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)
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)
