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)