Matrix-Capsules-pytorch

This is a pytorch implementation of Matrix Capsules with EM routing

In Capsules.py, there are two implemented classes: PrimaryCaps and ConvCaps. The ClassCapsules in the paper is actually a special case of ConvCaps with whole receptive field, transformation matrix sharing and Coordinate Addition.

In train.py, I define a CapsNet in the paper using classes in Capsules.py, and could be used to train a model for MNIST dataset.

Train a small CapsNet on MNIST

python train.py -batch_size=64 -lr=2e-2 -num_epochs=5 -r=1 -print_freq=5.

Note:

• more args can be found in utils.py, and if you want to change A,B,C,D, go to line 62 of train.py
• m and lambda schedule need to be changed if you want to train a capsnet with r=2 or 3. The default schedule make capsnet does not converge in those cases.

Results

The test accuracy is around 97.6% after 1 epoch and 98.7% after 2 epochs of training with a small Capsule of A,B,C,D,r = 64,8,16,16,1. After 30 epochs of training, the best acc is around 99.3%. More results on different configurations are welcomed.

TODO

• using more matrix operation rather than for iteration in E-step of Capsules.py.
• make capsules work when height_in != width_in
• find better lambda/m schedule to speed up the convergence.