Method forward

gat/main.py:184–203

Performs a forward pass through the network. Args: input_tensor (torch.Tensor): Input tensor representing node features. adj_mat (torch.Tensor): Adjacency matrix representing graph structure. Returns: torch.Tensor: Output tensor after th

(self, input_tensor: torch.Tensor , adj_mat: torch.Tensor)

        

    def forward(self, input_tensor: torch.Tensor , adj_mat: torch.Tensor):
        """
        Performs a forward pass through the network.

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

        Returns:
            torch.Tensor: Output tensor after the forward pass.
        """

        # Apply the first Graph Attention layer
        x = self.gat1(input_tensor, adj_mat)
        x = F.elu(x) # Apply ELU activation function to the output of the first layer

        # Apply the second Graph Attention layer
        x = self.gat2(x, adj_mat)

        return F.log_softmax(x, dim=1) # Apply log softmax activation function

################################
### LOADING THE CORA DATASET ###