8182ea386e6529a1a2294d8e2d33fc040d0cbfb2,deepctr/inputs.py,,get_linear_logit,#Any#Any#Any#Any#Any#Any#Any#,153

Before Change

    _, dense_input_list = input_from_feature_columns(features,feature_columns,1,l2_reg,init_std,seed,prefix=prefix)

    if len(linear_emb_list[0]) > 1:
        linear_term = concat_fun([add(linear_emb) for linear_emb in linear_emb_list])
    elif len(linear_emb_list[0]) == 1:
        linear_term = concat_fun([linear_emb[0] for linear_emb in linear_emb_list])
    else:

After Change

    linear_emb_list = [input_from_feature_columns(features,feature_columns,1,l2_reg,init_std,seed,prefix=prefix+str(i))[0] for i in range(units)]
    _, dense_input_list = input_from_feature_columns(features,feature_columns,1,l2_reg,init_std,seed,prefix=prefix)

    linear_logit_list = []
    for i in range(units):

        if len(linear_emb_list[0])>0 and len(dense_input_list) >0:
            sparse_input = concat_fun(linear_emb_list[i])
            dense_input = concat_fun(dense_input_list)
            linear_logit = Linear(l2_reg,mode=2)([sparse_input,dense_input])
        elif len(linear_emb_list[0])>0:
            sparse_input = concat_fun(linear_emb_list[i])
            linear_logit = Linear(l2_reg,mode=0)(sparse_input)
        elif len(dense_input_list) >0:
            dense_input = concat_fun(dense_input_list)
            linear_logit = Linear(l2_reg,mode=1)(dense_input)
        else:
            raise NotImplementedError
        linear_logit_list.append(linear_logit)

    return concat_fun(linear_logit_list)

def embedding_lookup(sparse_embedding_dict,sparse_input_dict,sparse_feature_columns,return_feat_list=(), mask_feat_list=()):
    embedding_vec_list = []
    for fc in sparse_feature_columns:

Instances