\ub525\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\uc778 \uc21c\ud658 \uc2e0\uacbd\ub9dd(RNN)\uc740 \uc8fc\ub85c \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4. RNN\uc740 \ubb38\uc7a5 \uc0dd\uc131, \uc74c\uc131 \uc778\uc2dd, \uc2dc\uacc4\uc5f4 \uc608\uce21\uacfc \uac19\uc740 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \ubc0f \uc608\uce21 \ubb38\uc81c\uc5d0 \uc0ac\uc6a9\ub418\uba70, \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 PyTorch<\/strong>\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc591\ubc29\ud5a5 RNN\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n \uc804\ud1b5\uc801\uc778 RNN\uc740 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ud55c \ubc29\ud5a5\uc73c\ub85c\ub9cc \ucc98\ub9ac\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub2e8\uc5b4 \uc2dc\ud000\uc2a4\ub97c \uc624\ub978\ucabd\uc5d0\uc11c \uc67c\ucabd\uc73c\ub85c \uc77d\uac8c \ub429\ub2c8\ub2e4. \ubc18\uba74 \uc591\ubc29\ud5a5 RNN\uc740 \ub450 \uac1c\uc758 RNN\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2dc\ud000\uc2a4\ub97c \uc591\ucabd \ubc29\ud5a5\uc5d0\uc11c \ucc98\ub9ac\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubb38\ub9e5\uc744 \ub354 \uc798 \uc774\ud574\ud558\uace0, \uc608\uce21 \uc131\ub2a5\uc774 \ud5a5\uc0c1\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \uc591\ubc29\ud5a5 RNN\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub450 \uac1c\uc758 RNN\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4:<\/p>\n \uc774 \ub450 \uacb0\uacfc\ub97c \uacb0\ud569\ud558\uc5ec \ucd9c\ub825\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc774\ub807\uac8c \ud568\uc73c\ub85c\uc368 \uc591\ubc29\ud5a5 RNN\uc740 \ub354 \ud48d\ubd80\ud55c \ubb38\ub9e5 \uc815\ubcf4\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \uc774\uc81c \ube44\ud2b8 \uc804\uc774 \ub124\ud2b8\uc6cc\ud06c\uc778 \uc591\ubc29\ud5a5 RNN\uc744 \uad6c\ud604\ud558\uae30 \uc704\ud574 PyTorch\ub97c \uc124\uc815\ud569\ub2c8\ub2e4. PyTorch\ub294 \ub525\ub7ec\ub2dd \uc5f0\uad6c\uc640 \uac1c\ubc1c\uc5d0 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \ub2e4\uc74c\uc740 PyTorch \uc124\uce58 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n \uc591\ubc29\ud5a5 RNN\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud55c \ub370\uc774\ud130\uc14b\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \uac04\ub2e8\ud55c \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc608\uc2dc\ub97c \ubcf4\uc5ec\uc904 \uac83\uc785\ub2c8\ub2e4.<\/p>\n \uc774\uc81c \uc2e4\uc81c\ub85c \uc591\ubc29\ud5a5 RNN \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n \uc774\uc81c \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uace0 \ud3c9\uac00\ud558\ub294 \uacfc\uc815\uc744 \ubcf4\uc5ec\uc8fc\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n \uc774 \uae00\uc5d0\uc11c\ub294 \uc591\ubc29\ud5a5 RNN\uc744 \uad6c\ud604\ud558\uace0 \ud6c8\ub828\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6e0\uc2b5\ub2c8\ub2e4. \uc591\ubc29\ud5a5 RNN\uc740 \ub2e4\uc591\ud55c \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \ucc98\ub9ac \uc791\uc5c5\uc5d0\uc11c \ud6a8\uacfc\uc801\uc778 \uacb0\uacfc\ub97c \ubcf4\uc5ec\uc8fc\uba70, PyTorch\ub97c \uc774\uc6a9\ud574 \uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\ub97c \ud1b5\ud574 \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc2dc\uacc4\uc5f4 \uc608\uce21 \ub4f1\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\ucd08\ub97c \ub9c8\ub828\ud560 \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n1. \uc591\ubc29\ud5a5 RNN(Bi-directional RNN) \uac1c\uc694<\/h2>\n
1.1 \uc591\ubc29\ud5a5 RNN\uc758 \uad6c\uc870<\/h3>\n
\n
2. \uc591\ubc29\ud5a5 RNN \uad6c\ud604\uc744 \uc704\ud55c \uc900\ube44<\/h2>\n
pip install torch torchvision<\/code><\/pre>\n2.1 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc784\ud3ec\ud2b8<\/h3>\n
import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nfrom torch.utils.data import Dataset, DataLoader<\/code><\/pre>\n2.2 \ub370\uc774\ud130\uc14b \uad6c\uc131<\/h3>\n
class SimpleDataset(Dataset):\n def __init__(self, input_data, target_data):\n self.input_data = input_data\n self.target_data = target_data\n\n def __len__(self):\n return len(self.input_data)\n\n def __getitem__(self, idx):\n return self.input_data[idx], self.target_data[idx]<\/code><\/pre>\n3. \uc591\ubc29\ud5a5 RNN \ubaa8\ub378 \uad6c\ud604<\/h2>\n
class BiRNN(nn.Module):\n def __init__(self, input_size, hidden_size, output_size):\n super(BiRNN, self).__init__()\n self.rnn = nn.RNN(input_size, hidden_size, num_layers=1, bidirectional=True, batch_first=True)\n self.fc = nn.Linear(hidden_size * 2, output_size)\n\n def forward(self, x):\n out, _ = self.rnn(x)\n out = self.fc(out[:, -1, :]) # \ub9c8\uc9c0\ub9c9 \ud0c0\uc784\uc2a4\ud0ec\ud504\uc758 \ucd9c\ub825\uc744 \uac00\uc838\uc634\n return out<\/code><\/pre>\n3.1 \ubaa8\ub378 \ud30c\ub77c\ubbf8\ud130 \uc124\uc815<\/h3>\n
input_size = 10 # \uc785\ub825 \ubca1\ud130\uc758 \ucc28\uc6d0\nhidden_size = 20 # RNN\uc758 hidden state \ucc28\uc6d0\noutput_size = 1 # \ucd9c\ub825 \ucc28\uc6d0 (\uc608\ub97c \ub4e4\uc5b4, \ud68c\uadc0 \ubb38\uc81c\ub97c \uc704\ud55c \uacbd\uc6b0)<\/code><\/pre>\n3.2 \ubaa8\ub378 \ucd08\uae30\ud654 \ubc0f \ucd5c\uc801\ud654<\/h3>\n
model = BiRNN(input_size, hidden_size, output_size)\ncriterion = nn.MSELoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)<\/code><\/pre>\n4. \ud6c8\ub828 \ubc0f \ud3c9\uac00 \uacfc\uc815<\/h2>\n
4.1 \ud6c8\ub828 \ud568\uc218 \uc815\uc758<\/h3>\n
def train_model(model, dataloader, criterion, optimizer, num_epochs=10):\n model.train()\n for epoch in range(num_epochs):\n for inputs, targets in dataloader:\n # Optimizer \ucd08\uae30\ud654\n optimizer.zero_grad()\n\n # Forward Pass\n outputs = model(inputs)\n\n # Loss \uacc4\uc0b0\n loss = criterion(outputs, targets)\n\n # Backward Pass \ubc0f Optimizer \uc2e4\ud589\n loss.backward()\n optimizer.step()\n\n print(f'Epoch [{epoch+1}\/{num_epochs}], Loss: {loss.item():.4f}')<\/code><\/pre>\n4.2 \ud3c9\uac00 \ud568\uc218 \uc815\uc758<\/h3>\n
def evaluate_model(model, dataloader):\n model.eval()\n total = 0\n correct = 0\n with torch.no_grad():\n for inputs, targets in dataloader:\n outputs = model(inputs)\n # \uc815\ud655\ub3c4 \uce21\uc815 (\ub610\ub294 \ud68c\uadc0 \ubb38\uc81c\uc758 \uacbd\uc6b0 \ucd94\uac00\uc801\uc778 \uba54\ud2b8\ub9ad \uc815\uc758)\n total += targets.size(0)\n correct += (outputs.round() == targets).sum().item()\n\n print(f'Accuracy: {100 * correct \/ total:.2f}%')<\/code><\/pre>\n4.3 \ub370\uc774\ud130\ub85c\ub354 \uc0dd\uc131 \ubc0f \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
# \ub370\uc774\ud130 \uc900\ube44\ninput_data = np.random.rand(100, 5, input_size).astype(np.float32)\ntarget_data = np.random.rand(100, output_size).astype(np.float32)\ndataset = SimpleDataset(input_data, target_data)\ndataloader = DataLoader(dataset, batch_size=10, shuffle=True)\n\n# \ubaa8\ub378 \ud6c8\ub828\ntrain_model(model, dataloader, criterion, optimizer, num_epochs=20)<\/code><\/pre>\n5. \uacb0\ub860<\/h2>\n
6. \ucd94\uac00 \uc790\ub8cc \ubc0f \ucc38\uace0 \ubb38\ud5cc<\/h2>\n