testnight2.m

clear acc
load M
digitDatasetPath = fullfile(matlabroot,'toolbox','nnet','nndemos','nndatasets','DigitDataset');
digitData = imageDatastore(digitDatasetPath,'IncludeSubfolders',true,'LabelSource','foldernames');
trainingNumFiles = 1000;
rng(1)
[trainDigitData,testDigitData] = splitEachLabel(digitData,trainingNumFiles,'randomize');
tmp_net = M.saveobj;
tmp_net.Layers(2).Weights = reshape(M.Layers(2).Weights,size(tmp_net.Layers(2).Weights));
tmp_net.Layers(2).Bias = reshape(M.Layers(2).Bias,size(tmp_net.Layers(2).Bias));
tmp_net.Layers(4).Weights = reshape(M.Layers(4).Weights,size(tmp_net.Layers(4).Weights));
tmp_net.Layers(4).Bias = reshape(M.Layers(4).Bias,size(tmp_net.Layers(4).Bias));
tmp_net.Layers(6).Bias = reshape(M.Layers(6).Bias,size(tmp_net.Layers(6).Bias));
tmp_net.Layers(8).Weights = reshape(M.Layers(8).Weights,size(tmp_net.Layers(8).Weights));
tmp_net.Layers(8).Bias = reshape(M.Layers(8).Bias,size(tmp_net.Layers(8).Bias));

%% Decomposition
%[W1,error,ee] = Reconstruct(M.Layers(6).Weights,,,bits)
%%
for bits = 1
    [W1,error,ee] = Reconstruct(M.Layers(6).Weights,[20,32,32,32],[1,20,640,32,1],bits)
    tmp_net.Layers(6).Weights = reshape(W1,size(tmp_net.Layers(6).Weights));
    convnet = convnet.loadobj(tmp_net);
    Y = classify(convnet, trainDigitData);
    YLabels = trainDigitData.Labels;
    acc = sum(Y==YLabels)/max(size(YLabels));
    filename = ['testF',num2str(bits),'.mat'];
    save(filename)
end