7ab8bc6a70982aa79c36c20678c98becb7f291b1,deepctr/models/dien.py,,DIEN,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,145

Before Change

    features = build_input_features(dnn_feature_columns)

    user_behavior_length = Input(shape=(1,), name="seq_length")

    sparse_feature_columns = list(
        filter(lambda x: isinstance(x, SparseFeat), dnn_feature_columns)) if dnn_feature_columns else []
    dense_feature_columns = list(
        filter(lambda x: isinstance(x, DenseFeat), dnn_feature_columns)) if dnn_feature_columns else []
    varlen_sparse_feature_columns = list(
        filter(lambda x: isinstance(x, VarLenSparseFeat), dnn_feature_columns)) if dnn_feature_columns else []

    history_feature_columns = []
    neg_history_feature_columns = []
    sparse_varlen_feature_columns = []
    history_fc_names = list(map(lambda x: "hist_" + x, history_feature_list))
    neg_history_fc_names = list(map(lambda x: "neg_" + x, history_fc_names))
    for fc in varlen_sparse_feature_columns:
        feature_name = fc.name
        if feature_name in history_fc_names:
            history_feature_columns.append(fc)
        elif feature_name in neg_history_fc_names:
            neg_history_feature_columns.append(fc)
        else:
            sparse_varlen_feature_columns.append(fc)

    inputs_list = list(features.values())

    embedding_dict = create_embedding_matrix(dnn_feature_columns, l2_reg_embedding, seed, prefix="",
                                             seq_mask_zero=False)

    query_emb_list = embedding_lookup(embedding_dict, features, sparse_feature_columns,
                                      return_feat_list=history_feature_list, to_list=True)

    keys_emb_list = embedding_lookup(embedding_dict, features, history_feature_columns,
                                     return_feat_list=history_fc_names, to_list=True)
    dnn_input_emb_list = embedding_lookup(embedding_dict, features, sparse_feature_columns,
                                          mask_feat_list=history_feature_list, to_list=True)
    dense_value_list = get_dense_input(features, dense_feature_columns)

    sequence_embed_dict = varlen_embedding_lookup(embedding_dict, features, sparse_varlen_feature_columns)
    sequence_embed_list = get_varlen_pooling_list(sequence_embed_dict, features, sparse_varlen_feature_columns,
                                                  to_list=True)
    dnn_input_emb_list += sequence_embed_list

    keys_emb = concat_func(keys_emb_list)
    deep_input_emb = concat_func(dnn_input_emb_list)
    query_emb = concat_func(query_emb_list)

    if use_negsampling:

        neg_uiseq_embed_list = embedding_lookup(embedding_dict, features, neg_history_feature_columns,
                                                neg_history_fc_names, to_list=True)

        neg_concat_behavior = concat_func(neg_uiseq_embed_list)

    else:
        neg_concat_behavior = None
    hist, aux_loss_1 = interest_evolution(keys_emb, query_emb, user_behavior_length, gru_type=gru_type,
                                          use_neg=use_negsampling, neg_concat_behavior=neg_concat_behavior,
                                          att_hidden_size=att_hidden_units,
                                          att_activation=att_activation,
                                          att_weight_normalization=att_weight_normalization, )

    deep_input_emb = Concatenate()([deep_input_emb, hist])

    deep_input_emb = tf.keras.layers.Flatten()(deep_input_emb)

    dnn_input = combined_dnn_input([deep_input_emb], dense_value_list)
    output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn,
                 dnn_dropout, use_bn, seed)(dnn_input)
    final_logit = Dense(1, use_bias=False)(output)
    output = PredictionLayer(task)(final_logit)

    // model_input_list = get_inputs_list(
    //    [sparse_input, dense_input, user_behavior_input])
    model_input_list = inputs_list

    // if use_negsampling:
    //    model_input_list += list(neg_user_behavior_input.values())

    model_input_list += [user_behavior_length]

    model = tf.keras.models.Model(inputs=model_input_list, outputs=output)

    if use_negsampling:

After Change

    
    features = build_input_features(dnn_feature_columns)

    user_behavior_length = features["seq_length"]

    sparse_feature_columns = list(
        filter(lambda x: isinstance(x, SparseFeat), dnn_feature_columns)) if dnn_feature_columns else []
    dense_feature_columns = list(

Instances