# prepare to count predictions for each class
correct_pred ={classname:0for classname in classes}
total_pred ={classname:0for classname in classes}# again no gradients neededwith torch.no_grad():for data in testloader:
images, labels = data
outputs = net(images)
_, predictions = torch.max(outputs,1)# collect the correct predictions for each classfor label, prediction inzip(labels, predictions):if label == prediction:
correct_pred[classes[label]]+=1
total_pred[classes[label]]+=1# print accuracy for each classfor classname, correct_count in correct_pred.items():
accuracy =100*float(correct_count)/ total_pred[classname]print("Accuracy for class {:5s} is: {:.1f} %".format(classname,
accuracy))