问使用BP神经网络进行深度学习时，在训练时获得平坦的误差曲线

EN

def build_model():
  model = keras.Sequential()
  model.add(layers.Dense(128, activation=tf.nn.relu, input_shape=len(normed_train_data.keys())]))
  model.add(layers.Dense(128,activation=tf.nn.relu, input_shape=(1,)))
  model.add(layers.Dense(4))
  model.compile(loss='mean_squared_error', optimizer='Adam',metrics=['mae', 'mse','accuracy'])
  return model

def plot_history(history):
   hist = pd.DataFrame(history.history)
   hist['epoch'] = history.epoch
   plt.figure()
   plt.xlabel('Epoch')
   plt.ylabel('Mean Abs Error [per]')
   plt.plot(hist['epoch'], hist['mean_absolute_error'],label='Train Error')
   plt.plot(hist['epoch'], hist['val_mean_absolute_error'],label = 'Val Error')
   plt.legend()
   plt.ylim([0,200])
   plt.show()

model = build_model()
model.summary()
history = model.fit(normed_train_data, train_labels,epochs=EPOCHS,validation_split = 0.2, verbose=0,callbacks=[PrintDot()])
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
plot_history(history)