numgrad 0.2.1

Simple gradient computation library in Python

NumGrad

Simple gradient computation library for Python.

Getting Started

pip install numgrad

Inspired by tensorflow, numgrad supports automatic differentiation in tensorflow v2 style using original numpy and scipy functions.

>>> import numgrad as ng
>>> import numpy as np  # Original numpy
>>>
>>> # Pure numpy function
>>> def tanh(x):
...     y = np.exp(-2 * x)
...     return (1 - y) / (1 + y)
...
>>> x = ng.Variable(1)
>>> with ng.Graph() as g:
...     # numgrad patches numpy functions automatically here
...     y = tanh(x)
...
>>> g.backward(y, [x])
(0.419974341614026,)
>>> (tanh(1.0001) - tanh(0.9999)) / 0.0002
0.41997434264973155

numgrad also supports jax style automatic differentiation.

>>> import numgrad as ng
>>> import numpy as np  # Original numpy unlike `jax`
>>>
>>> power_derivatives = [lambda a: np.power(a, 5)]
>>> for _ in range(6):
...     power_derivatives.append(ng.grad(power_derivatives[-1]))
...
>>> [f(2) for f in power_derivatives]
[32, 80.0, 160.0, 240.0, 240.0, 120.0, 0.0]
>>> [f(-1) for f in power_derivatives]
[-1, 5.0, -20.0, 60.0, -120.0, 120.0, -0.0]

Contribute

Be sure to run the following command before developing

$ git clone https://github.com/ctgk/numgrad.git
$ cd numgrad
$ pre-commit install