{"id":29672,"date":"2024-10-28T01:56:01","date_gmt":"2024-10-28T01:56:01","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29672"},"modified":"2024-11-26T06:51:46","modified_gmt":"2024-11-26T06:51:46","slug":"%ed%97%88%ea%b9%85%ed%8e%98%ec%9d%b4%ec%8a%a4-%ed%8a%b8%eb%a0%8c%ec%8a%a4%ed%8f%ac%eb%a8%b8-%ed%99%9c%ec%9a%a9%ea%b0%95%ec%a2%8c-%ec%83%98%ed%94%8c-%ec%9d%b4%eb%af%b8%ec%a7%80-%eb%8d%b0%ec%9d%b4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29672\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \uc0d8\ud50c \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc138\ud2b8"},"content":{"rendered":"

\ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5 \ubc0f \ub525\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uc640 \ucef4\ud4e8\ud130 \ube44\uc804(CV) \ubd84\uc57c \ubaa8\ub450\uc5d0\uc11c \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) \ub77c\uc774\ube0c\ub7ec\ub9ac\uac00 \ub110\ub9ac \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc14b\uc744 \uc5b4\ub5bb\uac8c \ucc98\ub9ac\ud558\uace0 \ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\ub294\uc9c0\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uace0, \uc608\uc81c \ucf54\ub4dc\ub97c \ud1b5\ud574 \uad6c\uccb4\uc801\uc73c\ub85c \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \ud5c8\uae45\ud398\uc774\uc2a4\uc640 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub780?<\/h2>\n

\ud5c8\uae45\ud398\uc774\uc2a4\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc640 \uad00\ub828\ub41c \ub2e4\uc591\ud55c \ubbf8\ub9ac \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, BERT, GPT-2, T5\uc640 \uac19\uc740 \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ucd5c\uadfc\uc5d0\ub294 \uc774\ubbf8\uc9c0 \ucc98\ub9ac \ubaa8\ub378\uc778 \ube44\uc804 \ud2b8\ub79c\uc2a4\ud3ec\uba38(ViT), CLIP \ub4f1\uc774 \ucd94\uac00\ub418\uc5b4 \ucef4\ud4e8\ud130 \ube44\uc804 \uc791\uc5c5\uc5d0\uc11c\ub3c4 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

2. \ud544\uc694\ud55c \ud328\ud0a4\uc9c0 \ubc0f \ud658\uacbd \uc124\uc815<\/h2>\n

\ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \ucf54\ub4dc\ub85c \uac04\ub2e8\ud788 \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

pip install transformers torchvision torch<\/code><\/pre>\n
3. \uc0d8\ud50c \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc14b<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 CIFAR-10 \ub370\uc774\ud130\uc14b\uc744 \uc0d8\ud50c \ub370\uc774\ud130\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. CIFAR-10\uc740 10\uac1c\uc758 \ud074\ub798\uc2a4(\ube44\ud589\uae30, \uc790\ub3d9\ucc28, \uc0c8, \uace0\uc591\uc774, \uc0ac\uc2b4, \uac1c, \ud3ad\uadc4, \uae30\ucc28, \uacf0, \uc790\ub3d9\ucc28)\ub85c \uad6c\uc131\ub41c 6\ub9cc \uac1c\uc758 32×32 \uc0c9\uc0c1 \uc774\ubbf8\uc9c0\ub97c \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\uc14b\uc740 \uc774\ubbf8\uc9c0 \ubd84\ub958 \ubb38\uc81c\uc5d0 \ub9e4\uc6b0 \uc801\ud569\ud569\ub2c8\ub2e4.<\/p>\n
3.1 \ub370\uc774\ud130\uc14b \ubd88\ub7ec\uc624\uae30<\/h3>\n
\ud30c\uc774\uc36c\uc758 torchvision \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec CIFAR-10 \ub370\uc774\ud130\uc14b\uc744 \ubd88\ub7ec\uc624\uace0, \ud559\uc2b5\uacfc \uac80\uc99d \uc138\ud2b8\ub85c \ub098\ub204\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import torchvision\nimport torchvision.transforms as transforms\n\n# \ub370\uc774\ud130 \ubcc0\ud658 \uc124\uc815\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n])\n\n# \ud559\uc2b5 \ubc0f \uac80\uc99d \ub370\uc774\ud130\uc14b \ubd88\ub7ec\uc624\uae30\ntrainset = torchvision.datasets.CIFAR10(root='.\/data', train=True, download=True, transform=transform)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)\n\ntestset = torchvision.datasets.CIFAR10(root='.\/data', train=False, download=True, transform=transform)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)\n<\/code><\/pre>\n
3.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\ubd88\ub7ec\uc628 \ub370\uc774\ud130\uc14b\uc740 \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\ub97c \ud150\uc11c \ud615\ud0dc\ub85c \ubcc0\ud658\ud558\uace0 \uc815\uaddc\ud654 \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4. \uc704\uc758 \ucf54\ub4dc\uc5d0\uc11c\ub294 ToTensor() \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\ub97c \ud150\uc11c\ub85c \ubcc0\ud658\ud558\uace0, Normalize() \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud3c9\uade0\uacfc \ud45c\uc900\ud3b8\ucc28\ub97c \uae30\ubc18\uc73c\ub85c \uc815\uaddc\ud654\ub97c \uc218\ud589\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n
4. \ube44\uc804 \ud2b8\ub79c\uc2a4\ud3ec\uba38(ViT) \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\uc774\uc81c ViT \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uc5ec CIFAR-10 \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud55c \uc774\ubbf8\uc9c0\ub97c \ubd84\ub958\ud558\ub294 \uc791\uc5c5\uc744 \uc9c4\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ubaa8\ub378 \uc815\uc758\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 transformers \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \uac04\ub2e8\ud558\uac8c \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from transformers import ViTForImageClassification, ViTFeatureExtractor\n\n# ViT \ubaa8\ub378\uacfc feature extractor \ucd08\uae30\ud654\nfeature_extractor = ViTFeatureExtractor.from_pretrained('google\/vit-base-patch16-224')\nmodel = ViTForImageClassification.from_pretrained('google\/vit-base-patch16-224', num_labels=10)\n<\/code><\/pre>\n
\uc704 \ucf54\ub4dc\ub294 \ube44\uc804 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc744 \ucd08\uae30\ud654\ud558\ub294 \uacfc\uc815\uc73c\ub85c, `num_labels` \ub9e4\uac1c\ubcc0\uc218\ub294 \ud074\ub798\uc2a4 \uc218\ub97c \uc124\uc815\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 10\uac1c\uc758 \ud074\ub798\uc2a4\ub97c \uac00\uc9c0\uace0 \uc788\uc73c\ubbc0\ub85c \ud574\ub2f9 \uac12\uc73c\ub85c \uc124\uc815\ud558\uc600\uc2b5\ub2c8\ub2e4.<\/p>\n
5. \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc815\uc758\ud574\uc57c \ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 CrossEntropyLoss \uc190\uc2e4 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uace0, Adam \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
import torch.optim as optim\n\n# \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998 \uc815\uc758\ncriterion = torch.nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# \ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \uc7a5\uce58 \uc124\uc815\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel.to(device)\n\n# \ubaa8\ub378 \ud559\uc2b5\nfor epoch in range(10):  # 10 \uc5d0\ud3ed \ub3d9\uc548 \ud559\uc2b5\n    running_loss = 0.0\n    for i, data in enumerate(trainloader, 0):\n        inputs, labels = data\n        inputs, labels = inputs.to(device), labels.to(device)\n\n        # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n        optimizer.zero_grad()\n\n        # \uc21c forward + \uc5ed\uc804\ud30c + \ucd5c\uc801\ud654\n        outputs = model(inputs).logits\n        loss = criterion(outputs, labels)\n        loss.backward()\n        optimizer.step()\n\n        running_loss += loss.item()\n        if i % 2000 == 1999:  # \ub9e4 2000 \ubbf8\ub2c8\ubc30\uce58\ub9c8\ub2e4 \ucd9c\ub825\n            print(f\"[{epoch + 1}, {i + 1}] loss: {running_loss \/ 2000:.3f}\")\n            running_loss = 0.0\n<\/code><\/pre>\n
\uc704 \ucf54\ub4dc\ub294 \uc5d0\ud3ed \uc218\uc640 \ubbf8\ub2c8\ubc30\uce58\uc5d0 \ub530\ub77c \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uac01 \ubc30\uce58\uc5d0 \ub300\ud55c \uc190\uc2e4\uc744 \uacc4\uc0b0\ud558\uace0, \uc5ed\uc804\ud30c\ub97c \ud1b5\ud574 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n
6. \ubaa8\ub378 \ud3c9\uac00<\/h2>\n
\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \ud3c9\uac00\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
correct = 0\ntotal = 0\n\nwith torch.no_grad():\n    for data in testloader:\n        images, labels = data\n        images, labels = images.to(device), labels.to(device)\n        outputs = model(images).logits\n        _, predicted = torch.max(outputs.data, 1)\n        total += labels.size(0)\n        correct += (predicted == labels).sum().item()\n\nprint(f'Accuracy of the network on the 10000 test images: {100 * correct \/ total:.2f}%')\n<\/code><\/pre>\n
\uc704 \ucf54\ub4dc\ub294 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc5d0\uc11c \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \uacc4\uc0b0\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uac01 \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud574 \uc608\uce21\uac12\uacfc \uc2e4\uc81c \ub77c\ubca8\uc744 \ube44\uad50\ud558\uc5ec \uc815\ud655\ub3c4\ub97c \uc0b0\ucd9c\ud569\ub2c8\ub2e4.<\/p>\n
7. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec CIFAR-10 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0, \ube44\uc804 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uc5ec \uc774\ubbf8\uc9c0\ub97c \ubd84\ub958\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uba74 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \uad6c\ucd95\ud558\uace0, \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud558\uc5ec \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \ub2e4\uc591\ud55c \ubaa8\ub378\uacfc \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud55c \ub525\ub7ec\ub2dd \uc801\uc6a9 \uc0ac\ub840\ub97c \uacc4\uc18d \ud0d0\uad6c\ud574\ubcf4\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
\ubb38\uc758 \ubc0f \ucd94\uac00 \uc815\ubcf4\uac00 \ud544\uc694\ud558\uc2e0 \uacbd\uc6b0 \ub313\uae00\ub85c \ub0a8\uaca8\uc8fc\uc2dc\uba74 \uac10\uc0ac\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n","protected":false},"excerpt":{"rendered":"
\ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5 \ubc0f \ub525\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\uc640 \ucef4\ud4e8\ud130 \ube44\uc804(CV) \ubd84\uc57c \ubaa8\ub450\uc5d0\uc11c \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face) \ub77c\uc774\ube0c\ub7ec\ub9ac\uac00 \ub110\ub9ac \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc14b\uc744 \uc5b4\ub5bb\uac8c \ucc98\ub9ac\ud558\uace0 \ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\ub294\uc9c0\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uace0, \uc608\uc81c \ucf54\ub4dc\ub97c \ud1b5\ud574 \uad6c\uccb4\uc801\uc73c\ub85c \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud5c8\uae45\ud398\uc774\uc2a4\uc640 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub780? \ud5c8\uae45\ud398\uc774\uc2a4\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc640 \uad00\ub828\ub41c \ub2e4\uc591\ud55c \ubbf8\ub9ac \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, BERT, GPT-2, T5\uc640 \uac19\uc740 … \ub354 \ubcf4\uae30 “\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \uc0d8\ud50c \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc138\ud2b8”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[30],"tags":[],"class_list":["post-29672","post","type-post","status-publish","format-standard","hentry","category-30"],"yoast_head":"\n\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, \uc0d8\ud50c \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc138\ud2b8 - 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