Paddle的Python预测接口

Paddle目前使用Swig对其常用的预测接口进行了封装，使在Python环境下的预测接口更加简单。 在Python环境下预测结果，主要分为以下几个步骤。

  • 读入解析训练配置
  • 构造GradientMachine
  • 准备数据
  • 预测

典型的预测代码如下，使用mnist手写识别作为样例。

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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from py_paddle import swig_paddle, DataProviderWrapperConverter
from paddle.trainer.PyDataProviderWrapper import DenseSlot
from paddle.trainer.config_parser import parse_config

TEST_DATA = [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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def main():
    conf = parse_config("./mnist_model/trainer_config.conf.norm", "")
    print conf.data_config.load_data_args
    network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
    assert isinstance(network, swig_paddle.GradientMachine)  # For code hint.
    network.loadParameters("./mnist_model/")
    converter = DataProviderWrapperConverter(False, [DenseSlot(784)])
    inArg = converter(TEST_DATA)
    print network.forwardTest(inArg)


if __name__ == '__main__':
    swig_paddle.initPaddle("--use_gpu=0")
    main()

主要的软件包为py_paddle.swig_paddle，这个软件包文档相对完善。可以使用python的 help() 函数查询文档。主要步骤为:

  • 在程序开始阶段，使用命令行参数初始化paddle

  • 在98行载入paddle的训练文件。读取config

  • 在100行创建神经网络，并在83行载入参数。

  • 103行创建一个从工具类，用来转换数据。
    • swig_paddle接受的原始数据是C++的Matrix，也就是直接写内存的float数组。
    • 这个接口并不用户友好。所以，我们提供了一个工具类DataProviderWrapperConverter.
    • 这个工具类接收和PyDataProviderWrapper一样的输入数据，请参考PyDataProviderWrapper的文档。

  • 在第105行执行预测。forwardTest是一个工具类，直接提取出神经网络Output层的输出结果。典型的输出结果为:

[{'id': None, 'value': array([[  5.53018653e-09,   1.12194102e-05,   1.96644767e-09,
      1.43630644e-02,   1.51111044e-13,   9.85625684e-01,
      2.08823112e-10,   2.32777140e-08,   2.00186201e-09,
      1.15501715e-08],
   [  9.99982715e-01,   1.27787406e-10,   1.72296313e-05,
      1.49316648e-09,   1.36540484e-11,   6.93137714e-10,
      2.70634608e-08,   3.48565123e-08,   5.25639710e-09,
      4.48684503e-08]], dtype=float32)}]

其中，value即为softmax层的输出。由于数据是两个，所以输出的value。