def train(train_cfg, model_cfg, common_cfg, dataflow):
epochs = train_cfg.epochs
assert epochs, epochs
epoch_size = train_cfg.epoch_size
assert epoch_size, epoch_size
config = TrainConfig(
model=ModelWrapSingle(train_cfg, model_cfg, common_cfg),
dataflow=dataflow,
#data=my_inputsource, # alternatively, use a customized InputSource
#callbacks=[...], # some default callbacks are automatically applied
# some default monitors are automatically applied
steps_per_epoch=
epoch_size, # default to the size of your InputSource/DataFlow
max_epoch=epochs)
print("Create trainer")
trainer = SimpleTrainer()
print("Run train")
launch_train_with_config(config, trainer)
if save_dir is None:
logger.auto_set_dir()
else:
logger.set_logger_dir(save_dir)
dataset_train = get_data('train')
dataset_test = get_data('test')
config = TrainConfig(
model=CifarResNet(n=NUM_UNITS,
mult_decay=mult_decay,
lr_init=lr_base * 0.1),
dataflow=dataset_train,
callbacks=[
ModelSaver(),
InferenceRunner(
dataset_test,
[ScalarStats('cost'),
ClassificationError('wrong_vector')]),
ScheduledHyperParamSetter('learning_rate',
[(1, lr_base), (82, lr_base * 0.1),
(123, lr_base * 0.01),
(164, lr_base * 0.002)])
],
max_epoch=200,
session_init=SmartInit(args.load),
)
num_gpu = max(get_num_gpu(), 1)
launch_train_with_config(config,
SyncMultiGPUTrainerParameterServer(num_gpu))
