Comments (3)
Thanks for the report!
The immediate breakage should be fixed by the release today of TFP 0.24.0 -- https://github.com/tensorflow/probability/releases/tag/v0.24.0 . TFP 0.24.0 should work if TF 2.16.1 and TF Keras 2.16 are installed.
We do not currently have any plans to migrate from Keras 2 to Keras 3.
from probability.
While a new release of tensorflow-probability
will be required to not get broken in the way I showed above, from @jburnim's 988f023 it seems like after this tensorflow-probability
release tf-keras
will be a required dependency for use with tensorflow
as well.
This seems like it should be provided through a tensorflow-probability[tensorflow]
extra as communicating dependencies to users through error messages is very annoying from the user side.
edit: If I had taken the time to read 988f023 more carefully before posting this, I would have realized that commit also add this extra as tensorflow-probability[tf]
:
Lines 110 to 112 in 988f023
Lines 51 to 56 in 988f023
from probability.
Thanks @jburnim.
We do not currently have any plans to migrate from Keras 2 to Keras 3.
That's useful to know. It seems that tensorflow-probability
is making choices to migrate further from tensorflow
(or perhaps the other way around). Are there any plans to just split the library development into one JAX based library and one TensorFlow based library? Or is the idea to just split the support internally and then not duplicate the existing codebase until tensorflow
drifts enough to just drop support?
Note to other people that are trying to balance supporting supporting tensorflow-probability
with tensorflow
in a library, you might want to do something like scikit-hep/pyhf#2452.
from probability.
Related Issues (20)
- TFP JAX: The transition kernel drastically decreases speed. HOT 3
- jax.dtypes.prng_key gives `AttributeError: module 'jax.dtypes' has no attribute 'prng_key'` HOT 1
- MultivariateNormalTriL Layer appears to be incompatible with tf.keras in tf 2.16.1 and tfp 0.24 HOT 2
- how to fix this error
- Error in documentation of experimental.sequential.extended_kalman_filter
- tfp.math.scan_associative doesn't work for all associative functions (it should be using `vmap` for `lowered_fn`)
- Variational inference for Conv2D layers
- MultivariateNormal* constructor crashes with Numpy 2.0 HOT 1
- Please clean up [email protected] mailing list. HOT 1
- instructions to build wheel from source not working HOT 1
- Mixture of tuple-valued distributions for TFP on JAX raises a TypeError
- Bayesian MCMC model and Bayesian regression analysis model HOT 2
- `JointDistribution`s yield different PRNG behaviour on GPU and CPU
- MLE of lambda parameter in PowerTransform bijector HOT 3
- AttributeError: Tensorflow-Probabilty does not have right attribute HOT 4
- Saving a Keras model that includes a mixture layer (e.g. MixtureNormal) might be unsafe
- How to build a bayesion hierarchical/multilevel modeling with tf-keras? HOT 5
- Unsafe variable naming in `JointDistributionCoroutine` HOT 5
- tf.keras and tfp.tfp.layers. are not compatible
- When using tfp.optimizer.lbfgs_minimize, encountered OperatorNotAllowedInGraphError: Iterating over a symbolic `tf.Tensor` is not allowed:
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