api_train_v2.py

import numpy
import paddle.v2 as paddle

import mnist_util


def train_reader():
    train_file = './data/raw_data/train'
    generator = mnist_util.read_from_mnist(train_file)
    for item in generator:
        yield item


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

    # define network topology
    images = paddle.layer.data(
        name='pixel', type=paddle.data_type.dense_vector(784))
    label = paddle.layer.data(
        name='label', type=paddle.data_type.integer_value(10))
    hidden1 = paddle.layer.fc(input=images, size=200)
    hidden2 = paddle.layer.fc(input=hidden1, size=200)
    inference = paddle.layer.fc(input=hidden2,
                                size=10,
                                act=paddle.activation.Softmax())
    cost = paddle.layer.classification_cost(input=inference, label=label)

    parameters = paddle.parameters.create(cost)

    adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01)

    def event_handler(event):
        if isinstance(event, paddle.event.EndIteration):
            if event.batch_id % 100 == 0:
                print "Pass %d, Batch %d, Cost %f, %s" % (
                    event.pass_id, event.batch_id, event.cost, event.metrics)
        else:
            pass

    trainer = paddle.trainer.SGD(update_equation=adam_optimizer)

    trainer.train(train_data_reader=train_reader,
                  topology=cost,
                  parameters=parameters,
                  event_handler=event_handler,
                  num_passes=100,
                  batch_size=200, # batch size should be refactor in Data reader
                  data_types={  # data_types will be removed, It should be in
                      # network topology
                      'pixel': images.type,
                      'label': label.type
                  })


if __name__ == '__main__':
    main()