Class wise precision and recall for multi class classification in Tensorflow?

Question

Is there a way to get per class precision or recall when doing multiclass classification using tensor flow.

For example, If I have y_true and y_pred from each batch, is there a functional way to get precision or recall per class if I have more than 2 classes.

Answer 1

Here's a solution that is working for me for a problem with n=6 classes. If you have many more classes this solution is probably slow and you should use some sort of mapping instead of a loop.

Assume you have one hot encoded class labels in rows of tensor labels and logits (or posteriors) in tensor labels. Then, if n is the number of classes, try this:

y_true = tf.argmax(labels, 1)
y_pred = tf.argmax(logits, 1)

recall = [0] * n
update_op_rec = [[]] * n

for k in range(n):
    recall[k], update_op_rec[k] = tf.metrics.recall(
        labels=tf.equal(y_true, k),
        predictions=tf.equal(y_pred, k)
    )

Note that inside tf.metrics.recall, the variables labels and predictions are set to boolean vectors like in the 2 variable case, which allows the use of the function.