Pytorch Model not initializing

1

I created a model like resnet but for tabular data here is model architecture

class Model(nn.Module):      # <-- Update
    def __init__(self, num_features, num_targets, hidden_size):
        super(Model, self).__init__()
        head1 = nn.Sequential(
        nn.BatchNorm1d(num_features),
        nn.Dropout(0.2),
        nn.utils.weight_norm(nn.Linear(num_features, hidden_size)),
        nn.ReLU()
        )
        
        head2 = nn.Sequential(
        nn.BatchNorm1d(hidden_size),
        nn.Dropout(0.25),
        nn.utils.weight_norm(nn.Linear(hidden_size, hidden_size)),
        nn.ReLU()
        )
        
        head3 = nn.Sequential(
        nn.BatchNorm1d(hidden_size),
        nn.Dropout(0.3),
        nn.utils.weight_norm(nn.Linear(hidden_size, num_targets))
        )
        
    def forward(self, x):
        head1_out = head1(x)
        concat_1 = torch.cat((x, head1_out), dim=1)
        head2_out = head2(concat)
        concat_2 = torch.cat((x, head2_out), dim=1)
        out = head3(concat_2)
        
        return out

When I initialize it it doesn’t give me any error but doesn’t show any output either.
Any help or suggestions would be appreciated.
Thanks.

2

Could you explain what kind of output you are expecting and how you are initializing the model?
If you are using:

model = Model(num_features, num_targets, hidden_size)

then model should be the object of your custom Model class.

3

Yes, this is how I am initializing it. And I am working with a multilabel classification problem

4

@ptrblck Any solution?

5

I don’t know exactly what the issue is.
Could you explain, if you are seeing any errors or what you would expect, which is not working at the moment?

6

I figured it out. I had to write self.head1.

7

Oh sorry, I completely missed this issue and couldn’t figure out what the problem is. :confused:
I’m glad you’ve figured it out.