def __init__(self, hparams):
self._session = None
self.hparams = hparams
# Set the random seed to be sure the same validation set
# is used for each model
np.random.seed(0)
self.dataset = data_utils.DataSet(hparams)
np.random.seed() # Put the random seed back to random
# extra stuff for ray
self._build_models()
self._new_session()
self._session.__enter__()
def __init__(self, hparams):
self._session = None
self.hparams = hparams
# Set the random seed to be sure the same validation set
# is used for each model
np.random.seed(0)
self.data_loader = data_utils.DataSet(hparams)
np.random.seed() # Put the random seed back to random
self.data_loader.reset()
# extra stuff for ray
self._build_models()
self._new_session()
self._setup_model(hparams)
self._setup_teacher()
if hparams.pretrained_model:
tf.logging.info('load pretrained model')
self.extract_model_spec(hparams.pretrained_model)
self._session.__enter__()