https://github.com/cybprojects65/VariationalAutoencoder
337
Presentation of the VAE: https://github.com/cybprojects65/VariationalAutoencoder
Train the model on the data.csv file with the 'environment 2017_land_distance,environment 2017_mean_depth' columns:
docker run --rm -v %cd%:/home/docker/ gianpaolocoro/variationalautoencoder:latest /bin/sh -c "java -cp vae.jar it.cnr.anomaly.JavaVAE -i/home/docker/data.csv -v'environment 2017_land_distance,environment 2017_mean_depth' -h5 -e1000 -o/home/docker/dockout/ -r16 -ttrue"
Test the model on the data.csv file with the 'environment 2017_land_distance,environment 2017_mean_depth' columns:
docker run --rm -v %cd%:/home/docker/ gianpaolocoro/variationalautoencoder:latest /bin/sh -c "java -cp vae.jar it.cnr.anomaly.JavaVAE -i/home/docker/data.csv -v'environment 2017_land_distance,environment 2017_mean_depth' -o/home/docker/dockout/ -r16 -tfalse -m/home/docker/dockout/model.bin"
Content type
Image
Digest
sha256:377cc4155…
Size
1.7 GB
Last updated
almost 2 years ago
docker pull gianpaolocoro/variationalautoencoder