scipy.optimize.check_grad¶
-
scipy.optimize.
check_grad
(func, grad, x0, *args, **kwargs)[source]¶ Check the correctness of a gradient function by comparing it against a (forward) finite-difference approximation of the gradient.
- Parameters
- funccallable
func(x0, *args)
Function whose derivative is to be checked.
- gradcallable
grad(x0, *args)
Gradient of func.
- x0ndarray
Points to check grad against forward difference approximation of grad using func.
- args\*args, optional
Extra arguments passed to func and grad.
- epsilonfloat, optional
Step size used for the finite difference approximation. It defaults to
sqrt(numpy.finfo(float).eps)
, which is approximately 1.49e-08.
- funccallable
- Returns
- errfloat
The square root of the sum of squares (i.e. the 2-norm) of the difference between
grad(x0, *args)
and the finite difference approximation of grad using func at the points x0.
See also
Examples
>>> def func(x): ... return x[0]**2 - 0.5 * x[1]**3 >>> def grad(x): ... return [2 * x[0], -1.5 * x[1]**2] >>> from scipy.optimize import check_grad >>> check_grad(func, grad, [1.5, -1.5]) 2.9802322387695312e-08