title: Reusing data in sliding window based Temporal Convolutional Network
body: <p>I'm processing 8 channel EMG data with 1D convolutional filters across time, regressing them joint angles. My training data consists of 200 hundred sample long windows of EMG data (so 8x200 values) for each target joint angle. Once learning is complete on one window, the next window is only one sample ahead in the future. This means that 8x199 values are the same as the last window. Since I'm working from home and only got a laptop at hand with 16 GB RAM, I'm struggling with memory as I'd like to include more training data and different channels as well.</p>
  
  <p>Is there a way to reuse the training data without storing the separate windows explicitly in memory? i.e. just have one copy of the recordings and have the network in training be given windows as it needs during training? A Keras based solution would be preferred as I've done all work using this library so far.</p>