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('--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(): tf.debugging.set_log_device_placement(True) import argparse parser = argparse.ArgumentParser() parser.add_argument('--batch_size', type=int, default=16) parser.add_argument('--prefix', type=str, default='default') parser.add_argument('--checkpoint', type=str, default=None) parser.add_argument('--dataset', type=str, default='MNIST', choices=['MNIST', 'SVHN', 'CIFAR10']) parser.add_argument('--learning_rate', type=float, default=1e-4) parser.add_argument('--alpha', type=float, default=1.0) parser.add_argument('--lr_weight_decay', action='store_true', default=False) parser.add_argument('--dump_result', action='store_true', default=False) parser.add_argument('--few_shot_class', type=int, default=None) parser.add_argument('--few_shot_cap', type=int, default=False) parser.add_argument('--train_sample_cap', type=int, default=None) parser.add_argument('--test_sample_cap', type=int, default=None) parser.add_argument('--weight_multiplier', type=int, default=1) parser.add_argument('--ignore_weighting', action='store_true', default=False) config = parser.parse_args() if config.dataset == 'MNIST': import sys sys.path.insert(1, '/scratch') 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.conv_info = dataset.get_conv_info() config.deconv_info = dataset.get_deconv_info() dataset_train, dataset_test = dataset.create_default_splits(config) m, l = dataset_train.get_data(dataset_train.ids[0]) config.data_info = np.concatenate( [np.asarray(m.shape), np.asarray(l.shape)]) trainer = Trainer(config, dataset_train, dataset_test) log.warning("dataset: %s, learning_rate: %f", config.dataset, config.learning_rate) with tf.device('/GPU:0'): trainer.train(dataset_train)
def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument('--batch_size', type=int, default=16) parser.add_argument('--prefix', type=str, default='default') parser.add_argument('--checkpoint', type=str, default=None) parser.add_argument('--dataset', type=str, default='MNIST', choices=['MNIST', 'SVHN', 'CIFAR10']) parser.add_argument('--learning_rate', type=float, default=1e-4) parser.add_argument('--alpha', type=float, default=1.0) parser.add_argument('--lr_weight_decay', action='store_true', default=False) parser.add_argument('--dump_result', action='store_true', default=False) parser.add_argument( '--distribution', type=str, default='Uniform', choices=['Uniform', 'Gaussian', 'Mixture', 'Gamma', 'Beta']) parser.add_argument('--dimension', type=int, default=100) 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.conv_info = dataset.get_conv_info() config.deconv_info = dataset.get_deconv_info() dataset_train, dataset_test = dataset.create_default_splits( distribution=config.distribution, dimension=config.dimension) m, l = dataset_train.get_data(dataset_train.ids[0]) config.data_info = np.concatenate( [np.asarray(m.shape), np.asarray(l.shape)]) trainer = Trainer(config, dataset_train, dataset_test) log.warning("dataset: %s, learning_rate: %f", config.dataset, config.learning_rate) trainer.train(dataset_train)
def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument('--batch_size', type=int, default=1) parser.add_argument('--prefix', type=str, default='default') parser.add_argument('--checkpoint_path', type=str, default=None) parser.add_argument('--train_dir', type=str) parser.add_argument('--dataset', type=str, default='MNIST', choices=['MNIST', 'SVHN', 'CIFAR10']) parser.add_argument('--reconstruct', action='store_true', default=False) parser.add_argument('--generate', action='store_true', default=False) parser.add_argument('--interpolate', action='store_true', default=False) parser.add_argument('--recontrain', action='store_true', default=False) parser.add_argument('--data_id', nargs='*', default=None) parser.add_argument('--few_shot_class', type=int, default=None) parser.add_argument('--few_shot_cap', type=int, default=False) parser.add_argument('--train_sample_cap', type=int, default=None) parser.add_argument('--test_sample_cap', type=int, default=None) parser.add_argument('--weight_multiplier', type=int, default=1) parser.add_argument('--alpha', type=float, default=None) parser.add_argument('--ignore_weighting', action='store_true', default=False) config = parser.parse_args() if config.dataset == 'MNIST': import sys sys.path.insert(1, '/scratch') 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.conv_info = dataset.get_conv_info() config.deconv_info = dataset.get_deconv_info() dataset_train, dataset_test = dataset.create_default_splits(config) m, l = dataset_train.get_data(dataset_train.ids[0]) config.data_info = np.concatenate([np.asarray(m.shape), np.asarray(l.shape)]) evaler = Evaler(config, dataset_test, dataset_train) log.warning("dataset: %s", config.dataset) with tf.device('/GPU:0'): evaler.eval_run()
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()
def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument('--batch_size', type=int, default=256) parser.add_argument('--prefix', type=str, default='default') parser.add_argument('--checkpoint_path', type=str, default=None) parser.add_argument('--train_dir', type=str) parser.add_argument('--dataset', type=str, default='MNIST', choices=['MNIST', 'SVHN', 'CIFAR10']) parser.add_argument('--reconstruct', action='store_true', default=False) parser.add_argument('--generate', action='store_true', default=False) parser.add_argument('--interpolate', action='store_true', default=False) 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.conv_info = dataset.get_conv_info() config.deconv_info = dataset.get_deconv_info() dataset_train, dataset_test = dataset.create_default_splits() m, l = dataset_train.get_data(dataset_train.ids[0]) config.data_info = np.concatenate( [np.asarray(m.shape), np.asarray(l.shape)]) evaler = Evaler(config, dataset_test, dataset_train) log.warning("dataset: %s", config.dataset) evaler.eval_run()
# def main(): if __name__ == '__main__': 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.conv_info = dataset.get_conv_info() config.deconv_info = dataset.get_deconv_info() dataset_train, dataset_test = dataset.create_default_splits() m, l = dataset_train.get_data( dataset_train.ids[0] ) # m: image size of length 3, l: label size of length 1 config.data_info = np.concatenate( [np.asarray(m.shape), np.asarray(l.shape)]) trainer = Trainer(config, dataset_train, dataset_test) log.warning("dataset: %s, learning_rate: %f", config.dataset, config.learning_rate) trainer.train(dataset_train)