Output : tensor([[ -28.4967,   14.9120,  146.2759, -132.4498],
[   3.0310,   41.4412,   18.8934,  -62.9328],
[  75.7614,  -30.5392, -390.3096,  344.8382],
[  70.0653,  -71.1584, -411.3787,  412.4350]], device=‘cuda:0’)
Target : tensor([2, 2, 3, 3], device=‘cuda:0’)
I think classify other classes as well.
[second plot]
def acc_check(net, test_set, epoch, save=1):
net.eval()
correct = 0
total = 0
with torch.no_grad():
for data in test_set:
inputs, labels = data
inputs = inputs.to(device)
labels = torch.argmax(labels, dim=1)
labels = labels.to(device)
outputs = net(inputs.permute(0,4,1,2,3))
        _, predicted = torch.max(outputs.data, 1)
        #print("labels : ",labels)
        #print("prediced :" ,predicted)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

acc = (100 * correct / total)
print('Accuracy of the network on the 10000 test images: %d %%' % acc)
if epoch%50==0:
    torch.save(net, "D:/data/3dcnn_weights/basic128_epoch_{}_acc_{}.pth".format(epoch, int(acc)))
    pass
return acc

acc = acc_check(model_ft, test_loader, epoch, save=1)
value_tracker(acc_plt, torch.Tensor([acc]), torch.Tensor([epoch]))
thanks