%%?CS294A/CS294W?Programming?Assignment?Starter?Code

%??Instructions
%??------------
%?
%??This?file?contains?code?that?helps?you?get?started?on?the
%??programming?assignment.?You?will?need?to?complete?the?code?in?sampleIMAGES.m
%??sparseAutoencoderCost.m?and?computeNumericalGradient.m.?
%??For?the?purpose?of?completing?the?assignment?you?do?not?need?to
%??change?the?code?in?this?file.?
%
%%======================================================================
%%?STEP?0:?Here?we?provide?the?relevant?parameters?values?that?will
%??allow?your?sparse?autoencoder?to?get?good?filters;?you?do?not?need?to?
%??change?the?parameters?below.

visibleSize?=?8*8;???%?number?of?input?units?
hiddenSize?=?25;?????%?number?of?hidden?units?
sparsityParam?=?0.01;???%?desired?average?activation?of?the?hidden?units.
?????????????????????%?（This?was?denoted?by?the?Greek?alphabet?rho?which?looks?like?a?lower-case?“p“
		?????%??in?the?lecture?notes）.?
lambda?=?0.0001;?????%?weight?decay?parameter???????
beta?=?3;????????????%?weight?of?sparsity?penalty?term???????

%%======================================================================
%%?STEP?1:?Implement?sampleIMAGES
%
%??After?implementing?sampleIMAGES?the?display_network?command?should
%??display?a?random?sample?of?200?patches?from?the?dataset

patches?=?sampleIMAGES;
display_network（patches（:randi（size（patches2）2041））8）;


%??Obtain?random?parameters?theta
theta?=?initializeParameters（hiddenSize?visibleSize）;

%%======================================================================
%%?STEP?2:?Implement?sparseAutoencoderCost
%
%??You?can?implement?all?of?the?components?（squared?error?cost?weight?decay?term
%??sparsity?penalty）?in?the?cost?function?at?once?but?it?may?be?easier?to?do?
%??it?step-by-step?and?run?gradient?checking?（see?STEP?3）?after?each?step.??We?
%??suggest?implementing?the?sparseAutoencoderCost?function?using?the?following?steps:
%
%??（a）?Implement?forward?propagation?in?your?neural?network?and?implement?the?
%??????squared?error?term?of?the?cost?function.??Implement?backpropagation?to?
%??????compute?the?derivatives.???Then?（using?lambda=beta=0）?run?Gradient?Checking?
%??????to?verify?that?the?calculations?corresponding?to?the?squared?error?cost?
%??????term?are?correct.
%
%??（b）?Add?in?the?weight?decay?term?（in?both?the?cost?function?and?the?derivative
%??????calculations）?then?re-run?Gradient?Checking?to?verify?correctness.?
%
%??（c）?Add?in?the?sparsity?penalty?term?then?re-run?Gradient?Checking?to?
%??????verify?correctness.
%
%??Feel?free?to?change?the?training?settings?when?debugging?your
%??code.??（For?example?reducing?the?training?set?size?or?
%??number?of?hidden?units?may?make?your?code?run?faster;?and?setting?beta?
%??and/or?lambda?to?zero?may?be?helpful?for?debugging.）??However?in?your?
%??final?submission?of?the?visualized?weights?please?use?parameters?we?