# chainer.functions.binary_accuracy¶

chainer.functions.binary_accuracy(y, t)[source]

Computes binary classification accuracy of the minibatch.

Parameters: y (Variable or N-dimensional array) – Array whose i-th element indicates the score of positive at the i-th sample. The prediction label $$\hat t[i]$$ is 1 if y[i] >= 0, otherwise 0. t (Variable or N-dimensional array) – Array holding a signed integer vector of ground truth labels. If t[i] == 1, it indicates that i-th sample is positive. If t[i] == 0, it indicates that i-th sample is negative. If t[i] == -1, corresponding y[i] is ignored. Accuracy is zero if all ground truth labels are -1. A variable holding a scalar array of the accuracy. Variable

Note

This function is non-differentiable.

Example

We show the most common case, when y is the two dimensional array.

>>> y = np.array([[-2.0, 0.0], # prediction labels are [0, 1]
...               [3.0, -5.0]]) # prediction labels are [1, 0]
>>> t = np.array([[0, 1],
...              [1, 0]], np.int32)
>>> F.binary_accuracy(y, t).data # 100% accuracy because all samples are correct.
array(1.)
>>> t = np.array([[0, 0],
...              [1, 1]], np.int32)
>>> F.binary_accuracy(y, t).data # 50% accuracy because y[0][0] and y[1][0] are correct.
array(0.5)
>>> t = np.array([[0, -1],
...              [1, -1]], np.int32)
>>> F.binary_accuracy(y, t).data # 100% accuracy because of ignoring y[0][1] and y[1][1].
array(1.)