def preTrainModel(net, data_path=None): D = AutoSpeechDataset( os.path.join("../sample_data/test_data1", 'data01.data')) D.read_dataset() metadata = D.get_metadata() data_manager = DataManager(metadata, D.get_train()) train_x, train_y, val_x, val_y = data_manager.get_train_data( train_loop_num=11, model_num=1, round_num=2, use_new_train=True, use_mfcc=True) net.init_model(train_x.shape[1:], metadata["class_num"]) net.fit(train_x, train_y, (val_x, val_y), 2) result = net.predict(val_x) print('a')
if self._context.is_finished: log("Finish all stages") self.done_training = True self._last_pred = preds except MemoryError as mem_error: log("MemoryError has occurred: {}".format(mem_error)) self._has_exception = True self.done_training = True except Exception as exp: log("Exception has occurred: {}".format(exp)) self._has_exception = True self.done_training = True return self._last_pred if __name__ == '__main__': from ingestion.dataset import AutoSpeechDataset D = AutoSpeechDataset(os.path.join("../sample_data/DEMO", 'train.data')) D.read_dataset() m = Model(D.get_metadata()) m.train(D.get_train()) m.test(D.get_test()) m.train(D.get_train()) m.test(D.get_test()) m.train(D.get_train()) m.test(D.get_test()) # m.train(D.get_train()) # m.test(D.get_test())