Reusing outputs


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 theano import tensor as T

def sum_square_difference(a, b):
    return T.sum((a - b)**2)

Even without taking Theano’s optimizations 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 Theano’s high order Ops when applicable

Theano 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 elementwise operations easily whereas DimShuffle can be used to make transpose-like transformations. These higher order Ops are mostly Tensor-related, as this is Theano’s specialty.

Op Checklist

Use this list to make sure you haven’t forgotten anything when defining a new Op. It might not be exhaustive but it covers a lot of common mistakes.