565f0c88fc5cfbcfd5476da8146e92c793af4834,examples/pytorch/gat/gat.py,GraphAttention,edge_softmax,#GraphAttention#,81

Before Change

        // compute the max
        self.g.update_all(fn.copy_edge("a", "a"), fn.max("a", "a_max"))
        // minus the max and exp
        self.g.apply_edges(lambda edges : {"a" : torch.exp(edges.data["a"] - edges.dst["a_max"])})
        // compute dropout
        self.g.apply_edges(lambda edges : {"a_drop" : self.attn_drop(edges.data["a"])})
        // compute normalizer
        self.g.update_all(fn.copy_edge("a", "a"), fn.sum("a", "z"))

After Change

        // Save normalizer
        self.g.ndata["z"] = normalizer
        // Dropout attention scores and save them
        self.g.edata["a_drop"] = self.attn_drop(scores)

class GAT(nn.Module):
    def __init__(self,
                 g,

Instances