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@@ -357,19 +357,19 @@ <h1>Source code for onmt.utils.loss</h1><div class="highlight"><pre>
             <span class="n">batch_stats</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">stats</span><span class="p">)</span>
         <span class="k">return</span> <span class="kc">None</span><span class="p">,</span> <span class="n">batch_stats</span>
 
-    <span class="k">def</span> <span class="nf">_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Args:</span>
 <span class="sd">            loss (:obj:`FloatTensor`): the loss computed by the loss criterion.</span>
 <span class="sd">            scores (:obj:`FloatTensor`): a score for each possible output</span>
-<span class="sd">            target (:obj:`FloatTensor`): true targets</span>
+<span class="sd">            labels (:obj:`FloatTensor`): true targets</span>
 
 <span class="sd">        Returns:</span>
 <span class="sd">            :obj:`onmt.utils.Statistics` : statistics for this batch.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">pred</span> <span class="o">=</span> <span class="n">scores</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">non_padding</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span><span class="p">)</span>
-        <span class="n">num_correct</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="p">)</span><span class="o">.</span><span class="n">masked_select</span><span class="p">(</span><span class="n">non_padding</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">non_padding</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span><span class="p">)</span>
+        <span class="n">num_correct</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">masked_select</span><span class="p">(</span><span class="n">non_padding</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
         <span class="n">num_non_padding</span> <span class="o">=</span> <span class="n">non_padding</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
         <span class="k">return</span> <span class="n">onmt</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">Statistics</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="n">num_non_padding</span><span class="p">,</span> <span class="n">num_correct</span><span class="p">)</span>
 
@@ -399,14 +399,14 @@ <h1>Source code for onmt.utils.loss</h1><div class="highlight"><pre>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">label_smoothing</span>
 
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        output (FloatTensor): batch_size x n_classes</span>
-<span class="sd">        target (LongTensor): batch_size</span>
+<span class="sd">        labels (LongTensor): batch_size</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">model_prob</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">one_hot</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">model_prob</span><span class="o">.</span><span class="n">scatter_</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">target</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span><span class="p">)</span>
-        <span class="n">model_prob</span><span class="o">.</span><span class="n">masked_fill_</span><span class="p">((</span><span class="n">target</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">ignore_index</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">model_prob</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">one_hot</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">model_prob</span><span class="o">.</span><span class="n">scatter_</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">labels</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence</span><span class="p">)</span>
+        <span class="n">model_prob</span><span class="o">.</span><span class="n">masked_fill_</span><span class="p">((</span><span class="n">labels</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">ignore_index</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">kl_div</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">model_prob</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s1">&#39;sum&#39;</span><span class="p">)</span>