def testAccuracy1DInt64(self): with self.test_session() as session: pred = array_ops.placeholder(dtypes.int64, shape=[None]) labels = array_ops.placeholder(dtypes.int64, shape=[None]) acc = classification.accuracy(pred, labels) result = session.run(acc, feed_dict={pred: [1, 0, 1, 0], labels: [1, 1, 0, 0]}) self.assertEqual(result, 0.5)
def testAccuracy1DString(self): with self.test_session() as session: pred = array_ops.placeholder(dtypes.string, shape=[None]) labels = array_ops.placeholder(dtypes.string, shape=[None]) acc = classification.accuracy(pred, labels) result = session.run( acc, feed_dict={pred: ['a', 'b', 'a', 'c'], labels: ['a', 'c', 'b', 'c']}) self.assertEqual(result, 0.5)
def testAccuracy1DString(self): with self.test_session() as session: pred = tf.placeholder(tf.string, shape=[None]) labels = tf.placeholder(tf.string, shape=[None]) acc = classification.accuracy(pred, labels) result = session.run(acc, feed_dict={ pred: ['a', 'b', 'a', 'c'], labels: ['a', 'c', 'b', 'c'] }) self.assertEqual(result, 0.5)
def testAccuracy1DInt64(self): with self.test_session() as session: pred = tf.placeholder(tf.int64, shape=[None]) labels = tf.placeholder(tf.int64, shape=[None]) acc = classification.accuracy(pred, labels) result = session.run(acc, feed_dict={ pred: [1, 0, 1, 0], labels: [1, 1, 0, 0] }) self.assertEqual(result, 0.5)
def testAccuracy1D(self): with self.test_session() as session: pred = tf.placeholder(tf.int32, shape=[None]) labels = tf.placeholder(tf.int32, shape=[None]) acc = classification.accuracy(pred, labels) result = session.run(acc, feed_dict={ pred: [1, 0, 1, 0], labels: [1, 1, 0, 0] }) self.assertEqual(result, 0.5)
def testAccuracy1DBool(self): with self.test_session() as session: pred = array_ops.placeholder(dtypes.bool, shape=[None]) labels = array_ops.placeholder(dtypes.bool, shape=[None]) acc = classification.accuracy(pred, labels) result = session.run(acc, feed_dict={ pred: [1, 0, 1, 0], labels: [1, 1, 0, 0] }) self.assertEqual(result, 0.5)
def testAccuracy1DWeighted(self): with self.test_session() as session: pred = array_ops.placeholder(dtypes.int32, shape=[None]) labels = array_ops.placeholder(dtypes.int32, shape=[None]) weights = array_ops.placeholder(dtypes.float32, shape=[None]) acc = classification.accuracy(pred, labels) result = session.run(acc, feed_dict={ pred: [1, 0, 1, 1], labels: [1, 1, 0, 1], weights: [3.0, 1.0, 2.0, 0.0] }) self.assertEqual(result, 0.5)
def testAccuracy1DWeightedBroadcast(self): with self.test_session() as session: pred = array_ops.placeholder(dtypes.int32, shape=[None]) labels = array_ops.placeholder(dtypes.int32, shape=[None]) weights = array_ops.placeholder(dtypes.float32, shape=[]) acc = classification.accuracy(pred, labels) result = session.run(acc, feed_dict={ pred: [1, 0, 1, 0], labels: [1, 1, 0, 0], weights: 3.0, }) self.assertEqual(result, 0.5)
def testAccuracy1DWeightedBroadcast(self): with self.test_session() as session: pred = tf.placeholder(tf.int32, shape=[None]) labels = tf.placeholder(tf.int32, shape=[None]) weights = tf.placeholder(tf.float32, shape=[]) acc = classification.accuracy(pred, labels) result = session.run(acc, feed_dict={ pred: [1, 0, 1, 0], labels: [1, 1, 0, 0], weights: 3.0, }) self.assertEqual(result, 0.5)
def testAccuracyFloatLabels(self): with self.assertRaises(ValueError): pred = tf.placeholder(tf.int32, shape=[None]) labels = tf.placeholder(tf.float32, shape=[None]) classification.accuracy(pred, labels)
def testAccuracyDtypeMismatch(self): with self.assertRaises(ValueError): pred = tf.placeholder(tf.int32, shape=[None]) labels = tf.placeholder(tf.int64, shape=[None]) classification.accuracy(pred, labels)