0b2fd0f6ac45feda4eb2ccd92cc7b15b279a448a,onmt/Models.py,Embeddings,__init__,#Embeddings#Any#Any#Any#,14

Before Change

                // When concatenating, derive the size of each feature"s
                // embedding from the number of values the embedding
                // takes
                feat_sizes = (int(feat_dict.size() ** feat_exp)
                              for feat_dict in feature_dicts)
            else:
                // sum feature merge (for now, the same as the old option
                // for merging features in OpenNMT-py)
                feat_sizes = (opt.feat_vec_size for feat_dict in feature_dicts)

After Change

                emb_sizes.extend([opt.word_vec_size] * len(feature_dicts))
            else:
                // mlp feature merge
                emb_sizes.extend([opt.feat_vec_size] * len(feature_dicts))
                // apply a layer of mlp to get it down to the correct dim
                self.mlp = nn.Sequential(onmt.modules.BottleLinear(
                                        sum(emb_sizes),
                                        opt.word_vec_size),

Instances