def test_lambda_rank(grad, value, output, gain, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
score = AA(output, dtype=dt).reshape(-1, 1, 1)
gain = AA(gain, dtype=dt).reshape(-1, 1, 1)
group = np.ones_like(score).reshape(-1, 1, 1)
expected_value = AA(value, dtype=dt)
expected_grad = AA(grad, dtype=dt)
from cntk.losses import lambda_rank
g = input((1, ))
s = input((1, ), needs_gradient=True)
n = input((1, ))
f = lambda_rank(s, n, g)
actual_grad, actual_value = f.grad({
s: score,
n: gain,
g: group
}, [s], [f.output])
assert np.allclose(actual_value, expected_value)
assert np.allclose(actual_grad, expected_grad)
def test_lambda_rank(grad, value, output, gain, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
score = AA(output, dtype=dt).reshape(-1,1,1)
gain = AA(gain, dtype=dt).reshape(-1,1,1)
group = np.ones_like(score).reshape(-1,1,1)
expected_value = AA(value, dtype=dt)
expected_grad = AA(grad, dtype=dt)
from cntk.losses import lambda_rank
g = C.input_variable((1,))
s = C.input_variable((1,), needs_gradient=True)
n = C.input_variable((1,))
f = lambda_rank(s, n, g)
actual_grad, actual_value = f.grad({s:score, n:gain, g:group}, [s], [f.output])
assert np.allclose(actual_value, expected_value)
assert np.allclose(actual_grad, expected_grad)