Comments (5)
Thanks again for reporting this silent bug! It's now fixed with 11d70e1 (numpy 1.19 compatible), and 148ba2b (makes the for
shell loop exit with code 1 if it fails mid-loop; here's a sample of a failing test https://travis-ci.org/github/google/neural-tangents/builds/705595567/config and passing master: https://travis-ci.org/github/google/neural-tangents/builds/705590810).
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Thanks for reporting this - looks like JAX or our code specifically doesn't go along with numpy 1.19, could you try with
pip install numpy=1.18.5
?
Interestingly, our tests have been also failing but reported as passing... https://travis-ci.org/github/google/neural-tangents/jobs/704287384
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Thanks for the reply, Roman! If you look into the execution log of travis CI, those tests actually failed.
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But as you mentioned, Numpy version is indeed the problem. Take Monte Carlo tests for example, all of them failed with Numpy 1.19, but they work fine with 1.18.
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Interestingly, our tests have been also failing but reported as passing... https://travis-ci.org/github/google/neural-tangents/jobs/704287384
Wrong exiting code (0) which misleads Travis CI might be the problem? https://stackoverflow.com/questions/24972098/unit-test-script-returns-exit-code-0-even-if-tests-fail
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Related Issues (20)
- The analytical output of GP can not fit the result of NNGP generated by the nt.predict.gp_inference HOT 1
- Question: Relu Kernel Computation HOT 3
- Question: Connection MLE "parametrized" GP in infinite Width Limit vs minimizing MSE "parametrized" Kernel in infinite Width HOT 4
- Question regarding OOM issues HOT 3
- Question regarding lr in Neural Tangents Cookbook
- eNTK implementation uses deprecated xla attribute HOT 2
- Colab notebooks issue HOT 2
- How to obtain aleatoric uncertainty? HOT 2
- How to compute the empirical after kernel? HOT 1
- pip install issues HOT 2
- Erf function goes beyond [-1,1] HOT 2
- using stax.Cos(a=1.0, b=1.0, c=0.0) to get kernel from conv layer gives error HOT 2
- NTK is not PD
- stax.serial PSDness HOT 1
- How to use batch to gradient_descent_mse_ensemble ? HOT 1
- NTK/NNGP behavior in the infinite regime when weights are drawn from Gaussians with high standard deviation HOT 7
- NKT_mean output Nan, when the number of training sample is increased HOT 3
- Inefficient jacobian computation for embedding layers. HOT 1
- Question regarding the cookbook
- Calling the empirical kernel function with different parameters returns same result
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