Question about 'trainbr' in BPNN

I use Bayesian regulation by implimenting 'trainbr' when calling newff function. Can anyone help me with the following two questions?

1. The performance function should be changed to 'msereg', i.e. msereg = γmse + (1 − γ). But why the command window display: NET.performFcn has been set to SSE.
2. The maximam epoch is set to 300, but the codes stoped at the epoch less than 300.

clc,clear
% samples

data=[  550  50.9114   4.9195 
        550  53.9864   4.8926 
        550  56.3262   4.8658 
        550  58.5372   4.5436 
        550  59.9610   3.3356 
        500  42.2350   4.1678 
        500  47.3824   4.1141 
        500  49.3885   4.0604 
        500  52.0637   3.9597 
        500  54.0059   3.7248 
        500  54.6201   2.9664 
        475  38.7662   3.7584 
        475  43.1781   3.7248 
        475  45.1173   3.6711 
        475  47.5921   3.5638 
        475  50.0018   3.3557 
        475  50.6136   2.7383 
        450  34.8298   3.3423  
        450  37.0355   3.3356 
        450  40.7792   3.2886 
        450  43.9895   3.1678 
        450  45.7969   3.0000 
        450  46.3401   2.4899 
        400  28.0909   2.6376 
        400  31.3660   2.6376 
        400  33.3715   2.6107 
        400  35.9125   2.5436 
        400  37.9872   2.3826 
        400  38.5303   1.8792 
        350  19.5438   2.1477 
        350  24.2224   2.1544 
        350  27.0301   2.1208 
        350  29.2368   2.0604 
        350  31.1102   1.9463 
        350  31.7844   1.5973 
        300  14.3362   1.8054 
        300  19.3493   1.7852 
        300  21.4217   1.7651 
        300  23.4944   1.7181 
        300  25.2336   1.6376 
        300  26.5071   1.4228]; 

      data=[data(:,1) data(:,3) data(:,2)];   
      p=data(:,[1 3])';
      t=data(:,2)';

      data1=   [525.0000       46.3723
                525.0000       50.6499
                525.0000       52.6551
                525.0000       53.6585
                525.0000       54.0599
                525.0000       55.1983
                525.0000       56.7400
                525.0000       57.6911];
       dataInterp=data1';

       n=10;
       dataInterp_425 = [ 425*ones(1,n);linspace(31,42.3,n)];      

       data2=  [250.0000    11.1324
                250.0000    15.6110
                250.0000    17.2824
                250.0000    18.3522
                250.0000    19.2214
                250.0000    21.8308];

      dataExtrap=data2';

[normInput,ps]=mapminmax(p);

[normTarget,ts]=mapminmax(t);

validatePercent=0.1;
testPercent=0.1;
[trainSamples,validateSamples,testSamples] = dividevec(normInput,normTarget,validatePercent,testPercent);

TF1 = 'tansig';
TF2 = 'tansig'; 
TF3 = 'purelin';
net=newff(normInput,normTarget,[10 10],{ },'trainbr');

net.trainParam.epochs=300;
net.trainParam.lr=0.1;
net.trainParam.goal=0.0001;
% net.divideFcn='';

% [net,tr] = train(net,trainSamples.P,trainSamples.T,[],[],validateSamples,testSamples);
[net,tr] = train(net,normInput,normTarget);

normInterp=mapminmax('apply',dataInterp,ps);
normExtrap=mapminmax('apply',dataExtrap,ps);
normInterp_425=mapminmax('apply',dataInterp_425,ps);

normTrainOutput=sim(net,normInput);
normInterpOutput=sim(net,normInterp);
normExtrapOutput=sim(net,normExtrap);
normInterp_425Output=sim(net,normInterp_425);

TrainOutput=mapminmax('reverse',normTrainOutput,ts);
InterpOutput=mapminmax('reverse',normInterpOutput,ts);
ExtrapOutput=mapminmax('reverse',normExtrapOutput,ts);
Interp_425Output=mapminmax('reverse',normInterp_425Output,ts);

figure(1)
% comparison of traning data and predictions
plot(p(2,:),TrainOutput,'o');
hold on
plot(p(2,:),t,'r*');

% interpolation
plot(dataInterp(2,:),InterpOutput,'o')

     refInterp=[  525  46.3723   4.5638 
                  525  50.6499   4.5705  
                  525  52.6551   4.5638 
                  525  53.6585   4.5168 
                  525  54.0599   4.4899 
                  525  55.1983   4.3624 
                  525  56.7400   4.1007 
                  525  57.6911   3.1812 ];
            refInterp=[refInterp(:,1) refInterp(:,3) refInterp(:,2)]';

plot(refInterp(3,:),refInterp(2,:),'rd','MarkerFaceColor','r')

% extrapolation
plot(dataExtrap(2,:),ExtrapOutput,'o')
hold on

     refExtrap=[250.0000    1.5369   11.1324
                250.0000    1.5101   15.6110
                250.0000    1.4832   17.2824
                250.0000    1.4631   18.3522
                250.0000    1.4430   19.2214
                250.0000    1.2819   21.8308]';

plot(refExtrap(3,:),refExtrap(2,:),'rd','MarkerFaceColor','r') 

plot(dataInterp_425(2,:),Interp_425Output,'o')