[Bug] Negative variances when using fast_pred_var()
See original GitHub issue🐛 Bug
Seeing negative variances when using fast_pred_var(). Seems to be related to cache-ing: goes away when caches are cleared by using .train().eval(). In addition, when evaluating the variance at some set of points test_x and the evaluating again at a single point in test_x, they may not match.
To reproduce
See attached notebook and the comments there.
** Stack trace/error message **
tensor([ 1.0139e+00, 8.5046e-02, 8.5050e-02, 5.4592e-01, 8.5045e-02,
1.1338e+00, 2.0938e+00, 2.3304e+00, 2.3283e+00, 9.9273e-01,
9.6964e-01, -2.7103e+00, -1.7627e+02, -4.9231e+01, -9.5254e-01,
6.7344e-01, 1.1548e+00, 8.4972e-02, 1.1811e+00, 2.1131e+00])
Expected Behavior
Variances that are positive and match when evaluated in different ways.
System information
Please complete the following information:
- gpytorch 0.2.1
- PyTorch 1.0.0a0
- Linux release 7.5.1804
Issue Analytics
- State:
- Created 4 years ago
- Comments:5 (1 by maintainers)
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Top GitHub Comments
Tested this again on the multi-batch PR and the issue has disappeared!
Great. Must have been some weird caching thing that got solved inadvertently…