TypeError: int() 参数必须是字符串、类似字节的对象或数字，而不是实现 ABC 中的“NoneType”

如何解决TypeError: int() 参数必须是字符串、类似字节的对象或数字，而不是实现 ABC 中的“NoneType”

我正在使用 [this][1] api 在 ANN 中实现人工蜂群优化。但我收到此错误。这是我的代码：

def ANN(optimizer = "adam",neurons = 32,batch_size = 32,epochs = 50,activation = "relu",patience =5,loss = 'mse'):

  model = Sequential()
  model.add(Dense(neurons,input_dim=look_back,activation= activation))
  model.add(Dense(neurons,activation= activation))
  model.add(Dense(1))
  model.compile(optimizer = optimizer,loss = loss)
  early_stopping = EarlyStopping(monitor = "loss",patience = patience)
  history = model.fit(x_train,y_train,batch_size = batch_size,epochs = epochs,callbacks = [early_stopping],verbose = 0)
  return model

boundaries = [(0,2),(0,(10,100),(20,50),(3,20)]
def performance(x_train,x_test,y_test,optimizer = None,activation = None,loss = None,batch_size = None,neurons = None,epochs = None,patience=None):

  model = ANN(optimizer=optimizer,activation= activation,loss=loss,batch_size=batch_size,neurons= neurons,patience=patience)

  trainScore = model.evaluate(x_train,verbose=0)
  print('Train Score: %.2f MSE (%.2f RMSE)' % (trainScore,math.sqrt(trainScore)))
  testScore = model.evaluate(x_test,verbose=0)
  print('Test Score: %.2f MSE (%.2f RMSE)' % (testScore,math.sqrt(testScore)))
  trainPredict = model.predict(x_train)
  testPredict = model.predict(x_test)
  #calculate mean absolute percent error
  trainMAPE = mean_absolute_error(y_train,trainPredict)
  testMAPE = mean_absolute_error(y_test,testPredict)
    
  return print('testMAPE: %.2f MAPE' % trainMAPE),print('testMAPE: %.2f MAPE' % testMAPE)
writer = pd.ExcelWriter('/content/Scores.xlsx')
for sheetNum in range(1,5):
    dataframe = pd.read_excel('Fri.xlsx',sheet_name='Sheet'+str(sheetNum))
    
    # load the dataset
    dataset = dataframe.values
    dataset = dataset.astype('float32')
    
    
    train_size = int(len(dataset) * 0.48)
    test_size = len(dataset) - train_size
    train,test = dataset[0:train_size,:],dataset[train_size:len(dataset),:]
    
    # reshape into X=t and Y=t+1
    look_back = 10
    x_train,y_train = create_dataset(train,look_back)
    x_test,y_test = create_dataset(test,look_back)
    
  
    
    # normalize the dataset
    scaler = MinMaxScaler(feature_range=(0,1))
    x_train = scaler.fit_transform(x_train)
    x_test = scaler.fit_transform(x_test)
    abc_obj = abc(performance(x_train,y_test),boundaries)
    abc_obj.fit()

    #Get solution obtained after fit() execution:
    solution = abc_obj.get_solution()

这是我的错误：

TypeError                                 Traceback (most recent call last)
<ipython-input-38-f9098d8d18fc> in <module>()
     23     x_train = scaler.fit_transform(x_train)
     24     x_test = scaler.fit_transform(x_test)
---> 25     abc_obj = abc(performance(x_train,boundaries)
     26     abc_obj.fit()
     27 

2 frames
/usr/local/lib/python3.7/dist-packages/keras/layers/core.py in __init__(self,units,activation,use_bias,kernel_initializer,bias_initializer,kernel_regularizer,bias_regularizer,activity_regularizer,kernel_constraint,bias_constraint,**kwargs)
   1144         activity_regularizer=activity_regularizer,**kwargs)
   1145 
-> 1146     self.units = int(units) if not isinstance(units,int) else units
   1147     self.activation = activations.get(activation)
   1148     self.use_bias = use_bias

TypeError: int() argument must be a string,a bytes-like object or a number,not 'NoneType'

你能帮我解决这个问题吗？我想我没有正确定义功能“性能”。但我不明白我怎样才能让它变得更好。 [1]：https://pypi.org/project/beecolpy/