A densenet implementation using tensorflow2
Project description
DenseNet implementation using Tensorflow 2
Quickstart
$ ./bin/start
Setup and use docker
Build the docker image,
$ docker build --rm -f dockerfiles/cpu-jupiter.Dockerfile -t sign-language-recognition:latest .
and now run the image
$ docker run -v "$(pwd)/notebooks:/tf/notebooks" --rm -u $(id -u):$(id -g) -p 6006:6006 -p 8888:8888 sign-language-recognition:latest
Visit that link, hey look your jupyter notebooks are ready to be created. Changes in ./notebooks will be saved.
If you want, you can attach a shell to the running container
$ docker exec -it <container-id> /bin/sh -c "[ -e /bin/bash ] && /bin/bash || /bin/sh"
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Filename, size | File type | Python version | Upload date | Hashes |
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Filename, size densenet-0.1-py3-none-any.whl (5.6 kB) | File type Wheel | Python version py3 | Upload date | Hashes View |