ValueError: Rank mismatch: Rank of labels (received 2) should equal rank of logits minus 1 (received 2) about bi-tempered-loss HOT 2 OPEN

I had a similar problem, and from what I was able to research online, this seems to do the trick:

def bi_tempered_loss(y_true, y_pred):
    return sparse_bi_tempered_logistic_loss(y_pred, K.cast(K.reshape(y_true, (-1,)), "int32"), 0.2, 1.2)

Seems like labels were of shape [None, 1] whereas the function was expecting shape [None].

However, I later get a different symptom of some issue:

Exception has occurred: InvalidArgumentError
 Incompatible shapes: [16,5] vs. [16]
	 [[{{node gradient_tape/bi_tempered_loss/sparse_bitempered_logistic/cond/StatelessIf/else/_15/gradient_tape/bi_tempered_loss/sparse_bitempered_logistic/cond/gradients/bi_tempered_loss/sparse_bitempered_logistic/cond/IdentityN_grad/Mul_2}}]] [Op:__inference_train_function_18841]

Not sure what to do about it... 5 is the number of classes, so somewhere the sparsity of labels is not handled or something..

from bi-tempered-loss.

Here's what I got working:

def bi_tempered_loss(y_true, y_pred):
    y_true = K.cast(K.reshape(y_true, (-1,)), "int64")
    labels = K.one_hot(y_true, N_CLASSES)
    return bi_tempered_logistic_loss(y_pred, labels, T1, T2)

from bi-tempered-loss.