用python将data分为test，train，validation三个集，并放入model中，但报错说validation集中的x，y不等

将数据分为三个集test，train，validation

from sklearn.model_selection import train_test_split
(X_train, X_test, Y_train, Y_test) = train_test_split(X, Y, test_size=0.2, random_state=1)

from sklearn.model_selection import train_test_split
(X_train, Y_train, X_val, Y_val) = train_test_split(X_train, Y_train, test_size=0.3, random_state=1)

然后创建model

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(80, 80, 3)),
  tf.keras.layers.Dense(512, activation='sigmoid'),
  tf.keras.layers.Dense(2, activation='sigmoid')
])

编译model

model.compile(optimizer = 'adam',
              loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics = ['accuracy'])

然后Fit model 用 the training data 和 the validation data

model.fit(X_test, Y_test, epochs=100)

# Fits the model to the validation

model.fit(X_val, Y_val, epochs=100)

结果显示fit validation的时候，X_val, Y_val的数量不等

想请教一下是什么原因，我应该怎么实现fit validation

将(X_train, Y_train, X_val, Y_val) = train_test_split(X_train, Y_train, test_size=0.3, random_state=1)那句改为如下：
(X_train, X_val,Y_train, Y_val) = train_test_split(X_train, Y_train, test_size=0.3, random_state=1)即可。