DGL part 6

dgl6.py

optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
all_preds = []
epochs = 50

for epoch in range(epochs):
    preds = net(G, inputs)
    all_preds.append(preds)
    # we only compute loss for labeled nodes
    loss = F.cross_entropy(preds[labeled_nodes], labels)
    # PyTorch accumulates gradients by default, we need to zero them
    optimizer.zero_grad() 
    loss.backward() 
    optimizer.step()
    print('Epoch %d | Loss: %.4f' % (epoch, loss.item()))