def load(self):
"""Load the best performing checkpoint."""
# Load artifacts needed to recreate the network
self.cfg = io_utils.load_pickle("config.pkl", self.artifact_dir)
network_params = io_utils.load_pickle("network_params.pkl", self.artifact_dir)
# Build network
self.net = model.build_network(self.cfg["model"], network_params)
# Load best checkpoint
path = services.get_best_checkpoint_filepath(self.artifact_dir)
self.net.load_weights(path).expect_partial() # not loading optimizer
return self
def test_load_pickle(artifact_path, sample_dict):
filename = "unit_test.pkl"
with open(os.path.join(artifact_path, filename), "wb") as fn:
pickle.dump(sample_dict, fn)
assert os.path.isfile(os.path.join(artifact_path, filename))
obj = io_utils.load_pickle(filename, artifact_path)
assert obj == sample_dict
def load(self):
"""Load the best performing checkpoint."""
# Load artifacts needed to recreate the network
self.cfg = io_utils.load_pickle("config.pkl", self._artifact_dir)
network_params = io_utils.load_pickle("network_params.pkl", self._artifact_dir)
# Build network
self.net = model.build_network(self.cfg["model"], network_params)
# Load best checkpoint
path = services.get_best_checkpoint_filepath(self._artifact_dir)
# TODO: remove expect_partial, _make_predict_function
self.net.load_weights(path).expect_partial() # not loading optimizer
self.net._make_predict_function() # needed for threading in scoring
self._is_loaded = True
return self
def load(self, path):
self.tokenizer = io_utils.load_pickle("tokenizer.pkl", path)
def load(self, path):
self.class_map = io_utils.load_pickle("class_map.pkl", path)
self.inverse_class_map = io_utils.load_pickle("inverse_class_map.pkl",
path)
def load(self, path):
self.obj = io_utils.load_pickle("obj.pkl", path)
def load(self, path):
self.fit_params = io_utils.load_pickle("fit_params.pkl", path)