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Enhanced the random seed generation function. #328
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rxwei
reviewed
Jul 2, 2019
rxwei
reviewed
Jul 2, 2019
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All the requested changes (except for the |
rxwei
reviewed
Jul 2, 2019
|
This should be ready for another look now. |
rxwei
reviewed
Jul 2, 2019
| hashCopy[4].rotate(rightBy: 41) | ||
| let ch = (hashCopy[4] & hashCopy[5]) ^ ((~hashCopy[4]) & hashCopy[6]) | ||
| let t1 = hashCopy[7] &+ s1 &+ ch &+ k[j] &+ UInt64(M[j]) | ||
| hashCopy[7] = hashCopy[6] |
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@stephentyrone Could you please advise?
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I found this which seems relevant. :)
rxwei
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Jul 2, 2019
rxwei
approved these changes
Jul 3, 2019
rxwei
reviewed
Jul 3, 2019
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@eaplatanios, If you need any help with all these tasks you are undertaking, I'd love to help you out. I really like the enhancements we've had in the past week. So if there are trivial/non-trivial tasks you need some help with, do tag me. |
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@Shashi456 Thanks a lot! Sorry for the late response but I was traveling. I don't have something planned right now, but I will definitely tag you if anything comes up. :) |
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This PR enhances the function that generates default random seeds for TensorFlow. This is needed because the previous approach is weak in multi-process settings, which are quite common when training or evaluating machine learning models. The implementation is inspired from OpenAI Gym.
I also added an implementation on SHA512 in the utilities to avoid having any external library dependencies along with a test for the hash function. The SHA512 implementation is based on that of CryptoSwift.