Multiple output neurons (multiclass classification) support about arm-net HOT 2 CLOSED

Hi GillianGrayson,

In our implementation, supporting multiple outputs for multiclass classification tasks is rather easy.

For models using an MLP layer to produce the final prediction, you can achieve so by specifying the number of outputs via the noutput parameter in the MLP layer. This applies to dnn, afn, gc_arm, dcn, cin, nfm, xdfm, ipnn, kpnn, wd, gat, gcn, dcn+, sa_glu. E.g., for gcn, you should update line 57 with noutput specified for MLP, and then directly return y in line 75 without calling the squeeze function.

Comments on (2):
It is recommended to use the provided MLP layer, which should be much more efficient than using the for loop.

Comments on (3):
Actually, models such as dfm, fm, hofm, afm are originally proposed for binary classification tasks (pls refer to respective papers). Nonetheless, we can also extend these models to support multiclass classification, by simply using our implementation of the FactorizationMachine layer, initializing it with reduce_dim=False, and then feeding the output of this layer to another MLP layer with ninput=nemb, and noutput=nclass to produce the final predictions.

Hope the above can address your concerns :)

from arm-net.

Thank you a lot for a very informative and quick response!

from arm-net.