如何修复这个 Wide & Deep RNN？

Question

阅读 Hands-On Machine Learning 一书，发现了 Wide and Deep 模型，这似乎是一个灵活、准确的 RNN 模型。但是，我不断收到以下错误：“AttributeError: 'tuple' object has no attribute 'ndim'”。我用谷歌搜索了这个错误，似乎没有明确的解决方案或解释为什么我会收到错误，至少对我作为初学者来说是这样。有人知道如何解决这个问题吗？

housing = fetch_california_housing()

X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data, housing.target, 
random_state=42)
X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, random_state=42)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_valid = scaler.transform(X_valid)
X_test = scaler.transform(X_test)

input_A = keras.layers.Input(shape=[5], name="wide_input")
input_B = keras.layers.Input(shape=[6], name="deep_input")
hidden1 = keras.layers.Dense(30, activation="relu")(input_B)
hidden2 = keras.layers.Dense(30, activation="relu")(hidden1)
concat = keras.layers.concatenate([input_A, hidden2])
output = keras.layers.Dense(1, name="output")(concat)
model = keras.models.Model(inputs=[input_A, input_B], outputs=[output])

model.compile(loss='mse', optimizer=keras.optimizers.SGD(lr=1e-3))

X_train_A, X_train_B = X_train[:, :5], X_train[:, 2:]
X_valid_A, X_valid_B = X_valid[:, :5], X_valid[:, 2:]
X_test_A, X_test_B = X_test[:, :5], X_test[:, 2:]
X_new_A, X_new_B = X_test_A[:3], X_test_B[:3]

history = model.fit((X_train_A, X_train_B), y_train, epochs=20,
                validation_data=((X_valid_A, X_valid_B), y_valid))
mse_test = model.evaluate((X_test_A, X_test_B), y_test)
y_pred = model.predict((X_new_A, X_new_B))

Answer 1

将 shape=[5] 更改为 shape=(5) 和 shape=[6] 一样