https://github.com/shzygmyx/Matrix-Capsules-pytorch/blob/bc9e26dae9390bc96a5420a94cbe89432a1ad10a/Capsules.py#L199

pose's shape is (b, self.B, H, W), how can it be reshaped into ( b, 16, self.B, H, W) in the next line????? why does no one point this out? no error reported????

when Debug at here y_range = (max(floor((j-self.K)/self.stride)+1,0),min(j//self.stride+1,width_out))will print error TypeError: range() integer start argument expected, got float.

Hi, @shzygmyx

I am a contributor to another matrix CapsNet in TensorFlow. Are you able to reproduce the result suggested by the author of the paper?

Thanks for your work! However, when I set r = 2, the loss turned into nan. Have you met this problem? How to fix it?

Hello. I'm trying to overfit to a toy batch with r=2. With batchsize >1 I am unable to overfit with r>1, though everything works with r=1. In particular, the network outputs the same results for all images in the batch.

1. for the loss funtion in 'class CapsNet' (train.py)
   you use mask = u.ge(0).float() to complete the max operation. but after a check about this function(tensor.ge) i think we should not us this function. because we want to preserve the value which >=0 and change the value which <0 into 0. If we use tensor.ge, we would turn all the value which is >=0 into 1. It's fault i think.
   2.loss = ((masku)2).sum()/b - m2 #float
   for this code, why we should masku and why we should - m**2. I could not find an introduction in the equ5 in the paper.

Thanks for your kind help!

Hi,
I am on windows 10 with python 3 and conda install. I have just cloned and ran as suggested and I get this error RuntimeError: zero-dimensional tensor (at position 0) cannot be concatenated. Is this something you may have seen?
Thanks.

When trying your demo on my 1050 Ti, this model only reaches 30% usage of CUDA cores and runs really slow. Meantime, another traditional CapsNet can reach 100% usage. Does this mean a performance problem of your model?