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GAT
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class GAT(nn.Module): | |
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads): | |
"""Dense version of GAT.""" | |
super(GAT, self).__init__() | |
self.dropout = dropout | |
self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True) for _ in range(nheads)] | |
for i, attention in enumerate(self.attentions): | |
self.add_module('attention_{}'.format(i), attention) | |
self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=dropout, alpha=alpha, concat=False) | |
def forward(self, x, adj): | |
x = F.dropout(x, self.dropout, training=self.training) | |
x = torch.cat([att(x, adj) for att in self.attentions], dim=1) | |
x = F.dropout(x, self.dropout, training=self.training) | |
x = F.elu(self.out_att(x, adj)) | |
return F.log_softmax(x, dim=1) | |
class GraphAttentionLayer(nn.Module): | |
""" | |
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903 | |
""" | |
def __init__(self, in_features, out_features, dropout, alpha, concat=True): | |
super(GraphAttentionLayer, self).__init__() | |
self.dropout = dropout | |
self.in_features = in_features | |
self.out_features = out_features | |
self.alpha = alpha | |
self.concat = concat | |
self.W = nn.Parameter(torch.zeros(size=(in_features, out_features))) | |
nn.init.xavier_uniform_(self.W.data, gain=1.414) | |
self.a = nn.Parameter(torch.zeros(size=(2*out_features, 1))) | |
nn.init.xavier_uniform_(self.a.data, gain=1.414) | |
self.leakyrelu = nn.LeakyReLU(self.alpha) | |
def forward(self, input, adj): | |
h = torch.mm(input, self.W) | |
N = h.size()[0] | |
a_input = torch.cat([h.repeat(1, N).view(N * N, -1), h.repeat(N, 1)], dim=1).view(N, -1, 2 * self.out_features) | |
e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(2)) | |
zero_vec = -9e15*torch.ones_like(e) | |
attention = torch.where(adj > 0, e, zero_vec) | |
attention = F.softmax(attention, dim=1) | |
attention = F.dropout(attention, self.dropout, training=self.training) | |
h_prime = torch.matmul(attention, h) | |
if self.concat: | |
return F.elu(h_prime) | |
else: | |
return h_prime | |
def __repr__(self): | |
return self.__class__.__name__ + ' (' + str(self.in_features) + ' -> ' + str(self.out_features) + ')' |
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