Non-categorical loss in Keras I am training a neural network (arbitrary architecture) and I have a label space that is not one-hot encoded, but continuous. The reason is that for the given problem, it is not possible to assign a single class only, it is more of a probability mapping. So in the end, my targets sum up to 1 again, but they are not 1-hot.

I wonder if I am misunderstanding Keras documentation, but for what I read, there is no Crossentropy implementation for this. There is categorical and sparse_categorical (which seem to do exactly the same, but only expect a different label format). My idea was to wrap every single target index into a binary crossentropy, but this feels wrong and I think there is a better solution. Can you please help me find an appropriate CE loss for my task?
It sounds like you want your model to output a probability distribution which matches a &quot;ground truth&quot; probability distribution.  Instead of a cross-entropy loss, you should try Kullback-Leiber divergence (keras.losses.kullback_leibler_divergence).
KL-divergence measures the difference between two probability distributions.  Minimizing KL-divergence should cause your predicted distribution to match the actual distribution.
By the way, KL-divergence is not just a workaround for Keras's cross-entropy limitations.  It's actually the better loss function for this task.  From the wikipedia page on KL-divergence (emphasis added by me):

$$D_{KL}(P \| Q) = H(P, Q) - H(P)$$
where $H(P, Q)$ is the cross entropy of P and Q, and $H(P)$ is the entropy of P (which is the same as the cross-entropy of P with itself).
The KL divergence $D_{KL}(P \| Q)$ can be thought of as something like a measurement of how far the distribution Q is from the distribution P.  The cross-entropy $H(P, Q)$ is itself such a measurement, but it has the defect that $H(P, P) = H(P)$ is not zero, so we subtract $H(P)$ to make $D_{KL}(P \| Q)$ agree more closely with our notion of distance.