def init_value_network(self,
shared_network=None,
activation='linear',
loss='mse'):
if self.net == 'dnn':
self.value_network = DNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'lstm':
self.value_network = LSTMNetwork(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'cnn':
self.value_network = CNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
if self.reuse_models and \
os.path.exists(self.value_network_path): # reuse_models이 True이고, value_network_path 값이 있으면 신경망 모델 파일을 불러온다...
self.value_network.load_model(model_path=self.value_network_path)
def build_models(args, device='cuda'):
models = {}
models['encodercnn'] = CNN(input_shape=RGB_INPUT_SHAPE,
model_name=args.encoder_cnn_model).to(device)
models['encoder'] = Encoder(input_shape=models['encodercnn'].out_size,
encoder_block='convbilstm',
hidden_size=args.encoder_hid_size).to(device)
models['crossviewdecodercnn'] = CNN(input_shape=DEPTH_INPUT_SHAPE,
model_name=args.encoder_cnn_model,
input_channel=1).to(device)
crossviewdecoder_in_size = list(models['crossviewdecodercnn'].out_size)
crossviewdecoder_in_size[0] = crossviewdecoder_in_size[0] * 3
crossviewdecoder_in_size = torch.Size(crossviewdecoder_in_size)
models['crossviewdecoder'] = CrossViewDecoder(
input_shape=crossviewdecoder_in_size).to(device)
models['reconstructiondecoder'] = ReconstructionDecoder(
input_shape=models['encoder'].out_size[1:]).to(device)
models['viewclassifier'] = ViewClassifier(
input_size=reduce(operator.mul, models['encoder'].out_size[1:]),
num_classes=5,
reverse=(not args.disable_grl)).to(device)
return models
def init_policy_network(self,
shared_network=None,
activation='sigmoid',
loss='binary_crossentropy'):
if self.net == 'dnn':
self.policy_network = DNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'lstm':
self.policy_network = LSTMNetwork(
input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'cnn':
self.policy_network = CNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
if self.reuse_models and \
os.path.exists(self.policy_network_path):
self.policy_network.load_model(model_path=self.policy_network_path)
def __init__(self,
n_actions,
input_shape,
save_path=None,
load_path=None,
action_inverses={},
update_interval=256 * 2,
save_interval=5):
self.n_actions = n_actions
self.save_path = save_path
self.network = CNN(n_out=n_actions, input_shape=input_shape)
if load_path != None:
self.network.load(load_path)
self.n_features = input_shape
self.data = []
self.current_episode_count = 1
self.random_actions = 0
self.last_action = None
self.last_action_random = False
self.action_inverses = action_inverses
self.lifetime = 1
self.update_interval = update_interval
self.save_interval = save_interval
self.n_updates = 1
def do_stuff(opt):
print(f'\nTraining {opt} for {args.num_epochs} epochs...')
net = CNN() if args.dataset == 'cifar' else MLP()
_, kwargs = misc.split_optim_dict(misc.optim_dict[opt])
optimizer = misc.task_to_optimizer(opt)(params=net.parameters(),
**kwargs)
optimizer = misc.wrap_optimizer(opt, optimizer)
return fit(net,
data,
optimizer,
num_epochs=args.num_epochs,
lr_schedule=True)
def do_stuff(opt):
print(f'\nTraining {opt} for {args.num_epochs} epochs...')
net = CNN() if args.dataset == 'cifar' else MLP()
_, kwargs = misc.split_optim_dict(misc.optim_dict[opt])
optimizer = misc.task_to_optimizer(opt)(params=net.parameters(),
**kwargs)
if 'lookahead' in opt.lower():
optimizer = optimizers.Lookahead(optimizer, k=5, alpha=0.5)
return fit(net,
data,
optimizer,
num_epochs=args.num_epochs,
lr_schedule=True)
def define_agent(self, width, height, num_actions):
return NStepDQNAgent(config=Config(
num_actions=num_actions,
encoder=LayerEncoder(width, height, treasure_position=True),
optimizer=SharedAdamOptimizer(0.001),
network=CNN(hidden_units=[128]),
policy=EpsilonGreedyPolicy(1, 0.01, 25000),
discount=0.95,
n_step=16))
def define_agent(self, width, height, num_actions):
return DQNAgent(
config=Config(
num_actions=num_actions,
encoder=LayerEncoder(width, height, treasure_position=True),
optimizer=AdamOptimizer(0.001),
network=CNN(hidden_units=[128]),
policy=EpsilonGreedyPolicy(1, 0.01, 50000),
discount=0.95,
capacity=10000,
batch_size=8,
target_sync=100,
double_q=True))
def init_value_network(self,
shared_network=None,
activation='linear',
loss='mse'):
if self.rl_method == 'ddpg':
self.critic = CriticNetwork(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
num_steps=self.num_steps,
activation=activation,
loss=loss,
lr=self.lr)
elif self.net == 'dnn':
self.value_network = DNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'lstm':
self.value_network = LSTMNetwork(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'cnn':
self.value_network = CNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'cnn':
self.value_network = CNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
if self.reuse_models and \
os.path.exists(self.value_network_path):
self.value_network.load_model(model_path=self.value_network_path)
labels.append(label.item())
p, r, f, _ = report(labels, preds2)
ps_2 += [p]
rs_2 += [r]
fs_2 += [f]
# '''
print('1.5+ PRF on', name, ':')
print('\tprecision:', p)
print('\trecall:', r)
print('\tf score:', f)
# '''
return ps_1, rs_1, fs_1, ps_2, rs_2, fs_2
if __name__ == '__main__':
cnn = CNN('./cnn_config_1.5T_optimal.json', 0)
#valid_optimal_accu = cnn.train()
cnn.epoch=146
cnn.model.load_state_dict(torch.load('{}CNN_{}.pth'.format(cnn.checkpoint_dir, cnn.epoch)))
cnn.test()
# valid = 89.02
# test = 84.15
# gan = GAN('./gan_config_optimal.json', 0)
#gan.train()
#'''
# gan.optimal_epoch=105
# gan.netG.load_state_dict(torch.load('{}G_{}.pth'.format(gan.checkpoint_dir, gan.optimal_epoch)))
# print(gan.valid_model_epoch())
#gan.test(False)
# gan.eval_iqa(zoom=False, metric='brisque')
class ReinforcementLearner:
__metaclass__ = abc.ABCMeta
lock = threading.Lock()
def __init__(self,
rl_method='rl',
stock_code=None,
chart_data=None,
training_data=None,
min_trading_unit=1,
max_trading_unit=2,
delayed_reward_threshold=.05,
net='dnn',
num_steps=1,
lr=0.001,
value_network=None,
policy_network=None,
output_path='',
reuse_models=True):
# 인자 확인
assert min_trading_unit > 0
assert max_trading_unit > 0
assert max_trading_unit >= min_trading_unit
assert num_steps > 0
assert lr > 0
# 강화학습 기법 설정
self.rl_method = rl_method
# 환경 설정
self.stock_code = stock_code
self.chart_data = chart_data
self.environment = Environment(chart_data)
# 에이전트 설정
self.agent = Agent(self.environment,
min_trading_unit=min_trading_unit,
max_trading_unit=max_trading_unit,
delayed_reward_threshold=delayed_reward_threshold)
# 학습 데이터
self.training_data = training_data
self.sample = None
self.training_data_idx = -1
# 벡터 크기 = 학습 데이터 벡터 크기 + 에이전트 상태 크기
self.num_features = self.agent.STATE_DIM
if self.training_data is not None:
self.num_features += self.training_data.shape[1]
# 신경망 설정
self.net = net
self.num_steps = num_steps
self.lr = lr
self.value_network = value_network
self.policy_network = policy_network
self.reuse_models = reuse_models
self.critic = value_network
self.actor = policy_network
self.tau = 0.001
# 가시화 모듈
self.visualizer = Visualizer()
# 메모리
self.memory_sample = []
self.memory_action = []
self.memory_reward = []
self.memory_value = []
self.memory_policy = []
self.memory_target_policy = []
self.memory_target_value = []
self.memory_target_action = []
self.memory_pv = []
self.memory_num_stocks = []
self.memory_exp_idx = []
self.memory_learning_idx = []
# 에포크 관련 정보
self.loss = 0.
self.itr_cnt = 0
self.exploration_cnt = 0
self.batch_size = 0
self.learning_cnt = 0
# 로그 등 출력 경로
self.output_path = output_path
def init_policy_network(self,
shared_network=None,
activation='sigmoid',
loss='binary_crossentropy'):
if self.rl_method == 'ddpg':
print("actor")
self.actor = ActorNetwork(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
num_steps=self.num_steps,
activation=activation,
loss=loss,
lr=self.lr)
print(self.actor)
elif self.net == 'dnn':
self.policy_network = DNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'lstm':
self.policy_network = LSTMNetwork(
input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'cnn':
self.policy_network = CNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'cnn':
self.policy_network = CNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
if self.reuse_models and \
os.path.exists(self.policy_network_path):
self.policy_network.load_model(model_path=self.policy_network_path)
def init_value_network(self,
shared_network=None,
activation='linear',
loss='mse'):
if self.rl_method == 'ddpg':
self.critic = CriticNetwork(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
num_steps=self.num_steps,
activation=activation,
loss=loss,
lr=self.lr)
elif self.net == 'dnn':
self.value_network = DNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'lstm':
self.value_network = LSTMNetwork(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'cnn':
self.value_network = CNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'cnn':
self.value_network = CNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
if self.reuse_models and \
os.path.exists(self.value_network_path):
self.value_network.load_model(model_path=self.value_network_path)
def reset(self):
self.sample = None
self.training_data_idx = -1
# 환경 초기화
self.environment.reset()
# 에이전트 초기화
self.agent.reset()
# 가시화 초기화
self.visualizer.clear([0, len(self.chart_data)])
# 메모리 초기화
self.memory_sample = []
self.memory_action = []
self.memory_target_policy = []
self.memory_target_value = []
self.memory_target_action = []
self.memory_reward = []
self.memory_value = []
self.memory_policy = []
self.memory_pv = []
self.memory_num_stocks = []
self.memory_exp_idx = []
self.memory_learning_idx = []
# 에포크 관련 정보 초기화
self.loss = 0.
self.itr_cnt = 0
self.exploration_cnt = 0
self.batch_size = 0
self.learning_cnt = 0
def build_sample(self):
self.environment.observe()
if len(self.training_data) > self.training_data_idx + 1:
self.training_data_idx += 1
self.sample = self.training_data.iloc[
self.training_data_idx].tolist()
self.sample.extend(self.agent.get_states())
return self.sample
return None
@abc.abstractmethod
def get_batch(self, batch_size, delayed_reward, discount_factor):
pass
@abc.abstractmethod
def train(self, batch_size, delayed_reward, discount_factor):
pass
def update_networks(self, batch_size, delayed_reward, discount_factor):
# 배치 학습 데이터 생성
x, y_value, y_policy = self.get_batch(batch_size, delayed_reward,
discount_factor)
if len(x) > 0:
loss = 0
if y_value is not None:
# 가치 신경망 갱신
loss += self.critic.train_on_batch(x, y_value)
self.critic.transfer_weights()
if y_policy is not None:
# 정책 신경망 갱신
loss += self.actor.train_on_batch(x, y_policy)
self.actor.transfer_weights()
return loss
return None
def fit(self, delayed_reward, discount_factor, full=False):
batch_size = len(self.memory_reward) if full \
else self.batch_size
# 배치 학습 데이터 생성 및 신경망 갱신
if batch_size > 0:
_loss = self.update_networks(batch_size, delayed_reward,
discount_factor)
if _loss is not None:
self.loss += abs(_loss)
self.learning_cnt += 1
self.memory_learning_idx.append(self.training_data_idx)
self.batch_size = 0
def visualize(self, epoch_str, num_epoches, epsilon):
self.memory_action = [Agent.ACTION_HOLD] \
* (self.num_steps - 1) + self.memory_action
self.memory_num_stocks = [0] * (self.num_steps - 1) \
+ self.memory_num_stocks
if self.value_network is not None:
self.memory_value = [np.array([np.nan] \
* len(Agent.ACTIONS))] * (self.num_steps - 1) \
+ self.memory_value
if self.policy_network is not None:
self.memory_policy = [np.array([np.nan] \
* len(Agent.ACTIONS))] * (self.num_steps - 1) \
+ self.memory_policy
self.memory_pv = [self.agent.initial_balance] \
* (self.num_steps - 1) + self.memory_pv
self.visualizer.plot(
epoch_str=epoch_str,
num_epoches=num_epoches,
epsilon=epsilon,
action_list=Agent.ACTIONS,
actions=self.memory_action,
num_stocks=self.memory_num_stocks,
outvals_value=self.memory_value,
outvals_policy=self.memory_policy,
exps=self.memory_exp_idx,
learning_idxes=self.memory_learning_idx,
initial_balance=self.agent.initial_balance,
pvs=self.memory_pv,
)
self.visualizer.save(
os.path.join(self.epoch_summary_dir,
'epoch_summary_{}.png'.format(epoch_str)))
def run(self,
num_epoches=100,
balance=10000000,
discount_factor=0.9,
start_epsilon=0.5,
learning=True):
info = "[{code}] RL:{rl} Net:{net} LR:{lr} " \
"DF:{discount_factor} TU:[{min_trading_unit}," \
"{max_trading_unit}] DRT:{delayed_reward_threshold}".format(
code=self.stock_code, rl=self.rl_method, net=self.net,
lr=self.lr, discount_factor=discount_factor,
min_trading_unit=self.agent.min_trading_unit,
max_trading_unit=self.agent.max_trading_unit,
delayed_reward_threshold=self.agent.delayed_reward_threshold
)
with self.lock:
logging.info(info)
# 시작 시간
time_start = time.time()
# 가시화 준비
# 차트 데이터는 변하지 않으므로 미리 가시화
self.visualizer.prepare(self.environment.chart_data, info)
# 가시화 결과 저장할 폴더 준비
self.epoch_summary_dir = os.path.join(
self.output_path, 'epoch_summary_{}'.format(self.stock_code))
if not os.path.isdir(self.epoch_summary_dir):
os.makedirs(self.epoch_summary_dir)
else:
for f in os.listdir(self.epoch_summary_dir):
os.remove(os.path.join(self.epoch_summary_dir, f))
# 에이전트 초기 자본금 설정
self.agent.set_balance(balance)
# 학습에 대한 정보
max_portfolio_value = 0
epoch_win_cnt = 0
# 학습 반복
for epoch in range(num_epoches):
time_start_epoch = time.time()
# step 샘플을 만들기 위한 큐
q_sample = collections.deque(maxlen=self.num_steps)
# 환경, 에이전트, 신경망, 가시화, 메모리 초기화
self.reset()
# 학습을 진행할 수록 탐험 비율 감소
if learning:
epsilon = start_epsilon \
* (1. - float(epoch) / (num_epoches - 1))
self.agent.reset_exploration()
else:
epsilon = start_epsilon
while True:
# 샘플 생성
next_sample = self.build_sample()
if next_sample is None:
break
# num_steps만큼 샘플 저장
q_sample.append(next_sample)
if len(q_sample) < self.num_steps:
continue
# 가치, 정책 신경망 예측
pred_value = None
pred_policy = None
pred_target_policy = None
pred_target_value = None
if self.critic is not None:
pred_value = self.critic.predict(list(q_sample))
pred_target_value = self.critic.target_predict(
list(q_sample))
if self.actor is not None:
pred_policy = self.actor.predict(list(q_sample))
pred_target_policy = self.actor.target_predict(
list(q_sample))
# 신경망 또는 탐험에 의한 행동 결정
action, confidence, exploration = \
self.agent.decide_action(pred_value, pred_policy, epsilon)
# target 값을 이용한 행동 결정
target_action, target_confidence, target_exploration = \
self.agent.decide_action(pred_target_policy, pred_target_value, epsilon)
#결정한 행동을 수행하고 즉시 보상과 지연 보상 획득
immediate_reward, delayed_reward = \
self.agent.act(action, confidence)
# 행동 및 행동에 대한 결과를 기억
self.memory_sample.append(list(q_sample))
self.memory_action.append(action)
self.memory_reward.append(immediate_reward)
self.memory_target_action.append(target_action)
self.memory_target_policy.append(pred_target_policy)
self.memory_target_value.append(pred_target_value)
if self.value_network is not None:
self.memory_value.append(pred_value)
if self.policy_network is not None:
self.memory_policy.append(pred_policy)
self.memory_pv.append(self.agent.portfolio_value)
self.memory_num_stocks.append(self.agent.num_stocks)
if exploration:
self.memory_exp_idx.append(self.training_data_idx)
# 반복에 대한 정보 갱신
self.batch_size += 1
self.itr_cnt += 1
self.exploration_cnt += 1 if exploration else 0
# 지연 보상 발생된 경우 미니 배치 학습
if learning and (delayed_reward != 0):
self.fit(delayed_reward, discount_factor)
# 에포크 종료 후 학습
if learning:
self.fit(self.agent.profitloss, discount_factor, full=True)
# 에포크 관련 정보 로그 기록
num_epoches_digit = len(str(num_epoches))
epoch_str = str(epoch + 1).rjust(num_epoches_digit, '0')
time_end_epoch = time.time()
elapsed_time_epoch = time_end_epoch - time_start_epoch
if self.learning_cnt > 0:
logging.info("[{}][Epoch {}/{}] Epsilon:{:.4f} "
"#Expl.:{}/{} #Buy:{} #Sell:{} #Hold:{} "
"#Stocks:{} PV:{:,.0f} "
"LC:{} Loss:{:.6f} ET:{:.4f}".format(
self.stock_code, epoch_str, num_epoches,
epsilon, self.exploration_cnt, self.itr_cnt,
self.agent.num_buy, self.agent.num_sell,
self.agent.num_hold, self.agent.num_stocks,
self.agent.portfolio_value, self.learning_cnt,
self.loss, elapsed_time_epoch))
# 에포크 관련 정보 가시화
self.visualize(epoch_str, num_epoches, epsilon)
# 학습 관련 정보 갱신
max_portfolio_value = max(max_portfolio_value,
self.agent.portfolio_value)
if self.agent.portfolio_value > self.agent.initial_balance:
epoch_win_cnt += 1
# 종료 시간
time_end = time.time()
elapsed_time = time_end - time_start
# 학습 관련 정보 로그 기록
with self.lock:
logging.info("[{code}] Elapsed Time:{elapsed_time:.4f} "
"Max PV:{max_pv:,.0f} #Win:{cnt_win}".format(
code=self.stock_code,
elapsed_time=elapsed_time,
max_pv=max_portfolio_value,
cnt_win=epoch_win_cnt))
def save_models(self):
if self.value_network is not None and \
self.value_network_path is not None:
self.value_network.save_model(self.value_network_path)
if self.policy_network is not None and \
self.policy_network_path is not None:
self.policy_network.save_model(self.policy_network_path)
def pre_train(dataloader,
test_loader,
dict_loader,
dataloader_test,
mask_labels,
total_epochs=50,
learning_rate=1e-4,
use_gpu=True,
seed=123):
args = parser.parse_args()
pprint(args)
num_bits = args.num_bits
model = CNN(model_name='alexnet', bit=num_bits, class_num=args.num_class)
criterion = custom_loss(num_bits=num_bits)
arch = 'cnn_'
filename = arch + args.dataset + '_' + str(num_bits) + "bits"
checkpoint_filename = os.path.join(args.checkpoint, filename + '.pt')
if use_gpu:
model = model.cuda()
model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count()))
criterion = criterion.cuda()
torch.cuda.manual_seed(seed)
running_loss = 0.0
start_epoch = 0
batch_time = AverageMeter()
data_time = AverageMeter()
end = time.time()
best_prec = -99999
k = 10500
n_samples = 200000
alpha = 0.4
alpha_1 = 0.99
mask_labels = torch.from_numpy(mask_labels).long().cuda()
Z_h1 = torch.zeros(n_samples,
num_bits).float().cuda() # intermediate values
z_h1 = torch.zeros(n_samples, num_bits).float().cuda() # temporal outputs
h1 = torch.zeros(n_samples, num_bits).float().cuda() # current outputs
Z_h2 = torch.zeros(args.anchor_num,
num_bits).float().cuda() # intermediate values
z_h2 = torch.zeros(args.anchor_num,
num_bits).float().cuda() # temporal outputs
h2 = torch.zeros(args.anchor_num,
num_bits).float().cuda() # current outputs
for epoch in range(start_epoch, total_epochs):
model.train(True)
rampup_value = rampup(epoch)
rampdown_value = rampdown(epoch)
learning_rate = rampup_value * rampdown_value * 0.00005
adam_beta1 = rampdown_value * 0.9 + (1.0 - rampdown_value) * 0.5
adam_beta2 = step_rampup(epoch) * 0.99 + (1 -
step_rampup(epoch)) * 0.999
if epoch == 0:
u_w = 0.0
else:
u_w = rampup_value
u_w_m = u_w * 5
u_w_m = torch.autograd.Variable(torch.FloatTensor([u_w_m]).cuda(),
requires_grad=False)
optimizer = Adam(model.parameters(),
lr=learning_rate,
betas=(adam_beta1, adam_beta2),
eps=1e-8,
amsgrad=True)
anchors_data, anchor_Label = generate_anchor_vectors(dict_loader)
for iteration, data in enumerate(dataloader, 0):
anchor_index = np.arange(args.anchor_num)
np.random.shuffle(anchor_index)
anchor_index = anchor_index[:100]
anchor_index = torch.from_numpy(anchor_index).long().cuda()
anchor_inputs = anchors_data[anchor_index, :, :, :]
anchor_labels = anchor_Label[anchor_index, :]
inputs, labels, index = data['image'], data['labels'], data[
'index']
labels = labels.float()
mask_flag = Variable(mask_labels[index], requires_grad=False)
idx = (mask_flag > 0)
if index.shape[0] == args.batch_size:
anchor_batch_S, anchor_batch_W = CalcSim(
labels[idx, :].cuda(), anchor_labels.cuda())
if inputs.size(3) == 3:
inputs = inputs.permute(0, 3, 1, 2)
inputs = inputs.type(torch.FloatTensor)
zcomp_h1 = z_h1[index.cuda(), :]
zcomp_h2 = z_h2[anchor_index, :]
labeled_batch_S, labeled_batch_W = CalcSim(
labels[idx, :].cuda(), labels[idx, :].cuda())
if use_gpu:
inputs = Variable(inputs.cuda(), requires_grad=False)
anchor_batch_S = Variable(anchor_batch_S.cuda(),
requires_grad=False)
anchor_batch_W = Variable(anchor_batch_W.cuda(),
requires_grad=False)
labeled_batch_S = Variable(labeled_batch_S.cuda(),
requires_grad=False)
labeled_batch_W = Variable(labeled_batch_W.cuda(),
requires_grad=False)
# zero the parameter gradients
optimizer.zero_grad()
y_h1 = model(inputs)
y_h2 = model(anchor_inputs)
y = F.sigmoid(48 / num_bits * 0.4 *
torch.matmul(y_h1, y_h2.permute(1, 0)))
loss, l_batch_loss, m_loss = criterion(
y, y_h1, y_h2, anchor_batch_S, anchor_batch_W,
labeled_batch_S, labeled_batch_W, zcomp_h1, zcomp_h2,
mask_flag, u_w_m, epoch, num_bits)
h1[index, :] = y_h1.data.clone()
h2[anchor_index, :] = y_h2.data.clone()
# backward+optimize
loss.backward()
optimizer.step()
running_loss += loss.item()
Z_h2 = alpha_1 * Z_h2 + (1. - alpha_1) * h2
z_h2 = Z_h2 * (1. / (1. - alpha_1**(epoch + 1)))
print(
"Epoch[{}]({}/{}): Time:(data {:.3f}/ batch {:.3f}) Loss_H: {:.4f}/{:.4f}/{:.4f}"
.format(epoch, iteration, len(dataloader), data_time.val,
batch_time.val, loss.item(), l_batch_loss.item(),
m_loss.item()))
Z_h1 = alpha * Z_h1 + (1. - alpha) * h1
z_h1 = Z_h1 * (1. / (1. - alpha**(epoch + 1)))
if epoch % 1 == 0:
MAP = helpers.validate(model, dataloader_test, test_loader)
print("Test image map is:{}".format(MAP))
is_best = MAP > best_prec
best_prec = max(best_prec, MAP)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
},
is_best,
prefix=arch,
num_bits=num_bits,
filename=checkpoint_filename)
return model
print('Running on', device)
print("Building Models")
print("SoftMax")
buildModel(SoftMax(), 0.1, True, True)
print("MLP")
buildModel(MLP(), 0.1, True, True)
print("DAE")
buildDAELayer(DAELayer(10304, 1000), lRate=1e-4, epochs=5000, plot=True)
buildDAELayer(DAELayer(1000, 300), lRate=1e-4, epochs=5000, plot=True)
buildDAESoftmaxModel(DAESoftMax(), lRate=1e-2, epochs=1000, plot=True)
print("CNN")
buildModel(CNN(), 0.001, True, True)
print("\nModel(s) is reconstructed with alpha =", 5000, "beta =", 100,
"gamma =", 0.01, "delta =", 0.1)
print("Attacking Models")
# SoftMax Model from paper
print("Softmax")
reconstructionAttack(SoftMax())
# MLP Model from paper
print("MLP")
reconstructionAttack(MLP())
# DAE Model from paper
print("DAE")
reconstructionAttack(DAESoftMax())
import sys, os
sys.path.append("..")
sys.path.extend([
os.path.join(root, name) for root, dirs, _ in os.walk("../")
for name in dirs
])
from _config import NNConfig
from networks import CNN, LSTM, Encoder, Decoder
nnconfig = NNConfig()
nnconfig.show()
cnn = CNN("cnn_layer1")
lstm = LSTM("lstm_layer1")
cnn.show()
lstm.show()
encoder = Encoder(cnn)
decoder = Decoder(lstm)
import random
from networks import CNN
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Network File
path = "checkpoints/%s.pt" % sys.argv[1]
# Environment
env = gym.make("Breakout-v0")
env = wrappers.make_env(env)
# Network
q_network = CNN(4, env.action_space.n).to(device)
q_network.load_state_dict(torch.load(path, map_location=torch.device('cpu')))
q_network.eval()
# Greedy Policy
@torch.no_grad()
def select_action(q, state):
if random.random() < 0.05:
print('Random')
return env.action_space.sample()
k = q(state.to(device))
print(k)
return k.max(1)[1].view(1, 1)
from networks import CNN, SoftMax, MLP
from modules import buildModel, reconstructionAttack
print("Building Models")
print("SoftMax")
buildModel(SoftMax(), 0.1, True, True)
print("MLP")
buildModel(MLP(), 0.1, True, True)
print("DAE")
buildDAELayer(DAELayer(10304, 1000), lRate=1e-4, epochs=5000, plot=True)
buildDAELayer(DAELayer(1000, 300), lRate=1e-4, epochs=5000, plot=True)
buildDAESoftmaxModel(DAESoftMax(), lRate=1e-2, epochs=1000, plot=True)
print("CNN")
buildModel(CNN(), 0.001, True, True)
if args.headless: # GPU optimization
mpl.use('Agg') # Suppress rendering
import matplotlib.pyplot as plt
env = gym.make(args.env)
if not args.toy:
env = FrameStackWrapper(env, args.frames)
env = ResetLifeLostWrapper(env)
epsilon_max = args.epsilon_max
epsilon_min = args.epsilon_min
epsilon_decay = args.epsilon_decay
eps_threshold = 1
total_moves = 0
net = CNN(conv_channels=args.frames, n_actions=env.action_space.n) if not args.toy \
else FCN(n_actions=env.action_space.n)
if chainer.cuda.available:
net.to_gpu()
optim = optimizers.RMSpropGraves(lr=args.alpha, momentum=args.momentum)
# optim = optimizers.RMSprop(lr=args.alpha)
# optim = optimizers.Adam(alpha=args.alpha)
optim.setup(net)
if not os.path.exists("results/{}".format(args.env)):
os.makedirs("results/{}".format(args.env))
train()
class Mind:
def __init__(self,
n_actions,
input_shape,
save_path=None,
load_path=None,
action_inverses={},
update_interval=256 * 2,
save_interval=5):
self.n_actions = n_actions
self.save_path = save_path
self.network = CNN(n_out=n_actions, input_shape=input_shape)
if load_path != None:
self.network.load(load_path)
self.n_features = input_shape
self.data = []
self.current_episode_count = 1
self.random_actions = 0
self.last_action = None
self.last_action_random = False
self.action_inverses = action_inverses
self.lifetime = 1
self.update_interval = update_interval
self.save_interval = save_interval
self.n_updates = 1
def q(self, state):
return self.network.predict(np.expand_dims(np.array(state), axis=0))[0]
def should_explore(self, state):
if np.random.random() < 1000 / (1000 + self.lifetime):
return True
return False
def explore_action(self, state):
return np.random.randint(0, self.n_actions)
def action(self, state):
q = self.q(state)
# if self.last_action_random:
# if self.last_action in self.action_inverses:
# q[self.action_inverses[self.last_action]] = float('-inf')
action = np.argmax(q)
if self.should_explore(state):
self.random_actions += 1
action = self.explore_action(state)
self.last_action_random = True
else:
self.last_action_random = False
if self.lifetime % self.update_interval == 0:
self.update(alpha=0.9)
self.n_updates += 1
if self.n_updates % self.save_interval == 0:
if self.save_path != None:
self.save(self.save_path)
print('saved')
self.last_action = action
self.current_episode_count += 1
self.lifetime += 1
return action
def save(self, path):
self.network.save(path)
def reset(self):
self.count = 1
print('Random actions: ', self.random_actions)
self.random_actions = 0
def q_target(self, reward, best_next, alpha):
return reward + alpha * best_next
def feedback(self, old_action, old_state, reward, new_state):
self.data.append({
'Q_max': np.max(self.q(new_state)),
'reward': reward,
'old_state': old_state,
'old_action': old_action
})
def update(self, alpha=0.6):
np.random.shuffle(self.data)
samples = self.data
self.data = []
states = []
ys = []
for sample in samples:
y = self.q(sample['old_state'])
y[sample['old_action']] = self.q_target(sample['reward'],
best_next=sample['Q_max'],
alpha=alpha)
#y[sample['old_action']] = sarsa_target(sample['reward'], next_action = sample['Q_max'], alpha = alpha)
states.append(sample['old_state'])
ys.append(y)
self.network.train(np.array(states), np.array(ys))
class ReinforcementLearner:
__metaclass__ = abc.ABCMeta
lock = threading.Lock()
# rl_method: 강화학습 기법을 의미, 이 값은 하위 클래스에 따라 달라진다. (DQNLener는 dq, A2CLener는 ac 등)
# stock_code: 학습을 진행하는 주식 종목 코드
# chart_data: 주식 일봉 차트(환경에 해당)
# training_data: 학습을 위해서 전처리된 데이터
# min_trading_unit: 투자 최소 단위
# max_trading_unit: 투자 최대 단위
# delayed_reward_threshold: 지연 보상 임계값, 수익 or 손실률이 임계값보다 크면 지연 보상이 발생
# mini_batch_size: ??
# net: 신경망 종류, 이 값에 따라서, 가치 신경망, 정챙신경망으로 사용할 신경망 클래스가 달라짐
# n_steps: LSTM, CNN 신경망에서 사용하는 샘플 묶음 크기
# lr: (learn rate?), 학습 속도, 너무 크면 학습이 진행 안되고, 너무 작으면 학습이 오래 걸림
# value_network, policy_network: 값을 들어오는 경우, 해당 모델을 가치 신경망, 정책신경망으로 사용
# output_path: 학습 과정에서 발생하는 로그, 가시화 결과 및 학습 종료 후 저장되는 신겯망 모델 파일의 저장 위치 결정
def __init__(self,
rl_method='rl',
stock_code=None,
chart_data=None,
training_data=None,
min_trading_unit=1,
max_trading_unit=2,
delayed_reward_threshold=.05,
net='dnn',
num_steps=1,
lr=0.001,
value_network=None,
policy_network=None,
output_path='',
reuse_models=True):
# 인자 확인
assert min_trading_unit > 0
assert max_trading_unit > 0
assert max_trading_unit >= min_trading_unit
assert num_steps > 0
assert lr > 0
# 강화학습 기법 설정
self.rl_method = rl_method
# 환경 설정
self.stock_code = stock_code # 강화학습 대상이 되는 주식 종목 코드
self.chart_data = chart_data # 주식 종목의 차트 데이터
self.environment = Environment(chart_data) # 강화학습 환경 객체
# 에이전트 설정
self.agent = Agent(self.environment,
min_trading_unit=min_trading_unit,
max_trading_unit=max_trading_unit,
delayed_reward_threshold=delayed_reward_threshold)
# 학습 데이터
self.training_data = training_data
self.sample = None
self.training_data_idx = -1
# 벡터 크기 = 학습 데이터 벡터 크기 + 에이전트 상태 크기
self.num_features = self.agent.STATE_DIM
if self.training_data is not None:
self.num_features += self.training_data.shape[1]
# 신경망 클래스 객체는, 본 클래스의 하위 클래스에서 생성
# 신경망 설정
self.net = net
self.num_steps = num_steps
self.lr = lr
self.value_network = value_network # 가치 신경망
self.policy_network = policy_network # 정책 신경망
self.reuse_models = reuse_models
# 가시화 모듈
self.visualizer = Visualizer()
# 메모리
# 강화 학습 과정에서 발생하는 각종 데이터를 쌓아두기 위해서, memory라는 변수 정의
self.memory_sample = [] # 학습 데이터 샘플
self.memory_action = [] # 수행한 행동
self.memory_reward = [] # 획득한 보상
self.memory_value = [] # 행동의 예측 가치
self.memory_policy = [] # 핻동의 예측?확률?
self.memory_pv = [] # 포트폴리오 가치
self.memory_num_stocks = [] # 보유 주식수
self.memory_exp_idx = [] # 탐험 위치
self.memory_learning_idx = [] # 학습 위치
# 에포크 관련 정보
self.loss = 0. # 손실
self.itr_cnt = 0 # 수익발생 횟수
self.exploration_cnt = 0 # 탐험 횟수
self.batch_size = 0 # 배치 크기?
self.learning_cnt = 0 # 학습 횟수
# 로그 등 출력 경로
self.output_path = output_path
# 가치 신경망 생성 함수
# 팩토리 함수 느낌
# 가치 신경망은, 손익율을 회귀분석하는 모델로 보면 된다. 따라서, activation은 선형, loss는 mse이다.???
def init_value_network(self,
shared_network=None,
activation='linear',
loss='mse'):
if self.net == 'dnn':
self.value_network = DNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'lstm':
self.value_network = LSTMNetwork(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'cnn':
self.value_network = CNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
if self.reuse_models and \
os.path.exists(self.value_network_path): # reuse_models이 True이고, value_network_path 값이 있으면 신경망 모델 파일을 불러온다...
self.value_network.load_model(model_path=self.value_network_path)
# 정책 신경망 생성 함수
# activation이 sigmoid로 다르다.
# 정책신경망은 PV을 높이기 위해 취하기 좋은 행동에 대한 '분류' 모델
# 활성 함수로 sigmoid을 써서 0 ~ 1 시아의 값으로 확률로 사용하기 위함
def init_policy_network(self,
shared_network=None,
activation='sigmoid',
loss='binary_crossentropy'):
if self.net == 'dnn':
self.policy_network = DNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'lstm':
self.policy_network = LSTMNetwork(
input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
elif self.net == 'cnn':
self.policy_network = CNN(input_dim=self.num_features,
output_dim=self.agent.NUM_ACTIONS,
lr=self.lr,
num_steps=self.num_steps,
shared_network=shared_network,
activation=activation,
loss=loss)
if self.reuse_models and \
os.path.exists(self.policy_network_path):
self.policy_network.load_model(model_path=self.policy_network_path)
# 초기화 함수
# 에포크 초기화 함수
# 에포크마다 데이터가 새로 쌓이는 변수들을 초기화 한다.
def reset(self):
self.sample = None # 읽어온 학습 데이터가 샘플에 할당됨(초기화에선 None)
self.training_data_idx = -1 # 학습 데이터를 처음부터 다시 읽기위해서 -1로 설정
# 환경 초기화
self.environment.reset() # 환경클래스의 reset호출
# 에이전트 초기화
self.agent.reset() # 에이전트가 제공하는 reset호출
# 가시화 초기화
self.visualizer.clear([0, len(self.chart_data)]) # 가시화 클래스의 clear호출
# 메모리 초기화
self.memory_sample = []
self.memory_action = []
self.memory_reward = []
self.memory_value = []
self.memory_policy = []
self.memory_pv = []
self.memory_num_stocks = []
self.memory_exp_idx = []
self.memory_learning_idx = []
# 에포크 관련 정보 초기화
self.loss = 0. # 신경망의 결과가 학습 데이터와 얼마나 차이가 있는지를 저장하는 변수 loss가 줄어야 좋은거임!
self.itr_cnt = 0 # 수행한 에포크 수를 저장
self.exploration_cnt = 0 # 탐험 수 저장, epsilon이 0.1dlrh 100번 투자 결정이 있다고 한다면 약 10번의 무작위 투자
self.batch_size = 0 # 학습할 미니 배치 크기
self.learning_cnt = 0 # 한 에포크 동안 수행한 미니 배치 학습 횟수
# 환경 객체에서 샘플을 획득하는 함수
# 학습 데이터플 구성하는 샘플 하나를 생성하는 함수
def build_sample(self):
self.environment.observe() # 차트 데이터의 현재 인덱스에서, 다음 인덱스 데이터를 읽게한다.
if len(self.training_data
) > self.training_data_idx + 1: # 학습 데이터 존재 확인
self.training_data_idx += 1
self.sample = self.training_data.iloc[
self.training_data_idx].tolist() # 샘플 가져옴, 샘플은 26개의 값임
self.sample.extend(
self.agent.get_states()) # 에이전트에서 2개 값을 추가! (28개값!)
return self.sample
return None
# 배치 학습 데이터 생성 함수, 추상 메소드로 하위 클래스가 반드시 구현해야 한다.!
@abc.abstractmethod
def get_batch(self, batch_size, delayed_reward, discount_factor):
pass
# 가치 신경망 및 정책 신경망 학습 함수
# get_batch을 호출해서 배치 학습 데이터를 생성
# 가치 신경망 및 정책 신경망의 train_on_batch을 호출하여, 학습 시킴
# 가치 신경망: DQN, AC, A2C
# 정책 신경망: PolicyGradient, AC, A2C
def update_networks(self, batch_size, delayed_reward, discount_factor):
# 배치 학습 데이터 생성
x, y_value, y_policy = self.get_batch(batch_size, delayed_reward,
discount_factor)
if len(x) > 0:
loss = 0
if y_value is not None:
# 가치 신경망 갱신
loss += self.value_network.train_on_batch(x, y_value)
if y_policy is not None:
# 정책 신경망 갱신
loss += self.policy_network.train_on_batch(x, y_policy)
return loss # 학습 후 손실 반환
return None
# 가치 신경망 및 정책 신경망 학습 요청 함수
# 배치 학습 데이터의 크기를 정하고, update_networks 호출(위함수)
# _loss에 로 총 loss을 생성
def fit(self, delayed_reward, discount_factor, full=False):
batch_size = len(self.memory_reward) if full \
else self.batch_size
# 배치 학습 데이터 생성 및 신경망 갱신
if batch_size > 0:
_loss = self.update_networks(batch_size, delayed_reward,
discount_factor)
if _loss is not None:
self.loss += abs(_loss)
self.learning_cnt += 1 # 학습 횟수 저장, loss / learning_cnt하면 에포크의 학습 손실을 알 수 있음
self.memory_learning_idx.append(
self.training_data_idx) # 학습 위치 저장
self.batch_size = 0
# 에포크 정보 가시화 함수
def visualize(self, epoch_str, num_epoches, epsilon):
self.memory_action = [Agent.ACTION_HOLD] \
* (self.num_steps - 1) + self.memory_action
self.memory_num_stocks = [0] * (self.num_steps - 1) \
+ self.memory_num_stocks
if self.value_network is not None:
self.memory_value = [np.array([np.nan] \
* len(Agent.ACTIONS))] * (self.num_steps - 1) \
+ self.memory_value
if self.policy_network is not None:
self.memory_policy = [np.array([np.nan] \
* len(Agent.ACTIONS))] * (self.num_steps - 1) \
+ self.memory_policy
self.memory_pv = [self.agent.initial_balance] \
* (self.num_steps - 1) + self.memory_pv
self.visualizer.plot(
epoch_str=epoch_str,
num_epoches=num_epoches,
epsilon=epsilon,
action_list=Agent.ACTIONS,
actions=self.memory_action,
num_stocks=self.memory_num_stocks,
outvals_value=self.memory_value,
outvals_policy=self.memory_policy,
exps=self.memory_exp_idx,
learning_idxes=self.memory_learning_idx,
initial_balance=self.agent.initial_balance,
pvs=self.memory_pv,
)
self.visualizer.save(
os.path.join(self.epoch_summary_dir,
'epoch_summary_{}.png'.format(epoch_str)))
# 강화학습 수행 함수
# 핵심 함수!
def run(
self,
num_epoches=100, # 총 수행할 반복 학습 횟수, 너무 크면 학습에 걸리는 시간이 길어짐
balance=10000000, # 초기 투자금
discount_factor=0.9, # 상태-행동 가치를 구할때 적용할 할인율, 과거로 갈수록 현재 보상을 약하게 적용한다.
start_epsilon=0.5, # 초기 탐험 비율
learning=True # 학습을 마치면 학습된 가치 신경망모델, 정책 신경망 모델이 생성된다. 이런 신경망 모델으 만들꺼면 True, 이미 학습된 모델로, 투자 시뮬레이션일때는 False
):
info = "[{code}] RL:{rl} Net:{net} LR:{lr} " \
"DF:{discount_factor} TU:[{min_trading_unit}," \
"{max_trading_unit}] DRT:{delayed_reward_threshold}".format(
code=self.stock_code, rl=self.rl_method, net=self.net,
lr=self.lr, discount_factor=discount_factor,
min_trading_unit=self.agent.min_trading_unit,
max_trading_unit=self.agent.max_trading_unit,
delayed_reward_threshold=self.agent.delayed_reward_threshold
)
with self.lock:
logging.info(info) # 강화 학습의 설정값을 로깅 한다.
# 시작 시간
time_start = time.time()
# 가시화 준비
# 차트 데이터는 변하지 않으므로 미리 가시화
self.visualizer.prepare(self.environment.chart_data, info)
# 가시화 결과 저장할 폴더 준비
# epoch_summary_ 라는 폴더에 저장
self.epoch_summary_dir = os.path.join(
self.output_path, 'epoch_summary_{}'.format(self.stock_code))
if not os.path.isdir(self.epoch_summary_dir):
os.makedirs(self.epoch_summary_dir)
else:
for f in os.listdir(self.epoch_summary_dir):
os.remove(os.path.join(self.epoch_summary_dir, f))
# 에이전트 초기 자본금 설정
self.agent.set_balance(balance)
# 학습에 대한 정보 초기화
max_portfolio_value = 0
epoch_win_cnt = 0
# 학습 반복
for epoch in range(num_epoches):
time_start_epoch = time.time()
# step 샘플을 만들기 위한 큐
# deque사용 - 참고: https://opensourcedev.tistory.com/3
q_sample = collections.deque(maxlen=self.num_steps)
# 환경, 에이전트, 신경망, 가시화, 메모리 초기화
self.reset()
# 학습을 진행할 수록 탐험 비율 감소
if learning:
epsilon = start_epsilon \
* (1. - float(epoch) / (num_epoches - 1))
self.agent.reset_exploration()
else:
epsilon = start_epsilon
self.agent.reset_exploration(alpha=0)
while True:
# 샘플 생성
next_sample = self.build_sample()
if next_sample is None:
break # 샘플 만큼 while문 반복
# num_steps만큼 샘플 저장
q_sample.append(next_sample)
if len(q_sample) < self.num_steps:
continue
# 가치, 정책 신경망 예측
# 각 신경망의 predict함수 호출
pred_value = None
pred_policy = None
if self.value_network is not None:
pred_value = self.value_network.predict(list(q_sample))
if self.policy_network is not None:
pred_policy = self.policy_network.predict(list(q_sample))
# 신경망 또는 탐험에 의한 행동 결정
# 행동, 결정에 대한 확신도, 무작위 탐험 유무
action, confidence, exploration = \
self.agent.decide_action(
pred_value, pred_policy, epsilon)
# 결정한 행동을 수행하고 즉시 보상과 지연 보상 획득
immediate_reward, delayed_reward = \
self.agent.act(action, confidence)
# 행동 및 행동에 대한 결과를 기억
self.memory_sample.append(list(q_sample))
self.memory_action.append(action)
self.memory_reward.append(immediate_reward)
if self.value_network is not None:
self.memory_value.append(pred_value)
if self.policy_network is not None:
self.memory_policy.append(pred_policy)
self.memory_pv.append(self.agent.portfolio_value)
self.memory_num_stocks.append(self.agent.num_stocks)
if exploration:
self.memory_exp_idx.append(self.training_data_idx)
# 반복에 대한 정보 갱신
self.batch_size += 1
self.itr_cnt += 1
self.exploration_cnt += 1 if exploration else 0 # 3항연산???
# 지연 보상 발생된 경우 미니 배치 학습
# 지연보상은 지연보상 임계치가 넘는 손익률이 발생하면 주어진다.
if learning and (delayed_reward != 0):
self.fit(delayed_reward, discount_factor)
# 에포크 종료 후 학습 (while문 이후 미니 배치 학습)
if learning:
self.fit(self.agent.profitloss, discount_factor, full=True)
# 에포크 관련 정보 로그 기록
num_epoches_digit = len(str(num_epoches))
epoch_str = str(epoch + 1).rjust(num_epoches_digit,
'0') # 문자열을 자리수에 맞게 정렬(우측) 함수
time_end_epoch = time.time()
elapsed_time_epoch = time_end_epoch - time_start_epoch
if self.learning_cnt > 0:
self.loss /= self.learning_cnt
logging.info("[{}][Epoch {}/{}] Epsilon:{:.4f} "
"#Expl.:{}/{} #Buy:{} #Sell:{} #Hold:{} "
"#Stocks:{} PV:{:,.0f} "
"LC:{} Loss:{:.6f} ET:{:.4f}".format(
self.stock_code, epoch_str, num_epoches, epsilon,
self.exploration_cnt, self.itr_cnt,
self.agent.num_buy, self.agent.num_sell,
self.agent.num_hold, self.agent.num_stocks,
self.agent.portfolio_value, self.learning_cnt,
self.loss, elapsed_time_epoch))
# 에포크 관련 정보 가시화
self.visualize(epoch_str, num_epoches, epsilon)
# 학습 관련 정보 갱신
max_portfolio_value = max(max_portfolio_value,
self.agent.portfolio_value)
if self.agent.portfolio_value > self.agent.initial_balance:
epoch_win_cnt += 1
# 종료 시간
time_end = time.time()
elapsed_time = time_end - time_start
# 학습 관련 정보 로그 기록
with self.lock:
logging.info("[{code}] Elapsed Time:{elapsed_time:.4f} "
"Max PV:{max_pv:,.0f} #Win:{cnt_win}".format(
code=self.stock_code,
elapsed_time=elapsed_time,
max_pv=max_portfolio_value,
cnt_win=epoch_win_cnt))
# 가치 신경망 및 정책 신경망 저장 함수
def save_models(self):
if self.value_network is not None and \
self.value_network_path is not None:
self.value_network.save_model(self.value_network_path)
if self.policy_network is not None and \
self.policy_network_path is not None:
self.policy_network.save_model(self.policy_network_path)
if model == 'all':
# SoftMax Model from paper
print("Softmax")
reconstructionAttack(SoftMax(), alpha, beta, gamma, delta, True, False)
# MLP Model from paper
print("MLP")
reconstructionAttack(MLP(), alpha, beta, gamma, delta, True, False)
# DAE Model from paper
print("DAE")
reconstructionAttack(DAESoftMax(), alpha, beta, gamma, delta, True, False)
# CNN for comparison
print("CNN")
reconstructionAttack(CNN(), alpha, beta, gamma, delta, True, False)
else:
if model == 'Softmax':
# SoftMax Model from paper
print("Softmax")
reconstructionAttack(SoftMax(), alpha, beta, gamma, delta, True, False)
if model == 'MLP':
# MLP Model from paper
print("MLP")
reconstructionAttack(MLP(), alpha, beta, gamma, delta, True, False)
if model == 'DAE':
# DAE Model from paper
print("DAE")
reconstructionAttack(DAESoftMax(), alpha, beta, gamma, delta, True,
