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docs/_modules/torch/nn/functional.html

@@ -907,7 +907,14 @@ <h1>Source code for torch.nn.functional</h1><div class="highlight"><pre>
 <span class="sd">        &gt;&gt;&gt; output = F.nll_loss(F.log_softmax(input), target)</span>
 <span class="sd">        &gt;&gt;&gt; output.backward()</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">NLLLoss</span><span class="p">(</span><span class="n">size_average</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">)(</span><span class="nb">input</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span></div>
+    <span class="n">dim</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">f</span> <span class="o">=</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">NLLLoss</span><span class="p">(</span><span class="n">size_average</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="n">f</span> <span class="o">=</span> <span class="n">_functions</span><span class="o">.</span><span class="n">thnn</span><span class="o">.</span><span class="n">NLLLoss2d</span><span class="p">(</span><span class="n">size_average</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="n">weight</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Expected 2 or 4 dimensions (got </span><span class="si">{}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dim</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span></div>
 
 
 <div class="viewcode-block" id="kl_div"><a class="viewcode-back" href="../../../nn.html#torch.nn.functional.kl_div">[docs]</a><span class="k">def</span> <span class="nf">kl_div</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">size_average</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>

docs/_modules/torch/nn/utils/rnn.html

@@ -539,6 +539,9 @@ <h1>Source code for torch.nn.utils.rnn</h1><div class="highlight"><pre>
 <span class="sd">    Returns:</span>
 <span class="sd">        a :class:`PackedSequence` object</span>
 <span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">lengths</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;length of all samples has to be greater than 0, &quot;</span>
+                         <span class="s2">&quot;but found an element in &#39;lengths&#39; that is &lt;=0&quot;</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">batch_first</span><span class="p">:</span>
         <span class="nb">input</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
 

docs/torchvision/models.html

@@ -556,20 +556,20 @@
 <td>20.91</td>
 </tr>
 <tr class="row-odd"><td>VGG-11</td>
-<td>32.14</td>
-<td>12.12</td>
+<td>30.98</td>
+<td>11.37</td>
 </tr>
 <tr class="row-even"><td>VGG-13</td>
-<td>31.04</td>
-<td>11.40</td>
+<td>30.07</td>
+<td>10.75</td>
 </tr>
 <tr class="row-odd"><td>VGG-16</td>
-<td>29.11</td>
-<td>10.17</td>
+<td>28.41</td>
+<td>9.62</td>
 </tr>
 <tr class="row-even"><td>VGG-19</td>
-<td>28.42</td>
-<td>9.69</td>
+<td>27.62</td>
+<td>9.12</td>
 </tr>
 <tr class="row-odd"><td>SqueezeNet 1.0</td>
 <td>41.90</td>