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2026-07-13 13:35:51 +08:00

35 lines
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Python

"""
This code was copied from the GCN implementation in DGL examples.
"""
import torch
import torch.nn as nn
from dgl.nn.pytorch import GraphConv
class GCN(nn.Module):
def __init__(
self, g, in_feats, n_hidden, n_classes, n_layers, activation, dropout
):
super(GCN, self).__init__()
self.g = g
self.layers = nn.ModuleList()
# input layer
self.layers.append(GraphConv(in_feats, n_hidden, activation=activation))
# hidden layers
for i in range(n_layers - 1):
self.layers.append(
GraphConv(n_hidden, n_hidden, activation=activation)
)
# output layer
self.layers.append(GraphConv(n_hidden, n_classes))
self.dropout = nn.Dropout(p=dropout)
def forward(self, features):
h = features
for i, layer in enumerate(self.layers):
if i != 0:
h = self.dropout(h)
h = layer(self.g, h)
return h