chainer.trainingのextensions全部試す

extensions_test.py

import numpy as np
import chainer
from chainer import Function, gradient_check, report, training, utils, Variable
from chainer import datasets, iterators, optimizers, serializers
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
from chainer.training import extensions

class MyModel(Chain):
    def __init__(self):
        super(MyModel,self).__init__(
                l1 = L.Linear(None,100),
                l2 = L.Linear(None,100),
                l3 = L.Linear(None,10))

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

def train():
    model = L.Classifier(MyModel())
    dev = 0
    if dev >= 0:
        chainer.cuda.get_device(dev).use()
        model.to_gpu()

    optimizer = chainer.optimizers.Adam()
    optimizer.setup(model)

    train, test = chainer.datasets.get_mnist()
    train_iter = chainer.iterators.SerialIterator(train, 200)
    test_iter = chainer.iterators.SerialIterator(test, 200,repeat=False, shuffle=False)

    updater = training.StandardUpdater(train_iter, optimizer, device=dev)
    trainer = training.Trainer(updater, (100, 'epoch'), out="result")

    # load trainer snapshot
    #serializers.load_npz('./result/snapshot_iter_40800', trainer)

    # dump_graph
    #trainer.extend(extensions.dump_graph("main/loss"))

    # Evaluator
    trainer.extend(extensions.Evaluator(test_iter, model, device=dev))

    # ExponentialShift
    #trainer.extend(extensions.ExponentialShift("alpha", 1.000001))
    #trainer.extend(extensions.ExponentialShift("alpha", 1.0001))

    # LinearShift
    #trainer.extend(extensions.LinearShift("alpha", (0.001,0.0001), (20000,30000)))
    #trainer.extend(extensions.LinearShift("alpha", (0.01,0.001), (20000,30000)))

    # LogReport
    trainer.extend(extensions.LogReport())

    # snapshot
    #trainer.extend(extensions.snapshot())

    # snapshot_object
    # trainer.extend(extensions.snapshot_object(optimizer, 'optimizer_snapshot_{.updater.epoch}', trigger=(10,'epoch')))

    # PrintReport
    trainer.extend(extensions.PrintReport( entries=['epoch', 'main/loss', 'main/accuracy', 'elapsed_time' ]))

    # ProgressBar
    trainer.extend(extensions.ProgressBar())
    print("run")
    trainer.run()

if __name__ == "__main__":
    train()