def main():
model = MnistModel()
optimizer = Optimizer()
data_source = MnistDataSource()
cfg = TrainConfig()
cfg.load(model,
optimizer,
data_source,
task_name="mnist",
framework_name='Lasagne')
saver = TrainSaver(cfg.prm['work_dir'],
cfg.prm['project_name'],
cfg.prm['model_filename_prefix'],
data_source,
task_name="mnist",
suffix="_ls")
trainer = Trainer(model=model,
optimizer=optimizer,
data_source=data_source,
saver=saver)
trainer.train(num_epoch=cfg.prm['max_num_epoch'],
epoch_tail=cfg.prm['min_num_epoch'])
def main():
model = MnistModel()
optimizer = Optimizer()
data_source = MnistDataSource()
cfg = TrainConfig()
cfg.load(model,
optimizer,
data_source,
task_name="mnist",
framework_name='Gluon')
saver = TrainSaver(cfg.prm['work_dir'],
cfg.prm['project_name'],
cfg.prm['model_filename_prefix'],
data_source,
task_name="mnist",
suffix="_gl")
#ctx = [mx.gpu(i) for i in cfg.prm['gpus']] if cfg.prm['gpus'] else mx.cpu()
ctx = mx.gpu(0) if cfg.prm['gpus'] else mx.cpu()
trainer = Trainer(model=model,
optimizer=optimizer,
data_source=data_source,
saver=saver,
ctx=ctx)
trainer.train(num_epoch=cfg.prm['max_num_epoch'],
epoch_tail=cfg.prm['min_num_epoch'])
def main():
model = MnistModel()
optimizer = Optimizer()
data_source = MnistDataSource()
cfg = TrainConfig()
cfg.load(model,
optimizer,
data_source,
task_name="mnist",
framework_name='TFLearn')
saver = TrainSaver(cfg.prm['work_dir'],
cfg.prm['project_name'],
cfg.prm['model_filename_prefix'],
data_source=data_source,
task_name="mnist",
suffix="_tfl")
trainer = Trainer(model=model,
optimizer=optimizer,
data_source=data_source,
saver=saver)
# trainer.train(
# num_epoch=cfg.prm['max_num_epoch'],
# epoch_tail = cfg.prm['min_num_epoch'],
# dat_gaussian_blur_sigma_max=1.0,
# dat_gaussian_noise_sigma_max=0.05,
# dat_perspective_transform_max_pt_deviation=1,
# dat_max_scale_add=1.0 / (28.0 / 2),
# dat_max_translate=2.0,
# dat_rotate_max_angle_rad=0.2617994)
trainer.hyper_train(num_epoch=cfg.prm['max_num_epoch'],
epoch_tail=cfg.prm['min_num_epoch'],
bo_num_iter=cfg.prm['bo_num_iter'],
bo_kappa=cfg.prm['bo_kappa'],
bo_min_rand_num=cfg.prm['bo_min_rand_num'],
bo_results_filename='mnist_hyper.csv',
synch_file_list=cfg.prm['synch_list'],
sync_period=cfg.prm['sync_period'])
def main():
model = MnistModel()
optimizer = Optimizer()
data_source = MnistDataSource(use_augmentation=False)
cfg = TrainConfig()
cfg.load(
model,
optimizer,
data_source,
task_name="mnist",
framework_name='MXNet')
saver = TrainSaver(
cfg.prm['work_dir'],
cfg.prm['project_name'],
cfg.prm['model_filename_prefix'],
data_source,
task_name="mnist",
suffix="_mx")
ctx = [mx.gpu(i) for i in cfg.prm['gpus']] if cfg.prm['gpus'] else mx.cpu()
trainer = Trainer(
model=model,
optimizer=optimizer,
data_source=data_source,
saver=saver,
ctx=ctx)
trainer.hyper_train(num_epoch=cfg.prm['max_num_epoch'],
epoch_tail=cfg.prm['min_num_epoch'],
bo_num_iter=cfg.prm['bo_num_iter'],
bo_kappa=cfg.prm['bo_kappa'],
bo_min_rand_num=cfg.prm['bo_min_rand_num'],
bo_results_filename='mnist_hyper.csv',
synch_file_list=cfg.prm['synch_list'],
sync_period=cfg.prm['sync_period'])