function?[activation]?=?feedForwardAutoencoder（theta?hiddenSize?visibleSize?data）

%?theta:?trained?weights?from?the?autoencoder
%?visibleSize:?the?number?of?input?units?（probably?64）?
%?hiddenSize:?the?number?of?hidden?units?（probably?25）?
%?data:?Our?matrix?containing?the?training?data?as?columns.??So?data（:i）?is?the?i-th?training?example.?
??
%?We?first?convert?theta?to?the?（W1?W2?b1?b2）?matrix/vector?format?so?that?this?
%?follows?the?notation?convention?of?the?lecture?notes.?

W1?=?reshape（theta（1:hiddenSize*visibleSize）?hiddenSize?visibleSize）;
b1?=?theta（2*hiddenSize*visibleSize+1:2*hiddenSize*visibleSize+hiddenSize）;

%%?----------?YOUR?CODE?HERE?--------------------------------------
%??Instructions:?Compute?the?activation?of?the?hidden?layer?for?the?Sparse?Autoencoder.

activation??=?sigmoid（W1*data+repmat（b1[1size（data2）]））;
%-------------------------------------------------------------------

end

%-------------------------------------------------------------------
%?Here‘s?an?implementation?of?the?sigmoid?function?which?you?may?find?useful
%?in?your?computation?of?the?costs?and?the?gradients.??This?inputs?a?（row?or
%?column）?vector?（say?（z1?z2?z3））?and?returns?（f（z1）?f（z2）?f（z3））.?

function?sigm?=?sigmoid（x）
????sigm?=?1?./?（1?+?exp（-x））;
end

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----

?????文件????????610??2014-11-05?09:36??GUI數字識別系統\2.jpg

?????文件????????573??2014-11-05?10:25??GUI數字識別系統\3.jpg

?????文件???????1296??2014-10-15?11:43??GUI數字識別系統\feedForwardAutoencoder.m

?????文件???????4728??2014-11-05?08:22??GUI數字識別系統\nmist_softmax.fig

?????文件???????5408??2014-11-05?10:10??GUI數字識別系統\nmist_softmax.m

?????文件????2729032??2014-10-22?10:33??GUI數字識別系統\result2.mat

?????目錄??????????0??2014-11-05?11:08??GUI數字識別系統

-----------?---------??----------?-----??----

??????????????2741647????????????????????7