Timeline for How to treat small numbers in order to gain efficiency and precision in neural network algorithm?
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
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| Aug 31, 2023 at 16:13 | comment | added | GaloisFan | @DanielHuber thank you very much! I will see what I can do to test how the program works with exact precision calculations. | |
| Aug 31, 2023 at 16:12 | comment | added | GaloisFan | @MarcoB oh, of course I could work on a version of the code which reproduces the problem in the appropriate context and which was suitable for readers to understand. That is indeed way out of hand, however -- I have no shame admitting. My hopes were that the question, in its wordy form, is within the site parameters while being enough for some general suggestions I could try to wrap my head around. | |
| Aug 31, 2023 at 14:37 | comment | added | Daniel Huber | Machine numbers are represented by binary digits: d1, d2,..dmax and an exponent: ex like: 1.d1 d2 dmax x 2^ex,(note the starting "1")) where emin<= ex <= emax. These number are called "normalized machine numbers". It is clear that numbers smaller than 2'^emin can not be represented like this. They must be written with a starting zero. Therefore, we have at least one digit less, what account for the loss in precision. What you can do is to use rational numbers that do not have this problem, but the computing time may be much longer. | |
| Aug 31, 2023 at 14:27 | comment | added | MarcoB | "Unfortunately I won't be able to provide a MWE, as it would be too big and complex" - nah, you don't get out of doing your due diligence this easily :-) the point of a MINIMAL working example is that YOU work to reduce your problem to its most important point, or potentially generate a new, smaller and similar problem for us to work on. Otherwise, we can only guess. | |
| Aug 31, 2023 at 13:27 | history | asked | GaloisFan | CC BY-SA 4.0 |