def test_numerical_gradient_no_broadcast(data):
x = data.draw(
hnp.arrays(shape=hnp.array_shapes(max_side=3, max_dims=3),
dtype=float,
elements=st.floats(-100, 100)))
y = data.draw(
hnp.arrays(shape=x.shape, dtype=float, elements=st.floats(-100, 100)))
z = data.draw(
hnp.arrays(shape=x.shape, dtype=float, elements=st.floats(-100, 100)))
grad = data.draw(
hnp.arrays(shape=x.shape, dtype=float, elements=st.floats(-100, 100)))
# check variable-selection
assert numerical_gradient(unary_func, x, back_grad=grad,
vary_ind=[])[0] is None
# no broadcast
dx, = numerical_gradient(unary_func, x, back_grad=grad)
assert_allclose(dx, grad * 2 * x)
dx, dy = numerical_gradient(binary_func, x, y, back_grad=grad)
assert_allclose(dx, grad * y**2)
assert_allclose(dy, grad * 2 * x * y)
dx, dy, dz = numerical_gradient(ternary_func, x, y, z, back_grad=grad)
assert_allclose(dx, grad * z * y**2)
assert_allclose(dy, grad * z * 2 * x * y)
assert_allclose(dz, grad * x * y**2)
def test_numerical_gradient_no_broadcast(data, x):
atol, rtol = (1e-7, 1e-7)
y = data.draw(hnp.arrays(shape=x.shape,
dtype=float,
elements=st.floats(-100, 100)),
label="y")
z = data.draw(hnp.arrays(shape=x.shape,
dtype=float,
elements=st.floats(-100, 100)),
label="z")
grad = data.draw(
hnp.arrays(shape=x.shape, dtype=float, elements=st.floats(-100, 100)),
label="grad",
)
# check variable-selection
assert numerical_gradient(unary_func, x, back_grad=grad,
vary_ind=[])[0] is None
# no broadcast
dx, = numerical_gradient(unary_func, x, back_grad=grad)
assert_allclose(dx, grad * 2 * x, atol=atol, rtol=rtol)
dx, dy = numerical_gradient(binary_func, x, y, back_grad=grad)
assert_allclose(dx, grad * y**2, atol=atol, rtol=rtol)
assert_allclose(dy, grad * 2 * x * y, atol=atol, rtol=rtol)
dx, dy, dz = numerical_gradient(ternary_func, x, y, z, back_grad=grad)
assert_allclose(dx, grad * z * y**2, atol=atol, rtol=rtol)
assert_allclose(dy, grad * z * 2 * x * y, atol=atol, rtol=rtol)
assert_allclose(dz, grad * x * y**2, atol=atol, rtol=rtol)
def test_numerical_gradient_y_broadcast(data, x, y, grad, as_decimal):
atol, rtol = (1e-7, 1e-7) if as_decimal else (1e-2, 1e-2)
# broadcast x
dx, dy = numerical_gradient(binary_func, x, y, back_grad=grad)
assert_allclose(dx, grad * y**2, atol=atol, rtol=rtol)
assert_allclose(dy, (grad * 2 * x * y).sum(axis=0), atol=atol, rtol=rtol)
def test_numerical_gradient_x_broadcast(x, y, grad):
atol, rtol = (1e-7, 1e-7)
# broadcast x
dx, dy = numerical_gradient(binary_func, x, y, back_grad=grad)
assert_allclose(dx, (grad * y**2).sum(axis=0), atol=atol, rtol=rtol)
assert_allclose(dy, grad * 2 * x * y, atol=atol, rtol=rtol)
def test_numerical_gradient_xy_broadcast(data, x, y, grad, as_decimal):
atol, rtol = (1e-7, 1e-7) if as_decimal else (1e-2, 1e-2)
# broadcast x
dx, dy = numerical_gradient(binary_func, x, y, back_grad=grad)
x_grad = (grad * y**2).sum(axis=1, keepdims=True)
y_grad = (grad * 2 * x * y).sum(axis=0, keepdims=True)
assert_allclose(dx, x_grad, atol=atol, rtol=rtol)
assert_allclose(dy, y_grad, atol=atol, rtol=rtol)
def test_numerical_gradient_y_broadcast(data):
y = data.draw(
hnp.arrays(shape=(3, 4), dtype=float, elements=st.floats(-100, 100)))
x = data.draw(
hnp.arrays(shape=(2, 3, 4), dtype=float, elements=st.floats(-100,
100)))
grad = data.draw(
hnp.arrays(shape=(2, 3, 4), dtype=float, elements=st.floats(-100,
100)))
# broadcast x
dx, dy = numerical_gradient(binary_func, x, y, back_grad=grad)
assert_allclose(dx, grad * y**2)
assert_allclose(dy, (grad * 2 * x * y).sum(axis=0))
def test_numerical_gradient_xy_broadcast(data):
x = data.draw(
hnp.arrays(shape=(2, 1, 4), dtype=float, elements=st.floats(-100,
100)))
y = data.draw(
hnp.arrays(shape=(1, 3, 4), dtype=float, elements=st.floats(-100,
100)))
grad = data.draw(
hnp.arrays(shape=(2, 3, 4), dtype=float, elements=st.floats(-100,
100)))
# broadcast x
dx, dy = numerical_gradient(binary_func, x, y, back_grad=grad)
x_grad = (grad * y**2).sum(axis=1, keepdims=True)
y_grad = (grad * 2 * x * y).sum(axis=0, keepdims=True)
assert_allclose(dx, x_grad)
assert_allclose(dy, y_grad)