Python 3.x LSTM-Keras中的多个特征

当我运行模型时，我有多个特性要在timesteps=5（n）的LSTM中使用

def create_dataset(dataset, time_stamp=5):
    dataX, dataY = [], []
    print(len(dataset) - time_stamp - 1)
    for i in range(len(dataset) - time_stamp - 1):
        a = dataset[i:(i + time_stamp), :]
        dataX.append(a)
        dataY.append(dataset[i + time_stamp, 0])
    return np.array(dataX), np.array(dataY)


model = Sequential()
model.add(LSTM(16,activation="relu",input_shape=(timeFrame,2), return_sequences=True))
model.add(Dense(1,activation='relu'))
model.compile(optimizer="adam",loss='mean_squared_error')
model.summary()

model.fit(X_train, Y_train, epochs=30, batch_size=1, verbose=1)

X_train.shape = (8,5,2) // The data sets have 8 different datasets with 5 timesteps and 2 features
Y_train.shape =  (8)    // 


train_predict = model.predict(X_train)

列车形状=（8,5,1）

当我试着这么做的时候，这很好。。。现在的问题是，当我运行以下

scaler.inverse_transform(train_predict) //{ValueError}Found array with dim 3. Estimator expected <= 2.

scaler.inverse_transform(Y_train) //geting error 

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[math.sqrt(mean_squared_error(Y_train, train_predict))]