專家混合 - fminunc優化過早停止

Question

我有一個混合專家代碼，其中使用少量的參數。這是因爲如下：

global x_au; 
global x_vi; 
global x_alpha; 
global y; 
global parameter; 

options = optimoptions(@fminunc,'GradObj', 'on', 'Algorithm','quasi-newton','MaxIter', 10000,'Display','iter-detailed'); % change number of iterations 
optTheta=[]; 

x_au=x_au_train; 
x_vi=x_vi_train; 
x_alpha=x_alpha_train; 
y=y_train; 
parameter=zeros(8969,1); 

%expectation step 
fprintf('opt1 begins'); 
opt_1; 
fprintf('opt1 complete'); 
%maximaization step 
[x] = fminunc(@costfunction,parameter(1:4483),options); 
parameter(1:4483)=x; 
resnorm1=total_error(parameter(1:4483)); 
k=1; 
count = 1; 
while(1) 
opt_1; 
fprintf('expectation complete'); 
%maximaization step 
[x] = fminunc(@costfunction,parameter(1:4483),options); 
parameter(1:4483)=x; 
resnorm2=total_error(parameter(1:4483)); 
fprintf('resnorm1-resnorm2 - %f, resnorm2 - %f, k - %f',resnorm1-resnorm2,0.000001*resnorm2,k); 
if((resnorm1-resnorm2)< .000001*resnorm2 & k~=1) %% to decrease training time 
    break; 
end 

但現在，當我必須有大量的參數上的問題用這個，我得到以下日誌。

             First-order 
Iteration Func-count  f(x)  Step-size  optimality 
    0   1  5.31444e+10      4.75e+14 

Optimization stopped because the objective function cannot be decreased in the 
current search direction. Either the predicted change in the objective function, 
or the line search interval is less than eps. 
                First-order 
Iteration Func-count  f(x)  Step-size  optimality 
    0   1  5.31444e+10      4.75e+14 

Optimization stopped because the objective function cannot be decreased in the 
current search direction. Either the predicted change in the objective function, 
or the line search interval is less than eps. 

resnorm1-resnorm2 - 0.000000, resnorm2 - 53144.356560, k - 1.000000 
                First-order 
Iteration Func-count  f(x)  Step-size  optimality 
    0   1  5.31444e+10      4.75e+14 

Optimization stopped because the objective function cannot be decreased in the 
current search direction. Either the predicted change in the objective function, 
or the line search interval is less than eps. 

    resnorm1-resnorm2 - 0.000000, resnorm2 - 53144.356560, k - 2.000000 
>> 

然後過程完成，結果很糟糕。至於，可以看出fminunc無法正確優化。有人能幫我一下嗎？

Answer 1

我改變參數初始化從零到蘭特，並與標準化一起，我得到它的工作。

Answer 2

看起來你需要降低訓練速度係數，或者規範你的參數向量。