--- a/deep/stacked_dae/v_sylvain/sgd_optimization.py	Sun Mar 14 15:17:04 2010 -0400
+++ b/deep/stacked_dae/v_sylvain/sgd_optimization.py	Sun Mar 14 20:25:12 2010 -0400
@@ -118,9 +118,11 @@
             # go through pretraining epochs 
             for epoch in xrange(self.hp.pretraining_epochs_per_layer):
                 # go through the training set
+                batch_index=int(0)
                 for x,y in dataset.train(self.hp.minibatch_size):
                     c = self.classifier.pretrain_functions[i](x)
-
+                    batch_index+=1
+                    
                     self.series["reconstruction_error"].append((epoch, batch_index), c)
                         
                 print 'Pre-training layer %i, epoch %d, cost '%(i,epoch),c
@@ -140,6 +142,8 @@
 
         #index   = T.lscalar()    # index to a [mini]batch 
         minibatch_size = self.hp.minibatch_size
+        ensemble_x = T.matrix('ensemble_x')
+        ensemble_y = T.ivector('ensemble_y')
 
         # create a function to compute the mistakes that are made by the model
         # on the validation set, or testing set