MNIST count and show number of errors.

This removes dependency on sklearn and also makes it more transparent.
Additionally, people often report #errors on MNIST, i.e. the dropout paper.

Author: lucasb-eyer

examples/MNIST/test.py

@@ -1,15 +1,14 @@
 import numpy as np
 from progress_bar import *
 import theano as _th
-from sklearn.metrics import accuracy_score
 
 def validate(dataset_x, dataset_y, model, epoch, batch_size):
     progress = make_progressbar('Testing', epoch, len(dataset_x))
     progress.start()
 
     mini_batch_input = np.empty(shape=(batch_size, 28*28), dtype=_th.config.floatX)
     mini_batch_targets = np.empty(shape=(batch_size, ), dtype=_th.config.floatX)
-    accuracy = 0
+    nerrors = 0
 
     for j in range((dataset_x.shape[0] + batch_size - 1) // batch_size):
         progress.update(j * batch_size)
@@ -24,7 +23,8 @@ def validate(dataset_x, dataset_y, model, epoch, batch_size):
             mini_batch_prediction.resize((dataset_x.shape[0] - j * batch_size, ))
             mini_batch_targets.resize((dataset_x.shape[0] - j * batch_size, ))
 
-        accuracy = accuracy + accuracy_score(mini_batch_targets, mini_batch_prediction, normalize=False)
+        nerrors += sum(mini_batch_targets != mini_batch_prediction)
 
     progress.finish()
-    print("Epoch #" + str(epoch) + ", Classification: " + str(float(accuracy) / dataset_x.shape[0] * 100.0))
+    accuracy = 1 - float(nerrors)/dataset_x.shape[0]
+    print("Epoch #{}, Classification accuracy: {:.2%} ({} errors)".format(epoch, accuracy, nerrors))