Neural Net Fitting - How to get a training result table when using multiple hidden layers

I trained the neural network in the Neural Net Fitting app to regress my data using a multiple hidden layer(short for MHL) and brought the training script to the editor through the generation code.

I added hidden layers 2,3 as the attached code to implement MHL in the editor. After running that code, I realized that I couldn't get a training result table(short for TRT) like the picture.

When using only one hidden layer, the Neural Net Fitting app showed the TRT as shown in the picture. I need to get the TRT through the code using the MHL to write the report.

No matter how much I looked, I couldn't find the related command.

Is it not possible to create a TRT because I am using MHL?

If I use the Figure command, can I get the TRT even from the code using the MHL? Please tell me specific command.

Can I get the TRT in a way other than the Figure command? Please tell me the specific command.

% Solve an Input-Output Fitting problem with a Neural Network
% Script generated by Neural Fitting app
% Created 17-Jan-2022 05:03:24
%
% This script assumes these variables are defined:
%
%   inputdata - input data.
%   outputbeadwidth - target data.

x = inputdata';
t = outputbeadwidth';

% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainlm';  % Levenberg-Marquardt backpropagation.

% Create a Fitting Network
hiddenLayer1Size = 30;
hiddenLayer2Size = 20;
hiddenLayer3Size = 20;
net = fitnet([hiddenLayer1Size,hiddenLayer2Size,hiddenLayer3Size],trainFcn);

% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

% Train the Network
[net,tr] = train(net,x,t);

% Test the Network
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)

% View the Network
view(net)

% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, ploterrhist(e)
%figure, plotregression(t,y)
%figure, plotfit(net,x,t)