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callbacks.csv_logger.html
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---
title: callbacks.csv_logger
keywords: fastai
sidebar: home_sidebar
summary: "Callbacks that saves the tracked metrics during training"
---
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<h2 id="CSV-Logger">CSV Logger<a class="anchor-link" href="#CSV-Logger">¶</a></h2>
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<h2 id="CSVLogger" class="doc_header"><code>class</code> <code>CSVLogger</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/csv_logger.py#L12" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CSVLogger-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>CSVLogger</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>filename</code></strong>:<code>str</code>=<strong><em><code>'history'</code></em></strong>, <strong><code>append</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="CSVLogger-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CSVLogger-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>CSVLogger</code>:</p><ul><li><code>pytest -sv tests/test_callbacks_csv_logger.py::test_logger</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_callbacks_csv_logger.py#L37" 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>A <a href="/basic_train.html#LearnerCallback"><code>LearnerCallback</code></a> that saves history of metrics while training <code>learn</code> into CSV <code>filename</code>.</p>
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<p>First let's show an example of use, with a training on the usual MNIST dataset.</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_TINY</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="p">[</span><span class="n">accuracy</span><span class="p">,</span> <span class="n">error_rate</span><span class="p">],</span> <span class="n">callback_fns</span><span class="o">=</span><span class="p">[</span><span class="n">CSVLogger</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></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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Total time: 00:02 <p><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>error_rate</th>
<th>time</th>
</tr>
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<tr>
<td>0</td>
<td>0.643073</td>
<td>0.536124</td>
<td>0.931330</td>
<td>0.068670</td>
<td>00:00</td>
</tr>
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<td>1</td>
<td>0.523130</td>
<td>0.246320</td>
<td>0.952790</td>
<td>0.047210</td>
<td>00:00</td>
</tr>
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<td>2</td>
<td>0.402764</td>
<td>0.147998</td>
<td>0.938484</td>
<td>0.061516</td>
<td>00:00</td>
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<p>Training details have been saved in 'history.csv'.</p>
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<p>Note that it only saves float/int metrics, so time currently is not saved. This could be saved but requires changing the recording - you can submit a PR <a href="https://forums.fast.ai/t/expand-recorder-to-deal-with-non-int-float-data/41534">fixing that</a>.</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">path</span><span class="o">.</span><span class="n">ls</span><span class="p">()</span>
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<pre>[PosixPath('/home/stas/.fastai/data/mnist_tiny/models'),
PosixPath('/home/stas/.fastai/data/mnist_tiny/labels.csv'),
PosixPath('/home/stas/.fastai/data/mnist_tiny/cleaned.csv'),
PosixPath('/home/stas/.fastai/data/mnist_tiny/train'),
PosixPath('/home/stas/.fastai/data/mnist_tiny/test'),
PosixPath('/home/stas/.fastai/data/mnist_tiny/valid'),
PosixPath('/home/stas/.fastai/data/mnist_tiny/export.pkl'),
PosixPath('/home/stas/.fastai/data/mnist_tiny/history.csv')]</pre>
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<p>Note that, as with all <a href="/basic_train.html#LearnerCallback"><code>LearnerCallback</code></a>, you can access the object as an attribute of <code>learn</code> after it has been created. Here it's <code>learn.csv_logger</code>.</p>
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<h4 id="CSVLogger.read_logged_file" class="doc_header"><code>read_logged_file</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/csv_logger.py#L19" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CSVLogger-read_logged_file-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>read_logged_file</code>()</p>
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<div class="collapse" id="CSVLogger-read_logged_file-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CSVLogger-read_logged_file-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>read_logged_file</code>:</p><p>Some other tests where <code>read_logged_file</code> is used:</p><ul><li><code>pytest -sv tests/test_callbacks_csv_logger.py::test_logger</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_callbacks_csv_logger.py#L37" 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>Read the content of saved file</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">csv_logger</span><span class="o">.</span><span class="n">read_logged_file</span><span class="p">()</span>
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<style scoped>
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<th></th>
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>error_rate</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>0.643073</td>
<td>0.536124</td>
<td>0.931330</td>
<td>0.068670</td>
</tr>
<tr>
<th>1</th>
<td>1</td>
<td>0.523130</td>
<td>0.246320</td>
<td>0.952790</td>
<td>0.047210</td>
</tr>
<tr>
<th>2</th>
<td>2</td>
<td>0.402764</td>
<td>0.147998</td>
<td>0.938484</td>
<td>0.061516</td>
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<p>Optionally you can set <code>append=True</code> to log results of consequent stages of training.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># don't forget to remove the old file</span>
<span class="k">if</span> <span class="n">learn</span><span class="o">.</span><span class="n">csv_logger</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span> <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">learn</span><span class="o">.</span><span class="n">csv_logger</span><span class="o">.</span><span class="n">path</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></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="p">[</span><span class="n">accuracy</span><span class="p">,</span> <span class="n">error_rate</span><span class="p">],</span>
<span class="n">callback_fns</span><span class="o">=</span><span class="p">[</span><span class="n">partial</span><span class="p">(</span><span class="n">CSVLogger</span><span class="p">,</span> <span class="n">append</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># stage-1</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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Total time: 00:02 <p><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>error_rate</th>
<th>time</th>
</tr>
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<td>0</td>
<td>0.604549</td>
<td>0.493241</td>
<td>0.766810</td>
<td>0.233190</td>
<td>00:00</td>
</tr>
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<td>1</td>
<td>0.486367</td>
<td>0.226189</td>
<td>0.948498</td>
<td>0.051502</td>
<td>00:00</td>
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<td>2</td>
<td>0.377847</td>
<td>0.127142</td>
<td>0.958512</td>
<td>0.041488</td>
<td>00:00</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># stage-2</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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Total time: 00:02 <p><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>error_rate</th>
<th>time</th>
</tr>
</thead>
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<td>0</td>
<td>0.189441</td>
<td>0.118532</td>
<td>0.954220</td>
<td>0.045780</td>
<td>00:00</td>
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<td>1</td>
<td>0.177432</td>
<td>0.110913</td>
<td>0.965665</td>
<td>0.034335</td>
<td>00:00</td>
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<td>2</td>
<td>0.168626</td>
<td>0.097646</td>
<td>0.968526</td>
<td>0.031474</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">csv_logger</span><span class="o">.</span><span class="n">read_logged_file</span><span class="p">()</span>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>error_rate</th>
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<th>0</th>
<td>0</td>
<td>0.604549</td>
<td>0.493241</td>
<td>0.766810</td>
<td>0.233190</td>
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<th>1</th>
<td>1</td>
<td>0.486367</td>
<td>0.226189</td>
<td>0.948498</td>
<td>0.051502</td>
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<th>2</th>
<td>2</td>
<td>0.377847</td>
<td>0.127142</td>
<td>0.958512</td>
<td>0.041488</td>
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<th>3</th>
<td>epoch</td>
<td>train_loss</td>
<td>valid_loss</td>
<td>accuracy</td>
<td>error_rate</td>
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<th>4</th>
<td>0</td>
<td>0.189441</td>
<td>0.118532</td>
<td>0.954220</td>
<td>0.045780</td>
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<th>5</th>
<td>1</td>
<td>0.177432</td>
<td>0.110913</td>
<td>0.965665</td>
<td>0.034335</td>
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<th>6</th>
<td>2</td>
<td>0.168626</td>
<td>0.097646</td>
<td>0.968526</td>
<td>0.031474</td>
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<h3 id="Calback-methods">Calback methods<a class="anchor-link" href="#Calback-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="CSVLogger.on_train_begin" class="doc_header"><code>on_train_begin</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/csv_logger.py#L23" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CSVLogger-on_train_begin-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>on_train_begin</code>(<strong>**<code>kwargs</code></strong>:<code>Any</code>)</p>
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<div class="collapse" id="CSVLogger-on_train_begin-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CSVLogger-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>Prepare file with metric names.</p>
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<h4 id="CSVLogger.on_epoch_end" class="doc_header"><code>on_epoch_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/csv_logger.py#L32" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CSVLogger-on_epoch_end-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>on_epoch_end</code>(<strong><code>epoch</code></strong>:<code>int</code>, <strong><code>smooth_loss</code></strong>:<code>Tensor</code>, <strong><code>last_metrics</code></strong>:<code>MetricsList</code>, <strong>**<code>kwargs</code></strong>:<code>Any</code>) → <code>bool</code></p>
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<div class="collapse" id="CSVLogger-on_epoch_end-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CSVLogger-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>Add a line with <code>epoch</code> number, <code>smooth_loss</code> and <code>last_metrics</code>.</p>
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<h4 id="CSVLogger.on_train_end" class="doc_header"><code>on_train_end</code><a href="https://github.com/fastai/fastai/blob/master/fastai/callbacks/csv_logger.py#L41" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CSVLogger-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="CSVLogger-on_train_end-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CSVLogger-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>Close the file.</p>
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