# scipy.optimize.check_grad

scipy.optimize.check_grad(func, grad, x0, *args, epsilon=1.4901161193847656e-08, direction='all', seed=None)

  Check the correctness of a gradient function by comparing it against a (forward) finite-difference approximation of the gradient.

  Parameters:
    func callable `func(x0, *args)`

      Function whose derivative is to be checked.

    grad callable `grad(x0, *args)`

      Jacobian of func.

    x0 ndarray

      Points to check grad against forward difference approximation of grad using func.

    args*args, optional

      Extra arguments passed to func and grad.

    epsilon float, optional

      Step size used for the finite difference approximation. It defaults to `sqrt(np.finfo(float).eps)`, which is approximately 1.49e-08.

    direction str, optional

      If set to `'random'`, then gradients along a random vector are used to check grad against forward difference approximation using func. By default it is `'all'`, in which case, all the one hot direction vectors are considered to check grad. If func is a vector valued function then only `'all'` can be used.

    seed{None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional

      If seed is None (or np.random), the `numpy.random.RandomState` singleton is used. If seed is an int, a new `RandomState` instance is used, seeded with seed. If seed is already a `Generator` or `RandomState` instance then that instance is used. Specify seed for reproducing the return value from this function. The random numbers generated with this seed affect the random vector along which gradients are computed to check `grad`. Note that seed is only used when direction argument is set to ‘random’.

  Returns:
    err float

      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

  `approx_fprime`

  Examples

      >>> import numpy as np
      >>> 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  # may vary
      >>> rng = np.random.default_rng()
      >>> check_grad(func, grad, [1.5, -1.5],
      ...             direction='random', seed=rng)
      2.9802322387695312e-08