def test_make_ggnvp():
A = npr.randn(5, 4)
x = npr.randn(4)
v = npr.randn(4)
fun = lambda x: np.dot(A, x)
check_equivalent(make_ggnvp(fun)(x)(v), _make_explicit_ggnvp(fun)(x)(v))
fun2 = lambda x: np.tanh(np.dot(A, x))
check_equivalent(make_ggnvp(fun2)(x)(v), _make_explicit_ggnvp(fun2)(x)(v))
def test_make_ggnvp():
A = npr.randn(5, 4)
x = npr.randn(4)
v = npr.randn(4)
fun = lambda x: np.dot(A, x)
check_equivalent(make_ggnvp(fun)(x)(v), _make_explicit_ggnvp(fun)(x)(v))
fun2 = lambda x: np.tanh(np.dot(A, x))
check_equivalent(make_ggnvp(fun2)(x)(v), _make_explicit_ggnvp(fun2)(x)(v))
def test_make_ggnvp_nondefault_g():
A = npr.randn(5, 4)
x = npr.randn(4)
v = npr.randn(4)
g = lambda y: np.sum(2.*y**2 + y**4)
fun = lambda x: np.dot(A, x)
check_equivalent(make_ggnvp(fun, g)(x)(v), _make_explicit_ggnvp(fun, g)(x)(v))
fun2 = lambda x: np.tanh(np.dot(A, x))
check_equivalent(make_ggnvp(fun2, g)(x)(v), _make_explicit_ggnvp(fun2, g)(x)(v))
def test_make_ggnvp_nondefault_g():
A = npr.randn(5, 4)
x = npr.randn(4)
v = npr.randn(4)
g = lambda y: np.sum(2. * y**2 + y**4)
fun = lambda x: np.dot(A, x)
check_equivalent(
make_ggnvp(fun, g)(x)(v),
_make_explicit_ggnvp(fun, g)(x)(v))
fun2 = lambda x: np.tanh(np.dot(A, x))
check_equivalent(
make_ggnvp(fun2, g)(x)(v),
_make_explicit_ggnvp(fun2, g)(x)(v))
def test_make_ggnvp_broadcasting(): A = npr.randn(4, 5) x = npr.randn(10, 4) v = npr.randn(10, 4) fun = lambda x: np.tanh(np.dot(x, A)) res1 = np.stack([_make_explicit_ggnvp(fun)(xi)(vi) for xi, vi in zip(x, v)]) res2 = make_ggnvp(fun)(x)(v) check_equivalent(res1, res2)
def test_make_ggnvp_broadcasting():
A = npr.randn(4, 5)
x = npr.randn(10, 4)
v = npr.randn(10, 4)
fun = lambda x: np.tanh(np.dot(x, A))
res1 = np.stack(
[_make_explicit_ggnvp(fun)(xi)(vi) for xi, vi in zip(x, v)])
res2 = make_ggnvp(fun)(x)(v)
check_equivalent(res1, res2)