gcn.py

gcn.py

# import required packages
import torch
import torch.nn.functional as F
from torch_geometric.datasets import Planetoid
from torch_geometric.data import DataLoader
from torch_geometric.nn import GCNConv

# load Cora dataset
dataset = Planetoid(root='/tmp/Cora', name='Cora')
loader = DataLoader(dataset, batch_size=32, shuffle=True)

# define GCN model
class GCN(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = GCNConv(dataset.num_features, 16)
        self.conv2 = GCNConv(16, dataset.num_classes)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)
        return F.log_softmax(x, dim=1)


# multi-gpu support
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = GCN().to(device)
data = dataset[0].to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)

# train model for 100 epochs
model.train()
for epoch in range(100):
    optimizer.zero_grad()
    out = model(data)
    loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
    loss.backward()
    optimizer.step()