y_target: np.transpose([test_label_inst]), keep_prob:1.0}) test_loss_all.append(cur_test_loss) except tf.errors.OutOfRangeError: func.print_time() print('Done testing -- epoch limit reached') finally: coord.request_stop() coord.join(threads) # calculate auc test_pred_score_re = func.list_flatten(test_pred_score_all) test_label_re = func.list_flatten(test_label_all) test_auc, _, _ = func.cal_auc(test_pred_score_re, test_label_re) test_rmse = func.cal_rmse(test_pred_score_re, test_label_re) test_loss = np.mean(test_loss_all) # rounding test_auc = np.round(test_auc, 4) test_rmse = np.round(test_rmse, 4) test_loss = np.round(test_loss, 5) train_loss_list = [np.round(xx, 4) for xx in train_loss_list] val_auc_list = [np.round(xx, 4) for xx in val_auc_list] print('test_auc = ', test_auc) print('test_rmse =', test_rmse) print('test_loss =', test_loss) print('train_loss_list =', train_loss_list) print('val_auc_list =', val_auc_list)
y_target_2: test_label_inst_2, keep_prob:1.0}) test_loss_all_2.append(cur_test_loss_2) except tf.errors.OutOfRangeError: func.print_time() print('Done testing 2 -- epoch limit reached') finally: coord.request_stop() coord.join(threads) ################################# # calculate auc test_pred_score_re_1 = func.list_flatten(test_pred_score_all_1) test_label_re_1 = func.list_flatten(test_label_all_1) test_auc_1, _, _ = func.cal_auc(test_pred_score_re_1, test_label_re_1) test_rmse_1 = func.cal_rmse(test_pred_score_re_1, test_label_re_1) test_loss_1 = np.mean(test_loss_all_1) test_pred_score_re_2 = func.list_flatten(test_pred_score_all_2) test_label_re_2 = func.list_flatten(test_label_all_2) test_auc_2, _, _ = func.cal_auc(test_pred_score_re_2, test_label_re_2) test_rmse_2 = func.cal_rmse(test_pred_score_re_2, test_label_re_2) test_loss_2 = np.mean(test_loss_all_2) # rounding test_auc_1 = np.round(test_auc_1, 4) test_rmse_1 = np.round(test_rmse_1, 4) test_loss_1 = np.round(test_loss_1, 5) test_auc_2 = np.round(test_auc_2, 4) test_rmse_2 = np.round(test_rmse_2, 4)