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CapsNet for MNIST

CapsNet을 이용하여 MNIST 손글씨 인식 테스트를 하고 있습니다.

hyperparameter description

  • --batch_size=100 : batch size
  • --epochs=1 : 이포크 학습 반복수
  • --lam_recon=0.392 : 784 * 0.0005, paper uses sum of SE, here uses MSE 0.392
  • --num_routing=2 : routing 수(disit caps에서 primary caps에 routing 되는 횟수)
  • --shift_fraction=0.2 : 비율만큼 쉬프트(이동)하여 학습
  • --debug=1 : 디버그모드여부(1일경우 디버그 파일을 생성함)
  • --save_dir='./result/trained_model_test.h5' : train모드일경우 모델을 저장할 파일명 지정
  • --is_training=0 : 학습모드 여부(1이면 train, 0이면 test)
  • --weights='/trained_model.h5' : 테스트할때 불러들일 모델 파일명
  • --lr=0.001 : learning rate

Command 창에서 실행

  • python capsulenet_test.py --batch_size=100 --epochs=1 --lam_recon=0.392 --num_routing=2 --shift_fraction=0.2 --debug=1 --save_dir='./result/trained_model_test.h5' --is_training=0 --weights='/trained_model.h5' --lr=0.001

CapsNet Related

Resources of Capsule Network

Papers & Thesis

  • Optimizing Neural Networks that Generate Images, 2014 (pdf)
  • Dynamic Routing Between Capsules, 2017 (pdf)
  • MATRIX CAPSULES WITH EM ROUTING, 2018 (pdf)

Educational Resources

  • Capsule Networks: An Improvement to Convolutional Networks (video)
  • Geoffrey Hinton Capsule theory (video)
  • What is a CapsNet or Capsule Network? (blog)
  • Capsule Networks (CapsNets) – Tutorial (video)

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