--- a/deep/convolutional_dae/salah_exp/nist_csda.py	Thu Apr 22 00:49:42 2010 -0400
+++ b/deep/convolutional_dae/salah_exp/nist_csda.py	Thu Apr 22 19:50:21 2010 -0400
@@ -121,24 +121,20 @@
     
     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(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
         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(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
         channel.save()
     if finetune_choice == 2:
         print('\n\n\tfinetune with P07 followed by NIST\n\n')
-        optimizer.reload_parameters('params_pretrain.txt')
         optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20,decrease=decrease_lr)
         optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
         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(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
         
         
@@ -146,7 +142,6 @@
         print('\nSERIE OF 4 DIFFERENT FINETUNINGS')
         print('\n\n\tfinetune with NIST\n\n')
         sys.stdout.flush()
-        optimizer.reload_parameters('params_pretrain.txt')
         optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
         channel.save()
         print('\n\n\tfinetune with P07\n\n')