softmax scaling for entropy 1.25*0.875, entropy scaling 2 after 100 epochs, encoder bound 2.5,
global L1 scaling 0.5 (cifar-specific), L1 scaling 5, elastic net scaling 0.25,
12x12 berkeley window, 400 units, 11 ista iterations, 1 regularization iterations
learning rate 5e-3, minibatch size 10, 1000 epochs pretraining
decoding_feature_extraction_dictionary rows are fully normalized
global, not per-image, L2 norm on input, append 0.1 to hidden vals before computing entropy