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In categorical_crossentropy, I suspect this normalization line to be not useful and leads to 2 unexpected behaviors https://github.com/keras-team/keras/blob/b80dd12da9c0bc3f569eca3455e77762cf2ee8ef/keras/backend.py#L5540

The upper block handle logits and returns something if it is a logit. Therefore, L5540 is called when it is NOT a logit (sum of proba=1). AFAIK the values are already normalized here so L5540 is not useful.

Let’s consider the normalization L5540 is useful and we kept it unchanged. It produces an error in those two edge cases:

• Unexpected behavior 1 when predictions are zeroed it produces a division by 0: out=categorical_crossentropy(output=np.array([[0.,0.,0.]]), target=np.array([[1.,0.,0.]]), from_logits=False, axis=-1) returns tf.Tensor([nan], shape=(1,), dtype=float64)

• Unexpected behavior 2 is #17029 . In the edge case where we have 1 single class. Removing this L5540 allows training the model to return always 1. Therefore, the loss will not be always 0 but will quickly converge to 0. Line L5540 seems the root cause of what is described in this previous issue.

• As discussed in the 17029 issues, returning a warning to the user may be also useful.

Reproductibility https://colab.research.google.com/drive/1GFXQxfw_4gwMU6vIo8fNDPGCwnbOSp2o?usp=sharing

I may contribute and push a PR

tf.math.divide_no_nan would fix the first edge case. However, assuming a correct usage of the API, I'm not sure it would ever happen in the first place: it's impossible for the softmax function to produce an output [0., 0., 0.] because $e^x > 0, \forall x\in \mathbb{R}$.

@PierrickPochelu Could you refer to the comment above and let us know if it helps? Thank you!

yes it is

@PierrickPochelu Thank you for the confirmation!

@PierrickPochelu Could you please confirm if this issue is resolved? Thank you!

reduce_sum should be called only when it is mandatory.

By removing this line I reduce the computing time from 16 sec to 12 sec without changing the output: https://colab.research.google.com/drive/1GFXQxfw_4gwMU6vIo8fNDPGCwnbOSp2o?usp=sharing

To make the benchmark I take an example similar to Imagenet dimensions: 1 million samples (1e5 called 10 times due to memory limit) and 1 thousand classes.

PR: https://github.com/keras-team/keras/pull/17140

reduce_sum should be called only when it is mandatory.

By removing this line I reduce the computing time from 16 sec to 12 sec without changing the output: https://colab.research.google.com/drive/1GFXQxfw_4gwMU6vIo8fNDPGCwnbOSp2o?usp=sharing

To make the benchmark I take an example similar to Imagenet dimensions: 1 million samples (1e5 called 10 times due to memory limit) and 1 thousand classes.

can you please give public access to the colab?

Done

@PierrickPochelu - Th PR has been reviewed - https://github.com/keras-team/keras/pull/17140