def test_weighted_mean_semantics(self):
inputs = onp.array([1, 2, 3], dtype=onp.float32)
weights1 = onp.array([1, 1, 1], dtype=onp.float32)
layer = metrics.WeightedMean()
full_signature = (signature(inputs), signature(weights1))
layer.initialize_once(full_signature)
mean1 = layer((inputs, weights1))
onp.testing.assert_allclose(mean1, 2.0)
weights2 = onp.array([0, 0, 1], dtype=onp.float32)
mean2 = layer((inputs, weights2))
onp.testing.assert_allclose(mean2, 3.0)
weights3 = onp.array([1, 0, 0], dtype=onp.float32)
mean3 = layer((inputs, weights3))
onp.testing.assert_allclose(mean3, 1.0)
def test_weighted_mean_semantics(self):
inputs = onp.array([1, 2, 3], dtype=onp.float32)
weights1 = onp.array([1, 1, 1], dtype=onp.float32)
layer = metrics.WeightedMean()
rng = backend.random.get_prng(0)
layer.initialize_once((inputs.shape, weights1.shape),
(inputs.dtype, weights1.dtype), rng)
mean1 = layer((inputs, weights1))
onp.testing.assert_allclose(mean1, 2.0)
weights2 = onp.array([0, 0, 1], dtype=onp.float32)
mean2 = layer((inputs, weights2))
onp.testing.assert_allclose(mean2, 3.0)
weights3 = onp.array([1, 0, 0], dtype=onp.float32)
mean3 = layer((inputs, weights3))
onp.testing.assert_allclose(mean3, 1.0)
def test_weighted_mean_shape(self):
input_shape = ((29, 4, 4, 20), (29, 4, 4, 20))
result_shape = base.check_shape_agreement(
metrics.WeightedMean(), input_shape)
self.assertEqual(result_shape, ())