Python 在神经网络中计算每次迭代的分数

我正在使用不同的节点大小和其他参数，结合参数调整等，构建一个神经网络。我试图迭代多个模型，以便能够将每次迭代的分数存储在一个列表中，并最终计算平均分数

然而，我只得到每个循环一次计算。我不确定迭代是否正确，因为对于每个节点大小，我只得到一个分数，而不是三个分数

node_list = [64, 128, 256]  # list with number of nodes in hidden layer per model
test_outcomes = [] # list of model scores
r2_outcomes = [] #list of r2 scores

for nodes in node_list:
    for i in range(0,5):
        # Initialising the ANN
        model = Sequential()

        # Adding the input layer and the first hidden layer
        model.add(Dense(units = 128, activation = 'relu', input_shape = (x_train.shape[1],)))
        model.add(BatchNormalization())
        #model.add(Dropout(0.2))
    
        # Adding the second hidden layer
        model.add(Dense(units = nodes, activation = 'relu'))
        model.add(BatchNormalization())
        #model.add(Dropout(0.3))
   
        # Adding the output layer
        model.add(Dense(units = 1))

        # Compile the ANN
        model.compile(loss='mean_squared_error', optimizer = Adam(lr = 0.001), metrics=['mean_absolute_error'])

        # ANN Summary
        model.summary()
    
        # Fit model
        model.fit(x_train, y_train, batch_size = 25, epochs = 50, verbose = 0 ,validation_data = (x_valid, y_valid))
    
        # Get predictions
        y_pred = model.predict(x_valid)
        
        # Model Evaluation Score
        score = model.evaluate(x_valid, y_valid, verbose=1)
        test_loss = round(score[0], 3)
        print ('Test loss:', test_loss)
        
        test_outcomes.append(test_loss)

        #Calculate R_Squared
        r_squared = r2_score(y_valid, y_pred)
        r2_outcomes.append(r_squared)
    

    'Evaluation Scores'
    # Get the mean of that list
    mean_test= np.mean(test_outcomes)
    # Append to another list
    mean_test_scores = []
    mean_test_scores.append(mean_test)        

    # Get the mean of that list
    mean_r2 = np.mean(test_outcomes)
    # Append to another list
    mean_r2_scores = []
    mean_r2_scores.append(mean_r2)    

在进行迭代之后，是否有可能存储其中最好的评分模型