random-seed

So I have a very simple NN script written in Tensorflow, and I am having a hard time trying to trace down where some "randomness" is coming in from. I have recorded the Weights, Gradients, Logits of my network as I train, and for the first iteration, it is clear that everything starts off the same. I have a SEED value both for how data is read in, and a SEED value for initializing the weights of the net. Those I never change. My problem is that on say the second iteration of every re-run I do, I start to see the gradients diverge, (by a small amount, like say, 1e-6 or so). However over time,

I am creating a deep clone for some object. The object contains a Random . Is it good practice to retrieve the seed from the Random ? If so, how? There isn't a Random.getSeed() . A Random is intended to be random. Usually you want two Random to produce different numbers rather than to produce the same numbers. You can copy a Random using serialisation/de-serialisation and get the "seed" field using reflection. (But I doubt you should be doing either) Unless the sequence is critical to you, you can take the view that the clone of a Random is itself or any new Random() What you can do is get the

In my code I use random numbers in different classes. How to define random seed? Can I define this seed for all the classes in the main code? double rnd = Math.random(); Thijser You will probably want to use the special Random class. It gives you more control over the random numbers. To do this you first need to create a new random object. Random generator = new Random(seed); Then generate a new number by double random = generator.nextDouble(); http://docs.oracle.com/javase/6/docs/api/java/util/Random.html 来源： https://stackoverflow.com/questions/17445813/random-seed-math-random-in-java

I am using Keras to build a deep learning LSTM model, using TensorFlow backend. Each time I run the model, the result is different. Is there a way to fix the seed to create reproducible results? Thank you! As @Poete_Maudit said here: How to get reproducible results in keras To get reproducible results you will have to do the following at the very beginning of your script (that will be forced to use a single CPU ): # Seed value (can actually be different for each attribution step) seed_value= 0 # 1. Set `PYTHONHASHSEED` environment variable at a fixed value import os os.environ['PYTHONHASHSEED'

I am working on programming a Markov chain in Lua, and one element of this requires me to uniformly generate random numbers. Here is a simplified example to illustrate my question: example = function(x) local r = math.random(1,10) print(r) return x[r] end exampleArray = {"a","b","c","d","e","f","g","h","i","j"} print(example(exampleArray)) My issue is that when I re-run this program multiple times (mash F5 ) the exact same random number is generated resulting in the example function selecting the exact same array element. However, if I include many calls to the example function within the