PyTorch implementation of ARM-Net: Adaptive Relation Modeling Network for Structured Data.

Hi!

How to correctly support multiple output neurons in your models (for example for multiclass classification task)?

1. armnet and armnet_1h support multiple output neurons via noutput parameter - there is no problem.

2. For some models, I added support for multiple output neurons by explicitly specifying the noutput parameter in the last MLP-layer, or by adding support for multiple output neurons for the Linear as follows:

class Linear(nn.Module):

    def __init__(self, nfeat, noutput=1):
        super().__init__()
        self.noutput = noutput
        self.weight = nn.Embedding(nfeat, noutput)
        self.bias = nn.Parameter(torch.zeros((1,)))

    def forward(self, x):
        """
        :param x:   {'id': LongTensor B*F, 'value': FloatTensor B*F}
        :return:    linear transform of x
        """
        wights = self.weight(x['id'])
        linear = []
        for i in range(self.noutput):
            a_i = wights[:, :, i]
            a_i_mul_b_i = torch.mul(a_i, x['value'])
            linear.append(a_i_mul_b_i)
        linear = torch.stack(linear, dim=2)

        val = torch.sum(linear, dim=1) + self.bias

        return val

The following models can be adjusted in this way: lr, dnn, afn, gc_arm, dcn, cin, nfm, xdfm, ipnn, kpnn, wd, gat, gcn, dcn+, sa_glu.

3. It is not quite clear how to add support for multiple output neurons for the following models: dfm, fm, hofm, afm because of it is unclear how to modify FactorizationMachine for multiple output.

Could you please comment on the correctness of changing the Linear layer from (2), and how to add support for multiple output neurons for models from (3)?