Using tensorflow input pipline with GAN

Question

I want to build a conditional GAN with tensorflow and use input pipline for loading my dataset. The problem is that in each iteration I want to the use same data batch for training both generative and discriminative models, but because their training operators are fetched in different runs they will receive different batches of data. Is there any solution for that or should I use a feed_dict?

Could you try putting outputs of G and D in one run so that inputs to them will be same ? Using feed_dict is an option but it might slows down because of data copying. — Harsha Pokkalla

Joel Shor Joel Shor · Accepted Answer · 2017-11-29T00:58:12

One way to use the same data is to use a tf.group on the generator and discriminator train ops so they are trained jointly, and set use_locking=True on your optimizers to prevent pathological race conditions. Note that there still will be some stochasticity due to the fact that TensorFlow runtime won't guarantee that either the generator or the discriminator will consistently be trained first.

This idea is already implemented in TensorFlow's TFGAN library in get_joint_train_hooks, although it uses hooks instead of grouping the training ops (the "joint" refers to the fact that the discriminator and generator are trained jointly, rather than sequentially).

Using tensorflow input pipline with GAN

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