I am currently about to take part 2 of the course soon, so the scale up in complexity is something I’m attempting to learn rather quickly as best I can. So when I create my model, this is what I get:

TabularModel(

(embeds): ModuleList()

(emb_drop): Dropout(p=0.0)

(bn_cont): BatchNorm1d(55, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(layers): Sequential(

(0): Linear(in_features=55, out_features=221, bias=True)

(1): ReLU(inplace)

(2): BatchNorm1d(221, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(3): Linear(in_features=221, out_features=1500, bias=True)

(4): ReLU(inplace)

(5): BatchNorm1d(1500, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(6): Linear(in_features=1500, out_features=1500, bias=True)

(7): ReLU(inplace)

(8): BatchNorm1d(1500, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(9): Linear(in_features=1500, out_features=1500, bias=True)

(10): ReLU(inplace)

(11): BatchNorm1d(1500, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(12): Linear(in_features=1500, out_features=221, bias=True)

(13): ReLU(inplace)

(14): BatchNorm1d(221, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(15): Linear(in_features=221, out_features=55, bias=True)

)

)

To implement BatchSwapNoise into said generated model, would I need to do a model.layers.add_module()? Also what is the p in the parameters?

Thanks!