def testAccuracyDtype(self): # Can override default dtype of float64. m = metrics.Accuracy(dtype=dtypes.float32) m([0, 0], [0, 1]) self.assertEqual(0.5, m.result().numpy()) self.assertEqual(dtypes.float32, m.dtype) self.assertEqual(dtypes.float32, m.result().dtype)
def testWeightedAccuracy(self): m = metrics.Accuracy() # 1 correct, total weight of 2 m([0, 1, 2, 3], [0, 0, 0, 0], weights=[1, 1, 0, 0]) m([4], [4], weights=[0.5]) # 1 correct with a weight of 0.5 m([5], [0], weights=[0.5]) # 0 correct, weight 0.5 m([6], [6]) # 1 correct, weight 1 m([7], [2]) # 0 correct, weight 1 self.assertEqual(2.5/5, m.result().numpy())
def testAccuracy(self): m = metrics.Accuracy() m([0, 1, 2, 3], [0, 0, 0, 0]) # 1 correct m([4], [4]) # 1 correct m([5], [0]) # 0 correct m([6], [6]) # 1 correct m([7], [2]) # 0 correct self.assertEqual(3.0/8, m.result().numpy()) self.assertEqual(dtypes.float64, m.dtype) self.assertEqual(dtypes.float64, m.result().dtype)
def __init__(self,
model,
loss_key="loss",
label_key="label",
predicted_class_key="predicted_class",
weights_key="weights"):
super(SparseSoftmaxEvaluator, self).__init__(model)
# TODO(josh11b): Expand this to include everything from the standard
# SparseSoftmax Head.
self.avg_loss = self.track_metric(metrics.Mean("Avg Loss"))
self.accuracy = self.track_metric(metrics.Accuracy())
self.loss_key = loss_key
self.label_key = label_key
self.predicted_class_key = predicted_class_key
self.weights_key = weights_key
def testAccuracyDifferentShapes(self):
m = metrics.Accuracy()
with self.assertRaises(errors.InvalidArgumentError):
m([[0], [0]], [0, 1])