TFLearn: How to use image preloader for image in -> image out regression problems
<p>I'm new to tflearn and I'm trying to train a CNN to solve image in -> image out regression problems, where the input images are either MxN grayscale or MxNx3 RGB.</p>

<p>I have a very large training set that will not fit in memory, so I would like to train "on the fly", pulling images from disk as training is ready to consume them.</p>

<p>The image_preloader seems to make the assumption that we will always be solving problems where X is an input image and y is a set of scalar labels corresponding to X. </p>

<p>What is the best way within the tflearn framework to do something similar for regression problems where both X and y are images? I feel like I have to be missing something obvious, but I've looked through the Data Utils, Data Augmentation, and Data Preprocessing sections and don't see anything.</p>

<p>For the full scope of what I'm trying to do: I have a large set of images on disk. On the fly, I need to be able to load my images, then do some modifications to the image to define the input images X and y. The images on disk need to be augmented to properly define X and y. Given a minibatch X read from disk, I can define X and y a batch at a time.</p>