def test_transpose(x, data):
axes = data.draw(
valid_axes(x.ndim, min_dim=x.ndim, max_dim=x.ndim).map(
lambda out: (out, ) if isinstance(out, int) else out),
label="axes",
)
x_arr = Tensor(np.copy(x))
o = transpose(x_arr, axes, constant=False)
grad = data.draw(
hnp.arrays(shape=o.shape,
dtype=float,
elements=st.floats(1, 10),
unique=True),
label="grad",
)
o.backward(grad)
def f(x):
return np.transpose(x, axes)
assert_allclose(o.data, f(x))
dx, = numerical_gradient_full(f, x, back_grad=grad)
assert_allclose(x_arr.grad, dx)
out = transpose(x, constant=True)
assert out.constant and not x_arr.constant
def test_transpose(x, data):
axes = data.draw(valid_axes(x.ndim), label="axes")
if axes is not None:
assume(len(axes) == x.ndim)
x_arr = Tensor(np.copy(x))
o = transpose(x_arr, axes, constant=False)
grad = data.draw(
hnp.arrays(shape=o.shape,
dtype=float,
elements=st.floats(1, 10),
unique=True),
label="grad",
)
o.backward(grad)
def f(x):
return np.transpose(x, axes)
assert_allclose(o.data, f(x))
dx, = numerical_gradient_full(f, x, back_grad=grad)
assert_allclose(x_arr.grad, dx)
out = transpose(x, constant=True)
assert out.constant and not x_arr.constant
def test_input_validation():
x = Tensor([[1, 2]])
with raises(TypeError):
transpose(x, (0, ), 1)