="p">])</span>
</pre></div>
<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>
<div class="highlight"><pre><span></span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span>
</pre></div>
<p>Let's first import it </p>
<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>
</pre></div>
<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>
<p>The callback has to be added to the callbacks list in the fit method.</p>
<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">&quot;my_csv.csv&quot;</span><span class="p">,</span> <span class="n">separator</span><span class="o">=</span><span class="s1">&#39;;&#39;</span><span class="p">,</span> <span class="n">append</span><span class="o">=</span><span class="bp">False</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_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>
</pre></div>
<div class="highlight"><pre><span></span>Epoch 1/5
60000/60000 [==============================] - 9s 148us/step - loss: 0.5020 - acc: 0.8234 - mean_absolute_error: 4.4200
Epoch 2/5
60000/60000 [==============================] - 8s 138us/step - loss: 0.3765 - acc: 0.8630 - mean_absolute_error: 4.4200
Epoch 3/5
60000/60000 [==============================] - 8s 129us/step - loss: 0.3371 - acc: 0.8789 - mean_absolute_error: 4.4200
Epoch 4/5
60000/60000 [==============================] - 8s 133us/step - loss: 0.3129 - acc: 0.8843 - mean_absolute_error: 4.4200
Epoch 5/5
60000/60000 [==============================] - 9s 151us/step - loss: 0.2952 - acc: 0.8916 - mean_absolute_error: 4.4200
&amp;lt;keras.callbacks.History at 0x1582adc6780&amp;gt;
</pre></div>
<p>The results are stored in the my_csv.csv file and contain the epoch results </p>
<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;my_csv.csv&quot;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">)</span>
</pre></div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>epoch</th>
<th>acc</th>
<th>loss</th>
<th>mean_absolute_error</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>0.823400</td>
<td>0.502013</td>
<td>4.42</td>
</tr>
<tr>
<th>1</th>
<td>1</td>
<td>0.863050</td>
<td>0.376516</td>
<td>4.42</td>
</tr>
<tr>
<th>2</th>
<td>2</td>
<td>0.878867</td>
<td>0.337097</td>
<td>4.42</td>
</tr>
<tr>
<th>3</th>
<td>3</td>
<td>0.884317</td>
<td>0.312893</td>
<td>4.42</td>
</tr>
<tr>
<th>4</th>
<td>4</td>
<td>0.891583</td>
<td>0.295157</td>
<td>4.42</td>
</tr>
</tbody>
</table>
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dl/keras_install.html
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@ -152,7 +152,7 @@
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dl/tensorboard_keras.html
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@ -34,7 +34,7 @@
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<meta name="tags" content="Other" /> <meta name="tags" content="basics" />
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@ -121,7 +121,7 @@
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<li>DL</li> <li>DL</li>
<li>Other</li> <li>basics</li>
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index.html
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@ -305,6 +305,8 @@
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<li><a href="./dl/tensorboard_keras.html">Using tensorboard with Keras</a></li>
<li><a href="./dl/keras_install.html">Install keras using conda</a></li> <li><a href="./dl/keras_install.html">Install keras using conda</a></li>
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@ -356,7 +358,7 @@
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linux/Reloading_.bashrc.html
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@ -144,7 +144,7 @@ However, you can use the following command to do it </p>
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@ -167,7 +167,7 @@ none 0 0 0 - /config
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linux/get_ip_linux.html
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@ -256,7 +256,7 @@ wifi0 Link encap:UNSPEC HWaddr 5C-51-4F-41-7A-AD-00-00-00-00-00-00-00-00-00
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linux/linux_process_information.html
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@ -172,7 +172,7 @@ USER;PID;%CPU;%MEM;VSZ;RSS;TTY;STAT;START;TIME;COMMAND</p>
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linux/mount_nfs_share_fstab.html
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@ -208,7 +208,7 @@ The secret number is 126
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machine-learning/LeastSquareRegressionLine.html
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@ -234,7 +234,7 @@ Where Y=a+b*X
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machine-learning/Multiple_Linear_Regression.html
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@ -248,7 +248,7 @@ b_2\\
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mathematics/Geometric.html
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@ -192,7 +192,7 @@ If you try to open a non existing file in read mode 'r', a FileNotFoundError wil
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python/Optimized_numpy_random_intel.html
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@ -176,7 +176,7 @@ conda install numpy --channel intel --override-channels
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python/Reading_data_from_a_sql_database_with_pandas.html
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@ -237,7 +237,7 @@
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python/Saving_a_matplotlib_figure_with_a_high_resolution.html
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@ -186,7 +186,7 @@
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python/Setting_up_the_notebook_for_plotting_with_matplotlib.html
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python/Writing_data_to_a_sql_database_with_pandas.html
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@ -339,7 +339,7 @@ However, exporting to a sql database can prove very useful. Indeed, having a wel
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python/counting.html
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@ -181,7 +181,7 @@ The number 4 appears 1 times in the list
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python/dask_distributed_parallelism.html
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@ -338,7 +338,7 @@ http://127.0.0.1:8787/status</p>
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python/dask_infiniband.html
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python/design_own_libs.html
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@ -232,7 +232,7 @@ The list of dependencies required to test, build and generate the doc are listed
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python/dict_comprehension.html
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python/download_page.html
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python/ensure_dir.html
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@ -177,7 +177,7 @@
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python/list_files_directory.html
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python/logplot.html
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python/randint.html
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@ -156,7 +156,7 @@
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python/reverse.html
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@ -173,7 +173,7 @@ Reversed array : [4 3 2 1 0]
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python/sorting.html
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@ -192,7 +192,7 @@ Sorted array : [0.13876208 0.19939047 0.40021786 0.43734158 0.46015169]
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python/stock_pandas.html
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python/unique.html
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@ -171,7 +171,7 @@ Numpy array with unique values : [10 20 30 40 50 60]
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python/updating_all_python_package_with_anaconda.html
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search.html
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sitemap.xml
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@ -5,7 +5,7 @@ xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
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<loc>redoules.github.io/</loc> <loc>redoules.github.io/</loc>
<lastmod>2018-12-03T23:15:56-00:00</lastmod> <lastmod>2018-12-04T21:44:24-00:00</lastmod>
<changefreq>daily</changefreq> <changefreq>daily</changefreq>
<priority>0.5</priority> <priority>0.5</priority>
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@ -17,6 +17,13 @@ xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
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sql/Sorting_results.html
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sql/WHERE_SQL_keywords.html
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sql/display_table.html
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sql/display_table_filter.html
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tipuesearch_content.json
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