How to Trust Your Deep Learning Code
This is the support repository for the blog post How to Trust Your Deep Learning Code. It contains code for training a Variational Autoencoder (VAE) and the associated unit tests. The unit tests illustrate useful concepts to test in deep learning projects. The focus lay on writing tests that are readable and reusable.
For more information check out the blog post.
The project uses Python 3.7.
First, install the packages specified in the
conda create -n unittest_dl python=3.7 conda activate unittest_dl conda install --file requirements.txt -c pytorch ## or virtualenv -p python3.7 unittest_dl source unittest_dl/bin/activate pip install -r requirements.txt
This project was developed in PyCharm, so it is the easiest to use that way.
Open it in the IDE and mark the
src directory as Sources Root (right-click the folder > Mark directory as > Sources Root).
Everything should work out of the box now.
To run all tests, right-click the
tests directory and select
"Run 'Unittests in tests'".
As an alternative, you can manually add
src to your
PYTHON_PATH environment variable.