(len(features), len(nextMoves))) print( "==========================================================" ) batch = get_batch(features, nextMoves, BATCH_SIZE) while len(batch['features']) != 0: # train 1 batch at a time: network.train(batch) if i % 5 == 0: network.average_summary() if i % 100 == 0 and i != 0: network.save_checkpoint(CHECKPOINT_DIR, network.get_global_step()) # get rid of the data used in previous batch # and get the next batch features = features[BATCH_SIZE:] nextMoves = nextMoves[BATCH_SIZE:] batch = get_batch(features, nextMoves, BATCH_SIZE) i += 1 print("%d batches ran. Remaining Feature Length is %d" % (network.get_global_step(), len(features))) network.average_summary() print("%d batches ran." % i)