Continuing to implement MIX+GAN

    else:

      # Create learnable weights for the generators and discriminators.

      gen_alpha = layers.Variable(np.ones((n_generators, 1)))

      gen_alpha = layers.Variable(np.ones((1, n_generators)))

      gen_weights = layers.SoftMax(gen_alpha)

      discrim_alpha = layers.Variable(np.ones((n_discriminators, 1)))

      discrim_alpha = layers.Variable(np.ones((1, n_discriminators)))

      discrim_weights = layers.SoftMax(discrim_alpha)

      # Compute the weighted errors

      gen_layers.add(gen_alpha)

      discrim_layers.add(gen_alpha)

      discrim_layers.add(discrim_alpha)

      self.gen_alpha = gen_alpha

      self.discrim_alpha = discrim_alpha

      # Add an entropy term to the loss.

      entropy = -(layers.ReduceSum(layers.Log(gen_weights))/n_generators + layers.ReduceSum(layers.Log(discrim_weights))/n_discriminators)

      total_discrim_loss += entropy

    # Create submodels for training the generators and discriminators.

        saver.save(self.session, self.save_file, global_step=self.global_step)

        time2 = time.time()

        print("TIMING: model fitting took %0.3f s" % (time2 - time1))

      print(self.session.run(self.gen_alpha))

      print(self.session.run(self.discrim_alpha))

  def predict_gan_generator(self,

                            batch_size=1,