diff --git a/exps/NAS-Bench-102/visualize.py b/exps/NAS-Bench-102/visualize.py
index f73adc0..992bb61 100644
--- a/exps/NAS-Bench-102/visualize.py
+++ b/exps/NAS-Bench-102/visualize.py
@@ -367,6 +367,160 @@ def write_video(save_dir):
 
 
 
+
+def plot_results_nas(api, dataset, xset, root, file_name, y_lims):
+  print ('root-path={:}, dataset={:}, xset={:}'.format(root, dataset, xset))
+  checkpoints = ['./output/search-cell-nas-bench-102/R-EA-cifar10/results.pth',
+                 './output/search-cell-nas-bench-102/REINFORCE-cifar10/results.pth',
+                 './output/search-cell-nas-bench-102/RAND-cifar10/results.pth',
+                 './output/search-cell-nas-bench-102/BOHB-cifar10/results.pth'
+                ]
+  legends, indexes = ['REA', 'REINFORCE', 'RANDOM', 'BOHB'], None
+  All_Accs = OrderedDict()
+  for legend, checkpoint in zip(legends, checkpoints):
+    all_indexes = torch.load(checkpoint, map_location='cpu')
+    accuracies  = []
+    for x in all_indexes:
+      info = api.arch2infos_full[ x ]
+      metrics = info.get_metrics(dataset, xset, None, False)
+      accuracies.append( metrics['accuracy'] )
+    if indexes is None: indexes = list(range(len(all_indexes)))
+    All_Accs[legend] = sorted(accuracies)
+  
+  color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
+  dpi, width, height = 300, 3400, 2600
+  LabelSize, LegendFontsize = 28, 28
+  figsize = width / float(dpi), height / float(dpi)
+  fig = plt.figure(figsize=figsize)
+  x_axis = np.arange(0, 600)
+  plt.xlim(0, max(indexes))
+  plt.ylim(y_lims[0], y_lims[1])
+  interval_x, interval_y = 100, y_lims[2]
+  plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
+  plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
+  plt.grid()
+  plt.xlabel('The index of runs', fontsize=LabelSize)
+  plt.ylabel('The accuracy (%)', fontsize=LabelSize)
+
+  for idx, legend in enumerate(legends):
+    plt.plot(indexes, All_Accs[legend], color=color_set[idx], linestyle='-', label='{:}'.format(legend), lw=2)
+    print ('{:} : mean = {:}, std = {:} :: {:.2f}$\\pm${:.2f}'.format(legend, np.mean(All_Accs[legend]), np.std(All_Accs[legend]), np.mean(All_Accs[legend]), np.std(All_Accs[legend])))
+  plt.legend(loc=4, fontsize=LegendFontsize)
+  save_path = root / '{:}-{:}-{:}'.format(dataset, xset, file_name)
+  print('save figure into {:}\n'.format(save_path))
+  fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
+
+
+def just_show(api):
+  xtimes = {'RSPS': [8082.5, 7794.2, 8144.7],
+            'DARTS-V1': [11582.1, 11347.0, 11948.2],
+            'DARTS-V2': [35694.7, 36132.7, 35518.0],
+            'GDAS'    : [31334.1, 31478.6, 32016.7],
+            'SETN'    : [33528.8, 33831.5, 35058.3],
+            'ENAS'    : [14340.2, 13817.3, 14018.9]}
+  for xkey, xlist in xtimes.items():
+    xlist = np.array(xlist)
+    print ('{:4s} : mean-time={:.1f} s'.format(xkey, xlist.mean()))
+
+  xpaths = {'RSPS'    : 'output/search-cell-nas-bench-102/RANDOM-NAS-cifar10/checkpoint/',
+            'DARTS-V1': 'output/search-cell-nas-bench-102/DARTS-V1-cifar10/checkpoint/',
+            'DARTS-V2': 'output/search-cell-nas-bench-102/DARTS-V2-cifar10/checkpoint/',
+            'GDAS'    : 'output/search-cell-nas-bench-102/GDAS-cifar10/checkpoint/',
+            'SETN'    : 'output/search-cell-nas-bench-102/SETN-cifar10/checkpoint/',
+            'ENAS'    : 'output/search-cell-nas-bench-102/ENAS-cifar10/checkpoint/',
+           }
+  xseeds = {'RSPS'    : [5349, 59613, 5983],
+            'DARTS-V1': [11416, 72873, 81184],
+            'DARTS-V2': [43330, 79405, 79423],
+            'GDAS'    : [19677, 884, 95950],
+            'SETN'    : [20518, 61817, 89144],
+            'ENAS'    : [30801, 75610, 97745],
+           }
+
+  def get_accs(xdata, index=-1):
+    if index == -1:
+      epochs = xdata['epoch']
+      genotype = xdata['genotypes'][epochs-1]
+      index = api.query_index_by_arch(genotype)
+    pairs = [('cifar10-valid', 'x-valid'), ('cifar10', 'ori-test'), ('cifar100', 'x-valid'), ('cifar100', 'x-test'), ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test')]
+    xresults = []
+    for dataset, xset in pairs:
+      metrics = api.arch2infos_full[index].get_metrics(dataset, xset, None, False)
+      xresults.append( metrics['accuracy'] )
+    return xresults
+
+  for xkey in xpaths.keys():
+    all_paths = [ '{:}/seed-{:}-basic.pth'.format(xpaths[xkey], seed) for seed in xseeds[xkey] ]
+    all_datas = [torch.load(xpath) for xpath in all_paths]
+    accyss = [get_accs(xdatas) for xdatas in all_datas]
+    accyss = np.array( accyss )
+    print('\nxkey = {:}'.format(xkey))
+    for i in range(accyss.shape[1]): print('---->>>> {:.2f}$\\pm${:.2f}'.format(accyss[:,i].mean(), accyss[:,i].std()))
+
+  print('\n{:}'.format(get_accs(None, 11472))) # resnet
+  pairs = [('cifar10-valid', 'x-valid'), ('cifar10', 'ori-test'), ('cifar100', 'x-valid'), ('cifar100', 'x-test'), ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test')]
+  for dataset, metric_on_set in pairs:
+    arch_index, highest_acc = api.find_best(dataset, metric_on_set)
+    print ('[{:10s}-{:10s} ::: index={:5d}, accuracy={:.2f}'.format(dataset, metric_on_set, arch_index, highest_acc))
+
+
+def show_nas_sharing_w(api, dataset, subset, vis_save_dir, file_name, y_lims):
+  color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
+  dpi, width, height = 300, 3400, 2600
+  LabelSize, LegendFontsize = 28, 28
+  figsize = width / float(dpi), height / float(dpi)
+  fig = plt.figure(figsize=figsize)
+  x_maxs = 250
+  x_axis = np.arange(0, x_maxs)
+  plt.xlim(0, x_maxs)
+  plt.ylim(y_lims[0], y_lims[1])
+  interval_x, interval_y = x_maxs // 5, y_lims[2]
+  plt.xticks(np.arange(0, x_maxs, interval_x), fontsize=LegendFontsize)
+  plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
+  plt.grid()
+  plt.xlabel('The searching epoch', fontsize=LabelSize)
+  plt.ylabel('The accuracy (%)', fontsize=LabelSize)
+
+  xpaths = {'RSPS'    : 'output/search-cell-nas-bench-102/RANDOM-NAS-cifar10/checkpoint/',
+            'DARTS-V1': 'output/search-cell-nas-bench-102/DARTS-V1-cifar10/checkpoint/',
+            'DARTS-V2': 'output/search-cell-nas-bench-102/DARTS-V2-cifar10/checkpoint/',
+            'GDAS'    : 'output/search-cell-nas-bench-102/GDAS-cifar10/checkpoint/',
+            'SETN'    : 'output/search-cell-nas-bench-102/SETN-cifar10/checkpoint/',
+            'ENAS'    : 'output/search-cell-nas-bench-102/ENAS-cifar10/checkpoint/',
+           }
+  xseeds = {'RSPS'    : [5349, 59613, 5983],
+            'DARTS-V1': [11416, 72873, 81184],
+            'DARTS-V2': [43330, 79405, 79423],
+            'GDAS'    : [19677, 884, 95950],
+            'SETN'    : [20518, 61817, 89144],
+            'ENAS'    : [30801, 75610, 97745],
+           }
+
+  def get_accs(xdata):
+    epochs, xresults = xdata['epoch'], []
+    for iepoch in range(epochs):
+      genotype = xdata['genotypes'][iepoch]
+      index = api.query_index_by_arch(genotype)
+      metrics = api.arch2infos_full[index].get_metrics(dataset, subset, None, False)
+      xresults.append( metrics['accuracy'] )
+    return xresults
+
+  for idx, method in enumerate(['RSPS', 'GDAS', 'SETN', 'ENAS']):
+    xkey = method
+    all_paths = [ '{:}/seed-{:}-basic.pth'.format(xpaths[xkey], seed) for seed in xseeds[xkey] ]
+    all_datas = [torch.load(xpath) for xpath in all_paths]
+    accyss = [get_accs(xdatas) for xdatas in all_datas]
+    accyss = np.array( accyss )
+    epochs = list(range(accyss.shape[1]))
+    plt.plot(epochs, [accyss[:,i].mean() for i in epochs], color=color_set[idx], linestyle='-', label='{:}'.format(method), lw=2)
+    plt.fill_between(epochs, [accyss[:,i].mean()-accyss[:,i].std() for i in epochs], [accyss[:,i].mean()+accyss[:,i].std() for i in epochs], alpha=0.2, color=color_set[idx])
+  plt.legend(loc=4, fontsize=LegendFontsize)
+  save_path = vis_save_dir / '{:}-{:}-{:}'.format(dataset, subset, file_name)
+  print('save figure into {:}\n'.format(save_path))
+  fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
+
+
+
 if __name__ == '__main__':
 
   parser = argparse.ArgumentParser(description='NAS-Bench-102', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@@ -384,3 +538,15 @@ if __name__ == '__main__':
   #visualize_info(str(meta_file), 'cifar100', vis_save_dir)
   #visualize_info(str(meta_file), 'ImageNet16-120', vis_save_dir)
   #visualize_relative_ranking(vis_save_dir)
+
+  api = API(args.api_path)
+  show_nas_sharing_w(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-plot.pdf', (5,95,10))
+  """
+  just_show(api)
+  plot_results_nas(api, 'cifar10-valid' , 'x-valid' , vis_save_dir, 'nas-com.pdf', (85,95, 1))
+  plot_results_nas(api, 'cifar10'       , 'ori-test', vis_save_dir, 'nas-com.pdf', (85,95, 1))
+  plot_results_nas(api, 'cifar100'      , 'x-valid' , vis_save_dir, 'nas-com.pdf', (55,75, 3))
+  plot_results_nas(api, 'cifar100'      , 'x-test'  , vis_save_dir, 'nas-com.pdf', (55,75, 3))
+  plot_results_nas(api, 'ImageNet16-120', 'x-valid' , vis_save_dir, 'nas-com.pdf', (35,50, 3))
+  plot_results_nas(api, 'ImageNet16-120', 'x-test'  , vis_save_dir, 'nas-com.pdf', (35,50, 3))
+  """
diff --git a/exps/vis/show-results.py b/exps/vis/show-results.py
deleted file mode 100644
index 55545c9..0000000
--- a/exps/vis/show-results.py
+++ /dev/null
@@ -1,135 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
-# python ./exps/vis/show-results.py --api_path ${HOME}/.torch/NAS-Bench-102-v1_0-e61699.pth
-##################################################
-import os, sys, argparse
-from pathlib import Path
-import torch
-import numpy as np
-from collections import OrderedDict
-import matplotlib
-matplotlib.use('agg')
-import matplotlib.pyplot as plt
-lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
-if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
-
-from nas_102_api import NASBench102API as API
-
-
-def plot_results_nas(api, dataset, xset, root, file_name, y_lims):
-  print ('root-path={:}, dataset={:}, xset={:}'.format(root, dataset, xset))
-  checkpoints = ['./output/search-cell-nas-bench-102/R-EA-cifar10/results.pth',
-                 './output/search-cell-nas-bench-102/REINFORCE-cifar10/results.pth',
-                 './output/search-cell-nas-bench-102/RAND-cifar10/results.pth',
-                 './output/search-cell-nas-bench-102/BOHB-cifar10/results.pth'
-                ]
-  legends, indexes = ['REA', 'REINFORCE', 'RANDOM', 'BOHB'], None
-  All_Accs = OrderedDict()
-  for legend, checkpoint in zip(legends, checkpoints):
-    all_indexes = torch.load(checkpoint, map_location='cpu')
-    accuracies  = []
-    for x in all_indexes:
-      info = api.arch2infos_full[ x ]
-      metrics = info.get_metrics(dataset, xset, None, False)
-      accuracies.append( metrics['accuracy'] )
-    if indexes is None: indexes = list(range(len(all_indexes)))
-    All_Accs[legend] = sorted(accuracies)
-  
-  color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
-  dpi, width, height = 300, 3400, 2600
-  LabelSize, LegendFontsize = 28, 28
-  figsize = width / float(dpi), height / float(dpi)
-  fig = plt.figure(figsize=figsize)
-  x_axis = np.arange(0, 600)
-  plt.xlim(0, max(indexes))
-  plt.ylim(y_lims[0], y_lims[1])
-  interval_x, interval_y = 100, y_lims[2]
-  plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
-  plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
-  plt.grid()
-  plt.xlabel('The index of runs', fontsize=LabelSize)
-  plt.ylabel('The accuracy (%)', fontsize=LabelSize)
-
-  for idx, legend in enumerate(legends):
-    plt.plot(indexes, All_Accs[legend], color=color_set[idx], linestyle='-', label='{:}'.format(legend), lw=2)
-    print ('{:} : mean = {:}, std = {:} :: {:.2f}$\\pm${:.2f}'.format(legend, np.mean(All_Accs[legend]), np.std(All_Accs[legend]), np.mean(All_Accs[legend]), np.std(All_Accs[legend])))
-  plt.legend(loc=4, fontsize=LegendFontsize)
-  save_path = root / '{:}-{:}-{:}'.format(dataset, xset, file_name)
-  print('save figure into {:}\n'.format(save_path))
-  fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
-
-
-def just_show(api):
-  xtimes = {'RSPS': [8082.5, 7794.2, 8144.7],
-            'DARTS-V1': [11582.1, 11347.0, 11948.2],
-            'DARTS-V2': [35694.7, 36132.7, 35518.0],
-            'GDAS'    : [31334.1, 31478.6, 32016.7],
-            'SETN'    : [33528.8, 33831.5, 35058.3],
-            'ENAS'    : [14340.2, 13817.3, 14018.9]}
-  for xkey, xlist in xtimes.items():
-    xlist = np.array(xlist)
-    print ('{:4s} : mean-time={:.1f} s'.format(xkey, xlist.mean()))
-
-  xpaths = {'RSPS'    : 'output/search-cell-nas-bench-102/RANDOM-NAS-cifar10/checkpoint/',
-            'DARTS-V1': 'output/search-cell-nas-bench-102/DARTS-V1-cifar10/checkpoint/',
-            'DARTS-V2': 'output/search-cell-nas-bench-102/DARTS-V2-cifar10/checkpoint/',
-            'GDAS'    : 'output/search-cell-nas-bench-102/GDAS-cifar10/checkpoint/',
-            'SETN'    : 'output/search-cell-nas-bench-102/SETN-cifar10/checkpoint/',
-            'ENAS'    : 'output/search-cell-nas-bench-102/ENAS-cifar10/checkpoint/',
-           }
-  xseeds = {'RSPS'    : [5349, 59613, 5983],
-            'DARTS-V1': [11416, 72873, 81184],
-            'DARTS-V2': [43330, 79405, 79423],
-            'GDAS'    : [19677, 884, 95950],
-            'SETN'    : [20518, 61817, 89144],
-            'ENAS'    : [30801, 75610, 97745],
-           }
-
-  def get_accs(xdata, index=-1):
-    if index == -1:
-      epochs = xdata['epoch']
-      genotype = xdata['genotypes'][epochs-1]
-      index = api.query_index_by_arch(genotype)
-    pairs = [('cifar10-valid', 'x-valid'), ('cifar10', 'ori-test'), ('cifar100', 'x-valid'), ('cifar100', 'x-test'), ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test')]
-    xresults = []
-    for dataset, xset in pairs:
-      metrics = api.arch2infos_full[index].get_metrics(dataset, xset, None, False)
-      xresults.append( metrics['accuracy'] )
-    return xresults
-
-  for xkey in xpaths.keys():
-    all_paths = [ '{:}/seed-{:}-basic.pth'.format(xpaths[xkey], seed) for seed in xseeds[xkey] ]
-    all_datas = [torch.load(xpath) for xpath in all_paths]
-    accyss = [get_accs(xdatas) for xdatas in all_datas]
-    accyss = np.array( accyss )
-    print('\nxkey = {:}'.format(xkey))
-    for i in range(accyss.shape[1]): print('---->>>> {:.2f}$\\pm${:.2f}'.format(accyss[:,i].mean(), accyss[:,i].std()))
-
-  print('\n{:}'.format(get_accs(None, 11472))) # resnet
-  pairs = [('cifar10-valid', 'x-valid'), ('cifar10', 'ori-test'), ('cifar100', 'x-valid'), ('cifar100', 'x-test'), ('ImageNet16-120', 'x-valid'), ('ImageNet16-120', 'x-test')]
-  for dataset, metric_on_set in pairs:
-    arch_index, highest_acc = api.find_best(dataset, metric_on_set)
-    print ('[{:10s}-{:10s} ::: index={:5d}, accuracy={:.2f}'.format(dataset, metric_on_set, arch_index, highest_acc))
-
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser(description='NAS-Bench-102', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-  parser.add_argument('--save_dir',  type=str, default='./output/search-cell-nas-bench-102/visuals', help='The base-name of folder to save checkpoints and log.')
-  parser.add_argument('--api_path',  type=str, default=None,                                         help='The path to the NAS-Bench-102 benchmark file.')
-  args = parser.parse_args()
-
-  api  = API(args.api_path)
-
-  root = Path(args.save_dir).resolve()
-  root.mkdir(parents=True, exist_ok=True)
-
-  just_show(api)
-  """
-  plot_results_nas(api, 'cifar10-valid' , 'x-valid' , root, 'nas-com.pdf', (85,95, 1))
-  plot_results_nas(api, 'cifar10'       , 'ori-test', root, 'nas-com.pdf', (85,95, 1))
-  plot_results_nas(api, 'cifar100'      , 'x-valid' , root, 'nas-com.pdf', (55,75, 3))
-  plot_results_nas(api, 'cifar100'      , 'x-test'  , root, 'nas-com.pdf', (55,75, 3))
-  plot_results_nas(api, 'ImageNet16-120', 'x-valid' , root, 'nas-com.pdf', (35,50, 3))
-  plot_results_nas(api, 'ImageNet16-120', 'x-test'  , root, 'nas-com.pdf', (35,50, 3))
-  """