#12.當資料集數目少時可用k-摺疊交叉驗證法(k fold cross validation)
k = 4
nb_val_samples = len(X_train) // k
nb_epochs = 80
mse_scores = []
mae_scores = []
for i in range(k):
    print("Processing Fold #" + str(i))
    # 取出驗證資料集
    X_val = X_train[i*nb_val_samples: (i+1)*nb_val_samples]
    Y_val = Y_train[i*nb_val_samples: (i+1)*nb_val_samples]
    # 結合出訓練資料集
    X_train_p = np.concatenate(
            [X_train[:i*nb_val_samples],
            X_train[(i+1)*nb_val_samples:]], axis=0)
    Y_train_p = np.concatenate(
            [Y_train[:i*nb_val_samples],
            Y_train[(i+1)*nb_val_samples:]], axis=0)
    model = build_model()
    # 訓練模型
    model.fit(X_train_p, Y_train_p, epochs=nb_epochs, 
              batch_size=16, verbose=0)
    # 評估模型
    mse, mae = model.evaluate(X_val, Y_val, verbose=0)
    mse_scores.append(mse)
    mae_scores.append(mae)