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---
title: callbacks.general_sched
keywords: fastai
sidebar: home_sidebar
summary: "Implementation of a flexible training API"
---
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<h2 id="TrainingPhase-and-General-scheduler">TrainingPhase and General scheduler<a class="anchor-link" href="#TrainingPhase-and-General-scheduler">¶</a></h2>
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<p>Creates a scheduler that lets you train a model with following different <a href="/callbacks.general_sched.html#TrainingPhase"><code>TrainingPhase</code></a>.</p>
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<h2 id="TrainingPhase" class="doc_header"><code>class</code> <code>TrainingPhase</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/general_sched.py#L8" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#TrainingPhase-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>TrainingPhase</code>(<strong><code>length</code></strong>:<code>int</code>)</p>
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<div class="collapse" id="TrainingPhase-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#TrainingPhase-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>TrainingPhase</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>Schedule hyper-parameters for a phase of <code>length</code> iterations.</p>
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<p>You can then schedule any hyper-parameter you want by using the following method.</p>
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<h4 id="TrainingPhase.schedule_hp" class="doc_header"><code>schedule_hp</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/general_sched.py#L13" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#TrainingPhase-schedule_hp-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>schedule_hp</code>(<strong><code>name</code></strong>, <strong><code>vals</code></strong>, <strong><code>anneal</code></strong>=<strong><em><code>None</code></em></strong>)</p>
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<div class="collapse" id="TrainingPhase-schedule_hp-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#TrainingPhase-schedule_hp-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>schedule_hp</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>Adds a schedule for <code>name</code> between <code>vals</code> using <code>anneal</code>.</p>
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<p>The phase will make the hyper-parameter vary from the first value in <code>vals</code> to the second, following <code>anneal</code>. If an annealing function is specified but <code>vals</code> is a float, it will decay to 0. If no annealing function is specified, the default is a linear annealing for a tuple, a constant parameter if it's a float.</p>
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<div markdown="span" class="alert alert-info" role="alert"><i class="fa fa-info-circle"></i> <b>Note: </b>If you want to use discriminative values, you can pass an numpy array in `vals` (or a tuple
of them for start and stop).</div>
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<p>The basic hyper-parameters are named:</p>
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<li>'lr' for learning rate</li>
<li>'mom' for momentum (or beta1 in Adam)</li>
<li>'beta' for the beta2 in Adam or the alpha in RMSprop</li>
<li>'wd' for weight decay</li>
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<p>You can also add any hyper-parameter that is in your optimizer (even if it's custom or a <a href="/general_optimizer.html#GeneralOptimizer"><code>GeneralOptimizer</code></a>), like 'eps' if you're using Adam.</p>
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<p>Let's make an example by using this to code <a href="https://arxiv.org/abs/1608.03983">SGD with warm restarts</a>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fit_sgd_warm</span><span class="p">(</span><span class="n">learn</span><span class="p">,</span> <span class="n">n_cycles</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">mom</span><span class="p">,</span> <span class="n">cycle_len</span><span class="p">,</span> <span class="n">cycle_mult</span><span class="p">):</span>
<span class="n">n</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">data</span><span class="o">.</span><span class="n">train_dl</span><span class="p">)</span>
<span class="n">phases</span> <span class="o">=</span> <span class="p">[(</span><span class="n">TrainingPhase</span><span class="p">(</span><span class="n">n</span> <span class="o">*</span> <span class="p">(</span><span class="n">cycle_len</span> <span class="o">*</span> <span class="n">cycle_mult</span><span class="o">**</span><span class="n">i</span><span class="p">))</span>
<span class="o">.</span><span class="n">schedule_hp</span><span class="p">(</span><span class="s1">'lr'</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">anneal</span><span class="o">=</span><span class="n">annealing_cos</span><span class="p">)</span>
<span class="o">.</span><span class="n">schedule_hp</span><span class="p">(</span><span class="s1">'mom'</span><span class="p">,</span> <span class="n">mom</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="n">n_cycles</span><span class="p">)]</span>
<span class="n">sched</span> <span class="o">=</span> <span class="n">GeneralScheduler</span><span class="p">(</span><span class="n">learn</span><span class="p">,</span> <span class="n">phases</span><span class="p">)</span>
<span class="n">learn</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sched</span><span class="p">)</span>
<span class="k">if</span> <span class="n">cycle_mult</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">total_epochs</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">cycle_len</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">cycle_mult</span><span class="p">)</span><span class="o">**</span><span class="n">n_cycles</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">cycle_mult</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span> <span class="n">total_epochs</span> <span class="o">=</span> <span class="n">n_cycles</span> <span class="o">*</span> <span class="n">cycle_len</span>
<span class="n">learn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">total_epochs</span><span class="p">)</span>
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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="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">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="n">accuracy</span><span class="p">)</span>
<span class="n">fit_sgd_warm</span><span class="p">(</span><span class="n">learn</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mf">1e-3</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</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>accuracy</th>
<th>time</th>
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<td>0</td>
<td>0.162146</td>
<td>0.153532</td>
<td>0.942100</td>
<td>00:02</td>
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<td>1</td>
<td>0.126112</td>
<td>0.117267</td>
<td>0.960255</td>
<td>00:02</td>
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<td>2</td>
<td>0.112045</td>
<td>0.110586</td>
<td>0.962218</td>
<td>00:02</td>
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<td>3</td>
<td>0.097603</td>
<td>0.090838</td>
<td>0.967615</td>
<td>00:02</td>
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<td>4</td>
<td>0.086883</td>
<td>0.081375</td>
<td>0.973013</td>
<td>00:02</td>
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<td>5</td>
<td>0.083673</td>
<td>0.076160</td>
<td>0.973994</td>
<td>00:02</td>
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<td>6</td>
<td>0.084835</td>
<td>0.076211</td>
<td>0.973994</td>
<td>00:02</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">recorder</span><span class="o">.</span><span class="n">plot_lr</span><span class="p">()</span>
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<h2 id="GeneralScheduler" class="doc_header"><code>class</code> <code>GeneralScheduler</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/general_sched.py#L18" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#GeneralScheduler-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>GeneralScheduler</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>phases</code></strong>:<code>Collection</code>[<a href="/callbacks.general_sched.html#TrainingPhase"><code>TrainingPhase</code></a>], <strong><code>start_epoch</code></strong>:<code>int</code>=<strong><em><code>None</code></em></strong>) :: <a href="/basic_train.html#LearnerCallback"><code>LearnerCallback</code></a></p>
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<div class="collapse" id="GeneralScheduler-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#GeneralScheduler-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>GeneralScheduler</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>Schedule multiple <a href="/callbacks.general_sched.html#TrainingPhase"><code>TrainingPhase</code></a> for a <a href="/basic_train.html#Learner"><code>Learner</code></a>.</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="GeneralScheduler.on_batch_end" class="doc_header"><code>on_batch_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/general_sched.py#L40" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#GeneralScheduler-on_batch_end-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>on_batch_end</code>(<strong><code>train</code></strong>, <strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<div class="collapse" id="GeneralScheduler-on_batch_end-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#GeneralScheduler-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>
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<p>Takes a step in the current phase and prepare the hyperparameters for the next batch.</p>
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<h4 id="GeneralScheduler.on_train_begin" class="doc_header"><code>on_train_begin</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/general_sched.py#L24" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#GeneralScheduler-on_train_begin-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>on_train_begin</code>(<strong><code>epoch</code></strong>:<code>int</code>, <strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<div class="collapse" id="GeneralScheduler-on_train_begin-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#GeneralScheduler-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>
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<p>Initiates the hyperparameters to the start values of the first phase.</p>
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