Using for Regression

[Soodeh-K]

Hi

Thank you for sharing your implementation in Pytorch.
I am using a similar GCN structure for regression analysis. Therefor the last layer would be the same as others. My proposed GCN follows the below structure.
model GCN(
(gc1): GraphConvolution (2 -> 2)
(gc2): GraphConvolution (2 -> 20)
(gc3): GraphConvolution (20 -> 20)
(gc4): GraphConvolution (20 -> 20)
(gc5): GraphConvolution (20 -> 2)
(gc6): GraphConvolution (2 -> 2)
)
The inputs are locations of 2D vertices and adjacency matrix of synthetic data (for simplicity a circular shape graphs).
The activation functions are tanh and the loss function is L2norm (because the problem is regression).
I’ve also initialized the weights and bias parameters as following:
def reset_parameters(self):
stdv = 1. / math.sqrt(10/self.nhid)
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.fill_(0)
I feed the network with some noisy data (as input graphs) and the target is a circle. It is expected to networks can regressed a circular shape but outputs have elliptic shape. I got that this network comes to hight sensitivity respect to weight initialization.
Why this GCN couldn’t work to solve a regression problem? Could you please give me your advise and some feedback about this.