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Minor improvement of the GCN doc #1231

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Feb 13, 2024
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Minor improvement of the GCN doc #1231

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9 changes: 5 additions & 4 deletions gcn/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@ class GraphConv(nn.Module):
input features per node.
A: Adjacency matrix of the graph with shape (N, N), representing the relationships between nodes.
W: Learnable weight matrix with shape (F_in, F_out), where F_out is the number of output features per node.
D: The degree matrix.
"""
def __init__(self, input_dim, output_dim, use_bias=False):
super(GraphConv, self).__init__()
Expand All @@ -48,7 +49,7 @@ def forward(self, input_tensor, adj_mat):

Args:
input_tensor (torch.Tensor): Input tensor representing node features.
adj_mat (torch.Tensor): Adjacency matrix representing graph structure.
adj_mat (torch.Tensor): Normalized adjacency matrix representing graph structure.

Returns:
torch.Tensor: Output tensor after the graph convolution operation.
Expand Down Expand Up @@ -92,7 +93,7 @@ def forward(self, input_tensor, adj_mat):
Args:
input_tensor (torch.Tensor): Input node feature matrix with shape (N, input_dim), where N is the number of nodes
and input_dim is the number of input features per node.
adj_mat (torch.Tensor): Adjacency matrix of the graph with shape (N, N), representing the relationships between
adj_mat (torch.Tensor): Normalized adjacency matrix of the graph with shape (N, N), representing the relationships between
nodes.

Returns:
Expand All @@ -113,7 +114,7 @@ def forward(self, input_tensor, adj_mat):

def load_cora(path='./cora', device='cpu'):
"""
The graph convolutional operation rquires normalize the adjacency matrix: D^(-1/2) * A * D^(-1/2). This step
The graph convolutional operation rquires the normalized adjacency matrix: D^(-1/2) * A * D^(-1/2). This step
scales the adjacency matrix such that the features of neighboring nodes are weighted appropriately during
aggregation. The steps involved in the renormalization trick are as follows:
- Compute the degree matrix.
Expand Down Expand Up @@ -249,7 +250,7 @@ def test(model, criterion, input, target, mask):
idx = torch.randperm(len(labels)).to(device)
idx_test, idx_val, idx_train = idx[:1000], idx[1000:1500], idx[1500:]

gcn = GCN(features.shape[1], args.hidden_dim, labels.max().item() + 1,args.include_bias, args.dropout_p).to(device)
gcn = GCN(features.shape[1], args.hidden_dim, labels.max().item() + 1, args.include_bias, args.dropout_p).to(device)
optimizer = Adam(gcn.parameters(), lr=args.lr, weight_decay=args.l2)
criterion = nn.NLLLoss()

Expand Down
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