Created by: zpyzlsd

import paddle.v2 as paddle
def softmax_regression(img):
    predict = paddle.layer.fc(input=img,
                              size=10,
                              act=paddle.activation.Softmax())
    return predict
paddle.init(use_gpu=False, trainer_count=1)

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))

# predict = softmax_regression(images) # Softmax回归
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)
from paddle.v2.plot import Ploter

train_title = "Train cost"
test_title = "Test cost"
cost_ploter = Ploter(train_title, test_title)

step = 0

# event_handler to plot a figure
def event_handler_plot(event):
    global step
    if isinstance(event, paddle.event.EndIteration):
        if step % 100 == 0:
            cost_ploter.append(train_title, step, event.cost)
            cost_ploter.plot()
        step += 1
    if isinstance(event, paddle.event.EndPass):
        # save parameters
        with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
            trainer.save_parameter_to_tar(f)

        result = trainer.test(reader=paddle.batch(
            paddle.dataset.mnist.test(), batch_size=128))
        cost_ploter.append(test_title, step, result.cost)
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):
        # save parameters
        with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
            trainer.save_parameter_to_tar(f)

        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_plot,
    num_passes=5)