如何在keras中创建序列预测

Question

我想用keras LSTM预测输出序列。我有6个功能和6个输出值。但是，我的代码会在标签值中引发错误。

Error when checking model target: expected dense_1 to have shape (None, 1) but got array with shape (4000, 6)

将numpy导入为np     np.random.seed（种子= 7）     将pandas导入为pd

numbers = pd.read_csv(r'C:\...\Desktop\LSTM.csv', sep=';')

from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import Dropout
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error

numval = numbers.values.astype('float32')
scaler =MinMaxScaler()
scaler.fit_transform(numval)

X = numval[:4000,0:6]

y = numval[:4000,6:]

y_test = numval[4000,:6:]

X = np.reshape(X,(X.shape[0],1,X.shape[1]))

X_test = numval[4000:,0:6]
X_test  = np.reshape(X_test,(X_test.shape[0],1,X_test.shape[1]))

print(X.shape)


model = Sequential()
model.add(LSTM(6,input_dim=6,stateful=True))
model.add(Dense(6))
model.compile(loss='sparse_categorical_crossentropy',optimizer='adam')
model.fit(X,y,batch_size=200,nb_epoch=100,verbose=2)
scores =  model.evaluate(X_test,y_test,batch_size=32,verbose=1)
print(scores[1])

如何获得多个labeloutput？ THX