why i am having 'Training data for an autoencoder must either be a matrix, or a cell array of equal size images.' error though my images have the same size?

Question

Hadeer tawfik 2016-10-7

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/306306-why-i-am-having-training-data-for-an-autoencoder-must-either-be-a-matrix-or-a-cell-array-of-equal

编辑： Hadeer tawfik 2016-10-7

my feature vector file is csvlistdnewtarget.mat and it is a 1x83 feature vector of 83 images, all of the 83 images have the same size which is 160x160, this feature vector has the coffiecents of every image after applying wavelet with sym8 filter and then normalizing it. then i have a target matrix which is 5x83

now when i run the code i have this error:

Error using Autoencoder>iConvertToDouble (line 863)
Training data for an autoencoder must either be a matrix, or a cell array of equal size images.
Error in Autoencoder.train (line 489)
            [X,visualizationDimensions, trainedOnImages] = iConvertToDouble(X);
Error in trainAutoencoder (line 105)
autoenc = Autoencoder.train(X, autonet, paramsStruct.UseGPU);
Error in cataractd (line 11)
autoenc1 = trainAutoencoder(x,hiddenSize1, ...
>>

here is the code:

function [x,t] = cataractdataset
  datastruct_x = load('csvlistdnewtarget.mat');
  x = datastruct_x;
  datastruct_t = load('targetnew.mat');
  t = datastruct_t;
end
%%%%%%in another file%%
[x,t] = cataractdataset;
rng('default')
hiddenSize1 = 100;
autoenc1 = trainAutoencoder(x,hiddenSize1, ...
    'MaxEpochs',400, ...
    'L2WeightRegularization',0.004, ...
    'SparsityRegularization',4, ...
    'SparsityProportion',0.15, ...
    'ScaleData', false);
view(autoenc1)
figure()
plotWeights(autoenc1);
feat1 = encode(autoenc1,x);
hiddenSize2 = 50;
autoenc2 = trainAutoencoder(feat1,hiddenSize2, ...
    'MaxEpochs',100, ...
    'L2WeightRegularization',0.002, ...
    'SparsityRegularization',4, ...
    'SparsityProportion',0.1, ...
    'ScaleData', false);
view(autoenc2)
feat2 = encode(autoenc2,feat1);
softnet = trainSoftmaxLayer(feat2,t,'MaxEpochs',400);
view(softnet)
deepnet = stack(autoenc1,autoenc2,softnet);
view(deepnet)