autoptim 0.3

Description:

autoptim 0.3

# autoptim: automatic differentiation + optimizationDo 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.## Short presentationAutoptim is a small Python package that blends `autograd` automatic differentiation in `scipy.optimize.minimize`.The gradients are computed under the hood using automatic differentiation; the user only provides the objective function:```pythonimport numpy as npfrom autoptim import minimizedef rosenbrock(x): return (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2x0 = 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.minimize` are Numpy arrays.- **Smart input processing**: `scipy.optimize.minimize` is only meant to deal with one-dimensional arrays as input. In `autoptim`, 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. `autoptim` makes preconditioning straightforward. ### DisclaimerThis package is meant to be as easy to use as possible. As so, some compromises on the speed of minimization are made.## Installation To install, use `pip`: ``` pip install autoptim ```## Dependencies- numpy>=1.12- scipy>=0.18.0- autograd >= 1.2## ExamplesSeveral examples can be found in `autoptim/tutorials`