Sample stable inputs in tests of group normalization #7894
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emcastillo
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Aug 9, 2019
| if x_groups.std(axis=2).min() >= min_std: | ||
| break | ||
| retry += 1 | ||
| assert retry <= 10, 'Too many retries to generate inputs' |
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I find this fine, I am just curious about how many retries does it take on average to sample a good input :).
Thanks!
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For example, shape=(3, 20), groups=4 samples 5 per group. For each group std ≥ 0.2 with probability ~97.8%.
>>> (np.random.uniform(-1, 1, (1000000, 5)).std(axis=1) > 0.2).sum()
978456
A try satisfies the condition for all groups with probability at least 0.97 ** (3 * 4). Thus it fails 10 tries with probability
>>> (1 - 0.97 ** (3 * 4)) ** 10
7.235207665894931e-06
... I see. It's not sufficiently small.
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Thanks a lot for the great explanation!
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Jenkins, test this please |
emcastillo
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Jenkins CI test (for commit c66a532, target branch master) failed with status FAILURE. |
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CI unrelated |
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Aug 9, 2019
Sample stable inputs in tests of group normalization
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Fix #6911.