As part of UCSC - CS218 "Deep Learning" class, we wanted to work on possible ways to improve our test accuracy. One idea was to introduce more images to mini-batches. We did it by creating new images.
This code is based on Michael Nielsen's code, which uses MNIST dataset. We also used the same data.
To get the data file, please right click and save mnist.pkl.gz.
| Min Test Cost | Max Test Accuracy | |
|---|---|---|
| Benchmark | 0.6108 | 97.00% |
| With Image Morphing | 1.3728 | 95.07% |
Please visit https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/README.md for license details of Code samples for "Neural Networks and Deep Learning".