def main():
print("Starting run (v{})".format(__version__))
init_directories()
model_name = "model_1"
model = create_initial_model(name=model_name)
while True:
model = load_latest_model()
best_model = load_best_model()
train(model, game_model_name=best_model.name)
evaluate(best_model, model)
K.clear_session()
def main():
init_directories()
model_name = "model_1"
model = create_initial_model(name=model_name)
while True:
model = load_latest_model()
best_model = load_model(os.path.join(conf['MODEL_DIR'],
conf['BEST_MODEL']),
custom_objects={'loss': loss})
train(model, game_model_name=best_model.name)
evaluate(best_model, model)
def setUp(self):
init_directories()
model_name = "model_1"
model = create_initial_model(name=model_name)
best_model = load_best_model()
if best_model.name == model.name:
train(model, game_model_name=best_model.name)
evaluate(best_model, model)
# We save wether or not it was a better model
full_filename = os.path.join(conf['MODEL_DIR'], conf['BEST_MODEL'])
model.save(full_filename)
else:
model = best_model
self.model = model
def main():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
K.set_session(tf.Session(config=config))
init_directories()
model_name = "model_1"
model = create_initial_model(name=model_name)
while True:
best_model = load_best_model()
self_play(best_model,
n_games=10,
mcts_simulations=conf['MCTS_SIMULATIONS'])
K.clear_session()