def testAccuracyMetric(self): predictions = np.random.randint(1, 5, size=(12, 12, 12, 1)) targets = np.random.randint(1, 5, size=(12, 12, 12, 1)) expected = np.mean((predictions == targets).astype(float)) with self.test_session() as session: scores, _ = metrics.padded_accuracy( tf.one_hot(predictions, depth=5, dtype=tf.float32), tf.constant(targets, dtype=tf.int32)) a = tf.reduce_mean(scores) session.run(tf.global_variables_initializer()) actual = session.run(a) self.assertAlmostEqual(actual, expected)
def testAccuracyMetric(self): predictions = np.random.randint(1, 5, size=(12, 12, 12, 1)) targets = np.random.randint(1, 5, size=(12, 12, 12, 1)) expected = np.mean((predictions == targets).astype(float)) with self.test_session() as session: scores, _ = metrics.padded_accuracy( tf.one_hot(predictions, depth=5, dtype=tf.float32), tf.constant(targets, dtype=tf.int32)) a = tf.reduce_mean(scores) session.run(tf.global_variables_initializer()) actual = session.run(a) self.assertAlmostEqual(actual, expected)