--- a/baseline/log_reg/log_reg.py	Sun Mar 14 20:25:12 2010 -0400
+++ b/baseline/log_reg/log_reg.py	Mon Mar 15 09:22:52 2010 -0400
@@ -145,6 +145,7 @@
                     dataset=datasets.nist_digits, image_size = 32 * 32, nb_class = 10,  \
                     patience = 5000, patience_increase = 2, improvement_threshold = 0.995):
     
+    #28 * 28 = 784
     """
     Demonstrate stochastic gradient descent optimization of a log-linear 
     model
@@ -296,20 +297,24 @@
                  ( best_validation_loss * 100., test_score * 100.))
     print ('The code ran for %f minutes' % ((end_time-start_time) / 60.))
     
- ######   return validation_error, test_error, nb_exemples, time
+    return best_validation_loss, test_score, iter*batch_size, (end_time-start_time) / 60.
 
 if __name__ == '__main__':
     log_reg()
     
  
 def jobman_log_reg(state, channel):
-    (validation_error, test_error, nb_exemples, time) = log_reg( learning_rate = state.learning_rate,\
-                                                                                        nb_max_examples = state.nb_max_examples,\
-                                                                                                    batch_size  = state.batch_size,\
-                                                                                                dataset_name = state.dataset_name, \
+    print state
+    (validation_error, test_error, nb_exemples, time) = log_reg( learning_rate = state.learning_rate, \
+                                                                                        nb_max_examples = state.nb_max_examples, \
+                                                                                                   batch_size  = state.batch_size,\
+                                                                                               dataset_name = state.dataset_name, \
                                                                                                     image_size = state.image_size,  \
-                                                                                                       nb_class  = state.nb_class )
-
+                                                                                                      nb_class  = state.nb_class, \
+                                                                                                   patience = state.patience, \
+                                                                                                    patience_increase = state.patience_increase, \
+                                                                                                    improvement_threshold = state.improvement_threshold ) 
+    print state
     state.validation_error = validation_error
     state.test_error = test_error
     state.nb_exemples = nb_exemples