多分类中的提前停止 model Keras

Question

我面临一个多分类问题，为此我构建了以下 model：

def model_building():
  input = keras.Input(shape=(X_train[0]).shape)
  hidden1 = keras.layers.Dense(20)(input)
  dropout1 = keras.layers.Dropout(0.25)(hidden1)
  hidden2 = keras.layers.Dense(20)(dropout1)
  output = keras.layers.Dense(1)(hidden3)
  model = keras.Model(inputs=input, outputs=output, name='ClassModel')
  model.compile(loss='sparse_categorical_crossentropy', optimizer=keras.optimizers.Nadam(learning_rate=0.01), metrics=[acc])
  return model

但是在拟合时，它向我显示了这个错误

from keras import callbacks
earlystopping = callbacks.EarlyStopping(monitor ="loss", 
                                        mode ="min", patience = 5, 
                                        restore_best_weights = True)

history = model.fit(X_train, Y_train, epochs=40, validation_split=0.2, verbose=0,
                    shuffle=True, callbacks =[earlystopping])


# ERROR:
InvalidArgumentError:  Received a label value of 4 which is outside the valid range of [0, 1).  Label values: 2 3 1 3 2 3 2 2 3 2 2 2 3 3 3 3 4 2 2 2 2 3 3 2 2 3 2 2 3 2 2 4

是否可以在多分类问题中构建EarlyStopping ？ 如果可能，怎么做？