def save_optimizer(self, optimizer, path=''):
"""Save optimizer model
Example:
::
path = './test.optimizer'
self.save_optimizer(optimizer, path)
Args:
optimizer (chainer.optimizers): optimizer
path (str): path
Returns:
bool: True if saving successful
"""
# if path is ''
if path == '':
path = str(self.save_optimizer_epoch) + '.optimizer'
# increment self.nz_save_optimizer_epoch
self.nz_save_optimizer_epoch += 1
serializers.save_npz(path, optimizer)
return True
python类optimizers()的实例源码
def __init__(self, gpu=-1, optimizer=None, model=None, content_weight=1, texture_weight=1, average_pooling=False):
self.content_weight = content_weight
self.texture_weight = texture_weight
self.average_pooling = average_pooling
if optimizer is None:
self.optimizer = chainer.optimizers.Adam(alpha=4.0)
else:
self.optimizer = optimizer
if model is None:
self.model = neural_art.utility.load_nn("vgg")
else:
self.model = model
if gpu >= 0:
chainer.cuda.get_device(gpu).use()
self.xp = chainer.cuda.cupy
self.model.model.to_gpu()
else:
self.xp = numpy
def set_trainer(self, out_dir, gpu, n_epoch, g_clip, opt_name, lr=None):
if opt_name == "Adam":
opt = getattr(optimizers, opt_name)()
else:
opt = getattr(optimizers, opt_name)(lr)
opt.setup(self.model)
opt.add_hook(optimizer.GradientClipping(g_clip))
updater = training.StandardUpdater(self.train_iter, opt, device=gpu)
self.trainer = training.Trainer(updater, (n_epoch, 'epoch'), out=out_dir)
self.trainer.extend(extensions.Evaluator(self.test_iter, self.model, device=gpu))
self.trainer.extend(extensions.dump_graph('main/loss'))
self.trainer.extend(extensions.snapshot(), trigger=(n_epoch, 'epoch'))
self.trainer.extend(extensions.LogReport())
self.trainer.extend(extensions.PlotReport(['main/loss', 'validation/main/loss'],
'epoch', file_name='loss.png'))
self.trainer.extend(extensions.PlotReport(['main/accuracy', 'validation/main/accuracy'],
'epoch', file_name='accuracy.png'))
self.trainer.extend(extensions.PrintReport(['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy',
'elapsed_time']))
self.trainer.extend(extensions.ProgressBar())
def _build_optimizer(self, config):
kwargs = {k: float(v) for k, v in config.items() if k != 'name'}
o = getattr(chainer.optimizers, config['name'])(**kwargs)
return o
def init_optimizer(args, mdl):
logger = logging.getLogger(__name__)
logger.info('Making Adam optimizer:')
logger.info('* learning rate = %f', args.learning_rate)
logger.info('* gradient clipping = %f', args.gradient_clipping)
logger.info('* weight decay = %f', args.weight_decay)
opt = chainer.optimizers.Adam(alpha=args.learning_rate)
opt.setup(mdl)
opt.add_hook(chainer.optimizer.GradientClipping(args.gradient_clipping))
opt.add_hook(chainer.optimizer.WeightDecay(args.weight_decay))
return opt
def get_optimizer(model, method, optimizer_args, weight_decay=None):
optimizer = getattr(optimizers, method)(**optimizer_args)
optimizer.setup(model)
if weight_decay is not None:
optimizer.add_hook(chainer.optimizer.WeightDecay(weight_decay))
return optimizer
def get_optimizer_from_config(model, config):
opt_config = Optimizer(**config['optimizer'])
optimizer = getattr(optimizers, opt_config.method)(**opt_config.args)
optimizer.setup(model)
if opt_config.weight_decay is not None:
optimizer.add_hook(
chainer.optimizer.WeightDecay(opt_config.weight_decay))
return optimizer
def get_optimizer(model, method, optimizer_args, weight_decay=None):
optimizer = getattr(optimizers, method)(**optimizer_args)
optimizer.setup(model)
if weight_decay is not None:
optimizer.add_hook(chainer.optimizer.WeightDecay(weight_decay))
return optimizer
