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---
title: train
keywords: fastai
sidebar: home_sidebar
summary: "Extensions to Learner that easily implement Callback"
---
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<h2 id="Additional-training-functions">Additional training functions<a class="anchor-link" href="#Additional-training-functions">¶</a></h2>
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<p><a href="/train.html#train"><code>train</code></a> provides a number of extension methods that are added to <a href="/basic_train.html#Learner"><code>Learner</code></a> (see below for a list and details), along with three simple callbacks:</p>
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<li><a href="/train.html#ShowGraph"><code>ShowGraph</code></a></li>
<li><a href="/train.html#GradientClipping"><code>GradientClipping</code></a></li>
<li><a href="/train.html#BnFreeze"><code>BnFreeze</code></a></li>
<li><a href="/train.html#AccumulateScheduler"><code>AccumulateScheduler</code></a></li>
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<h2 id="Learner-extension-methods"><a href="/basic_train.html#Learner"><code>Learner</code></a> extension methods<a class="anchor-link" href="#Learner-extension-methods">¶</a></h2>
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<p>These methods are automatically added to all <a href="/basic_train.html#Learner"><code>Learner</code></a> objects created after importing this module. They provide convenient access to a number of callbacks, without requiring them to be manually created.</p>
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<h4 id="fit_one_cycle" class="doc_header"><code>fit_one_cycle</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L14" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#fit_one_cycle-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>fit_one_cycle</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>cyc_len</code></strong>:<code>int</code>, <strong><code>max_lr</code></strong>:<code>Union</code>[<code>float</code>, <code>Collection</code>[<code>float</code>], <code>slice</code>]=<strong><em><code>slice(None, 0.003, None)</code></em></strong>, <strong><code>moms</code></strong>:<code>Point</code>=<strong><em><code>(0.95, 0.85)</code></em></strong>, <strong><code>div_factor</code></strong>:<code>float</code>=<strong><em><code>25.0</code></em></strong>, <strong><code>pct_start</code></strong>:<code>float</code>=<strong><em><code>0.3</code></em></strong>, <strong><code>final_div</code></strong>:<code>float</code>=<strong><em><code>None</code></em></strong>, <strong><code>wd</code></strong>:<code>float</code>=<strong><em><code>None</code></em></strong>, <strong><code>callbacks</code></strong>:<code>Optional</code>[<code>Collection</code>[<a href="/callback.html#Callback"><code>Callback</code></a>]]=<strong><em><code>None</code></em></strong>, <strong><code>tot_epochs</code></strong>:<code>int</code>=<strong><em><code>None</code></em></strong>, <strong><code>start_epoch</code></strong>:<code>int</code>=<strong><em><code>None</code></em></strong>)</p>
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<div class="collapse" id="fit_one_cycle-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#fit_one_cycle-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>fit_one_cycle</code>:</p><ul><li><code>pytest -sv tests/test_train.py::test_fit_one_cycle</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_train.py#L36" class="source_link" style="float:right">[source]</a></li></ul><p>Some other tests where <code>fit_one_cycle</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_train.py::test_empty_cont</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_train.py#L71" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_text_train.py::test_qrnn_works_if_split_fn_provided</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_text_train.py#L73" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_text_train.py::test_qrnn_works_with_no_split</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_text_train.py#L61" 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>Fit a model following the 1cycle policy.</p>
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<h4 id="one_cycle_scheduler" class="doc_header"><code>one_cycle_scheduler</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L10" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#one_cycle_scheduler-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>one_cycle_scheduler</code>(<strong><code>lr_max</code></strong>:<code>float</code>, <strong>**<code>kwargs</code></strong>:<code>Any</code>) → <a href="/callbacks.one_cycle.html#OneCycleScheduler"><code>OneCycleScheduler</code></a></p>
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<div class="collapse" id="one_cycle_scheduler-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#one_cycle_scheduler-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>one_cycle_scheduler</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>Instantiate a <a href="/callbacks.one_cycle.html#OneCycleScheduler"><code>OneCycleScheduler</code></a> with <code>lr_max</code>.</p>
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<p>See <a href="/callbacks.one_cycle.html#OneCycleScheduler"><code>OneCycleScheduler</code></a> for details.</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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<p>See <a href="/callbacks.lr_finder.html#LRFinder"><code>LRFinder</code></a> for details.</p>
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<h4 id="to_fp16" class="doc_header"><code>to_fp16</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L34" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#to_fp16-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>to_fp16</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>loss_scale</code></strong>:<code>float</code>=<strong><em><code>None</code></em></strong>, <strong><code>max_noskip</code></strong>:<code>int</code>=<strong><em><code>1000</code></em></strong>, <strong><code>dynamic</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>clip</code></strong>:<code>float</code>=<strong><em><code>None</code></em></strong>, <strong><code>flat_master</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong><code>max_scale</code></strong>:<code>float</code>=<strong><em><code>16777216</code></em></strong>) → <a href="/basic_train.html#Learner"><code>Learner</code></a></p>
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<div class="collapse" id="to_fp16-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#to_fp16-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>to_fp16</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>Put <code>learn</code> in FP16 precision mode.</p>
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<p>See <a href="/callbacks.fp16.html#MixedPrecision"><code>MixedPrecision</code></a> for details.</p>
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<h4 id="to_fp32" class="doc_header"><code>to_fp32</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L45" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#to_fp32-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>to_fp32</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>)</p>
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<div class="collapse" id="to_fp32-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#to_fp32-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>to_fp32</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>Put <code>learn</code> back to FP32 precision mode.</p>
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<h4 id="mixup" class="doc_header"><code>mixup</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L53" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#mixup-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>mixup</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>alpha</code></strong>:<code>float</code>=<strong><em><code>0.4</code></em></strong>, <strong><code>stack_x</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong><code>stack_y</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>) → <a href="/basic_train.html#Learner"><code>Learner</code></a></p>
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<div class="collapse" id="mixup-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#mixup-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>mixup</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>Add mixup <a href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a> to <code>learn</code>.</p>
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<p>See <a href="/callbacks.mixup.html#MixUpCallback"><code>MixUpCallback</code></a> for more details.</p>
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<h2 id="Interpretation" class="doc_header"><code>class</code> <code>Interpretation</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L136" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Interpretation-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>Interpretation</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>preds</code></strong>:<code>Tensor</code>, <strong><code>y_true</code></strong>:<code>Tensor</code>, <strong><code>losses</code></strong>:<code>Tensor</code>, <strong><code>ds_type</code></strong>:<a href="/basic_data.html#DatasetType"><code>DatasetType</code></a>=<strong><em><code><DatasetType.Valid: 2></code></em></strong>)</p>
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<div class="collapse" id="Interpretation-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Interpretation-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>Interpretation</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>Interpretation base class, can be inherited for task specific Interpretation classes</p>
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<h4 id="Interpretation.from_learner" class="doc_header"><code>from_learner</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L147" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Interpretation-from_learner-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>from_learner</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>ds_type</code></strong>:<a href="/basic_data.html#DatasetType"><code>DatasetType</code></a>=<strong><em><code><DatasetType.Valid: 2></code></em></strong>, <strong><code>activ</code></strong>:<a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>Module</code></a>=<strong><em><code>None</code></em></strong>)</p>
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<div class="collapse" id="Interpretation-from_learner-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Interpretation-from_learner-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>from_learner</code>:</p><p>Some other tests where <code>from_learner</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_train.py::test_confusion_tabular</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_train.py#L84" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_vision_train.py::test_ClassificationInterpretation</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L95" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_vision_train.py::test_interp</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L72" 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>Gets preds, y_true, losses to construct base class from a learner</p>
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<h4 id="Interpretation.top_losses" class="doc_header"><code>top_losses</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L153" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Interpretation-top_losses-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>top_losses</code>(<strong><code>k</code></strong>:<code>int</code>=<strong><em><code>None</code></em></strong>, <strong><code>largest</code></strong>=<strong><em><code>True</code></em></strong>)</p>
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<div class="collapse" id="Interpretation-top_losses-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Interpretation-top_losses-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>top_losses</code>:</p><p>Some other tests where <code>top_losses</code> is used:</p><ul><li><code>pytest -sv tests/test_vision_train.py::test_interp</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L72" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_vision_train.py::test_interp_shortcut</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L78" 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><code>k</code> largest(/smallest) losses and indexes, defaulting to all losses (sorted by <code>largest</code>).</p>
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<p>For example in <a href="/train.html#ClassificationInterpretation"><code>ClassificationInterpretation</code></a> is implemented using argmax on preds to set <code>self.pred_class</code> whereas and optional sigmoid is used for <code>MultilabelClassificationInterpretation</code></p>
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<h2 id="ClassificationInterpretation" class="doc_header"><code>class</code> <code>ClassificationInterpretation</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L163" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#ClassificationInterpretation-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>ClassificationInterpretation</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>preds</code></strong>:<code>Tensor</code>, <strong><code>y_true</code></strong>:<code>Tensor</code>, <strong><code>losses</code></strong>:<code>Tensor</code>, <strong><code>ds_type</code></strong>:<a href="/basic_data.html#DatasetType"><code>DatasetType</code></a>=<strong><em><code><DatasetType.Valid: 2></code></em></strong>) :: <a href="/train.html#Interpretation"><code>Interpretation</code></a></p>
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<div class="collapse" id="ClassificationInterpretation-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#ClassificationInterpretation-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>ClassificationInterpretation</code>:</p><ul><li><code>pytest -sv tests/test_vision_train.py::test_ClassificationInterpretation</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L95" class="source_link" style="float:right">[source]</a></li></ul><p>Some other tests where <code>ClassificationInterpretation</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_train.py::test_confusion_tabular</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_train.py#L84" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_vision_train.py::test_interp</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L72" 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>Interpretation methods for classification models.</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="n">learn</span> <span class="o">=</span> <span class="n">cnn_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</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">preds</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">losses</span> <span class="o">=</span> <span class="n">learn</span><span class="o">.</span><span class="n">get_preds</span><span class="p">(</span><span class="n">with_loss</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">interp</span> <span class="o">=</span> <span class="n">ClassificationInterpretation</span><span class="p">(</span><span class="n">learn</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">losses</span><span class="p">)</span>
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<h4 id="Interpretation.top_losses" class="doc_header"><code>top_losses</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L153" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Interpretation-top_losses-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>top_losses</code>(<strong><code>k</code></strong>:<code>int</code>=<strong><em><code>None</code></em></strong>, <strong><code>largest</code></strong>=<strong><em><code>True</code></em></strong>)</p>
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<div class="collapse" id="Interpretation-top_losses-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Interpretation-top_losses-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>top_losses</code>:</p><p>Some other tests where <code>top_losses</code> is used:</p><ul><li><code>pytest -sv tests/test_vision_train.py::test_interp</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L72" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_vision_train.py::test_interp_shortcut</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L78" 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><code>k</code> largest(/smallest) losses and indexes, defaulting to all losses (sorted by <code>largest</code>).</p>
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<p>Returns tuple of <em>(losses,indices)</em>.</p>
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<pre>torch.return_types.topk(
values=tensor([10.5023, 7.5183, 7.4790, 5.0515, 4.5135, 4.3364, 4.1073, 3.8588,
3.7758]),
indices=tensor([ 979, 977, 226, 1019, 161, 1393, 1500, 1697, 1276]))</pre>
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<h4 id="ClassificationInterpretation.plot_confusion_matrix" class="doc_header"><code>plot_confusion_matrix</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L181" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#ClassificationInterpretation-plot_confusion_matrix-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>plot_confusion_matrix</code>(<strong><code>normalize</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong><code>title</code></strong>:<code>str</code>=<strong><em><code>'Confusion matrix'</code></em></strong>, <strong><code>cmap</code></strong>:<code>Any</code>=<strong><em><code>'Blues'</code></em></strong>, <strong><code>slice_size</code></strong>:<code>int</code>=<strong><em><code>1</code></em></strong>, <strong><code>norm_dec</code></strong>:<code>int</code>=<strong><em><code>2</code></em></strong>, <strong><code>plot_txt</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>return_fig</code></strong>:<code>bool</code>=<strong><em><code>None</code></em></strong>, <strong>**<code>kwargs</code></strong>) → <code>Optional</code>[<code>Figure</code>]</p>
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<div class="collapse" id="ClassificationInterpretation-plot_confusion_matrix-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#ClassificationInterpretation-plot_confusion_matrix-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>plot_confusion_matrix</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>Plot the confusion matrix, with <code>title</code> and using <code>cmap</code>.</p>
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<p>If <a href="/vision.data.html#normalize"><code>normalize</code></a>, plots the percentages with <code>norm_dec</code> digits. <code>slice_size</code> can be used to avoid out of memory error if your set is too big. <code>kwargs</code> are passed to <code>plt.figure</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">interp</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">()</span>
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<h4 id="ClassificationInterpretation.confusion_matrix" class="doc_header"><code>confusion_matrix</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L169" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#ClassificationInterpretation-confusion_matrix-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>confusion_matrix</code>(<strong><code>slice_size</code></strong>:<code>int</code>=<strong><em><code>1</code></em></strong>)</p>
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<div class="collapse" id="ClassificationInterpretation-confusion_matrix-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#ClassificationInterpretation-confusion_matrix-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>confusion_matrix</code>:</p><ul><li><code>pytest -sv tests/test_tabular_train.py::test_confusion_tabular</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_train.py#L84" class="source_link" style="float:right">[source]</a></li></ul><p>Some other tests where <code>confusion_matrix</code> is used:</p><ul><li><code>pytest -sv tests/test_vision_train.py::test_ClassificationInterpretation</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L95" 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>Confusion matrix as an <code>np.ndarray</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">interp</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">()</span>
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<pre>array([[983, 27],
[ 30, 998]])</pre>
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<h4 id="ClassificationInterpretation.most_confused" class="doc_header"><code>most_confused</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L206" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#ClassificationInterpretation-most_confused-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>most_confused</code>(<strong><code>min_val</code></strong>:<code>int</code>=<strong><em><code>1</code></em></strong>, <strong><code>slice_size</code></strong>:<code>int</code>=<strong><em><code>1</code></em></strong>) → <code>Collection</code>[<code>Tuple</code>[<code>str</code>, <code>str</code>, <code>int</code>]]</p>
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<div class="collapse" id="ClassificationInterpretation-most_confused-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#ClassificationInterpretation-most_confused-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>most_confused</code>:</p><p>Some other tests where <code>most_confused</code> is used:</p><ul><li><code>pytest -sv tests/test_vision_train.py::test_ClassificationInterpretation</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L95" 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>Sorted descending list of largest non-diagonal entries of confusion matrix, presented as actual, predicted, number of occurrences.</p>
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<h2 id="MultiLabelClassificationInterpretation" class="doc_header"><code>class</code> <code>MultiLabelClassificationInterpretation</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L220" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#MultiLabelClassificationInterpretation-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>MultiLabelClassificationInterpretation</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>preds</code></strong>:<code>Tensor</code>, <strong><code>y_true</code></strong>:<code>Tensor</code>, <strong><code>losses</code></strong>:<code>Tensor</code>, <strong><code>ds_type</code></strong>:<a href="/basic_data.html#DatasetType"><code>DatasetType</code></a>=<strong><em><code><DatasetType.Valid: 2></code></em></strong>, <strong><code>sigmoid</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>thresh</code></strong>:<code>float</code>=<strong><em><code>0.3</code></em></strong>) :: <a href="/train.html#Interpretation"><code>Interpretation</code></a></p>
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<div class="collapse" id="MultiLabelClassificationInterpretation-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#MultiLabelClassificationInterpretation-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>MultiLabelClassificationInterpretation</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>Interpretation methods for classification models.</p>
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<div markdown="span" class="alert alert-danger" role="alert"><i class="fa fa-danger-circle"></i> <b>Warning: </b>MultiLabelClassificationInterpretation is not implemented yet. Feel free to implement it :)</div>
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<h4 id="Working-with-large-datasets">Working with large datasets<a class="anchor-link" href="#Working-with-large-datasets">¶</a></h4>
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<p>When working with large datasets, memory problems can arise when computing the confusion matrix. For example, an error can look like this:</p>
<pre><code>RuntimeError: $ Torch: not enough memory: you tried to allocate 64GB. Buy new RAM!
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<p>In this case it is possible to force <a href="/train.html#ClassificationInterpretation"><code>ClassificationInterpretation</code></a> to compute the confusion matrix for data slices and then aggregate the result by specifying slice_size parameter.</p>
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<pre>array([[984, 26],
[ 37, 991]])</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">interp</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">slice_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">interp</span><span class="o">.</span><span class="n">most_confused</span><span class="p">(</span><span class="n">slice_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
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<pre>[('7', '3', 37), ('3', '7', 26)]</pre>
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<h2 id="Additional-callbacks">Additional callbacks<a class="anchor-link" href="#Additional-callbacks">¶</a></h2>
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<p>We'll show examples below using our MNIST sample. As usual the <code>on_something</code> methods are directly called by the fastai library, no need to call them yourself.</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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<h3 id="ShowGraph" class="doc_header"><code>class</code> <code>ShowGraph</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L64" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#ShowGraph-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>ShowGraph</code>(<strong><code>learn</code></strong>) :: <a href="/basic_train.html#LearnerCallback"><code>LearnerCallback</code></a></p>
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<div class="collapse" id="ShowGraph-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#ShowGraph-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>ShowGraph</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>Update a graph of learner stats and metrics after each epoch.</p>
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<div class="highlight"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">cnn_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</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">callback_fns</span><span class="o">=</span><span class="n">ShowGraph</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">3</span><span class="p">)</span>
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<p><img src="imgs/train_graph.gif" alt="Training graph"></p>
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<h4 id="ShowGraph.on_epoch_end" class="doc_header"><code>on_epoch_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L66" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#ShowGraph-on_epoch_end-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>on_epoch_end</code>(<strong><code>n_epochs</code></strong>:<code>int</code>, <strong><code>last_metrics</code></strong>:<code>MetricsList</code>, <strong>**<code>kwargs</code></strong>) → <code>bool</code></p>
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<div class="collapse" id="ShowGraph-on_epoch_end-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#ShowGraph-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>If we have <code>last_metrics</code> plot them in our pbar graph</p>
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<h2 id="GradientClipping" class="doc_header"><code>class</code> <code>GradientClipping</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L83" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#GradientClipping-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>GradientClipping</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>clip</code></strong>:<code>float</code>=<strong><em><code>0.0</code></em></strong>) :: <a href="/basic_train.html#LearnerCallback"><code>LearnerCallback</code></a></p>
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<div class="collapse" id="GradientClipping-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#GradientClipping-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>GradientClipping</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>Gradient clipping during 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">cnn_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</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">callback_fns</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span><span class="n">GradientClipping</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="mf">0.1</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>
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<th>valid_loss</th>
<th>accuracy</th>
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<td>0</td>
<td>0.140471</td>
<td>0.079571</td>
<td>0.971541</td>
<td>00:08</td>
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<h4 id="GradientClipping.on_backward_end" class="doc_header"><code>on_backward_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L89" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#GradientClipping-on_backward_end-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>on_backward_end</code>(<strong>**<code>kwargs</code></strong>)</p>
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<div class="collapse" id="GradientClipping-on_backward_end-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#GradientClipping-on_backward_end-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>on_backward_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>Clip the gradient before the optimizer step.</p>
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<h2 id="BnFreeze" class="doc_header"><code>class</code> <code>BnFreeze</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L77" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#BnFreeze-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>BnFreeze</code>(<strong><code>learn</code></strong>) :: <a href="/basic_train.html#LearnerCallback"><code>LearnerCallback</code></a></p>
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<div class="collapse" id="BnFreeze-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#BnFreeze-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>BnFreeze</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>Freeze moving average statistics in all non-trainable batchnorm layers.</p>
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<p>For batchnorm layers where <code>requires_grad==False</code>, you generally don't want to update their moving average statistics, in order to avoid the model's statistics getting out of sync with its pre-trained weights. You can add this callback to automate this freezing of statistics (internally, it calls <code>eval</code> on these layers).</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">cnn_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</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">callback_fns</span><span class="o">=</span><span class="n">BnFreeze</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>
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<th>time</th>
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<td>0</td>
<td>0.147409</td>
<td>0.081370</td>
<td>0.972031</td>
<td>00:05</td>
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<h4 id="BnFreeze.on_epoch_begin" class="doc_header"><code>on_epoch_begin</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L79" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#BnFreeze-on_epoch_begin-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>on_epoch_begin</code>(<strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<div class="collapse" id="BnFreeze-on_epoch_begin-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#BnFreeze-on_epoch_begin-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>on_epoch_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>Put bn layers in eval mode just after <code>model.train()</code>.</p>
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<h2 id="AccumulateScheduler" class="doc_header"><code>class</code> <code>AccumulateScheduler</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L99" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#AccumulateScheduler-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>AccumulateScheduler</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>n_step</code></strong>:<code>int</code>=<strong><em><code>1</code></em></strong>, <strong><code>drop_last</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>) :: <a href="/basic_train.html#LearnerCallback"><code>LearnerCallback</code></a></p>
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<div class="collapse" id="AccumulateScheduler-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#AccumulateScheduler-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>AccumulateScheduler</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>Does accumlated step every nth step by accumulating gradients</p>
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<p>Let's force <code>batch_size=2</code> to mimic a scenario where we can't fit enough batch samples to our memory. We can then set <code>n_step</code> as desired to have an effective batch_size of <code>effective_batch_size=batch_size*n_step</code>.</p>
<p>It is also important to use loss func with <code>reduce='sum'</code> in order to calculate exact average accumulated gradients.</p>
<p>Another important note for users is that <code>batchnorm</code> is not yet adapted to accumulated gradients. So you should use this callback at your own risk until a hero fixes it :)</p>
<p>Here we demonstrate this callback with a model without <code>batchnorm</code> layers, alternatively you can use <code>nn.InstanceNorm</code> or <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.GroupNorm"><code>nn.GroupNorm</code></a>.</p>
<pre><code>from torchvision.models import vgg11
data = ImageDataBunch.from_folder(path, bs=2)
learn = cnn_learner(data, resnet18, metrics=accuracy, loss_func=CrossEntropyFlat(reduction='sum'),
callback_fns=partial(AccumulateScheduler, n_step=16))
learn.fit(1)</code></pre>
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