now with parameter chekcs

main_sortem.py

@@ -14,7 +14,7 @@ def main():
    
     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('--batch_size', type=int,default=[25,15,5,2,1], nargs='+',
+    parser.add_argument('--batch_size', type=int,default=[100,75,50,20,10], nargs='+',
                         help='deep learning model training batch')     
     
     parser.add_argument('--o', type=str,default='./results',
@@ -35,7 +35,7 @@ def main():
 
     parser.add_argument('--noise', dest='noise',action='store_true',default=False ,help='Use the noise generator to generate and scale the noise')
 
-    parser.add_argument('--steps',type=int,default=[5,5,5,5,5], nargs='+',help='how many epochs( runs through the dataset) before termination')
+    parser.add_argument('--steps',type=int,default=[10000,10000,10000,10000,10000], nargs='+',help='how many epochs( runs through the dataset) before termination')
 
     parser.add_argument('--l_reg',type=float,default=0.01,help='the lambda regulization of the diversity score loss if the noise generator is active')
               
@@ -63,6 +63,9 @@ def main():
             mkdir(join(args.o,'model'))
     if not isdir(join(args.o,'results')):
             mkdir(join(args.o,'results'))
+   # print(args.batch_size);exit()
+    if len(args.steps) != 5 or len(args.batch_size) != 5:
+            print("the number of steps and batch size must have a list of 5 containing integers");exit()
 
     args_dic = {'gpu_id': args.gpu_id,
                 'num_cpus': args.num_cpus,