Can you pass arguments to a function object in Python?

Question

For the sake of organization, I outline an ML optimizer with the rest of my config constants at the top of my file:

optimizer = torch.optim.SGD()

To use the optimizer, I have to pass in the model parameters, generated later on in the code

optimizer = torch.optim.SGD(model.parameters, lr=LEARNING_RATE)

Is there any way for me to pass arguments into the variable optimizer?

edit: I think my question is unclear, here's a simpler example of what I was asking:

#take the square of some arbitrary number

fn = math.prod()
x = 5

#how to feed x into the variable fn?

jonesy · Accepted Answer · 2020-06-01 20:10:53Z

2

Sure!

>>> def add(x, y):
...    return x+y
... 
>>> z = add
>>> z(1,1)
2

answered Jun 1, 2020 at 20:10

jonesy

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1 Comment

Alec Over a year ago

Yes, I figured it out but this is what I was looking for. I'll accept when it allows me to. Upvoted

chepner · Accepted Answer · 2020-06-01 20:10:49Z

0

It sounds like you are asking about partial application. This is a way of "wrapping" a function and some arguments into a single, new callable, which takes fewer (or even no) arguments.

>>> from functools import partial
>>> f = lambda x, y, z: x + y + z
>>> g = partial(f, 2, 3)
>>> g(4)
9
>>> h = partial(f, 1, 2, 3)
>>> h()
6

answered Jun 1, 2020 at 20:10

chepner

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Comments

Techniquab · Accepted Answer · 2020-06-01 20:13:42Z

0

I'm not sure if this is exactly what you are after, but I often use functools.partial to bind values to parameters.

import functools

optimizer = functools.partial(torch.optim.SGD, model.parameters, lr=LEARNING_RATE)

then you can call optimizer as a function and it will have the first positional paramerte and the "lr" keyword parameter already set, and you can pass whatever other parameters you need when you call optimizer.

answered Jun 1, 2020 at 20:13

Techniquab

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Comments

Alec · Accepted Answer · 2020-06-01 20:22:48Z

0

I figured it out, seems really simple but I'll leave it up because I couldn't find it online anywhere.

You can just pass the arguments to the new variable because it's now the same object as the function

optimizer(model.parameters, lr=LEARNING_RATE)

edited Jun 1, 2020 at 20:22

answered Jun 1, 2020 at 20:11

Alec

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