chainer.functions.accuracy¶
-
chainer.functions.
accuracy
(y, t, ignore_label=None)[source]¶ Computes multiclass classification accuracy of the minibatch.
- Parameters
y (
Variable
or N-dimensional array) – Array whose (i, j, k, …)-th element indicates the score of the class j at the (i, k, …)-th sample. The prediction label \(\hat t\) is calculated by the formula \(\hat t(i, k, ...) = \operatorname{\mathrm{argmax}}_j y(i, j, k, ...)\).t (
Variable
or N-dimensional array) – Array of ground truth labels.ignore_label (int or None) – Skip calculating accuracy if the true label is
ignore_label
.
- Returns
A variable holding a scalar array of the accuracy.
- Return type
Note
This function is non-differentiable.
Example
We show the most common case, when
y
is the two dimensional array.>>> y = np.array([[0.1, 0.7, 0.2], # prediction label is 1 ... [8.0, 1.0, 2.0], # prediction label is 0 ... [-8.0, 1.0, 2.0], # prediction label is 2 ... [-8.0, -1.0, -2.0]]) # prediction label is 1 >>> t = np.array([1, 0, 2, 1], np.int32) >>> F.accuracy(y, t).array # 100% accuracy because all samples are correct array(1.) >>> t = np.array([1, 0, 0, 0], np.int32) >>> F.accuracy(y, t).array # 50% accuracy because 1st and 2nd samples are correct. array(0.5) >>> F.accuracy(y, t, ignore_label=0).array # 100% accuracy because of ignoring the 2nd, 3rd and 4th samples. array(1.)