![]() ![]() # resetting seed does not reset sequence. With s.as_default(): print msg, a.eval(), a.eval(), a.eval(), a.eval() TensorFlow: Resetting the seed to a constant value does not yield repeating results, Tensorflow `set_random_seed` not working, TensorFlow: Non-repeatable results, how to get reproducible result in Tensorflow, How to get stable results with TensorFlow, setting random seed) however I can't get the behaviour I'm after. I've read the documentation and a ton of posts regarding random seeds in tensorflow (e.g. However, it remains unclear how random people can be during games. Setting the random seed again (tf.set_random_seed) doesn't seem to have an affect (I presume the seed from tf.set_random_seed is somehow combined with the op seed and baked into the op when it's created?) The human ability for random-sequence generation (RSG) is limited but improves in a competitive game environment with feedback. Destroying and reinitialising session isn't really ideal as I have GBs of variables which I lose, and reloading each time is a waste. However I'd like to be able to reset the random sequence without destroying the session, so I can call my function over and over (and get the same sequence). Reprogramming is a gradual process involving a number of steps, not all of which are understood. This page explains why its hard (and interesting) to get a computer to generate proper random numbers. Stocum, in Regenerative Biology and Medicine (Second Edition), 2012 2 Mechanism of Reprogramming. A free online tool made to generate random DNA, RNA and protein sequences. I can achieve this using random seeds, such that I always get the same sequence every time I run the script from scratch (i.e. RANDOM.ORG is a true random number service that generates randomness via atmospheric noise. This form allows you to generate randomized sequences of integers. feed a large number of x, and always get the exact same z and y). I'd like to be able to run a function, and always use the same random sequence (e.g. Resetting a stream's seed can invalidate independence with other streams. The value of seed must be an integer between 0 and 2 32 1. If you mix randomly-initialized trainable layers with. reset(s,seed) resets the generator for the random stream s to the internal state corresponding to seed (the seed value), and it updates the seed property of s. if the probability of a symbol is dependent on the previous k-symbols occurring in the data), many (prediction-based) compression algorithms will succeed. But if the data has some predictable patterns (for e.g. I have a graph with GBs of variables, and a random function (e.g. It is critical to only do this step after the model with frozen layers has been trained to convergence. If the data is truly random, on average no compression algorithm can compress it. ![]()
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