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<h2 id="newsclassifier.train.eval_step" class="doc doc-heading">
<code class="highlight language-python"><span class="n">eval_step</span><span class="p">(</span><span class="n">val_loader</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span></code>
</h2>
<div class="doc doc-contents ">
<p>Eval step.</p>
<details class="quote">
<summary> <code>newsclassifier\train.py</code></summary>
<div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">43</span>
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<span class="normal">62</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">eval_step</span><span class="p">(</span><span class="n">val_loader</span><span class="p">:</span> <span class="n">DataLoader</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">,</span> <span class="n">epoch</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Eval step.&quot;&quot;&quot;</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">loss</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">total_iterations</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">val_loader</span><span class="p">)</span>
<span class="n">desc</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Validation - Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span>
<span class="n">y_trues</span><span class="p">,</span> <span class="n">y_preds</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">inference_mode</span><span class="p">():</span>
<span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">val_loader</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="n">total_iterations</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="n">desc</span><span class="p">):</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="n">collate</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
<span class="n">targets</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">one_hot</span><span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">long</span><span class="p">(),</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">num_classes</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="c1"># one-hot (for loss_fn)</span>
<span class="n">J</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">loss</span> <span class="o">+=</span> <span class="p">(</span><span class="n">J</span> <span class="o">-</span> <span class="n">loss</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">step</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">y_trues</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">targets</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="n">y_preds</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="k">return</span> <span class="n">loss</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">y_trues</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">y_preds</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h2 id="newsclassifier.train.train_step" class="doc doc-heading">
<code class="highlight language-python"><span class="n">train_step</span><span class="p">(</span><span class="n">train_loader</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span></code>
</h2>
<div class="doc doc-contents ">
<p>Train step.</p>
<details class="quote">
<summary> <code>newsclassifier\train.py</code></summary>
<div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">22</span>
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<span class="w"> </span><span class="sd">&quot;&quot;&quot;Train step.&quot;&quot;&quot;</span>
<span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
<span class="n">loss</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">total_iterations</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)</span>
<span class="n">desc</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Training - Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">&quot;</span>
<span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="n">total_iterations</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="n">desc</span><span class="p">):</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="n">collate</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inputs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">inputs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span> <span class="c1"># reset gradients</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="c1"># forward pass</span>
<span class="n">targets</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">one_hot</span><span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">long</span><span class="p">(),</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">num_classes</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="c1"># one-hot (for loss_fn)</span>
<span class="n">J</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span> <span class="c1"># define loss</span>
<span class="n">J</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> <span class="c1"># backward pass</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span> <span class="c1"># update weights</span>
<span class="n">loss</span> <span class="o">+=</span> <span class="p">(</span><span class="n">J</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">-</span> <span class="n">loss</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">step</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># cumulative loss</span>
<span class="k">return</span> <span class="n">loss</span>
</code></pre></div></td></tr></table></div>
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