(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)
