# 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?

• If you have more images, train your model on them and time the training. It'll interesting to know the difference in training time. – Mohit Motwani Aug 25 '18 at 3:22
• Are you referring to the amount of time per epoch or the total time to train the network? – timleathart Sep 24 '18 at 3:18

1. If the data types are different for the two images - np.float32 vs np.float64
I would suggest you to use methods like timeit that do multiple runs to verify that this behaviour is consistent.