Generates heatmaps that highlight image pixels that were important for a CNN classification result. Currently only supports VGG16. The CNN and relevance propagation rules are implemented in TensorFlow.
Example:
For more details on LRP see:
http://heatmapping.org
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.
http://www.sciencedirect.com/science/article/pii/S0031320316303582?via%3Dihub
https://arxiv.org/abs/1512.02479
Setup:
- create Python 3 virtual environment and activate the environment
virtualenv -p python3 ~/nn-attribute
source ~/nn-attribute/bin/activate
- install Python dependencies with
pip3 install -r requirements.txt
- download VGG16 pretrained weights
wget -P data/vgg16 ftp://mi.eng.cam.ac.uk/pub/mttt2/models/vgg16.npy
Generate heatmaps:
- choose image file to generate heatmaps for (e.g. ./data/goose.jpg)
- choose alpha (recommendation: alpha=2)
python -m heatmap \
--image-file data/goose.jpg \
--target-file heatmap.pdf \
--model vgg16 \
--weights-file data/vgg16.npy \
--classes-file data/vgg16/classes.json \
--alpha 2
- generated heatmaps are saved in
resultsfolder