# HG changeset patch
# User SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
# Date 1268612712 14400
# Node ID ecb69e17950b11e7c8a36791c423619721dcce0d
# Parent  c452e3a0a3b11bdff73b5109a3dd9a8674c0320c
correction de bugs

diff -r c452e3a0a3b1 -r ecb69e17950b deep/stacked_dae/v_sylvain/nist_sda.py
--- a/deep/stacked_dae/v_sylvain/nist_sda.py	Sun Mar 14 15:17:04 2010 -0400
+++ b/deep/stacked_dae/v_sylvain/nist_sda.py	Sun Mar 14 20:25:12 2010 -0400
@@ -120,10 +120,10 @@
                                     n_ins=n_ins, n_outs=n_outs,\
                                     series=series)
 
-    optimizer.pretrain()
+    optimizer.pretrain(datasets.nist_all)
     channel.save()
 
-    optimizer.finetune()
+    optimizer.finetune(datasets.nist_all)
     channel.save()
 
     return channel.COMPLETE
diff -r c452e3a0a3b1 -r ecb69e17950b deep/stacked_dae/v_sylvain/sgd_optimization.py
--- 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
diff -r c452e3a0a3b1 -r ecb69e17950b deep/stacked_dae/v_sylvain/stacked_dae.py
--- a/deep/stacked_dae/v_sylvain/stacked_dae.py	Sun Mar 14 15:17:04 2010 -0400
+++ b/deep/stacked_dae/v_sylvain/stacked_dae.py	Sun Mar 14 20:25:12 2010 -0400
@@ -204,6 +204,9 @@
         self.x  = T.matrix('x')  # the data is presented as rasterized images
         self.y  = T.ivector('y') # the labels are presented as 1D vector of 
                                  # [int] labels
+        ensemble = T.matrix('ensemble')
+        ensemble_x = T.matrix('ensemble_x')
+        ensemble_y = T.ivector('ensemble_y')
 
         for i in xrange( self.n_layers ):
             # construct the sigmoidal layer