def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--model', type=str, default='conv', choices=['mlp', 'conv'])
parser.add_argument('--checkpoint_path', type=str)
parser.add_argument('--train_dir', type=str)
parser.add_argument('--dataset', type=str, default='CIFAR10', choices=['MNIST', 'SVHN', 'CIFAR10'])
parser.add_argument('--data_id', nargs='*', default=None)
config = parser.parse_args()
if config.dataset == 'MNIST':
import datasets.mnist as dataset
elif config.dataset == 'SVHN':
import datasets.svhn as dataset
elif config.dataset == 'CIFAR10':
import datasets.cifar10 as dataset
else:
raise ValueError(config.dataset)
config.data_info = dataset.get_data_info()
config.conv_info = dataset.get_conv_info()
config.deconv_info = dataset.get_deconv_info()
dataset_train, dataset_test = dataset.create_default_splits()
evaler = Evaler(config, dataset_test)
log.warning("dataset: %s", config.dataset)
evaler.eval_run()
def main():
import argparse
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--prefix', type=str, default='default')
parser.add_argument('--checkpoint', type=str, default=None)
parser.add_argument('--dataset', type=str, default='CIFAR10', choices=['MNIST', 'SVHN', 'CIFAR10', 'CRITERIA'])
parser.add_argument('--learning_rate', type=float, default=1e-4)
parser.add_argument('--update_rate', type=int, default=5)
parser.add_argument('--lr_weight_decay', action='store_true', default=False)
parser.add_argument('--dump_result', action='store_true', default=False)
config = parser.parse_args()
if config.dataset == 'MNIST':
import datasets.mnist as dataset
elif config.dataset == 'SVHN':
import datasets.svhn as dataset
elif config.dataset == 'CIFAR10':
import datasets.cifar10 as dataset
elif config.dataset == 'CRITERIA':
import datasets.criteria as dataset
else:
raise ValueError(config.dataset)
config.data_info = dataset.get_data_info()
config.conv_info = dataset.get_conv_info()
config.deconv_info = dataset.get_deconv_info()
dataset_train, dataset_test = dataset.create_default_splits()
trainer = Trainer(config,
dataset_train, dataset_test)
log.warning("dataset: %s, learning_rate: %f", config.dataset, config.learning_rate)
trainer.train()
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--prefix', type=str, default='default')
parser.add_argument('--checkpoint', type=str, default=None)
parser.add_argument('--dataset', type=str, default='CIFAR10', choices=['MNIST', 'SVHN', 'CIFAR10'])
parser.add_argument('--learning_rate', type=float, default=1e-4)
parser.add_argument('--lr_weight_decay', action='store_true', default=False)
parser.add_argument('--activation', type=str, default='selu', choices=['relu', 'lrelu', 'selu'])
config = parser.parse_args()
if config.dataset == 'MNIST':
import datasets.mnist as dataset
elif config.dataset == 'SVHN':
import datasets.svhn as dataset
elif config.dataset == 'CIFAR10':
import datasets.cifar10 as dataset
else:
raise ValueError(config.dataset)
config.data_info = dataset.get_data_info()
config.conv_info = dataset.get_conv_info()
config.visualize_shape = dataset.get_vis_info()
dataset_train, dataset_test = dataset.create_default_splits()
trainer = Trainer(config,
dataset_train, dataset_test)
log.warning("dataset: %s, learning_rate: %f", config.dataset, config.learning_rate)
trainer.train()
def main():
# 配置初始参数
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=16)
# 前缀（代号）自定义
parser.add_argument('--prefix', type=str, default='default')
parser.add_argument('--checkpoint', type=str, default=None)
parser.add_argument('--dataset',
type=str,
default='CIFAR10',
choices=['MNIST', 'SVHN', 'CIFAR10'])
parser.add_argument('--learning_rate', type=float, default=1e-4)
parser.add_argument('--update_rate', type=int, default=5)
parser.add_argument('--lr_weight_decay',
action='store_true',
default=False)
parser.add_argument('--dump_result', action='store_true', default=True)
config = parser.parse_args()
# 选择数据集
if config.dataset == 'MNIST':
import datasets.mnist as dataset
elif config.dataset == 'SVHN':
import datasets.svhn as dataset
elif config.dataset == 'CIFAR10':
import datasets.cifar10 as dataset
else:
raise ValueError(config.dataset)
# 根据不同数据集得到不同的数据所需的配置信息
config.data_info = dataset.get_data_info()
config.conv_info = dataset.get_conv_info()
config.deconv_info = dataset.get_deconv_info()
dataset_train, dataset_test = dataset.create_default_splits()
# 将配置信息和数据集初始化Trainer
trainer = Trainer(config, dataset_train, dataset_test)
log.warning("dataset: %s, learning_rate: %f", config.dataset,
config.learning_rate)
# 训练
trainer.train()
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--prefix', type=str, default='default')
parser.add_argument('--checkpoint', type=str, default=None)
parser.add_argument('--dataset',
type=str,
default='CIFAR10',
choices=['MNIST', 'SVHN', 'CIFAR10'])
parser.add_argument('--learning_rate', type=float, default=1e-4)
parser.add_argument('--update_rate', type=int, default=5)
parser.add_argument('--lr_weight_decay',
action='store_true',
default=False)
parser.add_argument('--dump_result', action='store_true', default=False)
config = parser.parse_args()
if config.dataset == 'MNIST':
import datasets.mnist as dataset
elif config.dataset == 'SVHN':
import datasets.svhn as dataset
elif config.dataset == 'CIFAR10':
import datasets.cifar10 as dataset
else:
raise ValueError(config.dataset)
config.data_info = dataset.get_data_info() # 修改训练数据的大小、类别、通道
config.conv_info = dataset.get_conv_info() # 卷积参数
config.deconv_info = dataset.get_deconv_info() # 反卷积参数
dataset_train, dataset_test = dataset.create_default_splits(
170) # 参数 训练数据的个数
trainer = Trainer(config, dataset_train, dataset_test)
log.warning("dataset: %s, learning_rate: %f", config.dataset,
config.learning_rate)
trainer.train()
