Do you have a new machine learning model that you want to optimize, and do not want to bother computing the gradients? Autoptim is for you.
As of version 0.3, pytorch has been replaced with autograd for automatic differentiation. It makes the interfacing with numpy even simpler.
Autoptim is a small Python package that blends autograd's automatic differentiation in scipy.optimize.minimize.
The gradients are computed under the hood using automatic differentiation; the user only provides the objective function:
import numpy as np
from autoptim import minimize
def rosenbrock(x):
return (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2
x0 = np.zeros(2)
x_min, _ = minimize(rosenbrock, x0)
print(x_min)
>>> [0.99999913 0.99999825]It comes with the following features:
-
Natural interfacing with Numpy: The objective function is written in standard Numpy. The input/ output of
autoptim.minimizeare Numpy arrays. -
Smart input processing:
scipy.optimize.minimizeis only meant to deal with one-dimensional arrays as input. Inautoptim, variables can be multi-dimensional arrays or lists of arrays. -
Preconditioning: Preconditioning is a simple way to accelerate minimization, by doing a change of variables.
autoptimmakes preconditioning straightforward.
This package is meant to be as easy to use as possible. As so, some compromises on the speed of minimization are made.
To install, use pip:
pip install autoptim
- numpy>=1.12
- scipy>=0.18.0
- autograd >= 1.2
Several examples can be found in autoptim/tutorials