Python create_dataset示例

示例#1

config = create_from_json(arguments.config_json_path)
arguments.output.mkdir(exist_ok=True)
config.save_as_json((arguments.output / 'config.json').absolute())

# model
if config.train.gpu >= 0:
    cuda.get_device_from_id(config.train.gpu).use()
predictor, discriminator = create(config.model)
models = {
    'predictor': predictor,
    'discriminator': discriminator,
}

# dataset
dataset = create_dataset(config.dataset)
train_iter = MultiprocessIterator(dataset['train'], config.train.batchsize)
test_iter = MultiprocessIterator(dataset['test'],
                                 config.train.batchsize,
                                 repeat=False,
                                 shuffle=False)
train_eval_iter = MultiprocessIterator(dataset['train_eval'],
                                       config.train.batchsize,
                                       repeat=False,
                                       shuffle=False)


# optimizer
def create_optimizer(model):
    optimizer = optimizers.Adam(alpha=0.0002, beta1=0.5, beta2=0.999)
    optimizer.setup(model)

示例#2

文件： train.py 项目： yusuke-kurita/become-yukarin

config = create_from_json(arguments.config_json_path)
arguments.output.mkdir(exist_ok=True)
config.save_as_json((arguments.output / 'config.json').absolute())

# model
if config.train.gpu >= 0:
    cuda.get_device_from_id(config.train.gpu).use()
predictor, discriminator = create(config.model)
models = {
    'predictor': predictor,
    'discriminator': discriminator,
}

# dataset
dataset = create_dataset(config.dataset)
train_iter = MultiprocessIterator(dataset['train'], config.train.batchsize)
test_iter = MultiprocessIterator(dataset['test'], config.train.batchsize, repeat=False, shuffle=False)
train_eval_iter = MultiprocessIterator(dataset['train_eval'], config.train.batchsize, repeat=False, shuffle=False)


# optimizer
def create_optimizer(model):
    optimizer = optimizers.Adam(alpha=0.0002, beta1=0.5, beta2=0.999)
    optimizer.setup(model)
    return optimizer


opts = {key: create_optimizer(model) for key, model in models.items()}

# updater