def test_mean_squared_loss_with_invalid_labels(self):
scores = [[1., 3., 2.]]
labels = [[0., -1., 1.]]
loss = losses.MeanSquaredLoss()
self.assertAlmostEqual(loss(labels, scores).numpy(), (1. + 1.) / 3.,
places=5)
def test_mean_squared_loss(self):
scores = [[0.2, 0.5, 0.3], [0.2, 0.3, 0.5], [0.2, 0.3, 0.5]]
labels = [[0., 0., 1.], [0., 0., 2.], [0., 0., 0.]]
weights = [[2.], [1.], [1.]]
loss = losses.MeanSquaredLoss()
self.assertAlmostEqual(loss(labels, scores).numpy(),
(_mean_squared_error(labels[0], scores[0]) +
_mean_squared_error(labels[1], scores[1]) +
_mean_squared_error(labels[2], scores[2])) /
9.,
places=5)
self.assertAlmostEqual(
loss(labels, scores, weights).numpy(),
(_mean_squared_error(labels[0], scores[0]) * 2.0 +
_mean_squared_error(labels[1], scores[1]) +
_mean_squared_error(labels[2], scores[2])) / 9.,
places=5)
def test_pointwise_losses_are_serializable(self):
self.assertIsLossSerializable(
losses.ClickEMLoss(exam_loss_weight=2.0, rel_loss_weight=5.0))
self.assertIsLossSerializable(losses.SigmoidCrossEntropyLoss())
self.assertIsLossSerializable(losses.MeanSquaredLoss())