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(KSC 2019) 학습된 신경망에서 카테고리 부분집합 분류를 위한 서브 네트워크 추출 기법

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SubNet

Keras implementation of SubNetwork

Results

Compare accuracy and number of parameters

The following results can be reproduced with command:

python main.py --dataset MNIST --subset 1 4
python main.py --dataset MNIST --subset 0 2 6
python main.py --dataset MNIST --subset 0 4 6 7
python main.py --dataset MNIST --subset 0 1 2 3 4 5 6 7 8 9
python main.py --dataset FashionMNIST --subset 5 7 9
python main.py --dataset FashionMNIST --subset 0 1 2 3 4 5 6 7 8 9
subset SubNetwork OriginalNetwork A/B (%)
# Params (A) Test - acc # Params (B) Test - acc
Using MNISTdataset Network
[1, 4] 21,251 0.999 124,825 0.983 17.02
[0, 2, 6] 29,947 0.992 0.980 23.99
[0, 4, 6, 7] 40,243 0.992 0.981 32.24
ALL 124,825 0.979 0.979 100.
Using FashionMNISTdataset Network
subset for shoes categories [5, 7, 9] 87,147 0.963 330,670 0.962 26.35
ALL 330,670 0.911 0.911 100.

Average number of nodes according to number of subset elements

The following results can be reproduced with command:

python main.py --dataset MNIST --subset 0 1 2 3 4 5 6 7 8 9 --meanNodes True
python main.py --dataset FashionMNIST --subset 0 1 2 3 4 5 6 7 8 9 --meanNodes True
Average number of Nodes
X-axis: Average number of nodes, Y-aixs: number of subset elements

Usage

Prerequisites

  1. Keras
  2. Python packages: numpy

Command

python main.py --dataset <choose dataset> --subset <subset of total categories>

Example: python main.py --dataset MNIST --subset 0 1 2 3

Arguments

Required:

  • --dataset: Choose datset. Option: MNIST or FasionMNIST
  • --subset: Subset elements of total categories. example: --subset 0 1 2

Optional:

  • --meanNodes: Whether or not to print the average number of nodes. type: bool, Default: False

Acknowledgements

This implementation has been tested with Keras 2.2.4 on Windows 10.

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(KSC 2019) 학습된 신경망에서 카테고리 부분집합 분류를 위한 서브 네트워크 추출 기법

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