def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config)
except:
print("missing or invalid arguments")
exit(0)
# create the experiments dirs
create_dirs([
config.callbacks.tensorboard_log_dir, config.callbacks.checkpoint_dir
])
print('Create the data generator.')
data_loader = SimpleMnistDataLoader(config)
print('Create the model.')
model = SimpleMnistModel(config)
print('Create the trainer')
trainer = SimpleMnistModelTrainer(model.model,
data_loader.get_train_data(), config)
print('Start training the model.')
trainer.train()
def main_train():
"""
训练模型
:return:
"""
print '[INFO] 解析配置...'
parser = None
config = None
try:
args, parser = get_train_args()
config = process_config(args.config)
except Exception as e:
print '[Exception] 配置无效, %s' % e
if parser:
parser.print_help()
print '[Exception] 参考: python main_train.py -c configs/simple_mnist_config.json'
exit(0)
# config = process_config('configs/simple_mnist_config.json')
print '[INFO] 加载数据...'
dl = SimpleMnistDL(config=config)
print '[INFO] 构造网络...'
model = SimpleMnistModel(config=config)
print '[INFO] 训练网络...'
trainer = SimpleMnistTrainer(
model=model.model,
data=[dl.get_train_data(), dl.get_test_data()],
config=config)
trainer.train()
print '[INFO] 训练完成...'
def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config)
except:
print("missing or invalid arguments")
exit(0)
# create the experiments dirs
create_dirs([config.tensorboard_log_dir, config.checkpoint_dir, "val_test"])
print('Create the data generator.')
if hasattr(config, "data_set"):
if config.data_set == "face_data_77":
data_loader = FaceLandmark77DataLoader(config)
else:
data_loader = SimpleMnistDataLoader(config)
else:
data_loader = SimpleMnistDataLoader(config)
print('Create the model.')
if hasattr(config, "model_name"):
if config.model_name == "mobile_net":
model = MobileNetV2Model(config)
else:
model = SimpleMnistModel(config)
else:
model = SimpleMnistModel(config)
print(model.model.input_names)
print([out.op.name for out in model.model.outputs])
return
if hasattr(config, "best_checkpoint"):
model.load(config.best_checkpoint)
frozen_graph = freeze_session(K.get_session(),
output_names=[out.op.name for out in model.model.outputs])
ckpt_path = Path(config.best_checkpoint)
tf.train.write_graph(frozen_graph, str(ckpt_path.parent), ckpt_path.with_suffix(".pb").name, as_text=False)
def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config)
except:
print("missing or invalid arguments")
exit(0)
# create the experiments dirs
create_dirs(
[config.tensorboard_log_dir, config.checkpoint_dir, "val_test"])
print('Create the data generator.')
if hasattr(config, "data_set"):
if config.data_set == "face_data_77":
data_loader = FaceLandmark77DataLoader(config)
else:
data_loader = SimpleMnistDataLoader(config)
else:
data_loader = SimpleMnistDataLoader(config)
print('Create the model.')
if hasattr(config, "model_name"):
if config.model_name == "mobile_net":
model = MobileNetV2Model(config)
else:
model = SimpleMnistModel(config)
else:
model = SimpleMnistModel(config)
print("Create the Evaluater.")
evaluator = FaceLandmarkEvaluater(model, data_loader, config)
print("Start evaluate.")
evaluator.evaluate()
def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config)
except:
print("missing or invalid arguments")
exit(0)
# create the experiments dirs
create_dirs(
[config.tensorboard_log_dir, config.checkpoint_dir, "val_test"])
print('Create the data generator.')
if hasattr(config, "data_set"):
if config.data_set == "face_data_77":
data_loader = FaceLandmark77DataLoader(config)
else:
data_loader = SimpleMnistDataLoader(config)
else:
data_loader = SimpleMnistDataLoader(config)
print('Create the model.')
if hasattr(config, "model_name"):
if config.model_name == "mobile_net":
model = MobileNetV2Model(config)
else:
model = SimpleMnistModel(config)
else:
model = SimpleMnistModel(config)
print(model.model.input_names)
print([out.op.name for out in model.model.outputs])
return
if hasattr(config, "best_checkpoint"):
model.load(config.best_checkpoint)
frozen_graph = freeze_session(
K.get_session(),
output_names=[out.op.name for out in model.model.outputs])
ckpt_path = Path(config.best_checkpoint)
tf.train.write_graph(frozen_graph,
str(ckpt_path.parent),
ckpt_path.with_suffix(".pb").name,
as_text=False)
def main_train():
"""
训练模型
:return:
"""
print('[INFO] 解析配置...')
parser = None
config = None
# try:
# args, parser = get_train_args()
# config = process_config(args.config)
# except Exception as e:
# print('[Exception] 配置无效, %s' % e)
# if parser:
# parser.print_help()
# print('[Exception] 参考: python main_train.py -c configs/simple_mnist_config.json')
# exit(0)
config = process_config('configs/simple_mnist_config.json')
np.random.seed(47) # 固定随机数
print('[INFO] 加载数据...')
dl = SimpleMnistDL(config=config)
print('[INFO] 构造网络...')
model = SimpleMnistModel(config=config)
print('[INFO] 训练网络...')
trainer = SimpleMnistTrainer(
model=model.model,
data=[dl.get_train_data(), dl.get_test_data()],
config=config)
trainer.train()
print('[INFO] 训练完成...')