Filter löschen
Filter löschen

The effect of rng() on neural network training result

6 Ansichten (letzte 30 Tage)
Amend
Amend am 7 Aug. 2015
Kommentiert: Greg Heath am 8 Aug. 2015
I use rng('default') to control random number generation, so each neural network has the same divide data (train, validation, test) and initial weight/bias, then I change node size and compare the error to decide the best node size. But when I change rng('default') to rng(3,'twister'), I find the best node size is different from the previous one. Two rng() function gives the different answers, which one should I choose?
rng function (eg. rng('default'), rng(0,'twister'), rng('sshuffle')...) does not produce unique result for neural network training!
  1 Kommentar
Greg Heath
Greg Heath am 7 Aug. 2015
I am not familiar with the different types. I just stick with the old ones: rng(0), rng(4151941) and rng('default'). It's hard to believe that one way of generating pseudo random numbers will consistently result in better designs.

Melden Sie sich an, um zu kommentieren.

Antworten (1)

Steven Lord
Steven Lord am 7 Aug. 2015
The pseudorandom number generators will eventually (after generating a LARGE number of values) repeat themselves. The 'twister' generator, the Mersenne Twister, has an approximate period of 2^19937-1. When you use RNG with a seed value, you're basically specifying where in the period to start generating numbers. [Before you ask, it's not as simple as "the second number generated after starting at seed value 1 is the first number generated after starting at seed value 2."]
So you're starting at different points in the period when you use rng('default') and rng(3, 'twister'). There's nothing inherently "better" about either one; they're just different. As an example, if I want to get to New York City, New York, USA, then a seed value that started me in Boston, Massachusetts, USA would be better than one that started me in Paris, France. But if I wanted to get to Berlin, Germany, the Paris starting point may be better.
  1 Kommentar
Greg Heath
Greg Heath am 8 Aug. 2015
This is not an answer to your question. However, it is an interesting point to ponder.
Most of the NN functions have a default of 10 hidden nodes. For a trained SISO 1-10-1 MLP, how many ways can the weighted nodes be reordered without changing the output?

Melden Sie sich an, um zu kommentieren.

Kategorien

Mehr zu Random Number Generation finden Sie in Help Center und File Exchange

Tags

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by