def main(_):
path_to_train_tfrecords_file = os.path.join(FLAGS.data_dir,
'train.tfrecords')
path_to_val_tfrecords_file = os.path.join(FLAGS.data_dir, 'val.tfrecords')
path_to_tfrecords_meta_file = os.path.join(FLAGS.data_dir, 'meta.json')
path_to_train_log_dir = FLAGS.train_logdir
path_to_train_log_dir = os.path.join(
FLAGS.train_logdir,
"ssim_{:.2f}-defend_{}-attacker_{}".format(FLAGS.ssim_weight,
FLAGS.defend_layer,
FLAGS.attacker_type))
print("log path: {}".format(path_to_train_log_dir))
path_to_restore_model_checkpoint_file = FLAGS.restore_checkpoint
training_options = {
'batch_size': FLAGS.batch_size,
'learning_rate': FLAGS.learning_rate,
'patience': FLAGS.patience,
'decay_steps': FLAGS.decay_steps,
'decay_rate': FLAGS.decay_rate
}
meta = Meta()
meta.load(path_to_tfrecords_meta_file)
_train(path_to_train_tfrecords_file, meta.num_train_examples,
path_to_val_tfrecords_file, meta.num_val_examples,
path_to_train_log_dir, path_to_restore_model_checkpoint_file,
training_options)
def main(_):
path_to_test_tfrecords_file = os.path.join(FLAGS.data_dir, 'generated.tfrecords')
path_to_tfrecords_meta_file = os.path.join(FLAGS.data_dir, 'meta.json')
path_to_checkpoint_dir = FLAGS.checkpoint_dir
save_file = 'result_generated.txt'
path_to_test_eval_log_dir = os.path.join(FLAGS.eval_logdir, 'test')
meta = Meta()
meta.load(path_to_tfrecords_meta_file)
_eval(path_to_checkpoint_dir, path_to_test_tfrecords_file, meta.num_test_examples, path_to_test_eval_log_dir, save_file)
def main(_):
path_to_train_tfrecords_file = os.path.join(FLAGS.data_dir,
'train.tfrecords')
path_to_val_tfrecords_file = os.path.join(FLAGS.data_dir, 'val.tfrecords')
path_to_tfrecords_meta_file = os.path.join(FLAGS.data_dir,
'tfrecords_meta.json')
path_to_train_log_dir = FLAGS.train_logdir
path_to_restore_checkpoint_file = FLAGS.restore_checkpoint
meta = Meta()
meta.load(path_to_tfrecords_meta_file)
_train(path_to_train_tfrecords_file, path_to_val_tfrecords_file,
meta.num_val_examples, path_to_train_log_dir,
path_to_restore_checkpoint_file)
def main(_):
path_to_train_tfrecords_file = os.path.join(FLAGS.data_dir, 'train.tfrecords')
path_to_val_tfrecords_file = os.path.join(FLAGS.data_dir, 'val.tfrecords')
path_to_test_tfrecords_file = os.path.join(FLAGS.data_dir, 'test.tfrecords')
path_to_tfrecords_meta_file = os.path.join(FLAGS.data_dir, 'meta.json')
path_to_checkpoint_dir = FLAGS.checkpoint_dir
path_to_train_eval_log_dir = os.path.join(FLAGS.eval_logdir, 'train')
path_to_val_eval_log_dir = os.path.join(FLAGS.eval_logdir, 'val')
path_to_test_eval_log_dir = os.path.join(FLAGS.eval_logdir, 'test')
meta = Meta()
meta.load(path_to_tfrecords_meta_file)
_eval(path_to_checkpoint_dir, path_to_train_tfrecords_file, meta.num_train_examples, path_to_train_eval_log_dir)
_eval(path_to_checkpoint_dir, path_to_val_tfrecords_file, meta.num_val_examples, path_to_val_eval_log_dir)
_eval(path_to_checkpoint_dir, path_to_test_tfrecords_file, meta.num_test_examples, path_to_test_eval_log_dir)
def main_eval(_):
parser = argparse.ArgumentParser(
description="Evaluation Routine for SVHNClassifier")
parser.add_argument("--data_dir",
required=True,
help="Directory to read TFRecords files")
parser.add_argument("--path_to_checkpoint_dir",
required=True,
help="Directory to read checkpoint files")
parser.add_argument("--eval_logdir",
required=True,
help="Directory to write evaluation logs")
parser.add_argument("--path_to_train_tfrecords_file",
required=True,
help="Tfrecords file in train directory")
parser.add_argument("--path_to_val_tfrecords_file",
required=True,
help="Tfrecords file in val directory")
parser.add_argument("--path_to_test_tfrecords_file",
required=True,
help="Tfrecords file in test directory")
parser.add_argument("--path_to_tfrecords_meta_file",
required=True,
help="Meta file in directory")
parser.add_argument("--path_to_train_eval_log_dir",
required=True,
help="Training and evaluating log directory")
parser.add_argument("--path_to_val_eval_log_dir",
required=True,
help="Validating and evaluating log directory")
parser.add_argument("--path_to_test_eval_log_dir",
required=True,
help="Testing and evaluating log directory")
args = parser.parse_args()
meta = Meta()
meta.load(args.path_to_tfrecords_meta_file)
_eval(args.path_to_checkpoint_dir, args.path_to_train_tfrecords_file,
meta.num_train_examples, args.path_to_train_eval_log_dir)
_eval(args.path_to_checkpoint_dir, args.path_to_val_tfrecords_file,
meta.num_val_examples, args.path_to_val_eval_log_dir)
_eval(args.path_to_checkpoint_dir, args.path_to_test_tfrecords_file,
meta.num_test_examples, args.path_to_test_eval_log_dir)
def main(_):
path_to_train_tfrecords_file = os.path.join(FLAGS.data_dir, 'train.tfrecords')
path_to_val_tfrecords_file = os.path.join(FLAGS.data_dir, 'val.tfrecords')
path_to_tfrecords_meta_file = os.path.join(FLAGS.data_dir, 'meta.json')
path_to_train_log_dir = FLAGS.train_logdir
path_to_restore_checkpoint_file = FLAGS.restore_checkpoint
training_options = {
'batch_size': FLAGS.batch_size,
'learning_rate': FLAGS.learning_rate,
'epoches': FLAGS.epoches,
'decay_steps': FLAGS.decay_steps,
'decay_rate': FLAGS.decay_rate
}
meta = Meta()
meta.load(path_to_tfrecords_meta_file)
_train(path_to_train_tfrecords_file, meta.num_train_examples,
path_to_val_tfrecords_file, meta.num_val_examples,
path_to_train_log_dir, path_to_restore_checkpoint_file,
training_options)
def main(_):
path_to_train_tfrecords_file = os.path.join(FLAGS.data_dir, 'train.tfrecords')
path_to_val_tfrecords_file = os.path.join(FLAGS.data_dir, 'val.tfrecords')
path_to_tfrecords_meta_file = os.path.join(FLAGS.data_dir, 'meta.json')
path_to_train_log_dir = FLAGS.train_logdir
path_to_restore_checkpoint_file = FLAGS.restore_checkpoint
training_options = {
'batch_size': FLAGS.batch_size,
'learning_rate': FLAGS.learning_rate,
'patience': FLAGS.patience,
'decay_steps': FLAGS.decay_steps,
'decay_rate': FLAGS.decay_rate
}
meta = Meta()
meta.load(path_to_tfrecords_meta_file)
_train(path_to_train_tfrecords_file, meta.num_train_examples,
path_to_val_tfrecords_file, meta.num_val_examples,
path_to_train_log_dir, path_to_restore_checkpoint_file,
training_options)
def main(_):
path_to_train_tfrecords_files = [
os.path.join(FLAGS.data_dir, 'train.tfrecords'),
os.path.join(
os.path.split(FLAGS.data_dir)[0],
'MNIST2SVHN/MNIST_Converted_train.tfrecords')
]
path_to_val_tfrecords_files = [
os.path.join(FLAGS.data_dir, 'val.tfrecords'),
os.path.join(
os.path.split(FLAGS.data_dir)[0],
'MNIST2SVHN/MNIST_Converted_val.tfrecords')
]
path_to_tfrecords_meta_files = [
os.path.join(FLAGS.data_dir, 'meta.json'),
os.path.join(
os.path.split(FLAGS.data_dir)[0],
'MNIST2SVHN/MNIST_Converted_meta.json')
]
path_to_train_log_dir = FLAGS.train_logdir
path_to_restore_checkpoint_file = FLAGS.restore_checkpoint
training_options = {
'batch_size': FLAGS.batch_size,
'learning_rate': FLAGS.learning_rate,
'epoches': FLAGS.epoches,
'decay_steps': FLAGS.decay_steps,
'decay_rate': FLAGS.decay_rate
}
meta = Meta()
for path_to_tfrecords_meta_file in path_to_tfrecords_meta_files:
meta.load(path_to_tfrecords_meta_file)
_train(path_to_train_tfrecords_files, meta.num_train_examples,
path_to_val_tfrecords_files, meta.num_val_examples,
path_to_train_log_dir, path_to_restore_checkpoint_file,
training_options)
def main(unused_argv):
path_to_train_tfrecords_file = os.path.join(FLAGS.data_dir, 'train.tfrecords') #./data/train.tfrecords
path_to_val_tfrecords_file = os.path.join(FLAGS.data_dir, 'val.tfrecords') #./data/val.tfrecords
path_to_tfrecords_meta_file = os.path.join(FLAGS.data_dir, 'meta.json') #./data/meta.json
path_to_train_log_dir = FLAGS.train_logdir #./logs/train
path_to_restore_checkpoint_file = FLAGS.restore_checkpoint # None or ./logs/train/latest.ckpt
training_options = {
'batch_size': FLAGS.batch_size,
'learning_rate': FLAGS.learning_rate,
'patience': FLAGS.patience,
'decay_steps': FLAGS.decay_steps,
'decay_rate': FLAGS.decay_rate
}
meta = Meta()
meta.load(path_to_tfrecords_meta_file)
num_train_examples = meta.num_train_examples
num_val_examples = meta.num_val_examples
_train(path_to_train_tfrecords_file, num_train_examples,
path_to_val_tfrecords_file, num_val_examples,
path_to_train_log_dir, path_to_restore_checkpoint_file,
training_options)
def main_train(_):
parser = argparse.ArgumentParser(
description="Training Routine for SVHNClassifier")
parser.add_argument("--data_dir",
required=True,
help="Path to SVHN (format 1) folders")
parser.add_argument("--path_to_train_log_dir",
required=True,
help="Directory to write training logs")
parser.add_argument(
"--path_to_restore_checkpoint_file",
required=False,
help=
"Path to restore checkpoint (without postfix), e.g. ./logs/train/model.ckpt-100"
)
parser.add_argument("--path_to_train_tfrecords_file",
required=True,
help="Tfrecords file in train directory")
parser.add_argument("--path_to_val_tfrecords_file",
required=True,
help="Tfrecords file in val directory")
parser.add_argument("--path_to_tfrecords_meta_file",
required=True,
help="Meta file in directory")
parser.add_argument("--batch_size",
type=int,
required=True,
help="Default 32")
parser.add_argument("--learning_rate",
type=float,
required=True,
help="Default 1e-2")
parser.add_argument("--patience",
type=int,
required=True,
help="Default 100, set -1 to train infinitely")
parser.add_argument("--decay_steps",
type=int,
required=True,
help="Default 10000")
parser.add_argument("--decay_rate",
type=float,
required=True,
help="Default 0.9")
args = parser.parse_args()
training_options = {
'batch_size': args.batch_size,
'learning_rate': args.learning_rate,
'patience': args.patience,
'decay_steps': args.decay_steps,
'decay_rate': args.decay_rate
}
meta = Meta()
meta.load(args.path_to_tfrecords_meta_file)
_train(args.path_to_train_tfrecords_file, meta.num_train_examples,
args.path_to_val_tfrecords_file, meta.num_val_examples,
args.path_to_train_log_dir, args.path_to_restore_checkpoint_file,
training_options)
