kyzn / minibatchimagemorphing

An attempt at introducing morphed in between images to mini batches while training a Deep Neural Network.

Image Morphing for Mini Batches in DNN

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.

Results

	Min Test Cost	Max Test Accuracy
Benchmark	0.6108	97.00%
With Image Morphing	1.3728	95.07%

License

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".