diff --git a/docs/NAS-Bench-201.md b/docs/NAS-Bench-201.md
index 7042bca..2a3e72d 100644
--- a/docs/NAS-Bench-201.md
+++ b/docs/NAS-Bench-201.md
@@ -14,7 +14,7 @@ Note: please use `PyTorch >= 1.2.0` and `Python >= 3.6.0`.
 
 You can simply type `pip install nas-bench-201` to install our api. Please see source codes of `nas-bench-201` module in [this repo](https://github.com/D-X-Y/NAS-Bench-201).
 
-If you have any questions or issues, please post it at [here](https://github.com/D-X-Y/AutoDL-Projects/issues) or email me.
+**If you have any questions or issues, please post it at [here](https://github.com/D-X-Y/AutoDL-Projects/issues) or email me.**
 
 ### Preparation and Download
 
diff --git a/lib/nas_201_api/api.py b/lib/nas_201_api/api.py
index 9712fe2..1c9e111 100644
--- a/lib/nas_201_api/api.py
+++ b/lib/nas_201_api/api.py
@@ -292,6 +292,11 @@ class NASBench201API(object):
         xifo['est-valid-accuracy'] = est_valid_info['accuracy']
       return xifo
 
+  """
+  This function will print the information of a specific (or all) architecture(s).
+  If the index < 0: it will loop for all architectures and print their information one by one.
+  else: it will print the information of the 'index'-th archiitecture.
+  """
   def show(self, index=-1):
     if index < 0: # show all architectures
       print(self)
@@ -299,10 +304,10 @@ class NASBench201API(object):
         print('\n' + '-' * 10 + ' The ({:5d}/{:5d}) {:06d}-th architecture! '.format(i, len(self.evaluated_indexes), idx) + '-'*10)
         print('arch : {:}'.format(self.meta_archs[idx]))
         strings = print_information(self.arch2infos_full[idx])
-        print('>' * 40 + ' 200 epochs ' + '>' * 40)
+        print('>' * 40 + ' {:03d} epochs '.format(self.arch2infos_full[idx].get_total_epoch()) + '>' * 40)
         print('\n'.join(strings))
         strings = print_information(self.arch2infos_less[idx])
-        print('>' * 40 + '  12 epochs ' + '>' * 40)
+        print('>' * 40 + ' {:03d} epochs '.format(self.arch2infos_less[idx].get_total_epoch()) + '>' * 40)
         print('\n'.join(strings))
         print('<' * 40 + '------------' + '<' * 40)
     else:
@@ -310,10 +315,10 @@ class NASBench201API(object):
         if index not in self.evaluated_indexes: print('The {:}-th architecture has not been evaluated or not saved.'.format(index))
         else:
           strings = print_information(self.arch2infos_full[index])
-          print('>' * 40 + ' 200 epochs ' + '>' * 40)
+          print('>' * 40 + ' {:03d} epochs '.format(self.arch2infos_full[index].get_total_epoch()) + '>' * 40)
           print('\n'.join(strings))
           strings = print_information(self.arch2infos_less[index])
-          print('>' * 40 + '  12 epochs ' + '>' * 40)
+          print('>' * 40 + ' {:03d} epochs '.format(self.arch2infos_less[index].get_total_epoch()) + '>' * 40)
           print('\n'.join(strings))
           print('<' * 40 + '------------' + '<' * 40)
       else:
@@ -419,7 +424,7 @@ class ArchResults(object):
     -- When dataset = cifar10-valid, you can use 'train', 'x-valid', 'ori-test'
     ------ 'train' : the metric on the training set.
     ------ 'x-valid' : the metric on the validation set.
-    ------ 'ori-test' : the metric on the validation + test set.
+    ------ 'ori-test' : the metric on the test set.
     -- When dataset = cifar10, you can use 'train', 'ori-test'.
     ------ 'train' : the metric on the training + validation set.
     ------ 'ori-test' : the metric on the test set.
@@ -472,6 +477,11 @@ class ArchResults(object):
   def get_dataset_seeds(self, dataset):
     return copy.deepcopy( self.dataset_seed[dataset] )
 
+  """
+  This function will return the trained network's weights on the 'dataset'.
+  When the 'seed' is None, it will return the weights for every run trial in the form of a dict.
+  When the 
+  """
   def get_net_param(self, dataset, seed=None):
     if seed is None:
       x_seeds = self.dataset_seed[dataset]
@@ -479,6 +489,21 @@ class ArchResults(object):
     else:
       return self.all_results[(dataset, seed)].get_net_param()
 
+  # get the total number of training epochs
+  def get_total_epoch(self, dataset=None):
+    if dataset is None:
+      epochss = []
+      for xdata, x_seeds in self.dataset_seed.items():
+        epochss += [self.all_results[(xdata, seed)].get_total_epoch() for seed in x_seeds]
+    elif isinstance(dataset, str):
+      x_seeds = self.dataset_seed[dataset]
+      epochss = [self.all_results[(dataset, seed)].get_total_epoch() for seed in x_seeds]
+    else:
+      raise ValueError('invalid dataset={:}'.format(dataset))
+    if len(set(epochss)) > 1: raise ValueError('Each trial mush have the same number of training epochs : {:}'.format(epochss))
+    return epochss[-1]
+
+  # return the ResultsCount object (containing all information of a single trial) for 'dataset' and 'seed'
   def query(self, dataset, seed=None):
     if seed is None:
       x_seeds = self.dataset_seed[dataset]
@@ -537,6 +562,8 @@ class ArchResults(object):
     x.load_state_dict(state_dict)
     return x
 
+  # This function is used to clear the weights saved in each 'result'
+  # This can help reduce the memory footprint.
   def clear_params(self):
     for key, result in self.all_results.items():
       result.net_state_dict = None
@@ -547,6 +574,11 @@ class ArchResults(object):
     
 
 
+"""
+This class (ResultsCount) is used to save the information of one trial for a single architecture.
+I did not write much comment for this class, because it is the lowest-level class in NAS-Bench-201 API, which will be rarely called.
+If you have any question regarding this class, please open an issue or email me.
+"""
 class ResultsCount(object):
 
   def __init__(self, name, state_dict, train_accs, train_losses, params, flop, arch_config, seed, epochs, latency):
@@ -604,10 +636,17 @@ class ResultsCount(object):
     set_name = '[' + ', '.join(self.eval_names) + ']'
     return ('{name}({xname}, arch={arch}, FLOP={flop:.2f}M, Param={param:.3f}MB, seed={seed}, {num_eval} eval-sets: {set_name})'.format(name=self.__class__.__name__, xname=self.name, arch=self.arch_config['arch_str'], flop=self.flop, param=self.params, seed=self.seed, num_eval=num_eval, set_name=set_name))
 
+  # get the total number of training epochs
+  def get_total_epoch(self):
+    return copy.deepcopy(self.epochs)
+  
+  # get the latency
+  # -1 represents not avaliable ; otherwise it should be a float value
   def get_latency(self):
     if self.latency is None: return -1
     else: return sum(self.latency) / len(self.latency)
 
+  # get the information regarding time
   def get_times(self):
     if self.train_times is not None and isinstance(self.train_times, dict):
       train_times = list( self.train_times.values() )
@@ -626,6 +665,7 @@ class ResultsCount(object):
   def get_eval_set(self):
     return self.eval_names
 
+  # get the training information
   def get_train(self, iepoch=None):
     if iepoch is None: iepoch = self.epochs-1
     assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
@@ -639,6 +679,7 @@ class ResultsCount(object):
             'cur_time': xtime,
             'all_time': atime}
 
+  # get the evaluation information ; there could be multiple evaluation sets (identified by the 'name' argument).
   def get_eval(self, name, iepoch=None):
     if iepoch is None: iepoch = self.epochs-1
     assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)