This method implements equation (3) in [1]. In this equation the probability
of the decided label being correct is used to estimate the calibration
property of the predictor.
References
[1]: Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger,
On Calibration of Modern Neural Networks.
Proceedings of the 34th International Conference on Machine Learning
(ICML 2017).
arXiv:1706.04599
https://arxiv.org/pdf/1706.04599.pdf
Args
num_bins
int, number of probability bins, e.g. 10.
logits
Tensor, (n,nlabels), with logits for n instances and nlabels.
labels_true
Tensor, (n,), with tf.int32 or tf.int64 elements containing
ground truth class labels in the range [0,nlabels].
labels_predicted
Tensor, (n,), with tf.int32 or tf.int64 elements
containing decisions of the predictive system. If None, we will use
the argmax decision using the logits.
name
Python str name prefixed to Ops created by this function.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-11-21 UTC."],[],[]]