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He is pictured above at theprime minister’s Holocaust Commission consultation event held in London on 5 May 2014.

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Azov Unit fighting for Ukraine’s government. Photo: Tom Parfitt (Telegraph)

Plungyán ( Plungė), 30 July 2014

Yankl-Yosl Bunk (13 July 1923 – 30 July 2014) Photo: Adam Ellick

Bunk’smassive wooden sculpture at the forest hill spot ( Plungyán / Plungė , western Lithuania), where 74teenage Jewish girls (from the local high school) were murdered by locals who knew them. Photo:


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QUESTION: Does the new musical Sugar Herbert Sugar perhaps violate the new law which bans (with punishment up to five years jailtime ) “ glorification, denial, justification or gross derogation of genocide, crimes against humanity, crimes against peace or war crimes against Latvia perpetrated by the Soviet Union or Nazi Germany.” Or is the law null and void when it comes to the Holocaust in Latvia?

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Here’s a brief example. The setup code is:

Here, ‘rv_u’ represents a random stream of 2x2 matrices of draws from a uniform distribution. Likewise, ‘rv_n’ represents a random stream of 2x2 matrices of draws from a normal distribution. The distributions that are implemented are defined in RandomStreams and, at a lower level, in DoraTasia 2018 Spring Summer Vulcanized Sneaker Lace Up 2018 Online p87w2Obl
. They only work on CPU. See Other Implementations for GPU version.

Now let’s use these objects. If we call f(), we get random uniform numbers. The internal state of the random number generator is automatically updated, so we get different random numbers every time.

When we add the extra argument no_default_updates=True to function (as in g ), then the random number generator state is not affected by calling the returned function. So, for example, calling g multiple times will return the same numbers.

An important remark is that a random variable is drawn at most once during any single function execution. So the nearly_zeros function is guaranteed to return approximately 0 (except for rounding error) even though the rv_u random variable appears three times in the output expression.

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() .

You can also seed all of the random variables allocated by a RandomStreams object by that object’s seed method. This seed will be used to seed a temporary random number generator, that will in turn generate seeds for each of the random variables.

As usual for shared variables, the random number generators used for random variables are common between functions. So our nearly_zeros function will update the state of the generators used in function f above.

For example:

In some use cases, a user might want to transfer the “state” of all random number generators associated with a given theano graph (e.g. g1, with compiled function f1 below) to a second graph (e.g. g2, with function f2). This might arise for example if you are trying to initialize the state of a model, from the parameters of a pickled version of a previous model. For theano.tensor.shared_randomstreams.RandomStreams and theano.sandbox.rng_mrg.MRG_RandomStreams this can be achieved by copying elements of the state_updates parameter.

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