def __init__(self, model_params=None, json_path=None):
super().__init__()
self.model_params = get_from_dicts(model_params, default_parameters)
self.model_params = get_from_json(json_path, self.model_params)
self._sanity_checks()
logging.info("Model parameters : %s", self.model_params)
self.input_type = self.model_params['input']['type']
self.model_dir = self.model_params['output']['save_model_dir']
self.config = get_tf_config(self.model_params)
self.model = tf.estimator.Estimator(
model_fn=self.model_fn, model_dir=self.model_dir,
params=self.model_params, config=self.config)
def __init__(self, params=None, json_path=None):
super().__init__()
self.params = get_from_dicts(params, default_parameters)
self.params = get_from_json(json_path, self.params)
self._sanity_check()
logging.info("Model parameters : %s", self.params)
self.input_type = self.params['input']['type']
self.init_process_group()
self.model = DistributedDataParallel(
self.model_fn(self.params).to(get_device(self.params)))
self.optimizer = get_optimizer(key=self.params['optimizer']['name'])(
params=self.model.parameters(),
**self.params['optimizer']['kwargs'])
self.loss = get_loss_fn(key=self.params['loss']['name'])(
**self.params['loss']['kwargs'])
self.metric = get_metric_fn(key=self.params['output']['metric'])
def __init__(self, params=None, json_path=None):
super().__init__()
self.params = get_from_dicts(params, default_parameters)
self.params = get_from_json(json_path, self.params)
self._sanity_check()
Path(self.params["output"]["save_model_dir"]).expanduser().resolve().mkdir(
parents=True, exist_ok=True
)
logging.info("Model parameters : %s", self.params)
self.input_type = self.params["input"]["type"]
self.init_process_group()
self.model = DistributedDataParallel(self.model_fn(self.params).to(get_device(self.params)))
self.optimizer = get_optimizer(key=self.params["optimizer"]["name"])(
params=self.model.parameters(), **self.params["optimizer"]["kwargs"]
)
self.loss = get_loss_fn(key=self.params["loss"]["name"])(**self.params["loss"]["kwargs"])
self.metric = get_metric_fn(key=self.params["output"]["metric"])
def test_merge_dicts():
params = {"learning_rate": 0.05}
default_params = {"learning_rate": 0.08, "embedding_size": 256}
final = get_from_dicts(params, default_params)
assert final['learning_rate'] == 0.05
assert final['embedding_size'] == 256