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)
