c6349f5f1a8955419d396c7ac74f782f6f00a443,examples/mnist_superpixel_75.py,,train,#Any#,60

Before Change

        optimizer.step()

        pred = output.data.max(1)[1]
        correct = pred.eq(target.data).cpu().sum()
        acc = correct / 64

        print("Epoch:", epoch, "Batch:", batch, "Loss:", loss.data[0],
              "Accuracy:", acc)

After Change

    // Unnecessary...
    if epoch == 20:
        for param_group in optimizer.param_groups:
            param_group["lr"] = 0.00001

    for batch, ((input, adjs, _), target) in enumerate(train_loader):
        adj_0, adj_1, adj_2 = adjs[0][0], adjs[2][0], adjs[4][0]
        ones = input.new(input.size(0)).fill_(1)
        input = torch.stack([input, ones], dim=1)

Instances