(With layers whose backward accumulates gradients), this effectively
decouples the computational batch from the SGD minibatch. Each
iteration accumulates gradients over iter_size batches, then parameters
are updated.

Divide local rate by `iter_size` to normalize the gradient according to
the full minibatch size and not only the computational batch size.

Multiply the local decay by `iter_size` to counter the division of the
local learning rate since the decay is multiplied by the rate in the
update equation.

Compare the parameters after solving with a given batch size and the
halved batch size + two iter accumulation of gradients equivalent.

Note: the test net dummy data layer now makes constant data and random
gaussian targets. This assures the standard and gradient accumulation
cases check the same data. Otherwise the difference in batch sizes
causes different orders of random number draws.

`SGDSolver::Normalize()` normalizes accumulated gradients by scaling
inversely to the accumulation as `1 / iter_size`.

This fixes accumulation for AdaGrad and is more obvious than fooling
with rates and decays in 55585f5.