To get the accuracy you can first initialize a total and correct amount above your dataloader loop but below your epoch loop like this:
for e in epochs:
     total = 0.
     correct = 0.
     for data in dataloader:

then in the dataloader loop you can do this
    output = model(input)
    pred = output.data.max(1, keepdim=True)[1] 
    compare predictions to true label
    correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
    total += images.size(0)

and just print it out when you print out your loss by something like this
print('\nTest Accuracy: %2d%% (%2d/%2d)' % (
        100. * correct / total, correct, total))  

and I am not sure what you mean by the image with CNN. This is also assuming you are using softmax.