1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
|
import os.path as osp
import torch
import torch.nn.functional as F
import torch_geometric.transforms as T
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import AGNNConv
dataset = 'Cora'
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', dataset)
dataset = Planetoid(path, dataset, transform=T.NormalizeFeatures())
data = dataset[0]
class Net(torch.nn.Module):
def __init__(self):
super().__init__()
self.lin1 = torch.nn.Linear(dataset.num_features, 16)
self.prop1 = AGNNConv(requires_grad=False)
self.prop2 = AGNNConv(requires_grad=True)
self.lin2 = torch.nn.Linear(16, dataset.num_classes)
def forward(self):
x = F.dropout(data.x, training=self.training)
x = F.relu(self.lin1(x))
x = self.prop1(x, data.edge_index)
x = self.prop2(x, data.edge_index)
x = F.dropout(x, training=self.training)
x = self.lin2(x)
return F.log_softmax(x, dim=1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model, data = Net().to(device), data.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
def train():
model.train()
optimizer.zero_grad()
F.nll_loss(model()[data.train_mask], data.y[data.train_mask]).backward()
optimizer.step()
@torch.no_grad()
def test():
model.eval()
out, accs = model(), []
for _, mask in data('train_mask', 'val_mask', 'test_mask'):
pred = out[mask].argmax(1)
acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()
accs.append(acc)
return accs
best_val_acc = test_acc = 0
for epoch in range(1, 201):
train()
train_acc, val_acc, tmp_test_acc = test()
if val_acc > best_val_acc:
best_val_acc = val_acc
test_acc = tmp_test_acc
print(f'Epoch: {epoch:03d}, Train: {train_acc:.4f}, '
f'Val: {best_val_acc:.4f}, Test: {test_acc:.4f}')
|
