KERAS模型性能变化

Question

我正在学习神经网络的学习曲线，使用Keras根据先前的指定窗口来预测下一个值。这是我的代码

from sklearn.preprocessing import MinMaxScaler,StandardScaler 
from keras.preprocessing.sequence import TimeseriesGenerator

scaler = StandardScaler() scaler.fit(train)
scaled_train = scaler.transform(train) 
scaled_test  = scaler.transform(test)


# define generator 
n_input = 47 
n_features = 1 
generator = TimeseriesGenerator(scaled_train, scaled_train, length = n_input, batch_size=12)
initializer = tf.keras.initializers.GlorotNormal()

model = Sequential()
model.add(LSTM(12,activation = 'relu', input_shape = (n_input, n_features),kernel_initializer = initializer))
model.add(Dense(1))
model.compile(optimizer = 'adam', loss = 'mae')

问题，每次我重新训练模型而无需进行任何更改时，我的模型性能就会更改。通常，它应该根据参数的任何更改（例如新隐藏层或激活功能的更改等）进行更改