I’m trying to make model which will classify text into about 500 different classes. I think that I have to customize architecture of the Pooling Classifier which looks now like this:
(1): PoolingLinearClassifier( (layers): Sequential( (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): Dropout(p=0.2, inplace=False) (2): Linear(in_features=1200, out_features=50, bias=True) (3): ReLU(inplace=True) (4): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): Dropout(p=0.1, inplace=False) (6): Linear(in_features=50, out_features=498, bias=True) )
I think that I have to change in (2): Linear layer to have more out_features because in the last (6) Linear layer I predict more out_features than I’ve got in_features. What do you think?