def regression_loss(predicty, ylabel, weight_map=None): """ Distance regression loss :param predicty: prediction results :param ylabel: ground truth :return: regression loss """ loss = l2_loss(predicty, ylabel, weight_map) return loss
def test_l2_loss(self): # expected loss: (0.04 + 4 + 1) /2 = 2.52 # (note - not the mean, just the sum) with self.test_session(): predicted = tf.constant([1.2, 1, 2], dtype=tf.float32, name='predicted') gold_standard = tf.constant([1, 3, 3], dtype=tf.float32, name='gold_standard') self.assertAlmostEqual( l2_loss(predicted, gold_standard).eval(), 2.52)
def test_l2_loss(self): # expected loss: (0.04 + 4 + 1) /2 = 2.52 # (note - not the mean, just the sum) with self.test_session(): predicted = tf.constant( [1.2, 1, 2], dtype=tf.float32, name='predicted') gold_standard = tf.constant( [1, 3, 3], dtype=tf.float32, name='gold_standard') self.assertAlmostEqual( l2_loss(predicted, gold_standard).eval(), 2.52)