train.py

import paddle.v2 as paddle


def softmax_regression(img):
    predict = paddle.layer.fc(
        input=img, size=10, act=paddle.activation.Softmax())
    return predict


def multilayer_perceptron(img):
    # The first fully-connected layer
    hidden1 = paddle.layer.fc(input=img, size=128, act=paddle.activation.Relu())
    # The second fully-connected layer and the according activation function
    hidden2 = paddle.layer.fc(
        input=hidden1, size=64, act=paddle.activation.Relu())
    # The thrid fully-connected layer, note that the hidden size should be 10,
    # which is the number of unique digits
    predict = paddle.layer.fc(
        input=hidden2, size=10, act=paddle.activation.Softmax())
    return predict


def convolutional_neural_network(img):
    # first conv layer
    conv_pool_1 = paddle.networks.simple_img_conv_pool(
        input=img,
        filter_size=5,
        num_filters=20,
        num_channel=1,
        pool_size=2,
        pool_stride=2,
        act=paddle.activation.Tanh())
    # second conv layer
    conv_pool_2 = paddle.networks.simple_img_conv_pool(
        input=conv_pool_1,
        filter_size=5,
        num_filters=50,
        num_channel=20,
        pool_size=2,
        pool_stride=2,
        act=paddle.activation.Tanh())
    # The first fully-connected layer
    fc1 = paddle.layer.fc(
        input=conv_pool_2, size=128, act=paddle.activation.Tanh())
    # The softmax layer, note that the hidden size should be 10,
    # which is the number of unique digits
    predict = paddle.layer.fc(
        input=fc1, size=10, act=paddle.activation.Softmax())
    return predict


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))

# Here we can build the prediction network in different ways. Please
# choose one by uncomment corresponding line.
predict = softmax_regression(images)
#predict = multilayer_perceptron(images)
#predict = convolutional_neural_network(images)

cost = paddle.layer.classification_cost(input=predict, label=label)

parameters = paddle.parameters.create(cost)

optimizer = paddle.optimizer.Momentum(
    learning_rate=0.1 / 128.0,
    momentum=0.9,
    regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128))

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

lists = []


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)
    if isinstance(event, paddle.event.EndPass):
        result = trainer.test(reader=paddle.batch(
            paddle.dataset.mnist.test(), batch_size=128))
        print "Test with Pass %d, Cost %f, %s\n" % (event.pass_id, result.cost,
                                                    result.metrics)
        lists.append((event.pass_id, result.cost,
                      result.metrics['classification_error_evaluator']))


trainer.train(
    reader=paddle.batch(
        paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=8192),
        batch_size=128),
    event_handler=event_handler,
    num_passes=100)

# find the best pass
best = sorted(lists, key=lambda list: float(list[1]))[0]
print 'Best pass is %s, testing Avgcost is %s' % (best[0], best[1])
print 'The classification accuracy is %.2f%%' % (100 - float(best[2]) * 100)