Further fixes following gh-19

code/convolutional_mlp.py

@@ -290,13 +290,13 @@ def evaluate_lenet5(learning_rate=0.1, n_epochs=200,
     print('Optimization complete.')
     print('Best validation score of %f %% obtained at iteration %i,'\
           'with test performance %f %%' %
-          (best_validation_loss * 100., best_iter, test_score * 100.))
+          (best_validation_loss * 100., best_iter + 1, test_score * 100.))
     print >> sys.stderr, ('The code for file ' +
                           os.path.split(__file__)[1] +
                           ' ran for %.2fm' % ((end_time - start_time) / 60.))
 
 if __name__ == '__main__':
-    evaluate_lenet5(n_epochs=1, batch_size=1)
+    evaluate_lenet5()
 
 
 def experiment(state, channel):

code/mlp.py

@@ -348,7 +348,7 @@ def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,
     end_time = time.clock()
     print(('Optimization complete. Best validation score of %f %% '
            'obtained at iteration %i, with test performance %f %%') %
-          (best_validation_loss * 100., best_iter, test_score * 100.))
+          (best_validation_loss * 100., best_iter + 1, test_score * 100.))
     print >> sys.stderr, ('The code for file ' +
                           os.path.split(__file__)[1] +
                           ' ran for %.2fm' % ((end_time - start_time) / 60.))

doc/mlp.txt

@@ -350,12 +350,12 @@ The output one should expect is of the form :
 
 .. code-block:: bash
 
-  Optimization complete with best validation score of 2.08%, with test performance 2.18%
-  The code ran for 55.57 minutes
+  Optimization complete. Best validation score of 1.690000 % obtained at iteration 2070000, with test performance 1.650000 %
+  The code for file mlp.py ran for 97.34m
 
-On an Intel(R) Core(TM)2 Duo CPU E8400 @ 3.00 Ghz  the code runs with
-approximately 1.78 epoch/minute and it took 99 epochs to reach a test
-error of 2.18%.
+On an Intel(R) Core(TM) i7-2600K CPU @ 3.40GHz the code runs with
+approximately 10.3 epoch/minute and it took 828 epochs to reach a test
+error of 1.65%.
 
 To put this into perspective, we refer the reader to the results section of `this
 <http://yann.lecun.com/exdb/mnist>`_  page.