short-mnist.py

import chainer
from chainer import functions as F
from chainer import links as L
from chainer import training
from chainer.training import extensions

BATCH_SIZE = 50
EPOCH = 10

class MLP(chainer.Chain):
    def __init__(self):
        super(MLP, self).__init__(
            l1 = L.Linear(784, 1000),
            l2 = L.Linear(1000, 1000),
            l3 = L.Linear(1000, 10)
        )

    def __call__(self, x):
        h = F.dropout(F.relu(self.l1(x)), ratio=0.3)
        h = F.dropout(F.relu(self.l2(h)), ratio=0.3)
        return self.l3(h)

model = L.Classifier(MLP())
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)

train, test = chainer.datasets.get_mnist()
train_iter = chainer.iterators.SerialIterator(train, batch_size=BATCH_SIZE)
test_iter = chainer.iterators.SerialIterator(test, batch_size=BATCH_SIZE, repeat=False, shuffle=False)

updater = training.StandardUpdater(train_iter, optimizer)
trainer = training.Trainer(updater, (EPOCH, 'epoch'), out='result')
trainer.extend(extensions.Evaluator(test_iter, model))
trainer.extend(extensions.LogReport())
trainer.extend(extensions.PrintReport(['epoch', 'main/loss', 'main/accuracy', 'validation/main/loss', 'validation/main/accuracy']))
trainer.extend(extensions.ProgressBar())

trainer.run()