def main(gpu_id=-1, bs=32, epoch=20, out='./not_layer_result', resume=''):
net = ShallowConv()
model = L.Classifier(net)
if gpu_id >= 0:
chainer.cuda.get_device_from_id(gpu_id)
model.to_gpu()
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
train, test = chainer.datasets.get_mnist(ndim=3)
train_iter = chainer.iterators.SerialIterator(train, bs)
test_iter = chainer.iterators.SerialIterator(test, bs, repeat=False,
shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer, device=gpu_id)
trainer = training.Trainer(updater, (epoch, 'epoch'), out=out)
trainer.extend(extensions.ParameterStatistics(model.predictor))
trainer.extend(extensions.Evaluator(test_iter, model, device=gpu_id))
trainer.extend(extensions.LogReport())
trainer.extend(extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))
trainer.extend(extensions.ProgressBar())
if resume:
chainer.serializers.load_npz(resume, trainer)
trainer.run()
not_layer_instance_norm_sample.py 文件源码
python
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