Simple gradient computation library for Python.
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]
Be sure to run the following command before developing
$ git clone https://github.com/ctgk/numgrad.git
$ cd numgrad
$ pre-commit install