diff --git a/README.md b/README.md
index 029d31f..26da72e 100644
--- a/README.md
+++ b/README.md
@@ -82,8 +82,10 @@ python main.py --refine [PATH TO THE PRUNED MODEL] --dataset cifar10 --arch vgg
 
 |  CIFAR100-Resnet-164  | Baseline |   Sparsity (1e-5) | Prune (40%) | Fine-tune-160(40%) |    Prune(60%)  | Fine-tune-160(60%) |
 | :---------------: | :------: | :--------------------------: | :-----------------: | :-------------------: |:--------------------: | :-----------------:|
-| Top1 Accuracy (%) |  -----   |            76.87             |        48.0        |         ---         |  ---       |     --     |
-|    Parameters     |  1.73M  |            1.73M            |        1.49M        |         ---         |---       |     --     |
+| Top1 Accuracy (%) |  76.79   |            76.87             |        48.0        |         77.36        |  ---       |     ---     |
+|    Parameters     |  1.73M  |            1.73M            |        1.49M        |         1.49M         |---       |     ---     |
+
+Note: For results of pruning 60% of the channels for resnet164-cifar100, in this implementation, sometimes some layers are all pruned and there would be error. However, we also provide a [mask implementation](https://github.com/Eric-mingjie/network-slimming/tree/master/mask-impl) where we apply a mask to the scaling factor in BN layer. For mask implementaion, when pruning 60% of the channels in resnet164-cifar100, we can also train the pruned network.
 
 |  CIFAR100-Densenet-40  | Baseline |    Sparsity (1e-5) | Prune (40%) | Fine-tune-160(40%) | Prune(60%)  | Fine-tune-160(60%) |
 | :---------------: | :------: | :--------------------------: | :-----------------: | :-------------------: |:--------------------: | :-----------------:|
diff --git a/main.py b/main.py
index 3480215..272791b 100644
--- a/main.py
+++ b/main.py
@@ -122,8 +122,6 @@
     else:
         print("=> no checkpoint found at '{}'".format(args.resume))
 
-history_score = np.zeros((args.epochs - args.start_epoch + 1, 3))
-
 # additional subgradient descent on the sparsity-induced penalty term
 def updateBN():
     for m in model.modules():
@@ -132,9 +130,6 @@ def updateBN():
 
 def train(epoch):
     model.train()
-    global history_score
-    avg_loss = 0.
-    train_acc = 0.
     for batch_idx, (data, target) in enumerate(train_loader):
         if args.cuda:
             data, target = data.cuda(), target.cuda()
@@ -142,9 +137,7 @@ def train(epoch):
         optimizer.zero_grad()
         output = model(data)
         loss = F.cross_entropy(output, target)
-        avg_loss += loss.data[0]
         pred = output.data.max(1, keepdim=True)[1]
-        train_acc += pred.eq(target.data.view_as(pred)).cpu().sum()
         loss.backward()
         if args.sr:
             updateBN()
@@ -153,8 +146,6 @@ def train(epoch):
             print('Train Epoch: {} [{}/{} ({:.1f}%)]\tLoss: {:.6f}'.format(
                 epoch, batch_idx * len(data), len(train_loader.dataset),
                 100. * batch_idx / len(train_loader), loss.data[0]))
-    history_score[epoch][0] = avg_loss / len(train_loader)
-    history_score[epoch][1] = train_acc / float(len(train_loader))
 
 def test():
     model.eval()
@@ -187,8 +178,6 @@ def save_checkpoint(state, is_best, filepath):
             param_group['lr'] *= 0.1
     train(epoch)
     prec1 = test()
-    history_score[epoch][2] = prec1
-    np.savetxt(os.path.join(args.save, 'record.txt'), history_score, fmt = '%10.5f', delimiter=',')
     is_best = prec1 > best_prec1
     best_prec1 = max(prec1, best_prec1)
     save_checkpoint({
@@ -198,6 +187,4 @@ def save_checkpoint(state, is_best, filepath):
         'optimizer': optimizer.state_dict(),
     }, is_best, filepath=args.save)
 
-print("Best accuracy: "+str(best_prec1))
-history_score[-1][0] = best_prec1
-np.savetxt(os.path.join(args.save, 'record.txt'), history_score, fmt = '%10.5f', delimiter=',')
\ No newline at end of file
+print("Best accuracy: "+str(best_prec1))
\ No newline at end of file
diff --git a/mask-impl/main_mask.py b/mask-impl/main_mask.py
index f3aa03e..7e89dd3 100644
--- a/mask-impl/main_mask.py
+++ b/mask-impl/main_mask.py
@@ -122,8 +122,6 @@
     else:
         print("=> no checkpoint found at '{}'".format(args.resume))
 
-history_score = np.zeros((args.epochs - args.start_epoch + 1, 3))
-
 # additional subgradient descent on the sparsity-induced penalty term
 def updateBN():
     for m in model.modules():
@@ -138,20 +136,8 @@ def BN_grad_zero():
             m.weight.grad.data.mul_(mask)
             m.bias.grad.data.mul_(mask)
 
-def compute_cfg():
-    cfg = []
-    for m in model.modules():
-        if isinstance(m, nn.BatchNorm2d):
-            weight_nonzero = (m.weight.data == 0).sum()
-            bias_nonzero = (m.bias.data == 0).sum()
-            cfg.append((weight_nonzero, bias_nonzero))
-    return cfg
-
 def train(epoch):
     model.train()
-    global history_score
-    avg_loss = 0.
-    train_acc = 0.
     for batch_idx, (data, target) in enumerate(train_loader):
         if args.cuda:
             data, target = data.cuda(), target.cuda()
@@ -159,9 +145,7 @@ def train(epoch):
         optimizer.zero_grad()
         output = model(data)
         loss = F.cross_entropy(output, target)
-        avg_loss += loss.data[0]
         pred = output.data.max(1, keepdim=True)[1]
-        train_acc += pred.eq(target.data.view_as(pred)).cpu().sum()
         loss.backward()
         if args.sr:
             updateBN()
@@ -171,8 +155,6 @@ def train(epoch):
             print('Train Epoch: {} [{}/{} ({:.1f}%)]\tLoss: {:.6f}'.format(
                 epoch, batch_idx * len(data), len(train_loader.dataset),
                 100. * batch_idx / len(train_loader), loss.data[0]))
-    history_score[epoch][0] = avg_loss / len(train_loader)
-    history_score[epoch][1] = train_acc / float(len(train_loader))
 
 def test():
     model.eval()
@@ -206,8 +188,6 @@ def save_checkpoint(state, is_best, filepath):
             param_group['lr'] *= 0.1
     train(epoch)
     prec1 = test()
-    history_score[epoch][2] = prec1
-    np.savetxt(os.path.join(args.save, 'record.txt'), history_score, fmt = '%10.5f', delimiter=',')
     is_best = prec1 > best_prec1
     best_prec1 = max(prec1, best_prec1)
     save_checkpoint({
@@ -217,6 +197,4 @@ def save_checkpoint(state, is_best, filepath):
         'optimizer': optimizer.state_dict(),
     }, is_best, filepath=args.save)
 
-print("Best accuracy: "+str(best_prec1))
-history_score[-1][0] = best_prec1
-np.savetxt(os.path.join(args.save, 'record.txt'), history_score, fmt = '%10.5f', delimiter=',')
\ No newline at end of file
+print("Best accuracy: "+str(best_prec1))
\ No newline at end of file