I am working on my problem where I would like to add the classification loss in the generator. For that case, I need to change the way this code loads in the data from disk. I am thinking to use .tfrecords to load my images and corresponding labels in memory.
The problem I am facing is how to modify the current training procedure as right now all the fetches run at once using sess obj and the progress is calculated in a loop. But what I want is to first load a batch of images (using .tfrecords) and then feed the data to the network.
I am new in Tensorflow and GANS so finding it hard. Any suggestions or related fork will be appreciated. Thanks.
I am working on my problem where I would like to add the classification loss in the generator. For that case, I need to change the way this code loads in the data from disk. I am thinking to use .tfrecords to load my images and corresponding labels in memory.
The problem I am facing is how to modify the current training procedure as right now all the fetches run at once using sess obj and the progress is calculated in a loop. But what I want is to first load a batch of images (using .tfrecords) and then feed the data to the network.
I am new in Tensorflow and GANS so finding it hard. Any suggestions or related fork will be appreciated. Thanks.