Just a simple frame for setting up a feed-forward neural net. This is partially just so, having built the framework I have a better understanding of the inner workings of neural nets.

Note: This does not proivde any backpropagation. This is only the neurons and net.

• Net weights can all easily be pulled and replaced simultaneously (useful for genetic algorithms.
• All layers simply use all the outputs from the previous layer as inputs.
• Supports an arbitrary number of hidden layers of arbitrary sizes.
• Initial state for all weights on all neurons is given by Python's random.random()

Ok, enough here. On to using this thing with a genetic algorithm.