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()