def testSequenceAccuracyMetric(self):
predictions = np.random.randint(4, size=(12, 12, 12, 1))
targets = np.random.randint(4, size=(12, 12, 12, 1))
expected = np.mean(
np.prod((predictions == targets).astype(float), axis=(1, 2)))
with self.test_session() as session:
scores, _ = metrics.padded_sequence_accuracy(
tf.one_hot(predictions, depth=4, 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.assertEqual(actual, expected)
def testSequenceAccuracyMetric(self):
predictions = np.random.randint(4, size=(12, 12, 12, 1))
targets = np.random.randint(4, size=(12, 12, 12, 1))
expected = np.mean(
np.prod((predictions == targets).astype(float), axis=(1, 2)))
with self.test_session() as session:
scores, _ = metrics.padded_sequence_accuracy(
tf.one_hot(predictions, depth=4, 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.assertEqual(actual, expected)
def testSequenceAccuracyMetric(self):
predictions = np.random.randint(4, size=(3, 2, 2, 1))
targets = np.random.randint(4, size=(3, 2, 2, 1))
print("Predictions are========: ")
print(predictions)
print(
"========================================================================================"
)
print("Targets are: ")
print(targets)
expected = np.mean(
np.prod((predictions == targets).astype(float), axis=(1, 2)))
with self.test_session() as session:
scores, _ = metrics.padded_sequence_accuracy(
tf.one_hot(predictions, depth=4, dtype=tf.float32),
tf.constant(targets, dtype=tf.int32))
print(
"11111111111111111111111111111111111111111111111111111111111111111111111111111111111"
)
print("The scores are: ")
print(scores.eval())
print(
"11111111111111111111111111111111111111111111111111111111111111111111111111111111111"
)
print("one hot predictions are: ")
print(tf.one_hot(predictions, depth=4, dtype=tf.float32).eval())
print(
"222222222222222222222222222222222222222222222222222222222222222222222"
)
print("targets are: ")
print(tf.constant(targets, dtype=tf.int32).eval())
print(
"11111111111111111111111111111111111111111111111111111111111111111111111111111111111"
)
a = tf.reduce_mean(scores)
print("mean score is: ")
print(a.eval())
session.run(tf.global_variables_initializer())
actual = session.run(a)
print("The actual score is: ")
print(actual)
self.assertEqual(actual, expected)
