Why would 2 sets of similar training samples take significantly longer to train? I've built a fully-connected feed-forward neural network to recognize handwritten digits. I used MNIST and another very similar dataset (containing Arabic digits - same training set and test set count as those of MNIST). 

For a network with exactly the same architecture (1 hidden layer, same learning rate, same weight sampling), I tested on a sub-sample that used 100 images for training. The test accuracy was very low, but what I noticed was that for the Arabic digits, the network took significantly longer to train (double the amount of time than for English digits - 33ms and 15ms, respectively). 

What could be the reason for this?
There could be multiple factors that could cause this to happen:


If the data types are different for the two images - np.float32 vs np.float64
Your computer might be performing some compression and decompression to optimize memory usage. If most of the pixels are very different from each other in the Arabic dataset, then the compressed size may be larger and require more time to decompress.
Your computer happened to have other competing processes running at the same time and distorted the execution results for one of your runs.


I would suggest you to use methods like timeit that do multiple runs to verify that this behaviour is consistent.