def load_dict_data(dataset_path, is_linear, shape): if is_linear: train, test = load_data_vec(dataset_path) else: train, test = load_data_img(dataset_path, shape) train['data'] = np.asarray(train['data'], dtype='float32') test['data'] = np.asarray(test['data'] , dtype='float32') train['data'] = train['data'] / 255 test['data'] = test['data'] / 255 return train, test
sys.exit(1) dataset_path = sys.argv[1] measure_type = sys.argv[2] output_folder = sys.argv[3] if not output_folder.endswith('/'): output_folder += '/' if not os.path.exists(output_folder): os.mkdir(output_folder) if not measure_type in ['cos', 'euc']: sys.stderr.write('Error: measure_type must be in [cos, euc]\n') sys.exit(1) train, test = load_data_vec(dataset_path) assert train['data'].shape[1] == test['data'].shape[1] # train['data'] = normalize_data(train['data']) # test['data'] = normalize_data(test['data']) train['data'] = np.asarray(train['data'], dtype='float64') test['data'] = np.asarray(test['data'], dtype='float64') print train['data'].shape print test['data'].shape sample_num, dim = test['data'].shape batch_len = 500 batch_num = sample_num / batch_len engine = Engine(train, test, measure_type) # engine = FileNameEngine(train, test, measure_type)