Programming Machine Learning: a tip and a gotcha

Tip: If you are on a slow or old machine like me, or if you want to run many different examples to explore the design space you can speed up the calculations by removing a border from the MNIST image data. Every image has a 1-pixel white border. Removing this border reduces the number of input variables by 108 or more than 13%. In fact, you can drop even a 3-pixel border without any impact that I can notice. Dropping more is also possible, but then the expected max accuracy will also start to drop. But it is quite remarkable that even using only the innermost 8x8 image fragment one can easily get above 80% accuracy.

Gotcha: I have run the one hidden layer with 100 nodes scenario with the original test set of 10,000 examples. I did not split it into the 5,000 for validation and 5,000 for testing. I was surprised that the maximum accuracy I could achieve was only 97.8%, not the 98.6% stated in the book. However, this is purely an effect of the training set. When I did the splitting into validation and testing set with 5,000 for testing I got the 98.6% accuracy with the same network weights. This was surprising to me, that there is that big a change in accuracy due to the size of the test set.