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)
