Inconsistent manner of the metric `SpecificityAtSensitivity` among different backends

[Star9daisy]

Hi, developers. I want to monitor the metric SpecificityAtSensitivity values during the training process. I've checked the doc to make sure it could be used with compile API.

However, I find this metric does not work out of box like others, for example TruePositives. It could not be used directly in different backends, and even sometimes with "accuracy"! Below are the tables of three backends to show the results I found. "with acc" means set metrics=["acc", SpecificityAtSensitivity(...)]. And a colab link here to reproduce the error: issue_inconsistent_manner_of_SpecificityAtSensitivity.ipynb

tensorflow

run_eager \ metrics	with "acc"	without "acc"
False (default)	❎	❎
True	✔️	✔️

<class 'NotImplementedError'> Cannot convert a symbolic tf.Tensor (Cast_12:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported.

torch

run_eager \ metrics	with "acc"	without "acc"
False (default)	❎	✔️
True	❎	✔️

<class 'NotImplementedError'> Cannot copy out of meta tensor; no data!

jax

run_eager \ metrics	with "acc"	without "acc"
False (default)	❎	❎
True	❎	❎

<class 'jax.errors.ConcretizationTypeError'> Abstract tracer value encountered where concrete value is expected: traced array with shape int32[]. The size argument of jnp.nonzero must be statically specified to use jnp.nonzero within JAX transformations. The error occurred while tracing the function wrapped_fn at /usr/local/lib/python3.10/dist-packages/keras/src/backend/jax/core.py:153 for make_jaxpr. This concrete value was not available in Python because it depends on the values of the arguments args[1] and args[2].