corrected ups

fac_sortem.py

@@ -103,7 +103,7 @@ class DynAE(super_class):
             if self.ctf:
                 fake_projections = tf.expand_dims(apply_ctf(fake_projections,ctf_pr_count,lambdas,spher_abb,ac),axis=-1)
                 add_images.append(fake_projections[0])
-                print("ups");exit()
+                #print("ups");exit()
             else:
                 add_images.append(np.zeros((self.large_rescale,self.large_rescale)))
             if self.noise:

main_sortem.py

@@ -16,7 +16,7 @@ def main():
     parser.add_argument('--num_cpus',type=int,default = 8,help='The maximum allowed cpus to use for preprocessing data and Kmeans clustering')
     parser.add_argument('--star', type=str, nargs='+',
                         help='list of path to the star files, wild cards are accepted. The star file must refer to the .mrc files')
-    parser.add_argument('--ab', type=int,default=50,
+    parser.add_argument('--ab', type=int,default=100,
                         help='deep learning model training batch')     
     parser.add_argument('--pb', type=int,default=200,
                         help='deep learning model training batch')  
@@ -34,7 +34,7 @@ def main():
 
     parser.add_argument('--log', type=str,default="False",help='log all possible values to file (loss, pca_components,NMI,Recall,false positives,false negatives.')   
         
-    parser.add_argument('--num_parts',type=int,default=30,help='Number of gaussian components to use. (This is the maximum number)')
+    parser.add_argument('--num_parts',type=int,default=4,help='Number of gaussian components to use. (This is the maximum number)')
 
     parser.add_argument('--lr',type=float,default=0.002,help='The learning rate of the model')