def main():
resnet_policy = ResNetPolicy(stack_nb=2)
# resnet_policy = UnitizedResNetPolicy(stack_nb=1)
resnet = resnet_policy.network
trainer = get_trainer(PrePolicyTrainer,
resnet,
0,
get_data_file('pre', 0),
batch_size=128,
epochs=200,
verbose=1)
def scheduler(epoch):
if epoch <= 60:
return 0.05
if epoch <= 120:
return 0.01
if epoch <= 160:
return 0.002
return 0.0004
callbacks = [LearningRateScheduler(scheduler)]
config_file_path = get_config_file('pre', resnet.name, 0)
weight_file_path = get_weight_file('pre', resnet.name, 0)
trainer.train(True, 0.9, callbacks=callbacks)
json_dump_tuple(resnet.get_config(), config_file_path)
resnet.save_weights(weight_file_path)
def main(index):
resnet_mixture = ResNetMixture(stack_nb=2)
resnet = resnet_mixture.network
trainer = get_trainer(PreMixtureTrainer,
resnet,
0,
get_data_file('pre', 0),
batch_size=128,
epochs=200,
verbose=1)
def scheduler(epoch):
if epoch <= 60:
return 0.05
if epoch <= 120:
return 0.01
if epoch <= 160:
return 0.002
return 0.0004
callbacks = [LearningRateScheduler(scheduler), Stop(0.16, 0.5)]
config_file_path = get_config_file('pre', resnet.name, index)
weight_file_path = get_weight_file('pre', resnet.name, index)
trainer.train(True, 0.9, callbacks=callbacks)
json_dump_tuple(resnet.get_config(), config_file_path)
resnet.save_weights(weight_file_path)
from AlphaGomoku.mcts.rollout_mcts import RolloutMCTS
from AlphaGomoku.neural_networks import get_network
from AlphaGomoku.neural_networks.keras.weights import get_weight_file
from AlphaGomoku.board import Board
from AlphaGomoku.play import Human, Game
policy = get_network('policy', 'resnet', 'keras', stack_nb=2)
policy.load_weights(get_weight_file('pre', policy.network.name, 0))
mcts = RolloutMCTS(policy, policy, 100, thread_number=4,
depth=6, delete_threshold=10)
mcts.get_action = lambda board: mcts.mcts(board, 1)
pattern = int(input('1: player vs ai, 2: ai vs player, 3: ai vs ai: '))
if pattern == 1:
black = Human()
white = mcts
elif pattern == 2:
black = mcts
white = Human()
else:
black = mcts
white = mcts
game = Game(black, white, visualization=True, visualization_time=3)
game.play()
from AlphaGomoku.mcts.evaluation_mcts import EvaluationMCTS
from AlphaGomoku.mcts.rl_evaluation_mcts import RLEvaluationMCTS
from AlphaGomoku.neural_networks import get_network
from AlphaGomoku.neural_networks.keras.weights import get_weight_file
from AlphaGomoku.board import Board
from AlphaGomoku.play import Human, Game
mixture = get_network('mixture', 'resnet', 'keras', stack_nb=2)
# mixture.load_weights(get_weight_file('pre', mixture.network.name, 0))
# mixture.load_weights(get_weight_file('zero', mixture.network.name, '1_0'))
mixture.load_weights(get_weight_file('zero', mixture.network.name, '4'))
mcts = EvaluationMCTS(mixture, 500, thread_number=1, delete_threshold=10)
# mcts = RLEvaluationMCTS(mixture, 500, thread_number=4, delete_threshold=10)
mcts.get_action = lambda board: mcts.mcts(board, 1)
pattern = int(input('1: player vs ai, 2: ai vs player, 3: ai vs ai: '))
if pattern == 1:
black = Human()
white = mcts
elif pattern == 2:
black = mcts
white = Human()
else:
black = mcts
white = mcts
game = Game(black, white, visualization=True, visualization_time=3)
game.play()
from AlphaGomoku.rl.rl import EvaluationMainLoop
from AlphaGomoku.mcts.evaluation_mcts import EvaluationMCTS
from AlphaGomoku.mcts.rl_evaluation_mcts import RLEvaluationMCTS
from AlphaGomoku.neural_networks import get_network
from AlphaGomoku.neural_networks.keras.weights import get_weight_file
mixture = get_network('mixture', 'resnet', 'keras', stack_nb=2)
mixture.load_weights(get_weight_file('pre', mixture.network.name, 0))
kwargs = {
'self_play_mcts_config': {
'traverse_time': 10,
'c_puct': None,
'thread_number': 1,
'delete_threshold': 100
},
'self_play_number': 3,
'self_play_batch_size': 3,
'self_play_cache_step': 10,
'evaluate_mcts_config': {
'traverse_time': 10,
'c_puct': None,
'thread_number': 1,
'delete_threshold': 100
},
'evaluate_number': 5,
'evaluate_batch_size': 10,
'evaluate_win_ratio': 0.55,
'evaluate_cache_step': 5,
'train': {
'batch_size': 128,