I am trying to implement a regression on a Graph Neural Network. Most of the examples that I see are that of classification in this area, none so far of regression. I saw one for classification as follows: from torch_geometric.nn import GCNConv
class GCN(torch.nn.Module):
def __init__(self, hidden_channels):
super(GCN, self).__init__()
torch.manual_seed(12345)
self.conv1 = GCNConv(dataset.num_features, hidden_channels)
self.conv2 = GCNConv(hidden_channels, dataset.num_classes)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index)
x = x.relu()
x = F.dropout(x, p=0.5, training=self.training)
x = self.conv2(x, edge_index)
return x
model = GCN(hidden_channels=16)
print(model)
I am trying to modify it for my task, which basically includes performing a regression on a network with 30 nodes, each having 3 features and the edge has one feature.
If anyone could point me to examples to do the same, that would be very helpful.
add a linear layer,and don't forget use a regression loss function
class GCN(torch.nn.Module):
def __init__(self, hidden_channels):
super(GCN, self).__init__()
torch.manual_seed(12345)
self.conv1 = GCNConv(dataset.num_features, hidden_channels)
self.conv2 = GCNConv(hidden_channels, dataset.num_classes)
self.linear1 = torch.nn.Linear(100,1)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index)
x = x.relu()
x = F.dropout(x, p=0.5, training=self.training)
x = self.conv2(x, edge_index)
x = self.linear1(x)
return x
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