shared_randomstreams – Friendly random numbers

Platforms: Unix, Windows

Guide

Since Theano uses a functional design, producing pseudo-random numbers in a graph is not quite as straightforward as it is in numpy. If you are using Theano’s shared variables, then a RandomStreams object is probably what you want. (If you are using Module then this tutorial will be useful but not exactly what you want. Have a look at the :api:`RandomFunction` Op.)

The way to think about putting randomness into theano’s computations is to put random variables in your graph. Theano will allocate a numpy RandomState object for each such variable, and draw from it as necessary. I’ll call this sort of sequence of random numbers a random stream.

For an example of how to use random numbers, see Using Random Numbers.

Reference

class shared_randomstreams.RandomStreams(raw_random.RandomStreamsBase)

This is a symbolic stand-in for numpy.random.RandomState. Random variables of various distributions are instantiated by calls to parent class raw_random.RandomStreamsBase.

updates()

Returns:a list of all the (state, new_state) update pairs from the random variables it has returned.

This can be a convenient shortcut to enumerating all the random variables in a large graph in the update parameter of function.

seed(meta_seed)

meta_seed will be used to seed a temporary random number generator, that will in turn generate seeds for each of the random variables that has been created by this object (via gen).

Returns:None

gen(op, *args, **kwargs)

Return the random variable from op(*args, **kwargs), but also install special attributes (.rng and update, see RandomVariable ) into it.

This function also adds the returned variable to an internal list so that it can be seeded later by a call to seed.

uniform, normal, binomial, multinomial, random_integers, ...

See raw_random.RandomStreamsBase.

class shared_randomstreams.RandomVariable(object)

rng

The shared variable whose .value is the numpy RandomState generator feeding this random variable.

update

A pair whose first element is a shared variable whose value is a numpy RandomState, and whose second element is an [symbolic] expression for the next value of that RandomState after drawing samples. Including this pair in the``updates`` list to function will cause the function to update the random number generator feeding this variable.