Apologies if I am misunderstanding something or this is not the correct venue, but I believe I found a small mistake in the deeplearning.net theano tutorial.
In the "Seeding Streams" section of the "More Examples" section, it is written the following: Random variables can be seeded individually or collectively. You can seed just one random variable by seeding or assigning to the .rng attribute, using .rng.set_value(). >>> rng_val = rv_u.rng.get_value(borrow=True) # Get the rng for rv_u>>> >>> rng_val.seed(89234) # seeds the generator>>> >>> rv_u.rng.set_value(rng_val, borrow=True) # Assign back seeded rng Now, it seems to me that using rng.set_value is entirely superfluous here: since we used borrow=True with get_value, rng_val is the random state of the (uniform, in this case) random function rv_u and NOT a copy, and, thus, there is no need to assigning it back to it after seeding it. This would be different, of course, if we had used borrow=False. A couple of quick tests seem to confirm my impression. Am I missing something? Thanks! -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
