> running training for 10 hrs only to have a validation loop fail because of a shape mismatch. I agree that tracking array shapes at build time could catch cer

> running training for 10 hrs only to have a validation loop fail because of a shape mismatch. I agree that tracking array shapes at build time could catch certain classes of bugs (and I would love to have that capability). However, it's not as easy as it may seem. Consider the following example. It shows that even a simple slicing operation can change the number of array dimensions in ways that are only known at run time. >>> def f(arr, elem): ... return arr[elem] ... >>> arr=np.zeros((1, 1)) >>> f(arr, 0).shape (1,) >>> f(arr, ...).shape (1, 1) >>> f(arr, np.newaxis).shape (1, 1, 1)