Test on smallNORM with parameters specified in the paper have very bad result about matrix-capsules-em-tensorflow HOT 5 OPEN

Facing a similar issue. In the initial runs, the result was quite bad with the smallNORB. After trying multiple runs, I was able to get a decent accuracy. I got similar huge variance in validation accuracy for the training runs with MNIST. @www0wwwjs1 do you know what might cause this much variance?

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Because the test script doesn't read the latest training parameters, it reads the whole models saved starting from the first iteration, hence the first training parameters will have a terrible accuracy, try using the latest training parameters only, the output should be stable.

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Has anyone here had similar results to me on smallNORB? Images for output are in issue #30 This is just the training loop.

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I also get very poor performance when using 3 iterations on smallNORB. The model failed to learn anything after almost 30k iterations. Any suggestions?

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I'm not sure if the dataset is loaded correctly: The smallNorb dataset contains 24300 stereo images, but the model loads 24300*2 images with depth 1. I think this is wrong and possible this leads to the bad performance...

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