function?[cost?grad]?=?softmaxCost（theta?numClasses?inputSize?lambda?data?labels）

%?numClasses?-?the?number?of?classes?
%?inputSize?-?the?size?N?of?the?input?vector
%?lambda?-?weight?decay?parameter
%?data?-?the?N?x?M?input?matrix?where?each?column?data（:?i）?corresponds?to
%????????a?single?test?set
%?labels?-?an?M?x?1?matrix?containing?the?labels?corresponding?for?the?input?data
%

%?Unroll?the?parameters?from?theta
theta?=?reshape（theta?numClasses?inputSize）;

numCases?=?size（data?2）;

groundTruth?=?full（sparse（labels?1:numCases?1））;
cost?=?0;

thetagrad?=?zeros（numClasses?inputSize）;

%%?----------?YOUR?CODE?HERE?--------------------------------------
%??Instructions:?Compute?the?cost?and?gradient?for?softmax?regression.
%????????????????You?need?to?compute?thetagrad?and?cost.
%????????????????The?groundTruth?matrix?might?come?in?handy.












%?------------------------------------------------------------------
%?Unroll?the?gradient?matrices?into?a?vector?for?minFunc
grad?=?[thetagrad（:）];
end

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????????1033??2020-11-28?12:27??softmaxCost.m
?????文件????????4775??2020-11-28?12:27??softmaxExercise.m
?????文件?????????713??2020-11-28?12:27??softmaxPredict.m
?????文件????????1891??2020-11-28?12:27??softmaxTrain.m