def test_confusion_matrix_forward(seed, ctx, axis, func_name):
ishape = [5, 6, 7]
rng = np.random.RandomState(seed)
l_shape = list(ishape)
l_shape[axis] = 1
n_class = ishape[axis]
inputs = [
rng.rand(5, 6, 7).astype(np.float32),
rng.randint(0, n_class, size=l_shape).astype(np.int)
]
ref = ref_confusion_matrix(inputs[0], inputs[1], axis)
x = nn.Variable(ishape)
l = nn.Variable(l_shape)
y = F.confusion_matrix(x, l, axis)
x.d = inputs[0]
l.d = inputs[1]
y.forward()
res = y.d
atol_f = 1e-6
assert_allclose(ref, res, atol=atol_f)
def test_confusion_matrix_forward(seed, ctx, axis, func_name):
ishape = [5, 6, 7]
rng = np.random.RandomState(seed)
l_shape = list(ishape)
l_shape[axis] = 1
n_class = ishape[axis]
inputs = [
rng.rand(5, 6, 7).astype(np.float32),
rng.randint(0, n_class, size=l_shape).astype(np.int)]
ref = ref_confusion_matrix(inputs[0], inputs[1], axis)
x = nn.Variable(ishape)
l = nn.Variable(l_shape)
y = F.confusion_matrix(x, l, axis)
x.d = inputs[0]
l.d = inputs[1]
y.forward()
res = y.d
atol_f = 1e-6
assert np.allclose(ref, res, atol=atol_f)