Created by: linrongyi

用python wrapper执行预测的时候, 编译模型的conf出错.

在执行swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)出出错, 提示

I1104 19:07:32.537729 17412 Util.cpp:126] Call runInitFunctions done.
[libprotobuf ERROR google/protobuf/message_lite.cc:123] Can't parse message of type "paddle.ModelConfig" because it is missing required fields: parameters[14].learning_rate, parameters[14].m
omentum, parameters[16].learning_rate, parameters[16].momentum, parameters[17].learning_rate, parameters[17].momentum, parameters[18].learning_rate, parameters[18].momentum, parameters[19].l
earning_rate, parameters[19].momentum, parameters[20].learning_rate, parameters[20].momentum, parameters[21].learning_rate, parameters[21].momentum

我的Layer是

Layer(#
    name = "query_vec",
    type = "fc",
    active_type='',
    size = TERM_EMB_DIM,
    bias = False,
    inputs = Input("query",
    initial_std = 1.0 / math.sqrt(TERM_TOK_NUM) / 3.0,
    parameter_name = "_term_emb",
    sparse_update = True,
    sparse_remote_update = True)
)

如果加上 momentum, learning_rate 就好了, 也就是

Layer(#
    name = "query_vec",
    type = "fc",
    active_type='',
    size = TERM_EMB_DIM,
    bias = False,
    inputs = Input("query",
    initial_std = 1.0 / math.sqrt(TERM_TOK_NUM) / 3.0,
    parameter_name = "_term_emb",
    sparse_update = True,
    momentum = 0.0, learning_rate = 0.,
    sparse_remote_update = True)
)