Drug-combination-synergy/models.py

import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv

class GCN(torch.nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        torch.manual_seed(1234)
        self.conv = GCNConv(in_channels, out_channels, add_self_loops=False)

    def forward(self, x, edge_index, edge_weight=None):
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.conv(x, edge_index, edge_weight).relu()
        return x


model = GCN(dataset.num_features, dataset.num_classes)
model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

print("Training on CPU.")

for epoch in range(1, 6):
    optimizer.zero_grad()
    out = model(data.x, data.edge_index, data.edge_attr)
    loss = F.cross_entropy(out, data.y)
    loss.backward()
    optimizer.step()
    print(f"Epoch: {epoch}, Loss: {loss}")