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---
title: Data Callbacks
keywords: fastai
sidebar: home_sidebar
summary: "Callbacks which work with a learner's data"
---
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">nbdev.showdoc</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">fastai2.test_utils</span> <span class="k">import</span> <span class="o">*</span>
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<h2 id="CollectDataCallback" class="doc_header"><code>class</code> <code>CollectDataCallback</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/callback/data.py#L10" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>CollectDataCallback</code>() :: <a href="/learner.html#Callback"><code>Callback</code></a></p>
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<p>Collect all batches, along with <code>pred</code> and <code>loss</code>, into <code>self.data</code>. Mainly for testing</p>
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<h2 id="WeightedDL" class="doc_header"><code>class</code> <code>WeightedDL</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/callback/data.py#L17" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>WeightedDL</code>(<strong><code>dataset</code></strong>=<em><code>None</code></em>, <strong><code>bs</code></strong>=<em><code>None</code></em>, <strong><code>wgts</code></strong>=<em><code>None</code></em>, <strong><code>shuffle</code></strong>=<em><code>False</code></em>, <strong><code>num_workers</code></strong>=<em><code>None</code></em>, <strong><code>pin_memory</code></strong>=<em><code>False</code></em>, <strong><code>timeout</code></strong>=<em><code>0</code></em>, <strong><code>drop_last</code></strong>=<em><code>False</code></em>, <strong><code>indexed</code></strong>=<em><code>None</code></em>, <strong><code>n</code></strong>=<em><code>None</code></em>, <strong><code>wif</code></strong>=<em><code>None</code></em>, <strong><code>before_iter</code></strong>=<em><code>None</code></em>, <strong><code>after_item</code></strong>=<em><code>None</code></em>, <strong><code>before_batch</code></strong>=<em><code>None</code></em>, <strong><code>after_batch</code></strong>=<em><code>None</code></em>, <strong><code>after_iter</code></strong>=<em><code>None</code></em>, <strong><code>create_batches</code></strong>=<em><code>None</code></em>, <strong><code>create_item</code></strong>=<em><code>None</code></em>, <strong><code>create_batch</code></strong>=<em><code>None</code></em>, <strong><code>retain</code></strong>=<em><code>None</code></em>) :: <a href="/data.core.html#TfmdDL"><code>TfmdDL</code></a></p>
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<p>Transformed <a href="/data.load.html#DataLoader"><code>DataLoader</code></a></p>
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<h4 id="weighted_databunch" class="doc_header"><code>weighted_databunch</code><a href="https://github.com/fastai/fastai_dev/tree/master/devfastai2/callback/data.py#L29" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>weighted_databunch</code>(<strong><code>wgts</code></strong>, <strong><code>bs</code></strong>=<em><code>16</code></em>, <strong><code>val_bs</code></strong>=<em><code>None</code></em>, <strong><code>shuffle_train</code></strong>=<em><code>True</code></em>, <strong><code>n</code></strong>=<em><code>None</code></em>, <strong><code>path</code></strong>=<em><code>'.'</code></em>, <strong><code>dl_type</code></strong>=<em><code>None</code></em>, <strong><code>dl_kwargs</code></strong>=<em><code>None</code></em>)</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">160</span>
<span class="n">dsrc</span> <span class="o">=</span> <span class="n">DataSource</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">())</span>
<span class="n">dbch</span> <span class="o">=</span> <span class="n">dsrc</span><span class="o">.</span><span class="n">weighted_databunch</span><span class="p">(</span><span class="n">wgts</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">),</span> <span class="n">bs</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">learn</span> <span class="o">=</span> <span class="n">synth_learner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">dbch</span><span class="p">,</span> <span class="n">cb_funcs</span><span class="o">=</span><span class="n">CollectDataCallback</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">1</span><span class="p">)</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">concat</span><span class="p">(</span><span class="o">*</span><span class="n">learn</span><span class="o">.</span><span class="n">collect_data</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">itemgot</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">t</span><span class="p">);</span>
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<pre>(#4) [0,nan,None,00:00]
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