Created by: Bella-Zhao

我用paddle-v2训练好了一个模型，网络配置如下：

    data = paddle.layer.data("word", paddle.data_type.integer_value_sequence(input_dim))
    emb = paddle.layer.embedding(input=data, size=emb_dim, 
    param_attr=paddle.attr.Param(name='embeddings', is_static=True))

    fc1 = paddle.layer.fc(input=emb, size=hid_dim, act=linear, bias_attr=bias_attr)
    lstm1 = paddle.layer.lstmemory(input=fc1, act=relu, bias_attr=bias_attr, layer_attr=layer_attr)
    inputs = [fc1, lstm1]

  
    for i in range(2, stacked_num + 1):
        fc = paddle.layer.fc(
            input=inputs,
            size=hid_dim,
            act=linear,
            param_attr=para_attr,
            bias_attr=bias_attr)

        lstm = paddle.layer.lstmemory(
            input=fc,
            reverse=(i % 2) == 0,
            act=relu,
            bias_attr=bias_attr,
            layer_attr=layer_attr)
       inputs = [fc, lstm]
    fc_last = paddle.layer.pooling( input=inputs[0], pooling_type=paddle.pooling.Max())
    lstm_last = paddle.layer.pooling(input=inputs[1], pooling_type=paddle.pooling.Max())
    output = paddle.layer.fc(
        input=[fc_last, lstm_last],
        size=class_dim,
        act=paddle.activation.Softmax(),
        bias_attr=bias_attr,
        param_attr=para_attr)

现在想将预测过程包装成一个rpc服务，用capi调用上述训练好的模型预测，在参考https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/dense/trainer_config.py 生成配置的过程中发现，配置文件的代码风格还是v1的。

请问：capi是否支持用paddle-v2训练出的模型？如果支持，我的v2配置如何改写成v1的？