\ubaa8\ub378 \uc800\uc7a5 \ubc0f \ud3c9\uac00<\/li>\n<\/ul>\n2.1 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378 \ub85c\ub4dc<\/h3>\n
PyTorch\uc5d0\uc11c\ub294 torchvision \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc27d\uac8c \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\uc11c\ub294 ResNet18 \ubaa8\ub378\uc744 \uc608\ub85c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch\nimport torch.nn as nn\nimport torchvision.models as models\n\n# ResNet18 \ubaa8\ub378 \ub85c\ub4dc\nmodel = models.resnet18(pretrained=True)<\/code><\/pre>\n2.2 \ubaa8\ub378\uc758 \uc77c\ubd80 \ub808\uc774\uc5b4\ub97c \uace0\uc815 \ub610\ub294 \uc218\uc815<\/h3>\n
\uc77c\ubc18\uc801\uc73c\ub85c \ubbf8\uc138 \uc870\uc815\uc5d0\uc11c\ub294 \ubaa8\ub378\uc758 \ub9c8\uc9c0\ub9c9 \ub808\uc774\uc5b4\ub97c \uc218\uc815\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ud074\ub798\uc2a4\uc218\uc5d0 \ub9de\uac8c \ubcc0\uacbd\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc774\ubbf8\uc9c0 \ubd84\ub958\uc5d0\uc11c \ud074\ub798\uc218\uac00 1000\uac1c\uc5d0\uc11c 10\uac1c\ub85c \ubcc0\uacbd\ub418\ub294 \uacbd\uc6b0 \ub9c8\uc9c0\ub9c9 \ub808\uc774\uc5b4\ub97c \uad50\uccb4\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
# \uae30\uc874\uc758 \ub9c8\uc9c0\ub9c9 \ub808\uc774\uc5b4\ub97c \uc0c8\ub85c\uc6b4 \ub808\uc774\uc5b4\ub85c \uad50\uccb4\nnum_ftrs = model.fc.in_features\nmodel.fc = nn.Linear(num_ftrs, 10) # 10\uc73c\ub85c \uc0c8\ub86d\uac8c \uc124\uc815<\/code><\/pre>\n2.3 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
\uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud574 \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uae30 \uc704\ud574 \ub370\uc774\ud130 \ub85c\ub354\ub97c \uc124\uc815\ud569\ub2c8\ub2e4.<\/p>\n
from torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader\n\n# \ub370\uc774\ud130 \ubcc0\ud658 \uc124\uc815\ntransform = transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n])\n\n# \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ntrain_dataset = datasets.FakeData(transform=transform) # \uc0d8\ud50c \ub370\uc774\ud130 \uc0ac\uc6a9\ntrain_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)<\/code><\/pre>\n2.4 \ud6c8\ub828 \ub8e8\ud504 \uc791\uc131<\/h3>\n
\ud559\uc2b5 \uacfc\uc815\uc744 \uc815\uc758\ud558\ub294 \ud6c8\ub828 \ub8e8\ud504\ub97c \uc791\uc131\ud569\ub2c8\ub2e4.<\/p>\n
device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = model.to(device)\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n\n# \ud6c8\ub828 \ub8e8\ud504\nfor epoch in range(10): # \uc5d0\ud3ed \uc218 \ubcc0\uacbd \uac00\ub2a5\n model.train()\n running_loss = 0.0\n for inputs, labels in train_loader:\n inputs, labels = inputs.to(device), labels.to(device)\n\n optimizer.zero_grad()\n outputs = model(inputs)\n loss = criterion(outputs, labels)\n loss.backward()\n optimizer.step()\n\n running_loss += loss.item()\n\n print(f'Epoch {epoch + 1}, Loss: {running_loss \/ len(train_loader)}')<\/code><\/pre>\n3. \ubbf8\uc138 \uc870\uc815 \uacb0\uacfc \ud3c9\uac00<\/h2>\n
\ud6c8\ub828\uc774 \uc644\ub8cc\ub418\uba74 \ubaa8\ub378\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uac80\uc99d \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud655\uc778\ud569\ub2c8\ub2e4.<\/p>\n
# \uac80\uc99d \ub370\uc774\ud130\uc14b \ub85c\ub4dc \ubc0f \ud3c9\uac00\nval_dataset = datasets.FakeData(transform=transform) # \uc0d8\ud50c \ub370\uc774\ud130 \uc0ac\uc6a9\nval_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n\nmodel.eval()\ncorrect = 0\ntotal = 0\nwith torch.no_grad():\n for inputs, labels in val_loader:\n inputs, labels = inputs.to(device), labels.to(device)\n outputs = model(inputs)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\nprint(f'Accuracy of the model: {100 * correct \/ total}%')<\/code><\/pre>\n4. \uacb0\ub860<\/h2>\n
\ub525\ub7ec\ub2dd\uc5d0\uc11c \ubbf8\uc138 \uc870\uc815\uc740 \ub370\uc774\ud130\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uc0ac\uc6a9\ud558\uace0 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\ub294 \ub9e4\uc6b0 \uc911\uc694\ud55c \uae30\ubc95\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c\ub294 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \uc190\uc27d\uac8c \ubbf8\uc138 \uc870\uc815 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\ub294 \ub2e4\uc591\ud55c \ub3c4\uad6c\uc640 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc744 \uc774\ud574\ud558\uace0 \uc801\uc6a9\ud558\ub294 \uac83\uc740 \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \uc2e4\uc6a9\uc801\uc73c\ub85c \uc0ac\uc6a9\ud558\uae30 \uc704\ud55c \uc911\uc694\ud55c \ub2e8\uacc4\uc785\ub2c8\ub2e4.<\/p>\n
\uc774 \uac15\uc88c\uac00 \ub3c4\uc6c0\uc774 \ub418\uc5c8\uae38 \ubc14\ub78d\ub2c8\ub2e4. \uac10\uc0ac\ud569\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc758 \uc138\ubd80 \uc601\uc5ed \uc911 \ud558\ub098\uc778 \ubbf8\uc138 \uc870\uc815(Fine-tuning)\uc740 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ub9de\uac8c \uc870\uc815\ud558\uc5ec \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc774 \uae30\ubc95\uc740 \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \ud6c8\ub828\uc5d0 \ub4dc\ub294 \uc2dc\uac04\uacfc \uc790\uc6d0\uc744 \uc808\uc57d\ud560 \uc218 \uc788\ub294 \ud6a8\uc728\uc801\uc778 \ubc29\ubc95\uc73c\ub85c, \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub429\ub2c8\ub2e4. 1. \ubbf8\uc138 \uc870\uc815 \uae30\ubc95\uc758 \uac1c\uc694 \ubbf8\uc138 \uc870\uc815 \uae30\ubc95\uc740 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc758 \uac00\uc911\uce58\ub97c \uc774\uc2dd\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc14b\uc5d0 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubbf8\uc138 \uc870\uc815 \uae30\ubc95”<\/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":[33],"tags":[],"class_list":["post-30046","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubbf8\uc138 \uc870\uc815 \uae30\ubc95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/30046\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubbf8\uc138 \uc870\uc815 \uae30\ubc95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc758 \uc138\ubd80 \uc601\uc5ed \uc911 \ud558\ub098\uc778 \ubbf8\uc138 \uc870\uc815(Fine-tuning)\uc740 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ub9de\uac8c \uc870\uc815\ud558\uc5ec \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc774 \uae30\ubc95\uc740 \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \ud6c8\ub828\uc5d0 \ub4dc\ub294 \uc2dc\uac04\uacfc \uc790\uc6d0\uc744 \uc808\uc57d\ud560 \uc218 \uc788\ub294 \ud6a8\uc728\uc801\uc778 \ubc29\ubc95\uc73c\ub85c, \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub429\ub2c8\ub2e4. 1. \ubbf8\uc138 \uc870\uc815 \uae30\ubc95\uc758 \uac1c\uc694 \ubbf8\uc138 \uc870\uc815 \uae30\ubc95\uc740 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc758 \uac00\uc911\uce58\ub97c \uc774\uc2dd\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc14b\uc5d0 … \ub354 \ubcf4\uae30 "\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubbf8\uc138 \uc870\uc815 \uae30\ubc95"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/30046\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:19:20+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:50:16+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/30046\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/30046\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubbf8\uc138 \uc870\uc815 \uae30\ubc95\",\"datePublished\":\"2024-10-28T03:19:20+00:00\",\"dateModified\":\"2024-11-26T06:50:16+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/30046\/\"},\"wordCount\":5,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"\ud30c\uc774\ud1a0\uce58 \uac15\uc88c\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/30046\/\",\"url\":\"https:\/\/atmokpo.com\/w\/30046\/\",\"name\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubbf8\uc138 \uc870\uc815 \uae30\ubc95 - 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