there is a bit of glitch about batch_size about capsnet_guide_pytorch HOT 4 CLOSED

I just replicated your problem, it is weird that the batch size affects performance so significantly, I suspect that it is due to #1. Yet have no idea why exactly this happens.

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emm…I see. I haven't notice the problem mentioned in #1 . Look forward to your fix.

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I just pushed an update in the dev branch, you can clone it and see what happens. I have run a few experiments but the issue still exists, and it seems irrelevant to the batch size. Let me summarize the problem (experiments below are all batch_size=64):

1. If I set lr=1e-3 as stated in the paper, then the reconstruction loss drops quickly, and finally stays below 30. The reconstructed pictures look good, but the marginal loss does not decline at all and always fixes at 3.644, thus the classifier stays untrained and the accuracy is about 10%.

2. If I set lr=1e-5, then the marginal loss decreases steadily below 0.01 after a few epochs, and the classification accuracy normally goes above 95%. But the reconstruction loss can maximally decreases to about 50, thus the reconstructed pictures look like this:
   

3. But in some special cases, even if I set lr=1e-3, I can still get both the marginal loss below 0.01 and reconstruction loss below 30, like the sample run in my repo. But that requires a bit of luck and you have to experiment many times to encounter this situation (I'm very lucky to have it in the sample run).

So it seems that the loss surface has more than 2 local minimum and it is tricky to optimize to the global minimum. I have no idea why this happens, maybe I need to recheck every line of code to see if there is any wrong implementation.

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I just found that the problem may be caused by the wrong implementation of Decoder, as I forgot to add ReLU nonlinearity in the network. I have fixed it and it seems everything normal now. Check out if your problem still exists, and feel free to reopen this issue.

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