[Relay] Improve numerical gradient check #3856
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@MarisaKirisame @vinx13 @junrushao1994 please review |
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SWu
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Aug 29, 2019
| if inputs is None: | ||
| params = fwd_func.params | ||
| # Generate random inputs on the same scale as epsilon to avoid numerical precision loss. | ||
| inputs = [_np_randn_from_type(x.checked_type, scale=(10 * eps)) for x in params] |
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In practice, I found that this heuristic works well for operations that involve multiplication/addition
For division you still get precision problems, especially when using float32. In those cases, either supply manually selected inputs, or use float64.
vinx13
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I was having trouble getting passing tests with the existing gradient check implementation, even though the gradient implementations match results from the keras framework. This change rewrites the gradient check helper to perform two-sided numerical checks on every gradient dimension. I have an upcoming pull request with several gradient implementations that I was able to verify with this implementation.