26421ce20c6b626ceacafbb3282cad1d5dce04ca,onmt/Models.py,Embeddings,forward,#Embeddings#Any#,60

Before Change

                        for j, feature_lut in enumerate(self.feature_luts)]

            // Apply one MLP layer.
            emb = self.activation(
                self.linear(torch.cat([word] + features, -1)))

        if self.positional_encoding:
            emb = emb + Variable(self.pe[:emb.size(0), :1, :emb.size(2)]
                                 .expand_as(emb))
            emb = self.dropout(emb)
        return emb


class Encoder(nn.Module):

After Change

            emb (FloatTensor): len x batch x sum of feature embedding sizes
        
        feat_inputs = (feat.squeeze(2) for feat in src_input.split(1, dim=2))
        features = [lut(feat) for lut, feat in zip(self.emb_luts, feat_inputs)]
        emb = self.merge(features)
        return emb

Instances