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)