raw_random – Low-level random numbers
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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 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. We will call this sort of sequence of random numbers a random stream.
For an example of how to use random numbers, see Using Random Numbers.
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.
Returns: | a list of all the (state, new_state) update pairs for the random variables created by this object |
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This can be a convenient shortcut to enumerating all the random variables in a large graph in the update parameter of function.
meta_seed will be used to seed a temporary random number generator, that will in turn generate seeds for all random variables created by this object (via gen).
Returns: | None |
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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.
The shared variable whose .value is the numpy RandomState generator feeding this random variable.
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.