Improve configurability of training hyperparameters

[aribrill]

At present, most of the training hyperparameters are required config options. Make these optional, providing reasonable defaults.

Add the ability to choose any optimizer available in TensorFlow (or at least all the commonly used ones), as well as the ability to configure their parameters. Since there are a variety of optimizers and they all take different arguments, this could be accomplished by just having an optimizer_arguments dictionary that is passed directly to the optimizer on initialization without any intermediate parsing. This would replace the current adam_epsilon configuration parameter.

Add options for additional training hyperparameters such as regularization type and strength and learning rate annealing.