title: Getting good mixing with many input datafiles in tensorflow
body: <p>I'm working with tensorflow hoping to train a deep CNN to do move prediction for the game Go. The dataset I created consists of 100,000 binary data files, where each datafile corresponds to a recorded game and contains roughly 200 training samples (one for each move in the game). I believe it will be very important to get good mixing when using SGD. I'd like my batches to contain samples from different games AND samples from different stages of the games. So for example simply reading one sample from the start of 100 files and shuffling isn't good b/c those 100 samples will all be the first move of each game. </p>
  
  <p>I have read the tutorial on feeding data from files but I'm not sure if their provided libraries do what I need. If I were to hard code it myself I would basically initialize a bunch of file pointers to random locations within each file and then pull samples from random files, incrementing the file pointers accordingly.</p>
  
  <p>So, my question is does tensorflow provide this sort of functionality or would it be easier to write my own code for creating batches?</p>