# HG changeset patch
# User SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
# Date 1270842582 14400
# Node ID 048898c1ee551755893341b64e9a194f8403f633
# Parent  1763c64030d16a2536e8500912ff5ebf4e8316c4
Ajout d'une fonction pour calculer l'erreur effectuee par le modele sur un ensemble pre-determine

diff -r 1763c64030d1 -r 048898c1ee55 deep/stacked_dae/v_sylvain/sgd_optimization.py
--- a/deep/stacked_dae/v_sylvain/sgd_optimization.py	Thu Apr 08 11:22:11 2010 -0400
+++ b/deep/stacked_dae/v_sylvain/sgd_optimization.py	Fri Apr 09 15:49:42 2010 -0400
@@ -341,6 +341,19 @@
                 self.classifier.params[idx].value=theano._asarray(copy(x),dtype=theano.config.floatX)
             else:
                 self.classifier.params[idx].value=copy(x)
+                
+    #Calculate error over the training set (or a part of)           
+    def training_error(self,data):
+        # create a function to compute the mistakes that are made by the model
+        # on the validation set, or testing set
+        test_model = \
+            theano.function(
+                [self.classifier.x,self.classifier.y], self.classifier.errors)
+                
+        iter2 = data.train(self.hp.minibatch_size,bufsize=buffersize)
+        train_losses2 = [test_model(x,y) for x,y in iter2]
+        train_score2 = numpy.mean(train_losses2)
+        print "Training error is: " + str(train_score2)