Add a new Op#
Don’t define new Ops unless you have to#
It is usually not useful to define Ops that can be easily
implemented using other already existing Ops. For example, instead of
writing a “sum_square_difference” Op, you should probably just write a
simple function:
from aesara import tensor as at
def sum_square_difference(a, b):
return at.sum((a - b)**2)
Even without taking Aesara’s rewrites into account, it is likely to work just as well as a custom implementation. It also supports all data types, tensors of all dimensions as well as broadcasting, whereas a custom implementation would probably only bother to support contiguous vectors/matrices of doubles…
Use Aesara’s high order Ops when applicable#
Aesara provides some generic Op classes which allow you to generate a
lot of Ops at a lesser effort. For instance, Elemwise can be used to
make elemwise operations easily, whereas DimShuffle can be
used to make transpose-like transformations. These higher order Ops
are mostly tensor-related, as this is Aesara’s specialty.