Added documentation on MIX+GAN

  create_generator_loss()

  create_discriminator_loss()

  get_noise_batch()

  This class allows a GAN to have multiple generators and discriminators, a model

  known as MIX+GAN.  It is described in Arora et al., "Generalization and

  Equilibrium in Generative Adversarial Nets (GANs)" (https://arxiv.org/abs/1703.00573).

  This can lead to better models, and is especially useful for reducing mode

  collapse, since different generators can learn different parts of the

  distribution.  To use this technique, simply specify the number of generators

  and discriminators when calling the constructor.  You can then tell

  predict_gan_generator() which generator to use for predicting samples.

  """

  def __init__(self, n_generators=1, n_discriminators=1, **kwargs):

    """Construct a GAN.

    This class accepts all the keyword arguments from TensorGraph.

    In addition to the parameters listed below, this class accepts all the

    keyword arguments from TensorGraph.

    Parameters

    ----------

    n_generators: int

      the number of generators to include

    n_discriminators: int

      the number of discriminators to include

    """

    super(GAN, self).__init__(use_queue=False, **kwargs)