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callback.tracker.html
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---
title: Tracking callbacks
keywords: fastai
sidebar: home_sidebar
summary: "Callbacks that make decisions depending how a monitored metric/loss behaves"
---
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">nbdev.showdoc</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">fastai2.test_utils</span> <span class="k">import</span> <span class="o">*</span>
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<h2 id="ShortEpochCallback" class="doc_header"><code>class</code> <code>ShortEpochCallback</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/callback/tracker.py#L12" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>ShortEpochCallback</code>(<strong><code>pct</code></strong>=<em><code>0.01</code></em>, <strong><code>short_valid</code></strong>=<em><code>True</code></em>) :: <a href="/learner.html#Callback"><code>Callback</code></a></p>
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<p>Fit just <code>pct</code> of an epoch, then stop</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">synth_learner</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">1</span><span class="p">,</span> <span class="n">cbs</span><span class="o">=</span><span class="n">ShortEpochCallback</span><span class="p">())</span>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>time</th>
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<td>0</td>
<td>00:00</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">synth_learner</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">1</span><span class="p">,</span> <span class="n">cbs</span><span class="o">=</span><span class="n">ShortEpochCallback</span><span class="p">(</span><span class="n">short_valid</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>time</th>
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<td>0</td>
<td>4.103489</td>
<td>00:00</td>
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<h2 id="TerminateOnNaNCallback" class="doc_header"><code>class</code> <code>TerminateOnNaNCallback</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/callback/tracker.py#L21" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>TerminateOnNaNCallback</code>() :: <a href="/learner.html#Callback"><code>Callback</code></a></p>
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<p>A <a href="/learner.html#Callback"><code>Callback</code></a> that terminates training if loss is NaN.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">synth_learner</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">10</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">cbs</span><span class="o">=</span><span class="n">TerminateOnNaNCallback</span><span class="p">())</span>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>time</th>
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<td>0</td>
<td>2862784155043519244231962471774027776.000000</td>
<td>00:00</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">learn</span><span class="o">.</span><span class="n">recorder</span><span class="o">.</span><span class="n">losses</span><span class="p">)</span> <span class="o"><</span> <span class="mi">10</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">learn</span><span class="o">.</span><span class="n">dbunch</span><span class="o">.</span><span class="n">train_dl</span><span class="p">)</span>
<span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">learn</span><span class="o">.</span><span class="n">recorder</span><span class="o">.</span><span class="n">losses</span><span class="p">:</span>
<span class="k">assert</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="n">l</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>
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<h2 id="TrackerCallback" class="doc_header"><code>class</code> <code>TrackerCallback</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/callback/tracker.py#L30" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>TrackerCallback</code>(<strong><code>monitor</code></strong>=<em><code>'valid_loss'</code></em>, <strong><code>comp</code></strong>=<em><code>None</code></em>, <strong><code>min_delta</code></strong>=<em><code>0.0</code></em>) :: <a href="/learner.html#Callback"><code>Callback</code></a></p>
</blockquote>
<p>A <a href="/learner.html#Callback"><code>Callback</code></a> that keeps track of the best value in <code>monitor</code>.</p>
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<p>When implementing a <a href="/learner.html#Callback"><code>Callback</code></a> that has behavior that depends on the best value of a metric or loss, subclass this <a href="/learner.html#Callback"><code>Callback</code></a> and use its <code>best</code> (for best value so far) and <code>new_best</code> (there was a new best value this epoch) attributes.</p>
<p><code>comp</code> is the comparison operator used to determine if a value is best than another (defaults to <code>np.less</code> if 'loss' is in the name passed in <code>monitor</code>, <code>np.greater</code> otherwise) and <code>min_delta</code> is an optional float that requires a new value to go over the current best (depending on <code>comp</code>) by at least that amount.</p>
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<h2 id="EarlyStoppingCallback" class="doc_header"><code>class</code> <code>EarlyStoppingCallback</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/callback/tracker.py#L55" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>EarlyStoppingCallback</code>(<strong><code>monitor</code></strong>=<em><code>'valid_loss'</code></em>, <strong><code>comp</code></strong>=<em><code>None</code></em>, <strong><code>min_delta</code></strong>=<em><code>0.0</code></em>, <strong><code>patience</code></strong>=<em><code>1</code></em>) :: <a href="/callback.tracker.html#TrackerCallback"><code>TrackerCallback</code></a></p>
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<p>A <a href="/callback.tracker.html#TrackerCallback"><code>TrackerCallback</code></a> that terminates training when monitored quantity stops improving.</p>
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<p><code>comp</code> is the comparison operator used to determine if a value is best than another (defaults to <code>np.less</code> if 'loss' is in the name passed in <code>monitor</code>, <code>np.greater</code> otherwise) and <code>min_delta</code> is an optional float that requires a new value to go over the current best (depending on <code>comp</code>) by at least that amount. <code>patience</code> is the number of epochs you're willing to wait without improvement.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">synth_learner</span><span class="p">(</span><span class="n">n_trn</span><span class="o">=</span><span class="mi">2</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="n">n_epoch</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">1e-7</span><span class="p">,</span> <span class="n">cbs</span><span class="o">=</span><span class="n">EarlyStoppingCallback</span><span class="p">(</span><span class="n">monitor</span><span class="o">=</span><span class="s1">'valid_loss'</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
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<th>train_loss</th>
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<th>time</th>
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<td>0</td>
<td>8.969569</td>
<td>7.550271</td>
<td>00:00</td>
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<td>1</td>
<td>8.964047</td>
<td>7.550250</td>
<td>00:00</td>
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<td>2</td>
<td>8.963997</td>
<td>7.550220</td>
<td>00:00</td>
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<pre>No improvement since epoch 0: early stopping
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<h2 id="SaveModelCallback" class="doc_header"><code>class</code> <code>SaveModelCallback</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/callback/tracker.py#L73" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>SaveModelCallback</code>(<strong><code>monitor</code></strong>=<em><code>'valid_loss'</code></em>, <strong><code>comp</code></strong>=<em><code>None</code></em>, <strong><code>min_delta</code></strong>=<em><code>0.0</code></em>, <strong><code>fname</code></strong>=<em><code>'model'</code></em>, <strong><code>every_epoch</code></strong>=<em><code>False</code></em>, <strong><code>add_save</code></strong>=<em><code>None</code></em>, <strong><code>with_opt</code></strong>=<em><code>False</code></em>) :: <a href="/callback.tracker.html#TrackerCallback"><code>TrackerCallback</code></a></p>
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<p>A <a href="/callback.tracker.html#TrackerCallback"><code>TrackerCallback</code></a> that saves the model's best during training and loads it at the end.</p>
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<p><code>comp</code> is the comparison operator used to determine if a value is best than another (defaults to <code>np.less</code> if 'loss' is in the name passed in <code>monitor</code>, <code>np.greater</code> otherwise) and <code>min_delta</code> is an optional float that requires a new value to go over the current best (depending on <code>comp</code>) by at least that amount. Model will be saved in <code>learn.path/learn.model_dir/name.pth</code>, maybe <code>every_epoch</code> or at each improvement of the monitored quantity.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">synth_learner</span><span class="p">(</span><span class="n">n_trn</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="n">Path</span><span class="o">.</span><span class="n">cwd</span><span class="p">()</span><span class="o">/</span><span class="s1">'tmp'</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="n">n_epoch</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">cbs</span><span class="o">=</span><span class="n">SaveModelCallback</span><span class="p">())</span>
<span class="k">assert</span> <span class="p">(</span><span class="n">Path</span><span class="o">.</span><span class="n">cwd</span><span class="p">()</span><span class="o">/</span><span class="s1">'tmp/models/model.pth'</span><span class="p">)</span><span class="o">.</span><span class="n">exists</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="n">n_epoch</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">cbs</span><span class="o">=</span><span class="n">SaveModelCallback</span><span class="p">(</span><span class="n">every_epoch</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span> <span class="k">assert</span> <span class="p">(</span><span class="n">Path</span><span class="o">.</span><span class="n">cwd</span><span class="p">()</span><span class="o">/</span><span class="n">f</span><span class="s1">'tmp/models/model_</span><span class="si">{i}</span><span class="s1">.pth'</span><span class="p">)</span><span class="o">.</span><span class="n">exists</span><span class="p">()</span>
<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="n">Path</span><span class="o">.</span><span class="n">cwd</span><span class="p">()</span><span class="o">/</span><span class="s1">'tmp'</span><span class="p">)</span>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>time</th>
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<td>0</td>
<td>12.352231</td>
<td>9.437100</td>
<td>00:00</td>
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<td>1</td>
<td>12.247625</td>
<td>9.188762</td>
<td>00:00</td>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>time</th>
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</thead>
<tbody>
<tr>
<td>0</td>
<td>11.802291</td>
<td>8.847794</td>
<td>00:00</td>
</tr>
<tr>
<td>1</td>
<td>11.562449</td>
<td>8.428464</td>
<td>00:00</td>
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<h2 id="ReduceLROnPlateau">ReduceLROnPlateau<a class="anchor-link" href="#ReduceLROnPlateau">¶</a></h2>
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<h2 id="ReduceLROnPlateau" class="doc_header"><code>class</code> <code>ReduceLROnPlateau</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/callback/tracker.py#L96" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>ReduceLROnPlateau</code>(<strong><code>monitor</code></strong>=<em><code>'valid_loss'</code></em>, <strong><code>comp</code></strong>=<em><code>None</code></em>, <strong><code>min_delta</code></strong>=<em><code>0.0</code></em>, <strong><code>patience</code></strong>=<em><code>1</code></em>, <strong><code>factor</code></strong>=<em><code>10.0</code></em>) :: <a href="/callback.tracker.html#TrackerCallback"><code>TrackerCallback</code></a></p>
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<p>A <a href="/callback.tracker.html#TrackerCallback"><code>TrackerCallback</code></a> that reduces learning rate when a metric has stopped improving.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">synth_learner</span><span class="p">(</span><span class="n">n_trn</span><span class="o">=</span><span class="mi">2</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="n">n_epoch</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">1e-7</span><span class="p">,</span> <span class="n">cbs</span><span class="o">=</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span><span class="n">monitor</span><span class="o">=</span><span class="s1">'valid_loss'</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>time</th>
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<td>0</td>
<td>13.372304</td>
<td>11.866179</td>
<td>00:00</td>
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<td>1</td>
<td>13.377043</td>
<td>11.866154</td>
<td>00:00</td>
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<td>2</td>
<td>13.395395</td>
<td>11.866118</td>
<td>00:00</td>
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<td>3</td>
<td>13.400988</td>
<td>11.866114</td>
<td>00:00</td>
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<pre>Epoch 2: reducing lr to 1e-08
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