--- a/deep/stacked_dae/v_sylvain/nist_sda.py	Thu Apr 01 13:44:30 2010 -0400
+++ b/deep/stacked_dae/v_sylvain/nist_sda.py	Thu Apr 01 14:25:40 2010 -0400
@@ -106,44 +106,44 @@
     if finetune_choice == 0:
         print('\n\n\tfinetune with NIST\n\n')
         optimizer.reload_parameters('params_pretrain.txt')
-        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1)
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1)
         channel.save()
     if finetune_choice == 1:
         print('\n\n\tfinetune with P07\n\n')
         optimizer.reload_parameters('params_pretrain.txt')
-        optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
+        optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
         channel.save()
     if finetune_choice == 2:
         print('\n\n\tfinetune with NIST followed by P07\n\n')
         optimizer.reload_parameters('params_pretrain.txt')
-        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=21)
-        optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20)
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21)
+        optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20)
         channel.save()
     if finetune_choice == 3:
         print('\n\n\tfinetune with NIST only on the logistic regression on top (but validation on P07).\n\
         All hidden units output are input of the logistic regression\n\n')
         optimizer.reload_parameters('params_pretrain.txt')
-        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1,special=1)
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1)
         
         
     if finetune_choice==-1:
         print('\nSERIE OF 3 DIFFERENT FINETUNINGS')
         print('\n\n\tfinetune with NIST\n\n')
         optimizer.reload_parameters('params_pretrain.txt')
-        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1)
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1)
         channel.save()
         print('\n\n\tfinetune with P07\n\n')
         optimizer.reload_parameters('params_pretrain.txt')
-        optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
+        optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
         channel.save()
         print('\n\n\tfinetune with NIST (done earlier) followed by P07 (written here)\n\n')
         optimizer.reload_parameters('params_finetune_NIST.txt')
-        optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20)
+        optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20)
         channel.save()
         print('\n\n\tfinetune with NIST only on the logistic regression on top.\n\
         All hidden units output are input of the logistic regression\n\n')
         optimizer.reload_parameters('params_pretrain.txt')
-        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1,special=1)
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1)
         channel.save()
     
     channel.save()