update doc

aksnzhy · Dec 9, 2017 · c99c14b · c99c14b
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diff --git a/_build/doctrees/command_line.doctree b/_build/doctrees/command_line.doctree
diff --git a/_build/doctrees/environment.pickle b/_build/doctrees/environment.pickle
diff --git a/_build/html/_sources/command_line.rst.txt b/_build/html/_sources/command_line.rst.txt
@@ -123,7 +123,9 @@ A portion of xLearn's output: ::
         9            0.404554            0.546218                0.00
 
 Here we can see that, the training loss continuously goes down. While, the validation loss (test loss) goes 
-down first, and then goes up. This is because our model has already overfitted current training data set.
+down first, and then goes up. This is because our model has already overfitted current training data set. On 
+default, xLearn will calculate the validation loss in each epoch, while users can also set different evaluation
+metric by using ``-x`` option.
 
 
 

diff --git a/_build/html/command_line.html b/_build/html/command_line.html
@@ -287,7 +287,9 @@ <h2>Set Validation Dataset<a class="headerlink" href="#set-validation-dataset" t
 </pre></div>
 </div>
 <p>Here we can see that, the training loss continuously goes down. While, the validation loss (test loss) goes
-down first, and then goes up. This is because our model has already overfitted current training data set.</p>
+down first, and then goes up. This is because our model has already overfitted current training data set. On
+default, xLearn will calculate the validation loss in each epoch, while users can also set different evaluation
+metric by using <code class="docutils literal"><span class="pre">-x</span></code> option.</p>
 <blockquote>
 <div><div class="toctree-wrapper compound">
 </div>

diff --git a/_build/html/searchindex.js b/_build/html/searchindex.js
diff --git a/command_line.rst b/command_line.rst
@@ -123,7 +123,9 @@ A portion of xLearn's output: ::
         9            0.404554            0.546218                0.00
 
 Here we can see that, the training loss continuously goes down. While, the validation loss (test loss) goes 
-down first, and then goes up. This is because our model has already overfitted current training data set.
+down first, and then goes up. This is because our model has already overfitted current training data set. On 
+default, xLearn will calculate the validation loss in each epoch, while users can also set different evaluation
+metric by using ``-x`` option.