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added an artiicle on how to checkpoint a keras model during training
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<h1>
Saving the models weights after each epoch
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<time class="published" datetime="2018-12-04T21:48:00+01:00">
04 décembre 2018
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<li>DL</li>
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<p>Let's see how we can save the model weights after every epoch.
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>
<div class="highlight"><pre><span></span>Using TensorFlow backend.
</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;acc&quot;</span><span class="p">])</span>
</pre></div>
<p>In order to automatically save the model weights to a hdf5 format after every epoch we need to import the ModelCheckpoint 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>
<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">ModelCheckpoint</span>
</pre></div>
<p>We now need to define the ModelCheckPoint callback, it takes 7 arguments :
<em> filepath: string, path to save the model file. monitor: quantity to monitor
</em> verbose: verbosity mode, 0 or 1
<em> save_best_only: if <code>save_best_only=True</code>, the latest best model according to the quantity monitored will not be overwritten.
</em> mode: one of {auto, min, max}
<em> If <code>save_best_only=True</code>, the decision to overwrite the current save file is made based on either the maximization or the minimization of the monitored quantity.
</em> save_weights_only: if True, then only the model's weights will be saved (<code>model.save_weights(filepath)</code>), else the full model is saved (<code>model.save(filepath)</code>).
* period: Interval (number of epochs) between checkpoints.</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">save_to_hdf5</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s2">&quot;my_model.h5&quot;</span><span class="p">,</span>
<span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;acc&#39;</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">save_best_only</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
<span class="n">save_weights_only</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
<span class="n">mode</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
<span class="n">period</span><span class="o">=</span><span class="mi">1</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">save_to_hdf5</span><span class="p">])</span>
</pre></div>
<div class="highlight"><pre><span></span>Epoch 1/5
60000/60000 [==============================] - 29s 478us/step - loss: 0.3975 - acc: 0.8575
Epoch 2/5
60000/60000 [==============================] - 98s 2ms/step - loss: 0.3498 - acc: 0.8721
Epoch 3/5
60000/60000 [==============================] - 95s 2ms/step - loss: 0.3213 - acc: 0.8825
Epoch 4/5
60000/60000 [==============================] - 64s 1ms/step - loss: 0.3021 - acc: 0.8887: 6s - loss:
Epoch 5/5
60000/60000 [==============================] - 61s 1ms/step - loss: 0.2855 - acc: 0.8953: 4s - loss: 0.284 - ETA: 3s - loss: - ETA:
&amp;lt;keras.callbacks.History at 0x1d70aa62f28&amp;gt;
</pre></div>
<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="k">if</span> <span class="s2">&quot;my_model.h5&quot;</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">()</span> <span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;The model is saved to my_model.h5&quot;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s1">&#39;no model saved to disk&#39;</span><span class="p">)</span>
</pre></div>
<div class="highlight"><pre><span></span>The model is saved to my_model.h5
</pre></div>
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index.html
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<li><a href="./dl/model_checkpoint_keras.html">Saving the models weights after each epoch</a></li>
<li><a href="./dl/csv_logger_keras.html">Logging the training progress in a CSV</a></li>
<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>
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linux/get_ip_linux.html
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python/ensure_dir.html
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@ -177,7 +177,7 @@
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<center>This project contains 71 pages and is available on <a href="https://github.com/redoules/redoules.github.io">GitHub</a>.
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2
python/list_files_directory.html
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@ -213,7 +213,7 @@
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<center>This project contains 71 pages and is available on <a href="https://github.com/redoules/redoules.github.io">GitHub</a>.
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python/logplot.html
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@ -248,7 +248,7 @@
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<center>This project contains 71 pages and is available on <a href="https://github.com/redoules/redoules.github.io">GitHub</a>.
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2
python/randint.html
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@ -156,7 +156,7 @@
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<center>This project contains 71 pages and is available on <a href="https://github.com/redoules/redoules.github.io">GitHub</a>.
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2
python/reverse.html
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@ -173,7 +173,7 @@ Reversed array : [4 3 2 1 0]
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<center>This project contains 71 pages and is available on <a href="https://github.com/redoules/redoules.github.io">GitHub</a>.
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2
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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<center>This project contains 71 pages and is available on <a href="https://github.com/redoules/redoules.github.io">GitHub</a>.
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python/stock_pandas.html
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@ -381,7 +381,7 @@
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2
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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@ -151,7 +151,7 @@
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2
search.html
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@ -112,7 +112,7 @@
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9
sitemap.xml
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@ -5,7 +5,7 @@ xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
<url>
<loc>redoules.github.io/</loc>
<lastmod>2018-12-04T21:44:24-00:00</lastmod>
<lastmod>2018-12-04T22:06:57-00:00</lastmod>
<changefreq>daily</changefreq>
<priority>0.5</priority>
</url>
@ -17,6 +17,13 @@ xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
<priority>0.5</priority>
</url>
<url>
<loc>redoules.github.io/dl/model_checkpoint_keras.html</loc>
<lastmod>2018-12-04T21:48:00+01:00</lastmod>
<changefreq>monthly</changefreq>
<priority>0.5</priority>
</url>
<url>
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2
sql/Sorting_results.html
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@ -344,7 +344,7 @@ Done.
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sql/WHERE_SQL_keywords.html
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@ -631,7 +631,7 @@ Done.
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sql/display_table.html
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sql/display_table_filter.html
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2
tipuesearch_content.json
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