be65ce986a45bf2f35b5494db3fa6e993b905aeb,deepctr/models/fnn.py,,FNN,#,17

Before Change

    linear_logit = get_linear_logit(linear_emb_list, dense_input_dict, l2_reg_linear)

    deep_input = tf.keras.layers.Flatten()(concat_fun(deep_emb_list))
    deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn,
                   dnn_dropout, False, seed)(deep_input)
    deep_logit = tf.keras.layers.Dense(
        1, use_bias=False, activation=None)(deep_out)

After Change

    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.
    
    features = build_input_features(linear_feature_columns + dnn_feature_columns)

    inputs_list = list(features.values())

    sparse_embedding_list, dense_value_list = input_from_feature_columns(features,dnn_feature_columns,
                                                                              embedding_size,
                                                                              l2_reg_embedding,init_std,
                                                                              seed)

    linear_logit = get_linear_logit(features, linear_feature_columns, l2_reg=l2_reg_linear, init_std=init_std,
                                    seed=seed, prefix="linear")

    dnn_input = combined_dnn_input(sparse_embedding_list,dense_value_list)
    deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn,
                   dnn_dropout, False, seed)(dnn_input)

Instances