loss and optimization:
criterion = torch.nn.MSELoss(reduction=‘sum’)
optimizer = optim.Adam(model.parameters(), lr=0.0001)
As you can see there are two inputs for data, one set has 375 elements the other 63, and I am checking all possible interactions so 23625 in total.
I am using a 80/20 split, so 18900 interactions for training and 4725 for testing.
This also means that all off the original 375 and 63 elements appear in both training and testing (it’s supposed to be like that).
Currently I am training for 5 epochs, but the model converges after two.
I am not using any type of learning rate scheduling