update docs

Author: memimo

code/logistic_sgd.py

@@ -441,7 +441,7 @@ def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000,
 
 def predict():
     """
-    An example of how to load a train model and use it
+    An example of how to load a trained model and use it
     to predict labels.
     """
 

doc/logreg.txt

@@ -264,6 +264,20 @@ approximately 1.936 epochs/sec and it took 75 epochs to reach a test
 error of 7.489%. On the GPU the code does almost 10.0 epochs/sec. For this
 instance we used a batch size of 600.
 
+
+Prediction Using a Trained Model
++++++++++++++++++++++++++++++++
+
+``sgd_optimization_mnist`` serialize and pickle the model each time new
+lowest validation error is reached. We can reload this model and predict
+labels of new data. ``predict`` function shows an example of how
+this could be done.
+
+.. literalinclude:: ../code/logistic_sgd.py
+  :start-after:  ' ran for %.1fs' % ((end_time - start_time)))
+  :end-before: if __name__ == '__main__':
+
+
 .. rubric:: Footnotes
 
 .. [#f1] For smaller datasets and simpler models, more sophisticated descent