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])
    for param_name in parameters.keys():
        array = parameters.get(param_name)
        array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape)
        parameters.set(parameter_name=param_name, value=array)

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

    def event_handler(event):
        if isinstance(event, paddle.event.EndIteration):
            para = parameters.get('___fc_2__.w0')
            print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % (
                event.pass_id, event.batch_id, event.cost, para.mean())

        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,
        batch_size=32)  # batch size should be refactor in Data reader


if __name__ == '__main__':
    main()