I am training a Generative Adversarial Network (GAN) in tensorflow, where basically we have two different networks each one with its own optimizer.
self.G, s
You can create a separate instance of tf.train.Saver() with the var_list argument set to the variables you want to restore. And create a separate instance to save the variables
tf.train.Saver()
var_list