def testHessian(self):
# test based on code from sindhwani@google
def fun(x, t):
return np.sum(np.power(np.maximum(x, 0.0), 2)) + t
x = onp.array([-1., -0.5, 0., 0.5, 1.0])
ans = hessian(lambda x: fun(x, 0.0))(x)
expected = onp.array([[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.],
[0., 0., 0.5, 0., 0.], [0., 0., 0., 2., 0.],
[0., 0., 0., 0., 2.]])
self.assertAllClose(ans, expected, check_dtypes=False)
def testIssue387(self):
# https://github.com/google/jax/issues/387
R = np.random.RandomState(0).rand(100, 2)
def dist_sq(R):
dR = R[:, jnp.newaxis, :] - R[jnp.newaxis, :, :]
zero = jnp.zeros_like(dR)
dR = dR - jnp.where(jnp.abs(dR) < 0.5, zero, 0.5 * jnp.sign(dR))
return jnp.sum(dR ** 2, axis=2)
@jit
def f(R):
_ = dist_sq(R)
return jnp.sum(R ** 2)
_ = hessian(f)(R) # don't crash on UnshapedArray