def test_MODEL_loading(self): nn = Neural_Network(preprocessor=self.pp3D) model_path = os.path.join(self.tmp_dir3D.name, "my_model.hdf5") nn.dump(model_path) nn_new = Neural_Network(preprocessor=self.pp3D) nn_new.load(model_path)
mode="min") #-----------------------------------------------------# # Run Pipeline for provided CV Fold # #-----------------------------------------------------# # Run pipeline for cross-validation fold run_fold(fold, model, epochs=1000, iterations=150, evaluation_path=path_eval, draw_figures=True, callbacks=[cb_lr, cb_es, cb_tb, cb_cl, cb_mc], save_models=False) # Dump latest model to disk model.dump(os.path.join(fold_subdir, "model.latest.hdf5")) #-----------------------------------------------------# # Inference for provided CV Fold # #-----------------------------------------------------# # Load best model weights during fitting model.load(os.path.join(fold_subdir, "model.best.hdf5")) # Obtain training and validation data set training, validation = load_disk2fold( os.path.join(fold_subdir, "sample_list.json")) # Compute predictions model.predict(validation, return_output=False)
def test_MODEL_storage(self): nn = Neural_Network(preprocessor=self.pp3D) model_path = os.path.join(self.tmp_dir3D.name, "my_model.hdf5") nn.dump(model_path) self.assertTrue(os.path.exists(model_path))