def testAccuracyTopKMetric(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:
predicted = tf.one_hot(predictions, depth=5, dtype=tf.float32)
scores1, _ = metrics.padded_accuracy_topk(
predicted, tf.constant(targets, dtype=tf.int32), k=1)
scores2, _ = metrics.padded_accuracy_topk(
predicted, tf.constant(targets, dtype=tf.int32), k=7)
a1 = tf.reduce_mean(scores1)
a2 = tf.reduce_mean(scores2)
session.run(tf.global_variables_initializer())
actual1, actual2 = session.run([a1, a2])
self.assertAlmostEqual(actual1, expected)
self.assertAlmostEqual(actual2, 1.0)
def testAccuracyTopKMetric(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:
predicted = tf.one_hot(predictions, depth=5, dtype=tf.float32)
scores1, _ = metrics.padded_accuracy_topk(
predicted, tf.constant(targets, dtype=tf.int32), k=1)
scores2, _ = metrics.padded_accuracy_topk(
predicted, tf.constant(targets, dtype=tf.int32), k=7)
a1 = tf.reduce_mean(scores1)
a2 = tf.reduce_mean(scores2)
session.run(tf.global_variables_initializer())
actual1, actual2 = session.run([a1, a2])
self.assertAlmostEqual(actual1, expected)
self.assertAlmostEqual(actual2, 1.0)
