gradient_check.py 文件源码

def grad_check_sparse(f, x, analytic_grad, num_checks=10, h=1e-5):
  """
  sample a few random elements and only return numerical
  in this dimensions.
  """

  for i in xrange(num_checks):
    ix = tuple([randrange(m) for m in x.shape])

    oldval = x[ix]
    x[ix] = oldval + h # increment by h
    fxph = f(x) # evaluate f(x + h)
    x[ix] = oldval - h # increment by h
    fxmh = f(x) # evaluate f(x - h)
    x[ix] = oldval # reset

    grad_numerical = (fxph - fxmh) / (2 * h)
    grad_analytic = analytic_grad[ix]
    rel_error = abs(grad_numerical - grad_analytic) / (abs(grad_numerical) + abs(grad_analytic))
    print('numerical: %f analytic: %f, relative error: %e' % (grad_numerical, grad_analytic, rel_error))