diff --git a/cifar.py b/cifar.py
index 9572cf81..9dfe0016 100644
--- a/cifar.py
+++ b/cifar.py
@@ -245,7 +245,7 @@ def train(trainloader, model, criterion, optimizer, epoch, use_cuda):
         data_time.update(time.time() - end)
 
         if use_cuda:
-            inputs, targets = inputs.cuda(), targets.cuda(async=True)
+            inputs, targets = inputs.cuda(), targets.cuda()
         inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)
 
         # compute output
@@ -254,9 +254,9 @@ def train(trainloader, model, criterion, optimizer, epoch, use_cuda):
 
         # measure accuracy and record loss
         prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
-        losses.update(loss.data[0], inputs.size(0))
-        top1.update(prec1[0], inputs.size(0))
-        top5.update(prec5[0], inputs.size(0))
+        losses.update(loss.item(), inputs.size(0))
+        top1.update(prec1.item(), inputs.size(0))
+        top5.update(prec5.item(), inputs.size(0))
 
         # compute gradient and do SGD step
         optimizer.zero_grad()
@@ -303,7 +303,9 @@ def test(testloader, model, criterion, epoch, use_cuda):
 
         if use_cuda:
             inputs, targets = inputs.cuda(), targets.cuda()
-        inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)
+        with torch.no_grad():
+            inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)
+
 
         # compute output
         outputs = model(inputs)
@@ -311,9 +313,9 @@ def test(testloader, model, criterion, epoch, use_cuda):
 
         # measure accuracy and record loss
         prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
-        losses.update(loss.data[0], inputs.size(0))
-        top1.update(prec1[0], inputs.size(0))
-        top5.update(prec5[0], inputs.size(0))
+        losses.update(loss.item(), inputs.size(0))
+        top1.update(prec1.item(), inputs.size(0))
+        top5.update(prec5.item(), inputs.size(0))
 
         # measure elapsed time
         batch_time.update(time.time() - end)
diff --git a/utils/eval.py b/utils/eval.py
index 50513501..d6f11c66 100644
--- a/utils/eval.py
+++ b/utils/eval.py
@@ -9,10 +9,10 @@ def accuracy(output, target, topk=(1,)):
 
     _, pred = output.topk(maxk, 1, True, True)
     pred = pred.t()
-    correct = pred.eq(target.view(1, -1).expand_as(pred))
+    correct = pred.eq(target.reshape(1, -1).expand_as(pred))
 
     res = []
     for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
+        correct_k = correct[:k].reshape(-1).float().sum(0)
         res.append(correct_k.mul_(100.0 / batch_size))
     return res
\ No newline at end of file