Programming Machine Learning

Hands On: Achieving 99%

In the beginning of Part II, we set a target for ourselves: 99% accuracy on MNIST. We just got so close, reaching 98.6%. That last 0.4%? That’s up to you.

I told you that configuring a network can be more art than science, and this Hands On is proof of that. Here’s my suggestion: to find better hyperparameters than the ones we have now, drop compare.py. Instead, use neural_network_quieter.py, an alternative version of the network that logs accuracy only once every 10 epochs—it’s much faster. Here are a few things that you can try:

• The standardized version of MNIST generally works better. There’s no reason not to use it.

• Sometimes, it pays off to be patient and wait for more epochs before giving up. However, if 20 or ...