Created
August 10, 2021 11:37
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Graph Convolutional Network
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import torch.nn as nn | |
import torch.nn.functional as F | |
from torch_geometric.nn import GCNConv | |
# GCN model with 2 layers | |
class Net(torch.nn.Module): | |
def __init__(self): | |
super(Net, self).__init__() | |
self.conv1 = GCNConv(data.num_features, 16) | |
self.conv2 = GCNConv(16, int(data.num_classes)) | |
def forward(self): | |
x, edge_index = data.x, data.edge_index | |
x = F.relu(self.conv1(x, edge_index)) | |
x = F.dropout(x, training=self.training) | |
x = self.conv2(x, edge_index) | |
return F.log_softmax(x, dim=1) | |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
data = data.to(device) | |
model = Net().to(device) |
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