Support copying posterior caches (and thereby float32 predictions from float64 models) #891
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uri-granta
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hstojic
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Jan 20, 2025
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| npt.assert_allclose(f_mean, f_mean32, rtol=1e-6) | ||
| assert f_var32.dtype is tf.float32 | ||
| npt.assert_allclose(f_var, f_var32, rtol=1e-6) |
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my assumption is that accuracy will depend a lot on the problem, here we are testing on a simple function and its not surprising we get pretty much the same value - have you tested it on more complex problems of higher dimensionality?
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Good call. Can you suggest a problem I can easily test it on?
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lets start with more complex functions that we have in our synthetic problems - we have some where we can specify arbitrary dimensionality as well I think
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Fully backwards compatible: yes / no
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