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():
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(args=None):
import argparse
tf.reset_default_graph()
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--max_batch_size', type=int, default=64)
parser.add_argument('--prefix', type=str, default='default',
help='the nickname of this training job')
parser.add_argument('--checkpoint', type=str, default=None)
parser.add_argument('--dataset', type=str, default='MNIST',
choices=['MNIST', 'Fashion', 'SVHN',
'CIFAR10', 'ImageNet', 'TinyImageNet'])
parser.add_argument('--norm_type', type=str, default='batch',
choices=['batch', 'group', 'batch_skew', 'batch_mine'])
# Log
parser.add_argument('--max_training_step', type=int, default=100000)
parser.add_argument('--log_step', type=int, default=10)
parser.add_argument('--test_sample_step', type=int, default=10)
parser.add_argument('--write_summary_step', type=int, default=10)
parser.add_argument('--ckpt_save_step', type=int, default=1000)
# Learning
parser.add_argument('--learning_rate', type=float, default=1e-5)
parser.add_argument('--no_adjust_learning_rate', action='store_true', default=False)
if not args is None:
import shlex
config = parser.parse_args(shlex.split(args))
print(config)
else:
config = parser.parse_args()
print(config)
if config.dataset == 'MNIST':
import datasets.mnist as dataset
elif config.dataset == 'Fashion':
import datasets.fashion_mnist as dataset
elif config.dataset == 'SVHN':
import datasets.svhn as dataset
elif config.dataset == 'CIFAR10':
import datasets.cifar10 as dataset
elif config.dataset == 'TinyImageNet':
import datasets.tiny_imagenet as dataset
elif config.dataset == 'ImageNet':
import datasets.imagenet as dataset
else:
raise ValueError(config.dataset)
dataset_train, dataset_test = dataset.create_default_splits()
image, label = dataset_train.get_data(dataset_train.ids[0])
config.data_info = np.concatenate([np.asarray(image.shape), np.asarray(label.shape)])
trainer = Trainer(config,
dataset_train, dataset_test)
log.warning("dataset: %s, learning_rate: %f", config.dataset, config.learning_rate)
trainer.train()
del trainer
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=64)
parser.add_argument('--model',
type=str,
default='conv',
choices=['mlp', 'conv'])
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('--update_rate', type=int, default=5)
parser.add_argument('--lr_weight_decay',
action='store_true',
default=False)
config = parser.parse_args()
if config.dataset == 'MNIST':
from datasets.mnist import create_default_splits
config.data_info = np.array([28, 28, 10, 1])
config.conv_info = np.array([32, 64, 128])
config.deconv_info = np.array([[100, 2, 1], [25, 3, 2], [6, 4, 2],
[1, 6, 2]])
elif config.dataset == 'SVHN':
from datasets.svhn import create_default_splits
config.data_info = np.array([32, 32, 10, 3])
config.conv_info = np.array([64, 128, 256])
config.deconv_info = np.array([[384, 2, 1], [128, 4, 2], [64, 4, 2],
[3, 6, 2]])
elif config.dataset == 'CIFAR10':
from datasets.cifar10 import create_default_splits
config.data_info = np.array([32, 32, 10, 3])
config.conv_info = np.array([64, 128, 256])
config.deconv_info = np.array([[384, 2, 1], [128, 4, 2], [64, 4, 2],
[3, 6, 2]])
else:
raise ValueError(config.dataset)
dataset_train, dataset_test = 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=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)
