def test_accuracy(self):
y_pred = tf.constant([[1.0, 2.0, 3.0, 4.0, 6.0, 5.0]])
loss_fn = losses.SoftErrorLoss(topk=1.0, power=1.0)
acc = loss_fn.accuracy([2], y_pred)
self.assertGreaterEqual(acc, 0.0)
self.assertLessEqual(acc, 1.0)
acc2 = loss_fn.accuracy([4], y_pred)
self.assertGreater(acc2, acc)
# Test that topk gives the same accuracy for all top 3.
loss_fn = losses.SoftErrorLoss(topk=3, power=1.0)
acc = loss_fn.accuracy([3], y_pred)
acc2 = loss_fn.accuracy([4], y_pred)
self.assertAllClose(acc, acc2)
self.assertAllClose(acc, tf.constant([1.0]))
acc3 = loss_fn.accuracy([0], y_pred)
self.assertLess(acc3, 1.0)
def test_softerror(self):
y_pred = tf.constant([[1.0, 2.0, 3.0, 4.0, 6.0, 5.0]])
loss_fn = losses.SoftErrorLoss(topk=1.0, power=1.0)
loss = loss_fn([2], y_pred)
self.assertGreaterEqual(loss, 0.0)
self.assertLessEqual(loss, 1.0)
# A better prediction should lead to a lower loss.
loss2 = loss_fn([4], y_pred)
self.assertLessEqual(loss2, loss)
