def train(good_luck=False):
"""
Train procedure:
0) Try to resume the training if it has been stopped
1) Self play
2) Train the net based on the selfplay
3) Evaluate the trained net
"""
if not good_luck:
raise Exception('You will need it')
# Init file and folders for training
globals.init_training_stuff()
# Resume training
iteration = globals.get_last_iteration()
for it in range(iteration, train_args.iters):
print('Starting iteration ', it)
# Self play
examples = selfplay()
examples = GameState.all_symmetries(examples)
globals.dump_selfplay_data(examples)
# Train
train_net(examples)
# Evaluate
score = evaluate_net(
net=str(globals.get_last_iteration() + 1), net_opponent='best'
) if not train_args.always_accept_model else np.inf
# If new net scored positively accept new model
if score >= 0:
print('Accepting model, score', score)
globals.accept_model()
else:
print('Rejecting model, score', score)
# Update iteration file to keep track of iterations done
globals.update_iteration_file(it + 1)
