Using the test data set for MNIST fashion example

test_dataset_use.py

correct = 0
total = 0
for images, labels in testloader:
 #   images = Variable(images.float())
    flattened_images = images.view(images.shape[0],-1)
    outputs = model(flattened_images)
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)
    correct += (predicted == labels).sum()

print('Test Accuracy of the model on the 10000 test images: %.4f %%' % (100 * correct / total))