- class RECT_L(in_channels: int, hidden_channels: int, normalize: bool = True, dropout: float = 0.0)
The RECT model, i.e. its supervised RECT-L part, from the “Network Embedding with Completely-imbalanced Labels” paper. In particular, a GCN model is trained that reconstructs semantic class knowledge.
For an example of using RECT, see examples/rect.py.
in_channels (int) – Size of each input sample.
hidden_channels (int) – Intermediate size of each sample.
dropout (float, optional) – The dropout probability. (default:
- forward(x: Tensor, edge_index: Union[Tensor, SparseTensor], edge_weight: Optional[Tensor] = None) Tensor
Resets all learnable parameters of the module.