Blog Post: http://kuldeepsinghsidhu.blogspot.com
A simple neural network that predicts the first character that is stored in a QR-code such as this:
At training time, QR codes are created randomly from alphanumeric strings
of length ten (sample in the example above). The validation set also
contains 5000 randomly generated QR codes.
virtualenv -p python3 venv --no-site-packages
source venv/bin/activate
pip3 list
pip3 install -r requirements.txt
Using Python 3.5+
To start the training, simply call:
python train.py
Then, you can run tensorboard to view the test-set accuracy:
tensorboard --logdir /tmp/qrnet-log --reload_interval 5
After approximately 50.000 iterations (with 200 QR codes per batch), it reaches a test-set accuracy of over 0.999.
[1] Note that there are (2 × 26 + 10)^10 ≈ 10^18 possible QR codes, so chances of collisions between the training and the test set are vanishingly small.
[2] Unfortunately, the online training is rather slow. Most of the time is actually spent in the (parallelized) QR code generation.
Kuldeep Singh Sidhu
Github: github/singhsidhukuldeep
https://github.com/singhsidhukuldeep
Website: Kuldeep Singh Sidhu (Website)
http://kuldeepsinghsidhu.com
LinkedIn: Kuldeep Singh Sidhu (LinkedIn)
https://www.linkedin.com/in/singhsidhukuldeep/