Can Cross Entropy Be Negative?

It's never negative, and it's 0 only when y and ˆy are the same. Note that minimizing cross entropy is the same as minimizing the KL divergence from ˆy to y.

What does negative binary cross entropy loss mean?

When binary cross entropy predictions are negative, it is because the true values are not [0,1]. In my case I was using [-1,1]. The model does not fail, but produces negative value.

Is cross entropy negative log likelihood?

if a neural network does have hidden layers and the raw output vector has a softmax applied, and it's trained using a cross-entropy loss, then this is a “softmax cross entropy loss” which can be interpreted as a negative log likelihood because the softmax creates a probability distribution.