example2_adv_example.py 文件源码

def train(epoch):
    if epoch > 2:
        import pdb; pdb.set_trace()

    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        # 1. Add requires_grad so Torch doesn't erase the gradient with its optimization pass
        data, target = Variable(data, requires_grad=True), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.data[0]))

            # 2. Get the `.grad` attribute of the variable.
            # This is a Torch tensor, so to get the data as numpy format, we have to use `.grad.data.numpy()`
            adversarial_example = data.grad.data.numpy()
            print(adversarial_example.max())

            if epoch > 2:
                # 3. Let's plot it, because we can!
                plt.clf()
                plt.subplot(121); plt.imshow(data.data.numpy()[0,0,...], cmap='gray_r')
                plt.subplot(122); plt.imshow(adversarial_example[0,0,...]); plt.colorbar()
                plt.show(block=False)
                plt.pause(0.01)