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---
title: callbacks.tracker
keywords: fastai
sidebar: home_sidebar
summary: "Callbacks that take decisions depending on the evolution of metrics during training"
---
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<h1 id="Tracking-Callbacks">Tracking Callbacks<a class="anchor-link" href="#Tracking-Callbacks">¶</a></h1>
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<p>This module regroups the callbacks that track one of the metrics computed at the end of each epoch to take some decision about training. To show examples of use, we'll use our sample of MNIST and a simple cnn model.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">path</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">MNIST_SAMPLE</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">ImageDataBunch</span><span class="o">.</span><span class="n">from_folder</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
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<h2 id="TerminateOnNaNCallback"><code>class</code> <code>TerminateOnNaNCallback</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L9" class="source_link">[source]</a></h2><blockquote><p><code>TerminateOnNaNCallback</code>() :: <a href="/callback.html#Callback"><code>Callback</code></a></p>
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<p>A <a href="/callback.html#Callback"><code>Callback</code></a> that terminates training if loss is NaN.</p>
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<p>Sometimes, training diverges and the loss goes to nan. In that case, there's no point continuing, so this callback stops the training.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">simple_cnn</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="n">learn</span> <span class="o">=</span> <span class="n">Learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="n">accuracy</span><span class="p">])</span>
<span class="n">learn</span><span class="o">.</span><span class="n">fit_one_cycle</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mf">1e4</span><span class="p">)</span>
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Total time: 00:02 <p><table style='width:300px; margin-bottom:10px'>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
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<th>1</th>
<th>nan</th>
<th>nan</th>
<th>0.495584</th>
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<p>Using it prevents that situation to happen.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">simple_cnn</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="n">learn</span> <span class="o">=</span> <span class="n">Learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="n">accuracy</span><span class="p">],</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">TerminateOnNaNCallback</span><span class="p">()])</span>
<span class="n">learn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mf">1e4</span><span class="p">)</span>
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<th>valid_loss</th>
<th>accuracy</th>
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<pre>Epoch/Batch (0/5): Invalid loss, terminating training.
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<h3 id="Callback-methods">Callback methods<a class="anchor-link" href="#Callback-methods">¶</a></h3>
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<p>You don't call these yourself - they're called by fastai's <a href="/callback.html#Callback"><code>Callback</code></a> system automatically to enable the class's functionality.</p>
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<h4 id="TerminateOnNaNCallback.on_batch_end"><code>on_batch_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L15" class="source_link">[source]</a></h4><blockquote><p><code>on_batch_end</code>(<strong><code>last_loss</code></strong>, <strong><code>epoch</code></strong>, <strong><code>num_batch</code></strong>, <strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<p>Test if <code>last_loss</code> is NaN and interrupts training.</p>
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<h4 id="TerminateOnNaNCallback.on_epoch_end"><code>on_epoch_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L23" class="source_link">[source]</a></h4><blockquote><p><code>on_epoch_end</code>(<strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<p>Stop the training if necessary.</p>
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<h2 id="EarlyStoppingCallback"><code>class</code> <code>EarlyStoppingCallback</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L56" class="source_link">[source]</a></h2><blockquote><p><code>EarlyStoppingCallback</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>monitor</code></strong>:<code>str</code>=<strong><em><code>'val_loss'</code></em></strong>, <strong><code>mode</code></strong>:<code>str</code>=<strong><em><code>'auto'</code></em></strong>, <strong><code>min_delta</code></strong>:<code>int</code>=<strong><em><code>0</code></em></strong>, <strong><code>patience</code></strong>:<code>int</code>=<strong><em><code>0</code></em></strong>) :: <a href="/callbacks.tracker.html#TrackerCallback"><code>TrackerCallback</code></a></p>
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<p>A <a href="/callbacks.tracker.html#TrackerCallback"><code>TrackerCallback</code></a> that terminates training when monitored quantity stops improving.</p>
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<p>This callback tracks the quantity in <code>monitor</code> during the training of <code>learn</code>. <code>mode</code> can be forced to 'min' or 'max' but will automatically try to determine if the quantity should be the lowest possible (validation loss) or the highest possible (accuracy). Will stop training after <code>patience</code> epochs if the quantity hasn't improved by <code>min_delta</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">simple_cnn</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="n">learn</span> <span class="o">=</span> <span class="n">Learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="n">accuracy</span><span class="p">],</span>
<span class="n">callback_fns</span><span class="o">=</span><span class="p">[</span><span class="n">partial</span><span class="p">(</span><span class="n">EarlyStoppingCallback</span><span class="p">,</span> <span class="n">monitor</span><span class="o">=</span><span class="s1">'accuracy'</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">3</span><span class="p">)])</span>
<span class="n">learn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span><span class="mf">1e-42</span><span class="p">)</span>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
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<th>1</th>
<th>0.694346</th>
<th>0.697105</th>
<th>0.504907</th>
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<th>2</th>
<th>0.694370</th>
<th>0.697105</th>
<th>0.504907</th>
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<th>3</th>
<th>0.695692</th>
<th>0.697105</th>
<th>0.504907</th>
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<th>4</th>
<th>0.694920</th>
<th>0.697105</th>
<th>0.504907</th>
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<pre>Epoch 5: early stopping
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<h3 id="Callback-methods">Callback methods<a class="anchor-link" href="#Callback-methods">¶</a></h3>
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<p>You don't call these yourself - they're called by fastai's <a href="/callback.html#Callback"><code>Callback</code></a> system automatically to enable the class's functionality.</p>
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<h4 id="EarlyStoppingCallback.on_train_begin"><code>on_train_begin</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L63" class="source_link">[source]</a></h4><blockquote><p><code>on_train_begin</code>(<strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<p>Initialize inner arguments.</p>
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<h4 id="EarlyStoppingCallback.on_epoch_end"><code>on_epoch_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L68" class="source_link">[source]</a></h4><blockquote><p><code>on_epoch_end</code>(<strong><code>epoch</code></strong>, <strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<p>Compare the value monitored to its best score and maybe stop training.</p>
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<h2 id="SaveModelCallback"><code>class</code> <code>SaveModelCallback</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L80" class="source_link">[source]</a></h2><blockquote><p><code>SaveModelCallback</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>monitor</code></strong>:<code>str</code>=<strong><em><code>'val_loss'</code></em></strong>, <strong><code>mode</code></strong>:<code>str</code>=<strong><em><code>'auto'</code></em></strong>, <strong><code>every</code></strong>:<code>str</code>=<strong><em><code>'improvement'</code></em></strong>, <strong><code>name</code></strong>:<code>str</code>=<strong><em><code>'bestmodel'</code></em></strong>) :: <a href="/callbacks.tracker.html#TrackerCallback"><code>TrackerCallback</code></a></p>
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<p>A <a href="/callbacks.tracker.html#TrackerCallback"><code>TrackerCallback</code></a> that saves the model when monitored quantity is best.</p>
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<p>This callback tracks the quantity in <code>monitor</code> during the training of <code>learn</code>. <code>mode</code> can be forced to 'min' or 'max' but will automatically try to determine if the quantity should be the lowest possible (validation loss) or the highest possible (accuracy). Will save the model in <code>name</code> whenever determined by <code>every</code> ('improvement' or 'epoch'). Loads the best model at the end of training is <code>every='improvement'</code>.</p>
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<h3 id="Callback-methods">Callback methods<a class="anchor-link" href="#Callback-methods">¶</a></h3>
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<p>You don't call these yourself - they're called by fastai's <a href="/callback.html#Callback"><code>Callback</code></a> system automatically to enable the class's functionality.</p>
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<h4 id="SaveModelCallback.on_epoch_end"><code>on_epoch_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L89" class="source_link">[source]</a></h4><blockquote><p><code>on_epoch_end</code>(<strong><code>epoch</code></strong>, <strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<p>Compare the value monitored to its best score and maybe save the model.</p>
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<h4 id="SaveModelCallback.on_train_end"><code>on_train_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L99" class="source_link">[source]</a></h4><blockquote><p><code>on_train_end</code>(<strong>**<code>kwargs</code></strong>)</p>
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<p>Load the best model.</p>
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<h2 id="ReduceLROnPlateauCallback"><code>class</code> <code>ReduceLROnPlateauCallback</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L104" class="source_link">[source]</a></h2><blockquote><p><code>ReduceLROnPlateauCallback</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>monitor</code></strong>:<code>str</code>=<strong><em><code>'val_loss'</code></em></strong>, <strong><code>mode</code></strong>:<code>str</code>=<strong><em><code>'auto'</code></em></strong>, <strong><code>patience</code></strong>:<code>int</code>=<strong><em><code>0</code></em></strong>, <strong><code>factor</code></strong>:<code>float</code>=<strong><em><code>0.2</code></em></strong>, <strong><code>min_delta</code></strong>:<code>int</code>=<strong><em><code>0</code></em></strong>) :: <a href="/callbacks.tracker.html#TrackerCallback"><code>TrackerCallback</code></a></p>
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<p>A <a href="/callbacks.tracker.html#TrackerCallback"><code>TrackerCallback</code></a> that reduces learning rate when a metric has stopped improving.</p>
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<p>This callback tracks the quantity in <code>monitor</code> during the training of <code>learn</code>. <code>mode</code> can be forced to 'min' or 'max' but will automatically try to determine if the quantity should be the lowest possible (validation loss) or the highest possible (accuracy). Will reduce the learning rate by <code>factor</code> after <code>patience</code> epochs if the quantity hasn't improved by <code>min_delta</code>.</p>
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<h3 id="Callback-methods">Callback methods<a class="anchor-link" href="#Callback-methods">¶</a></h3>
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<p>You don't call these yourself - they're called by fastai's <a href="/callback.html#Callback"><code>Callback</code></a> system automatically to enable the class's functionality.</p>
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<h4 id="ReduceLROnPlateauCallback.on_train_begin"><code>on_train_begin</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L112" class="source_link">[source]</a></h4><blockquote><p><code>on_train_begin</code>(<strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<p>Initialize inner arguments.</p>
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<h4 id="ReduceLROnPlateauCallback.on_epoch_end"><code>on_epoch_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L117" class="source_link">[source]</a></h4><blockquote><p><code>on_epoch_end</code>(<strong><code>epoch</code></strong>, <strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<p>Compare the value monitored to its best and maybe reduce lr.</p>
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<h2 id="TrackerCallback"><code>class</code> <code>TrackerCallback</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L27" class="source_link">[source]</a></h2><blockquote><p><code>TrackerCallback</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>monitor</code></strong>:<code>str</code>=<strong><em><code>'val_loss'</code></em></strong>, <strong><code>mode</code></strong>:<code>str</code>=<strong><em><code>'auto'</code></em></strong>) :: <a href="/basic_train.html#LearnerCallback"><code>LearnerCallback</code></a></p>
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<p>A <a href="/basic_train.html#LearnerCallback"><code>LearnerCallback</code></a> that keeps track of the best value in <code>monitor</code>.</p>
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<h4 id="TrackerCallback.get_monitor_value"><code>get_monitor_value</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L43" class="source_link">[source]</a></h4><blockquote><p><code>get_monitor_value</code>()</p>
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<p>Pick the monitored value.</p>
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<h3 id="Callback-methods">Callback methods<a class="anchor-link" href="#Callback-methods">¶</a></h3>
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<p>You don't call these yourself - they're called by fastai's <a href="/callback.html#Callback"><code>Callback</code></a> system automatically to enable the class's functionality.</p>
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<h4 id="TrackerCallback.on_train_begin"><code>on_train_begin</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/tracker.py#L39" class="source_link">[source]</a></h4><blockquote><p><code>on_train_begin</code>(<strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<p>Initializes the best value.</p>
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