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아하에서 찾은 1,000건의 질문
- 기타 고민상담고민상담Q. 인터넷 가입 암보험 어디가 좋은가요?제대로 된게 없어서...지식도 없어서 도움 받고 싶습니다. 인터넷으로 가입하는게 비용절감 좋다는데 어디거로 해야될지...나이대는 50대 ,60대입니다.
- 전기기사·기능사자격증Q. 전기기사아니면 소방설비기사전기분야중 어떤게 유용성이나 쓰임새가많나요?전기기사나 소방설비기사전기분야중 어떤게 유용성이나 쓰임새가많나요?직장다니는 50대회사원인데 노후대비전기기사또는 소방전기중에 뭐부터 아님 뭘 취득하는게 나을까요?물론 비전공자입니다
- 가압류·가처분법률Q. 간판 시공 계약금 환불 가능한지 궁금합니다.금일 간판 업체에서 계약금 50%를 요구해서 입금했습니다.다만 업체분께서 실측 후 시안이 마음에 안 들면 전액 환불해주겠다고 해서 입금했는데시안이 이상한데도 환불을 해주지 않고 있는 상황입니다.100퍼센트는 아니라도 일부라도 계약금 환불을 받을 수 있을까요?계약서는 쓰지 않았으며 시공일자도 확실하지 않은 상태입니다. 보통 시공일자를 확인 후 계약금을 입금하는데지금 아무것도 하지 않았고, 디자인비 정도는 떼일 각오 하고 있습니다.시공일자 없이 계약금을 입금한 상태이기 때문에 업체와의 신뢰를 잃어버린 상황입니다.일단 계약서도 없이 계약금을 입금한 잘못이 있긴 하지만, 이대로 모든 계약금을 돌려받을 수 없는지 궁금합니다. 업자분은 시안 마음에 들지 않을 시 환불해주겠다고 3번 정도 말씀한 상태구요
- 저축성 보험보험Q. 자동차보험은 다이렉트가 싼가요?이번에 중고차를 구매하려고하는데 스포티지r이구요 31살인데 보험비 얼마정도 나올까요 10년 운전하면서 사고는 한번도 안났습니다. 보통 50만원대라고 알고있는데 30대면 정확히 모르겠어서요 ㅜㅜ
- 주식·가상화폐경제Q. 비트코인이 투자 가치로써 전망이 있을까요?비트코인이 고점 대비 50% 가량 떨어졌다고 합니다. 바닥을 확인하는 것은 어렵겠지만, 시장이 비트코인을 바라보는 관점은 예측하거나 평가해 볼 수 있을 것으로 보입니다. 예전에는 디지털 금이라고 평가했었는데, 아직도 인식이 그런가요? 양자컴퓨터 얘기도 나오고 잘 모르겠어서 질문드립니다.
- 생활꿀팁생활Q. 파이썬 코드 idle python 에서 오류?be (where it stores its past experiences).batchSize = 50 # The mini-batch size for training. Samples are randomly taken from memory till mini-batch size.gridSize = 10 # The size of the grid that the agent is going to play the game on.nbStates = gridSize * gridSize # We eventually flatten to a 1d tensor to feed the network.discount = 0.9 # The discount is used to force the network to choose states that lead to the reward quicker (0 to 1) learningRate = 0.2 # Learning Rate for Stochastic Gradient Descent (our optimizer).# Create the base model.X = tf.placeholder(tf.float32, [None, nbStates])W1 = tf.Variable(tf.truncated_normal([nbStates, hiddenSize], stddev=1.0 / math.sqrt(float(nbStates))))b1 = tf.Variable(tf.truncated_normal([hiddenSize], stddev=0.01)) input_layer = tf.nn.relu(tf.matmul(X, W1) + b1)W2 = tf.Variable(tf.truncated_normal([hiddenSize, hiddenSize],stddev=1.0 / math.sqrt(float(hiddenSize))))b2 = tf.Variable(tf.truncated_normal([hiddenSize], stddev=0.01))hiddenlayer = tf.nn.relu(tf.matmul(inputlayer, W2) + b2)W3 = tf.Variable(tf.truncated_normal([hiddenSize, nbActions],stddev=1.0 / math.sqrt(float(hiddenSize))))b3 = tf.Variable(tf.truncated_normal([nbActions], stddev=0.01))outputlayer = tf.matmul(hiddenlayer, W3) + b3# True labelsY = tf.placeholder(tf.float32, [None, nbActions])# Mean squared error cost functioncost = tf.reducesum(tf.square(Y-outputlayer)) / (2*batchSize)# Stochastic Gradient Decent Optimizeroptimizer = tf.train.GradientDescentOptimizer(learningRate).minimize(cost)# Helper function: Chooses a random value between the two boundaries.def randf(s, e): return (float(random.randrange(0, (e - s) * 9999)) / 10000) + s;# The environment: Handles interactions and contains the state of the environmentclass CatchEnvironment(): def init(self, gridSize): self.gridSize = gridSize self.nbStates = self.gridSize * self.gridSize self.state = np.empty(3, dtype = np.uint8) # Returns the state of the environment. def observe(self): canvas = self.drawState() canvas = np.reshape(canvas, (-1,self.nbStates)) return canvas def drawState(self): canvas = np.zeros((self.gridSize, self.gridSize)) canvas[self.state[0]-1, self.state[1]-1] = 1 # Draw the fruit. # Draw the basket. The basket takes the adjacent two places to the position of basket. canvas[self.gridSize-1, self.state[2] -1 - 1] = 1 canvas[self.gridSize-1, self.state[2] -1] = 1 canvas[self.gridSize-1, self.state[2] -1 + 1] = 1 return canvas # Resets the environment. Randomly initialise the fruit position (always at the top to begin with) and bucket. def reset(self): initialFruitColumn = random.randrange(1, self.gridSize + 1) initialBucketPosition = random.randrange(2, self.gridSize + 1 - 1) self.state = np.array([1, initialFruitColumn, initialBucketPosition]) return self.getState() def getState(self): stateInfo = self.state fruit_row = stateInfo[0] fruit_col = stateInfo[1] basket = stateInfo[2] return fruitrow, fruitcol, basket # Returns the award that the agent has gained for being in the current environment state. def getReward(self): fruitRow, fruitColumn, basket = self.getState() if (fruitRow == self.gridSize - 1): # If the fruit has reached the bottom. if (abs(fruitColumn - basket) <= 1): # Check if the basket caught the fruit. return 1 else: return -1 else: return 0 def isGameOver(self): if (self.state[0] == self.gridSize - 1): return True else: return False def updateState(self, action): if (action == 1): action = -1 elif (action == 2): action = 0 else: action = 1 fruitRow, fruitColumn, basket = self.getState() newBasket = min(max(2, basket + action), self.gridSize - 1) # The min/max prevents the basket from moving out of the grid. fruitRow = fruitRow + 1 # The fruit is falling by 1 every action. self.state = np.array([fruitRow, fruitColumn, newBasket]) #Action can be 1 (move left) or 2 (move right) def act(self, action): self.updateState(action) reward = self.getReward() gameOver = self.isGameOver() return self.observe(), reward, gameOver, self.getState() # For purpose of the visual, I also return the state.# The memory: Handles the internal memory that we add experiences that occur based on agent's actions,# and creates batches of experiences based on the mini-batch size for training.class ReplayMemory: def init(self, gridSize, maxMemory, discount): self.maxMemory = maxMemory self.gridSize = gridSize self.nbStates = self.gridSize * self.gridSize self.discount = discount canvas = np.zeros((self.gridSize, self.gridSize)) canvas = np.reshape(canvas, (-1,self.nbStates)) self.inputState = np.empty((self.maxMemory, 100), dtype = np.float32) self.actions = np.zeros(self.maxMemory, dtype = np.uint8) self.nextState = np.empty((self.maxMemory, 100), dtype = np.float32) self.gameOver = np.empty(self.maxMemory, dtype = np.bool) self.rewards = np.empty(self.maxMemory, dtype = np.int8) self.count = 0 self.current = 0 # Appends the experience to the memory. def remember(self, currentState, action, reward, nextState, gameOver): self.actions[self.current] = action self.rewards[self.current] = reward self.inputState[self.current, ...] = currentState self.nextState[self.current, ...] = nextState self.gameOver[self.current] = gameOver self.count = max(self.count, self.current + 1) self.current = (self.current + 1) % self.maxMemory def getBatch(self, model, batchSize, nbActions, nbStates, sess, X): # We check to see if we have enough memory inputs to make an entire batch, if not we create the biggest # batch we can (at the beginning of training we will not have enough experience to fill a batch). memoryLength = self.count chosenBatchSize = min(batchSize, memoryLength) inputs = np.zeros((chosenBatchSize, nbStates)) targets = np.zeros((chosenBatchSize, nbActions)) # Fill the inputs and targets up. for i in xrange(chosenBatchSize): if memoryLength == 1: memoryLength = 2 # Choose a random memory experience to add to the batch. randomIndex = random.randrange(1, memoryLength) current_inputState = np.reshape(self.inputState[randomIndex], (1, 100)) target = sess.run(model, feeddict={X: currentinputState}) current_nextState = np.reshape(self.nextState[randomIndex], (1, 100)) currentoutputs = sess.run(model, feeddict={X: current_nextState}) # Gives us Q_sa, the max q for the next state. nextStateMaxQ = np.amax(current_outputs) if (self.gameOver[randomIndex] == True): target[0, [self.actions[randomIndex]-1]] = self.rewards[randomIndex] else: # reward + discount(gamma) * max_a' Q(s',a') # We are setting the Q-value for the action to r + gamma*max a' Q(s', a'). The rest stay the same # to give an error of 0 for those outputs. target[0, [self.actions[randomIndex]-1]] = self.rewards[randomIndex] + self.discount * nextStateMaxQ # Update the inputs and targets. inputs[i] = current_inputState targets[i] = target return inputs, targets def main(_): print("Training new model") # Define Environment env = CatchEnvironment(gridSize) # Define Replay Memory memory = ReplayMemory(gridSize, maxMemory, discount) # Add ops to save and restore all the variables. saver = tf.train.Saver() winCount = 0 with tf.Session() as sess: tf.initializeallvariables().run() for i in xrange(epoch): # Initialize the environment. err = 0 env.reset() isGameOver = False # The initial state of the environment. currentState = env.observe() while (isGameOver != True): action = -9999 # action initilization # Decides if we should choose a random action, or an action from the policy network. global epsilon if (randf(0, 1) <= epsilon): action = random.randrange(1, nbActions+1) else: # Forward the current state through the network. q = sess.run(outputlayer, feeddict={X: currentState}) # Find the max index (the chosen action). index = q.argmax() action = index + 1 # Decay the epsilon by multiplying by 0.999, not allowing it to go below a certain threshold. if (epsilon > epsilonMinimumValue): epsilon = epsilon * 0.999 nextState, reward, gameOver, stateInfo = env.act(action) if (reward == 1): winCount = winCount + 1 memory.remember(currentState, action, reward, nextState, gameOver) # Update the current state and if the game is over. currentState = nextState isGameOver = gameOver # We get a batch of training data to train the model. inputs, targets = memory.getBatch(output_layer, batchSize, nbActions, nbStates, sess, X) # Train the network which returns the error. , loss = sess.run([optimizer, cost], feeddict={X: inputs, Y: targets}) err = err + loss print("Epoch " + str(i) + ": err = " + str(err) + ": Win count = " + str(winCount) + " Win ratio = " + str(float(winCount)/float(i+1)*100)) # Save the variables to disk. save_path = saver.save(sess, os.getcwd()+"/model.ckpt") print("Model saved in file: %s" % save_path)if name == 'main': tf.app.run()""" TensorFlow translation of the torch example found here (written by SeanNaren). https://github.com/SeanNaren/TorchQLearningExample Original keras example found here (written by Eder Santana). https://gist.github.com/EderSantana/c7222daa328f0e885093#file-qlearn-py-L164 The agent plays a game of catch. Fruits drop from the sky and the agent can choose the actions left/stay/right to catch the fruit before it reaches the ground."""import tensorflow.compat.v1 as tftf.disablev2behavior()import numpy as npimport randomimport mathimport os# Parametersepsilon = 1 # The probability of choosing a random action (in training). This decays as iterations increase. (0 to 1)epsilonMinimumValue = 0.001 # The minimum value we want epsilon to reach in training. (0 to 1)nbActions = 3 # The number of actions. Since we only have left/stay/right that means 3 actions.epoch = 1001 # The number of games we want the system to run for.hiddenSize = 100 # Number of neurons in the hidden layers.maxMemory = 500 # How large should the memory be (where it stores its past experiences).batchSize = 50 # The mini-batch size for training. Samples are randomly taken from memory till mini-batch size.gridSize = 10 # The size of the grid that the agent is going to play the game on.nbStates = gridSize * gridSize # We eventually flatten to a 1d tensor to feed the network.discount = 0.9 # The discount is used to force the network to choose states that lead to the reward quicker (0 to 1) learningRate = 0.2 # Learning Rate for Stochastic Gradient Descent (our optimizer).# Create the base model.X = tf.placeholder(tf.float32, [None, nbStates])W1 = tf.Variable(tf.truncated_normal([nbStates, hiddenSize], stddev=1.0 / math.sqrt(float(nbStates))))b1 = tf.Variable(tf.truncated_normal([hiddenSize], stddev=0.01)) input_layer = tf.nn.relu(tf.matmul(X, W1) + b1)W2 = tf.Variable(tf.truncated_normal([hiddenSize, hiddenSize],stddev=1.0 / math.sqrt(float(hiddenSize))))b2 = tf.Variable(tf.truncated_normal([hiddenSize], stddev=0.01))hiddenlayer = tf.nn.relu(tf.matmul(inputlayer, W2) + b2)W3 = tf.Variable(tf.truncated_normal([hiddenSize, nbActions],stddev=1.0 / math.sqrt(float(hiddenSize))))b3 = tf.Variable(tf.truncated_normal([nbActions], stddev=0.01))outputlayer = tf.matmul(hiddenlayer, W3) + b3# True labelsY = tf.placeholder(tf.float32, [None, nbActions])# Mean squared error cost functioncost = tf.reducesum(tf.square(Y-outputlayer)) / (2*batchSize)# Stochastic Gradient Decent Optimizeroptimizer = tf.train.GradientDescentOptimizer(learningRate).minimize(cost)# Helper function: Chooses a random value between the two boundaries.def randf(s, e): return (float(random.randrange(0, (e - s) * 9999)) / 10000) + s;# The environment: Handles interactions and contains the state of the environmentclass CatchEnvironment(): def init(self, gridSize): self.gridSize = gridSize self.nbStates = self.gridSize * self.gridSize self.state = np.empty(3, dtype = np.uint8) # Returns the state of the environment. def observe(self): canvas = self.drawState() canvas = np.reshape(canvas, (-1,self.nbStates)) return canvas def drawState(self): canvas = np.zeros((self.gridSize, self.gridSize)) canvas[self.state[0]-1, self.state[1]-1] = 1 # Draw the fruit. # Draw the basket. The basket takes the adjacent two places to the position of basket. canvas[self.gridSize-1, self.state[2] -1 - 1] = 1 canvas[self.gridSize-1, self.state[2] -1] = 1 canvas[self.gridSize-1, self.state[2] -1 + 1] = 1 return canvas # Resets the environment. Randomly initialise the fruit position (always at the top to begin with) and bucket. def reset(self): initialFruitColumn = random.randrange(1, self.gridSize + 1) initialBucketPosition = random.randrange(2, self.gridSize + 1 - 1) self.state = np.array([1, initialFruitColumn, initialBucketPosition]) return self.getState() def getState(self): stateInfo = self.state fruit_row = stateInfo[0] fruit_col = stateInfo[1] basket = stateInfo[2] return fruitrow, fruitcol, basket # Returns the award that the agent has gained for being in the current environment state. def getReward(self): fruitRow, fruitColumn, basket = self.getState() if (fruitRow == self.gridSize - 1): # If the fruit has reached the bottom. if (abs(fruitColumn - basket) <= 1): # Check if the basket caught the fruit. return 1 else: return -1 else: return 0 def isGameOver(self): if (self.state[0] == self.gridSize - 1): return True else: return False def updateState(self, action): if (action == 1): action = -1 elif (action == 2): action = 0 else: action = 1 fruitRow, fruitColumn, basket = self.getState() newBasket = min(max(2, basket + action), self.gridSize - 1) # The min/max prevents the basket from moving out of the grid. fruitRow = fruitRow + 1 # The fruit is falling by 1 every action. self.state = np.array([fruitRow, fruitColumn, newBasket]) #Action can be 1 (move left) or 2 (move right) def act(self, action): self.updateState(action) reward = self.getReward() gameOver = self.isGameOver() return self.observe(), reward, gameOver, self.getState() # For purpose of the visual, I also return the state.# The memory: Handles the internal memory that we add experiences that occur based on agent's actions,# and creates batches of experiences based on the mini-batch size for training.class ReplayMemory: def init(self, gridSize, maxMemory, discount): self.maxMemory = maxMemory self.gridSize = gridSize self.nbStates = self.gridSize * self.gridSize self.discount = discount canvas = np.zeros((self.gridSize, self.gridSize)) canvas = np.reshape(canvas, (-1,self.nbStates)) self.inputState = np.empty((self.maxMemory, 100), dtype = np.float32) self.actions = np.zeros(self.maxMemory, dtype = np.uint8) self.nextState = np.empty((self.maxMemory, 100), dtype = np.float32) self.gameOver = np.empty(self.maxMemory, dtype = np.bool) self.rewards = np.empty(self.maxMemory, dtype = np.int8) self.count = 0 self.current = 0 # Appends the experience to the memory. def remember(self, currentState, action, reward, nextState, gameOver): self.actions[self.current] = action self.rewards[self.current] = reward self.inputState[self.current, ...] = currentState self.nextState[self.current, ...] = nextState self.gameOver[self.current] = gameOver self.count = max(self.count, self.current + 1) self.current = (self.current + 1) % self.maxMemory def getBatch(self, model, batchSize, nbActions, nbStates, sess, X): # We check to see if we have enough memory inputs to make an entire batch, if not we create the biggest # batch we can (at the beginning of training we will not have enough experience to fill a batch). memoryLength = self.count chosenBatchSize = min(batchSize, memoryLength) inputs = np.zeros((chosenBatchSize, nbStates)) targets = np.zeros((chosenBatchSize, nbActions)) # Fill the inputs and targets up. for i in xrange(chosenBatchSize): if memoryLength == 1: memoryLength = 2 # Choose a random memory experience to add to the batch. randomIndex = random.randrange(1, memoryLength) current_inputState = np.reshape(self.inputState[randomIndex], (1, 100)) target = sess.run(model, feeddict={X: currentinputState}) current_nextState = np.reshape(self.nextState[randomIndex], (1, 100)) currentoutputs = sess.run(model, feeddict={X: current_nextState}) # Gives us Q_sa, the max q for the next state. nextStateMaxQ = np.amax(current_outputs) if (self.gameOver[randomIndex] == True): target[0, [self.actions[randomIndex]-1]] = self.rewards[randomIndex] else: # reward + discount(gamma) * max_a' Q(s',a') # We are setting the Q-value for the action to r + gamma*max a' Q(s', a'). The rest stay the same # to give an error of 0 for those outputs. target[0, [self.actions[randomIndex]-1]] = self.rewards[randomIndex] + self.discount * nextStateMaxQ # Update the inputs and targets. inputs[i] = current_inputState targets[i] = target return inputs, targets def main(_): print("Training new model") # Define Environment env = CatchEnvironment(gridSize) # Define Replay Memory memory = ReplayMemory(gridSize, maxMemory, discount) # Add ops to save and restore all the variables. saver = tf.train.Saver() winCount = 0 with tf.Session() as sess: tf.initializeallvariables().run() for i in xrange(epoch): # Initialize the environment. err = 0 env.reset() isGameOver = False # The initial state of the environment. currentState = env.observe() while (isGameOver != True): action = -9999 # action initilization # Decides if we should choose a random action, or an action from the policy network. global epsilon if (randf(0, 1) <= epsilon): action = random.randrange(1, nbActions+1) else: # Forward the current state through the network. q = sess.run(outputlayer, feeddict={X: currentState}) # Find the max index (the chosen action). index = q.argmax() action = index + 1 # Decay the epsilon by multiplying by 0.999, not allowing it to go below a certain threshold. if (epsilon > epsilonMinimumValue): epsilon = epsilon * 0.999 nextState, reward, gameOver, stateInfo = env.act(action) if (reward == 1): winCount = winCount + 1 memory.remember(currentState, action, reward, nextState, gameOver) # Update the current state and if the game is over. currentState = nextState isGameOver = gameOver # We get a batch of training data to train the model. inputs, targets = memory.getBatch(output_layer, batchSize, nbActions, nbStates, sess, X) # Train the network which returns the error이. , loss = sess.run([optimizer, cost], feeddict={X: inputs, Y: targets}) err = err + loss print("Epoch " + str(i) + ": err = " + str(err) + ": Win count = " + str(winCount) + " Win ratio = " + str(float(winCount)/float(i+1)*100)) # Save the variables to disk. save_path = saver.save(sess, os.getcwd()+"/model.ckpt") print("Model saved in file: %s" % save_path)if name == 'main': tf.app.run() 입니다그런데 이런 오류가 생겼습니다WARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\tens2\lib\site-packages\tensorflowcore\python\compat\v2compat.py:65: disableresourcevariables (from tensorflow.python.ops.variablescope) is deprecated and will be removed in a future version.Instructions for updating:non-resource variables are not supported in the long termTraining new modelWARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\tens2\lib\site-packages\tensorflowcore\python\util\tfshoulduse.py:198: initializeallvariables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.Instructions for updating:Use tf.globalvariablesinitializer instead.W0820 22:17:13.656675 9068 deprecation.py:323] From C:\ProgramData\Anaconda3\envs\tens2\lib\site-packages\tensorflowcore\python\util\tfshoulduse.py:198: initializeallvariables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.Instructions for updating:Use tf.globalvariablesinitializer instead.Traceback (most recent call last): File "C:\Windows\system32\python", line 267, in <module> tf.app.run() File "C:\ProgramData\Anaconda3\envs\tens2\lib\site-packages\tensorflowcore\python\platform\app.py", line 40, in run run(main=main, argv=argv, flagsparser=parseflagstolerateundef) File "C:\ProgramData\Anaconda3\envs\tens_2\lib\site-packages\absl\app.py", line 299, in run runmain(main, args) File "C:\ProgramData\Anaconda3\envs\tens2\lib\site-packages\absl\app.py", line 250, in run_main sys.exit(main(argv)) File "C:\Windows\system32\python", line 216, in main for i in xrange(epoch):NameError: name 'xrange' is not defined어떻게 해결해야 할까요?매우 길지만 해결해 주시면 감사하겠습니다 ㅠㅠ
- 생활꿀팁생활Q. casper ffg에서 질문입니다!!알고있는 이론은checkpoint는 50의 배수 번호의 블록이다.justified 된다는것은 각 checkpoint블록이 검증되었다는것finalized된다는것은 자신은 source로 한 target 블록인 직계자손 checkpoint블록이 justified되면 자신은 finalized가 된다.이렇게 알고있습니다.r-> b2 -> b3 -> b4가 메인 체인이라고 할때질문 1 . b3가 justified되었으므로 b2는 finalized 되었을것입니다.그렇다면 b2가 100번째 블록이라면 b3는 150번째 블록일 것입니다.(직계자손체크포인트이므로)그런데 a2,a3,가 어떻게 b2, b3 사이에서 연결될수가 있는건가요??(50배수 블록은 이미 b2,b3인데 a2,a3는 50배 블록이 아니여도 상관이 없는건가요?)아니면 단순히 a2,와 a3는 justified가 된것이 아니라 그냥 이어지기만 한것인가요??저 전체적인 그림이 어떻게 구현이 될수있는지 이해가 안갑니다.질문 2 . FFG에서 무조건 #50 블록 -> #200 블록 이런식으로 다음 체크포인트 블록이 아닌 어느정도 건너뛰어서 연결될수있나요??(justified되고 finalized되는것까지)질문3 . 저러한 상황이 일어날 이유가 궁금합니다.기존의 메인체인보다 작은 높이의 블록의 투표를 할 이유가 있나요??어차피 fork choice rule은 가장 긴 justified된 블록을 할텐데... 저 방법으로 인한 어떠한 공격방법이나 나타날수있는 현상을 말씀해주시면 감사하겠습니다.질문4 . 저러한 상황을 막지않으면 충돌이 어떻게 발생되는지 궁금합니다.
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