Convolutional neural network accuracy against MNIST digit data

I have been working on building my own neural network framework since Nov. Lately I have been focusing on implementing convolutional neural network ("CNN") to add to the framework. Initially I built the CNN with a back of the envelope-type of calculation and it didn't work at all. So I spent two days calculating and documenting the result which I posted earlier. At least for me, it is very hard to debug a neural network framework. This is because when neural network doesn't work, it could be due to hyperparameters, specific values weights were initialized for the session, or one or more bugs in my framework itself. After spending significant amount of time debugging with a fear that I may not be able to get this to work, I finally found a few bugs in my framework. Fixed them resolved the problem of loss not going down. I ran a 4-epoch training session against MNIST digit data overnight, and woke up to find the accuracy was 97.95%. If you use a pre-built neural network framework, I believe the accuracy number could go higher. However this is a number that I feel comfortable with declaring a victory for my project. If you want to try it, this is the specific script: CNN MNIST Python script.