t-sne visualization of mnist images when feature is represented by raw pixels and cnn learned feature
- the training code is from pytorch mnist example. The accuracy is 98% when use the original code, when bn is used in convolution and fully connected layer, the accuracy is 99. The training code here is with bn.
- the code for t-sne visualization is from danielfrg/tsne
- you can find the original mnist train raw data(60000x784), lable(60000x1), cnn learned feature(60000x50), t-sne generated feature(60000x2) for raw data and cnn learned feature, trained model in Baidu Pan or Google Drive
- tsne_vis.ipynb is used to do tsne and visualization
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|---|---|
| t-sne of raw image pixel | t-sne of cnn learned feature |
from above visualization, it is shown that t-sne of cnn learned feature is more centered and cleaner than that of t-sne of raw image pixel