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callbacks.lr_finder.html
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---
title: callbacks.lr_finder
keywords: fastai
sidebar: home_sidebar
summary: "Implementation of the LR Range test from Leslie Smith"
---
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<h2 id="Learning-Rate-Finder">Learning Rate Finder<a class="anchor-link" href="#Learning-Rate-Finder">¶</a></h2>
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<p>Learning rate finder plots lr vs loss relationship for a <a href="/basic_train.html#Learner"><code>Learner</code></a>. The idea is to reduce the amount of guesswork on picking a good starting learning rate.</p>
<p><strong>Overview:</strong></p>
<ol>
<li>First run lr_find <code>learn.lr_find()</code></li>
<li>Plot the learning rate vs loss <code>learn.recorder.plot()</code></li>
<li>Pick a learning rate before it diverges then start training</li>
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<p><strong>Technical Details:</strong> (first <a href="'https://arxiv.org/abs/1506.01186'">described</a> by Leslie Smith)</p>
<blockquote><p>Train <a href="/basic_train.html#Learner"><code>Learner</code></a> over a few iterations. Start with a very low <code>start_lr</code> and change it at each mini-batch until it reaches a very high <code>end_lr</code>. <a href="/basic_train.html#Recorder"><code>Recorder</code></a> will record the loss at each iteration. Plot those losses against the learning rate to find the optimal value before it diverges.</p>
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<h2 id="Choosing-a-good-learning-rate">Choosing a good learning rate<a class="anchor-link" href="#Choosing-a-good-learning-rate">¶</a></h2>
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<p>For a more intuitive explanation, please check out <a href="https://sgugger.github.io/how-do-you-find-a-good-learning-rate.html">Sylvain Gugger's post</a></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>
<span class="k">def</span> <span class="nf">simple_learner</span><span class="p">():</span> <span class="k">return</span> <span class="n">Learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</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">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">simple_learner</span><span class="p">()</span>
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<p>First we run this command to launch the search:</p>
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<h4 id="lr_find" class="doc_header"><code>lr_find</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L24" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#lr_find-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>lr_find</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>start_lr</code></strong>:<code>Floats</code>=<strong><em><code>1e-07</code></em></strong>, <strong><code>end_lr</code></strong>:<code>Floats</code>=<strong><em><code>10</code></em></strong>, <strong><code>num_it</code></strong>:<code>int</code>=<strong><em><code>100</code></em></strong>, <strong><code>stop_div</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>wd</code></strong>:<code>float</code>=<strong><em><code>None</code></em></strong>)</p>
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<div class="collapse" id="lr_find-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#lr_find-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>lr_find</code>:</p><ul><li><code>pytest -sv tests/test_train.py::test_lr_find</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_train.py#L16" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_vision_train.py::test_lrfind</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L84" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Explore lr from <code>start_lr</code> to <code>end_lr</code> over <code>num_it</code> iterations in <code>learn</code>. If <code>stop_div</code>, stops when loss diverges.</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">lr_find</span><span class="p">(</span><span class="n">stop_div</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_it</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
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<pre>LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
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<p>Then we plot the loss versus the learning rates. We're interested in finding a good order of magnitude of learning rate, so we plot with a log scale.</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">recorder</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
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
"
>
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<p>Then, we choose a value that is approximately in the middle of the sharpest downward slope. This is given as an indication by the LR Finder tool, so let's try 1e-2.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">simple_learner</span><span class="p">()</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">1e-2</span><span class="p">)</span>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: left;">
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>0.127434</td>
<td>0.070243</td>
<td>0.973013</td>
<td>00:02</td>
</tr>
<tr>
<td>2</td>
<td>0.050703</td>
<td>0.039493</td>
<td>0.984789</td>
<td>00:02</td>
</tr>
</tbody>
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<p>Don't just pick the minimum value from the plot!</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">simple_learner</span><span class="p">()</span>
<span class="n">simple_learner</span><span class="p">()</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">1e-0</span><span class="p">)</span>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: left;">
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>0.727221</td>
<td>0.693147</td>
<td>0.495584</td>
<td>00:02</td>
</tr>
<tr>
<td>2</td>
<td>0.693826</td>
<td>0.693147</td>
<td>0.495584</td>
<td>00:02</td>
</tr>
</tbody>
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<p>Picking a value before the downward slope results in slow training:</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">simple_learner</span><span class="p">()</span>
<span class="n">simple_learner</span><span class="p">()</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">1e-3</span><span class="p">)</span>
</pre></div>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: left;">
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>0.152897</td>
<td>0.134366</td>
<td>0.950932</td>
<td>00:02</td>
</tr>
<tr>
<td>2</td>
<td>0.120961</td>
<td>0.117550</td>
<td>0.960746</td>
<td>00:02</td>
</tr>
</tbody>
</table>
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<h4 id="Suggested-LR">Suggested LR<a class="anchor-link" href="#Suggested-LR">¶</a></h4>
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<p>If you pass <code>suggestion=True</code> in <code>learn.recorder.plot</code>, you will see the point where the gardient is the steepest with a<br>
red dot on the graph. We can use that point as a first guess for an LR.</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">lr_find</span><span class="p">(</span><span class="n">stop_div</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_it</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
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<pre>LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">recorder</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">suggestion</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<pre>Min numerical gradient: 5.25E-03
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>
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<p>You can access the corresponding learning rate like this:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">min_grad_lr</span> <span class="o">=</span> <span class="n">learn</span><span class="o">.</span><span class="n">recorder</span><span class="o">.</span><span class="n">min_grad_lr</span>
<span class="n">min_grad_lr</span>
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<pre>0.005248074602497722</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">simple_learner</span><span class="p">()</span>
<span class="n">simple_learner</span><span class="p">()</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="n">min_grad_lr</span><span class="p">)</span>
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<table border="1" class="dataframe">
<thead>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>0.109475</td>
<td>0.081607</td>
<td>0.970559</td>
<td>00:02</td>
</tr>
<tr>
<td>2</td>
<td>0.070303</td>
<td>0.050977</td>
<td>0.982826</td>
<td>00:02</td>
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<h2 id="LRFinder" class="doc_header"><code>class</code> <code>LRFinder</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/lr_finder.py#L9" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#LRFinder-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>LRFinder</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>start_lr</code></strong>:<code>float</code>=<strong><em><code>1e-07</code></em></strong>, <strong><code>end_lr</code></strong>:<code>float</code>=<strong><em><code>10</code></em></strong>, <strong><code>num_it</code></strong>:<code>int</code>=<strong><em><code>100</code></em></strong>, <strong><code>stop_div</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>) :: <a href="/basic_train.html#LearnerCallback"><code>LearnerCallback</code></a></p>
</blockquote>
<div class="collapse" id="LRFinder-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#LRFinder-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>LRFinder</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Causes <code>learn</code> to go on a mock training from <code>start_lr</code> to <code>end_lr</code> for <code>num_it</code> iterations.</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="LRFinder.on_train_begin" class="doc_header"><code>on_train_begin</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/lr_finder.py#L16" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#LRFinder-on_train_begin-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>on_train_begin</code>(<strong><code>pbar</code></strong>, <strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
</blockquote>
<div class="collapse" id="LRFinder-on_train_begin-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#LRFinder-on_train_begin-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>on_train_begin</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Initialize optimizer and learner hyperparameters.</p>
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<h4 id="LRFinder.on_batch_end" class="doc_header"><code>on_batch_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/lr_finder.py#L25" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#LRFinder-on_batch_end-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>on_batch_end</code>(<strong><code>iteration</code></strong>:<code>int</code>, <strong><code>smooth_loss</code></strong>:<code>TensorOrNumber</code>, <strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
</blockquote>
<div class="collapse" id="LRFinder-on_batch_end-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#LRFinder-on_batch_end-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>on_batch_end</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Determine if loss has runaway and we should stop.</p>
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<h4 id="Callback.on_epoch_end" class="doc_header"><code>on_epoch_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callback.py#L186" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Callback-on_epoch_end-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>on_epoch_end</code>(<strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
</blockquote>
<div class="collapse" id="Callback-on_epoch_end-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Callback-on_epoch_end-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>on_epoch_end</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Called at the end of an epoch.</p>
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<h4 id="LRFinder.on_train_end" class="doc_header"><code>on_train_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/lr_finder.py#L33" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#LRFinder-on_train_end-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>on_train_end</code>(<strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<div class="collapse" id="LRFinder-on_train_end-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#LRFinder-on_train_end-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>on_train_end</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Cleanup learn model weights disturbed during LRFinder exploration.</p>
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