train.py

import paddle.v2 as paddle
import paddle.v2.dataset.uci_housing as uci_housing


def main():
    # init
    paddle.init(use_gpu=False, trainer_count=1)

    # network config
    x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
    y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
    y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
    cost = paddle.layer.mse_cost(input=y_predict, label=y)

    # create parameters
    parameters = paddle.parameters.create(cost)

    # create optimizer
    optimizer = paddle.optimizer.Momentum(momentum=0)

    trainer = paddle.trainer.SGD(
        cost=cost, parameters=parameters, update_equation=optimizer)

    feeding = {'x': 0, 'y': 1}

    # event_handler to print training and testing info
    def event_handler(event):
        if isinstance(event, paddle.event.EndIteration):
            if event.batch_id % 100 == 0:
                print "Pass %d, Batch %d, Cost %f" % (
                    event.pass_id, event.batch_id, event.cost)

        if isinstance(event, paddle.event.EndPass):
            if event.pass_id % 10 == 0:
                with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
                    parameters.to_tar(f)
            result = trainer.test(
                reader=paddle.batch(uci_housing.test(), batch_size=2),
                feeding=feeding)
            print "Test %d, Cost %f" % (event.pass_id, result.cost)

    # training
    trainer.train(
        reader=paddle.batch(
            paddle.reader.shuffle(uci_housing.train(), buf_size=500),
            batch_size=2),
        feeding=feeding,
        event_handler=event_handler,
        num_passes=30)

    # inference
    test_data_creator = paddle.dataset.uci_housing.test()
    test_data = []
    test_label = []

    for item in test_data_creator():
        test_data.append((item[0], ))
        test_label.append(item[1])
        if len(test_data) == 5:
            break

    # load parameters from tar file.
    # users can remove the comments and change the model name
    # with open('params_pass_20.tar', 'r') as f:
    #     parameters = paddle.parameters.Parameters.from_tar(f)

    probs = paddle.infer(
        output_layer=y_predict, parameters=parameters, input=test_data)

    for i in xrange(len(probs)):
        print "label=" + str(test_label[i][0]) + ", predict=" + str(probs[i][0])


if __name__ == '__main__':
    main()