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#### Imports ####
from torch_geometric.datasets import Planetoid
import torch
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree
#### Loading the Dataset ####
dataset = Planetoid(root='/tmp/Cora', name='Cora')
#### The Graph Convolution Layer ####
class GraphConvolution(MessagePassing):
def __init__(self, in_channels, out_channels,bias=True, **kwargs):
super(GraphConvolution, self).__init__(aggr='add', **kwargs)
self.lin = torch.nn.Linear(in_channels, out_channels,bias=bias)
def forward(self, x, edge_index):
edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))
x = self.lin(x)
return self.propagate(edge_index, size=(x.size(0), x.size(0)), x=x)
def message(self, x_j, edge_index, size):
row, col = edge_index
deg = degree(row, size[0], dtype=x_j.dtype)
deg_inv_sqrt = deg.pow(-0.5)
norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]
return norm.view(-1, 1) * x_j
def update(self, aggr_out):
return aggr_out
class Net(torch.nn.Module):
def __init__(self,nfeat, nhid, nclass, dropout):
super(Net, self).__init__()
self.conv1 = GraphConvolution(nfeat, nhid)
self.conv2 = GraphConvolution(nhid, nclass)
self.dropout=dropout
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, self.dropout, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
nfeat=dataset.num_node_features
nhid=16
nclass=dataset.num_classes
dropout=0.5
#### Training ####
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net(nfeat, nhid, nclass, dropout).to(device)
data = dataset[0].to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
model.train()
for epoch in range(200):
optimizer.zero_grad()
out = model(data)
loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
model.eval()
_, pred = model(data).max(dim=1)
correct = float (pred[data.test_mask].eq(data.y[data.test_mask]).sum().item())
acc = correct / data.test_mask.sum().item()
print('Accuracy: {:.4f}'.format(acc))
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