Minimize GPU memory usage throughout training/data management

GPU memory is the primary bottleneck that this code faces in terms of training reliable and scalable networks. Whether it be in data augmentation or the training process itself, hitting the barrier of GPU memory forces us to lower our standards and settle for less than ideal hyper parameters (smaller batch sizes, smaller networks, less data augmentation, etc.).