How to get structured learner to return a model with sigmoid as last layer

Is there any parameter that I can pass to md.get_learner to specify the last layer activation ?

You can just copy the MixedInputModel from and costumize like you want. But if it is a multilabel classification the last activation will be sigmoid by default.

Here is the code for the forward() method of the MixedInputModel:

def forward(self, x_cat, x_cont):
        if self.n_emb != 0:
            x = [e(x_cat[:,i]) for i,e in enumerate(self.embs)]
            x =, 1)
            x = self.emb_drop(x)
        if self.n_cont != 0:
            x2 =
            x =[x, x2], 1) if self.n_emb != 0 else x2
        for l,d,b in zip(self.lins, self.drops, self.bns):
            x = F.relu(l(x))
            if self.use_bn: x = b(x)
            x = d(x)
        x = self.outp(x)
        if not self.is_reg:
            if self.is_multi:
                x = F.sigmoid(x)
                x = F.log_softmax(x)
        elif self.y_range:
            x = F.sigmoid(x)
            x = x*(self.y_range[1] - self.y_range[0])
            x = x+self.y_range[0]
return x