Function test_softmax_grad

numpy_ml/tests/test_nn.py:585–612

(N=15)

def test_softmax_grad(N=15):
    from numpy_ml.neural_nets.layers import Softmax
    from functools import partial

    np.random.seed(12345)

    N = np.inf if N is None else N
    p_soft = partial(F.softmax, dim=1)
    gold = torch_gradient_generator(p_soft)

    i = 0
    while i < N:
        mine = Softmax()
        n_ex = np.random.randint(1, 3)
        n_dims = np.random.randint(1, 50)
        z = random_tensor((n_ex, n_dims), standardize=True)
        out = mine.forward(z)

        assert_almost_equal(
            gold(z),
            mine.backward(np.ones_like(out)),
            err_msg="Theirs:\n{}\n\nMine:\n{}\n".format(
                gold(z), mine.backward(np.ones_like(out))
            ),
            decimal=3,
        )
        print("PASSED")
        i += 1


def test_softplus_grad(N=15):