{"id":30008,"date":"2024-10-28T03:19:08","date_gmt":"2024-10-28T03:19:08","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30008"},"modified":"2024-11-26T06:50:26","modified_gmt":"2024-11-26T06:50:26","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c-%eb%8d%b0%ec%9d%b4%ed%84%b0-%ec%a4%80%eb%b9%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30008\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub370\uc774\ud130 \uc900\ube44"},"content":{"rendered":"<div>\n<p>\ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130 \uc900\ube44 \ub2e8\uacc4\uac00 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uc62c\ubc14\ub978 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc900\ube44\ud558\uc9c0 \uc54a\uc73c\uba74 \ubaa8\ub378\uc758 \uc131\ub2a5\uc774 \uc800\ud558\ub420 \uc218 \uc788\uc73c\uba70, \uc774\ub294 \uacb0\uad6d \uc2e4\uc81c \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc758 \ud488\uc9c8\uc5d0 \uc545\uc601\ud5a5\uc744 \ubbf8\uce60 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc774\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \uc900\ube44 \ubc29\ubc95\uc744 \ub2e8\uacc4\ubcc4\ub85c \uc124\uba85\ud558\uace0, \uc608\uc81c \ucf54\ub4dc\ub97c \ud1b5\ud574 \uc2e4\uc2b5\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. \ub370\uc774\ud130 \uc900\ube44\uc758 \uc911\uc694\uc131<\/h2>\n<p>\ub525\ub7ec\ub2dd\uc758 \uc131\uacf5 \uc5ec\ubd80\ub294 \uc885\uc885 \ub370\uc774\ud130\uc758 \ud488\uc9c8\uacfc \uc591\uc5d0 \uc758\ud574 \uacb0\uc815\ub429\ub2c8\ub2e4. \ub530\ub77c\uc11c \ub370\uc774\ud130 \uc900\ube44 \ubc0f \uc804\ucc98\ub9ac \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uc8fc\uc694 \ubaa9\uc801\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\uc815\ud655\uc131:<\/strong> \ub370\uc774\ud130\uc758 \uc815\ud655\uc131\uc744 \ubcf4\uc7a5\ud558\uc5ec \ubaa8\ub378\uc774 \ud559\uc2b5\ud560 \ub54c \uc798\ubabb\ub41c \uc815\ubcf4\ub97c \uc81c\uacf5\ud558\uc9c0 \uc54a\ub3c4\ub85d \ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc77c\uad00\uc131:<\/strong> \uc77c\uad00\ub41c \ub370\uc774\ud130 \ud615\uc2dd\uc744 \uc720\uc9c0\ud558\uc5ec \ubaa8\ub378\uc774 \uc27d\uac8c \uc774\ud574\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uade0\ud615:<\/strong> \ubd84\ub958 \ubb38\uc81c\uc758 \uacbd\uc6b0, \ud074\ub798\uc2a4 \uac04\uc758 \uade0\ud615\uc744 \ub9de\ucd94\uc5b4 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ub370\uc774\ud130 \uc99d\uac15:<\/strong> \ub370\uc774\ud130 \uc591\uc774 \ubd80\uc871\ud560 \uacbd\uc6b0, \ub370\uc774\ud130 \uc99d\uac15 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5 \ub370\uc774\ud130\ub97c \ub298\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>2. \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \uc900\ube44<\/h2>\n<p>\ud30c\uc774\ud1a0\uce58\uc5d0\uc11c\ub294 \ub370\uc774\ud130 \uc900\ube44\ub97c \uc704\ud574 <strong>torch.utils.data<\/strong> \ubaa8\ub4c8\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774 \ubaa8\ub4c8\uc740 \ub370\uc774\ud130\uc14b\uacfc \ub370\uc774\ud130 \ub85c\ub354\ub97c \uc27d\uac8c \uc0dd\uc131\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ub370\uc774\ud130 \uc900\ube44\uc758 \uae30\ubcf8\uc801\uc778 \ub2e8\uacc4\uc785\ub2c8\ub2e4:<\/p>\n<h3>2.1 \ub370\uc774\ud130\uc14b \uc0dd\uc131<\/h3>\n<p>\ub370\uc774\ud130\uc14b\uc740 \ubaa8\ub378\uc774 \ud559\uc2b5\ud558\ub294 \ub370 \ud544\uc694\ud55c \uc774\ubbf8\uc9c0\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc744 \ub9cc\ub4e4\uae30 \uc704\ud574\uc11c\ub294 <strong>torch.utils.data.Dataset<\/strong> \ud074\ub798\uc2a4\ub97c \uc0c1\uc18d\ud558\uace0, __getitem__\uacfc __len__ \uba54\uc18c\ub4dc\ub97c \uc7ac\uc815\uc758\ud574\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uac04\ub2e8\ud55c \uc608\uc81c\uc785\ub2c8\ub2e4:<\/p>\n<pre><code>import torch\nfrom torch.utils.data import Dataset\n\nclass CustomDataset(Dataset):\n    def __init__(self, data, labels):\n        self.data = data\n        self.labels = labels\n\n    def __len__(self):\n        return len(self.data)\n\n    def __getitem__(self, idx):\n        return self.data[idx], self.labels[idx]\n\n# \uc608\uc81c \ub370\uc774\ud130\ndata = torch.randn(100, 3, 32, 32)  # 100\uac1c\uc758 32x32 RGB \uc774\ubbf8\uc9c0\nlabels = torch.randint(0, 10, (100,))  # 100\uac1c\uc758 \ubb34\uc791\uc704 \ub808\uc774\ube14 (0~9)\n\n# \ub370\uc774\ud130\uc14b \uc0dd\uc131\ndataset = CustomDataset(data, labels)\nprint(f\"\ub370\uc774\ud130\uc14b \ud06c\uae30: {len(dataset)}\")  # 100\n    <\/code><\/pre>\n<h3>2.2 \ub370\uc774\ud130 \ub85c\ub354 \uc0dd\uc131<\/h3>\n<p>\ub370\uc774\ud130 \ub85c\ub354\ub294 \ubc30\uce58\ub85c \ub370\uc774\ud130\ub97c \uac00\uc838\uc624\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ub370\uc774\ud130 \ub85c\ub354\ub97c \uc0ac\uc6a9\ud558\uba74 \ub370\uc774\ud130\uc14b\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ubbf8\ub2c8\ubc30\uce58\ub85c \ub098\ub204\uc5b4 \ubaa8\ub378\uc5d0 \uc804\ub2ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ub370\uc774\ud130 \ub85c\ub354\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4:<\/p>\n<pre><code>from torch.utils.data import DataLoader\n\n# \ub370\uc774\ud130 \ub85c\ub354 \uc0dd\uc131\ndata_loader = DataLoader(dataset, batch_size=16, shuffle=True)\n\n# \ubc30\uce58 \ub370\uc774\ud130 \ucd9c\ub825\nfor batch_data, batch_labels in data_loader:\n    print(f\"\ubc30\uce58 \ub370\uc774\ud130 \ud06c\uae30: {batch_data.size()}\")  # [16, 3, 32, 32]\n    print(f\"\ubc30\uce58 \ub808\uc774\ube14 \ud06c\uae30: {batch_labels.size()}\")  # [16]\n    break  # \uccab \ubc30\uce58\ub9cc \ucd9c\ub825\n    <\/code><\/pre>\n<h2>3. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n<p>\ub525\ub7ec\ub2dd\uc5d0\uc11c \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uacfc\uc815\uc740 \uc911\uc694\ud569\ub2c8\ub2e4. \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\ub97c \uc608\ub85c \ub4e4\uc5b4, \uc804\ucc98\ub9ac \ub2e8\uacc4\uc5d0\uc11c \uc218\ud589\ud574\uc57c \ud560 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\uc815\uaddc\ud654:<\/strong> \ub370\uc774\ud130\ub97c \uc815\uaddc\ud654\ud558\uc5ec \ud559\uc2b5 \uc18d\ub3c4\ub97c \ud5a5\uc0c1\uc2dc\ud0a4\uace0, \ubaa8\ub378\uc774 \ub354 \uc798 \uc77c\ubc18\ud654\ub420 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ub9ac\uc0ac\uc774\uc9d5:<\/strong> \uc774\ubbf8\uc9c0 \ud06c\uae30\ub97c \ubaa8\ub378\uc5d0 \ub9de\uac8c \ubcc0\uacbd\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ub370\uc774\ud130 \uc99d\uac15:<\/strong> \ub370\uc774\ud130\ub97c \uc99d\uac15\ud558\uc5ec \uacfc\uc801\ud569(overfitting)\uc744 \ubc29\uc9c0\ud558\uace0, \ub354 \ub113\uc740 \ub370\uc774\ud130\uc14b\uc744 \ud655\ubcf4\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>3.1 \uc774\ubbf8\uc9c0 \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uc608\uc81c<\/h3>\n<p>\ub2e4\uc74c\uc740 torchvision.transforms\ub97c \uc774\uc6a9\ud55c \uc774\ubbf8\uc9c0 \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uc608\uc81c\uc785\ub2c8\ub2e4:<\/p>\n<pre><code>from torchvision import transforms\n\n# \uc804\ucc98\ub9ac \ub2e8\uacc4 \uc815\uc758\ntransform = transforms.Compose([\n    transforms.Resize((32, 32)),  # \uc774\ubbf8\uc9c0 \ub9ac\uc0ac\uc774\uc9d5\n    transforms.ToTensor(),  # \ud150\uc11c\ub85c \ubcc0\ud658\n    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])  # \uc815\uaddc\ud654\n])\n\n# \ub370\uc774\ud130\uc14b \ud074\ub798\uc2a4 \uc218\uc815\nclass CustomDatasetWithTransform(Dataset):\n    def __init__(self, data, labels, transform=None):\n        self.data = data\n        self.labels = labels\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.data)\n\n    def __getitem__(self, idx):\n        image = self.data[idx]\n        label = self.labels[idx]\n        \n        if self.transform:\n            image = self.transform(image)  # \uc804\ucc98\ub9ac \uc801\uc6a9\n        \n        return image, label\n\n# \uc218\uc815\ub41c \ub370\uc774\ud130\uc14b \uc0dd\uc131\ndataset_with_transform = CustomDatasetWithTransform(data, labels, transform=transform)\ndata_loader_with_transform = DataLoader(dataset_with_transform, batch_size=16, shuffle=True)\n\n# \ubc30\uce58 \ub370\uc774\ud130 \ucd9c\ub825\nfor batch_data, batch_labels in data_loader_with_transform:\n    print(f\"\ubc30\uce58 \ub370\uc774\ud130 \ud06c\uae30: {batch_data.size()}\")\n    print(f\"\ubc30\uce58 \ub808\uc774\ube14 \ud06c\uae30: {batch_labels.size()}\")\n    break\n    <\/code><\/pre>\n<h2>4. \ub370\uc774\ud130 \uc99d\uac15<\/h2>\n<p>\ub370\uc774\ud130 \uc99d\uac15\uc740 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \ucd94\uac00\uc801\uc778 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\ub97c \uc81c\uacf5\ud568\uc73c\ub85c\uc368 \ubaa8\ub378\uc774 \ub354 \uc77c\ubc18\ud654\ub418\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uba87 \uac00\uc9c0 \ub370\uc774\ud130 \uc99d\uac15 \uae30\ubc95\uc785\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\ud68c\uc804:<\/strong> \uc774\ubbf8\uc9c0\ub97c \uc784\uc758\uc758 \uac01\ub3c4\ub85c \ud68c\uc804\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc798\ub77c\ub0b4\uae30:<\/strong> \uc774\ubbf8\uc9c0\uc758 \uc784\uc758\uc758 \ubd80\ubd84\uc744 \uc798\ub77c\ub0c5\ub2c8\ub2e4.<\/li>\n<li><strong>\ubc18\uc804:<\/strong> \uc774\ubbf8\uc9c0\uc758 \uc0c9\uc0c1\uc744 \ubc18\uc804\uc2dc\ud0b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>4.1 \ub370\uc774\ud130 \uc99d\uac15 \uc608\uc81c<\/h3>\n<p>\ub2e4\uc74c\uc740 torchvision\uc744 \uc0ac\uc6a9\ud55c \ub370\uc774\ud130 \uc99d\uac15\uc758 \uc608\uc785\ub2c8\ub2e4:<\/p>\n<pre><code>from torchvision import transforms\n\n# \ub370\uc774\ud130 \uc99d\uac15 \ub2e8\uacc4 \uc815\uc758\naugment = transforms.Compose([\n    transforms.RandomHorizontalFlip(),  # \uc218\ud3c9 \ub4a4\uc9d1\uae30\n    transforms.RandomRotation(20),  # \ub79c\ub364 \ud68c\uc804\n    transforms.ToTensor(),  # \ud150\uc11c\ub85c \ubcc0\ud658\n    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])  # \uc815\uaddc\ud654\n])\n\n# \ub370\uc774\ud130\uc14b\uc5d0 \uc99d\uac15 \ub2e8\uacc4 \uc801\uc6a9\ndataset_with_augmentation = CustomDatasetWithTransform(data, labels, transform=augment)\ndata_loader_with_augmentation = DataLoader(dataset_with_augmentation, batch_size=16, shuffle=True)\n\n# \ubc30\uce58 \ub370\uc774\ud130 \ucd9c\ub825\nfor batch_data, batch_labels in data_loader_with_augmentation:\n    print(f\"\ubc30\uce58 \ub370\uc774\ud130 \ud06c\uae30: {batch_data.size()}\")\n    print(f\"\ubc30\uce58 \ub808\uc774\ube14 \ud06c\uae30: {batch_labels.size()}\")\n    break\n    <\/code><\/pre>\n<h2>5. \uacb0\ub860<\/h2>\n<p>\ub525\ub7ec\ub2dd\uc5d0\uc11c \ub370\uc774\ud130 \uc900\ube44\ub294 \ub9e4\uc6b0 \uc911\uc694\ud55c \ub2e8\uacc4\uc785\ub2c8\ub2e4. \uc801\uc808\ud55c \ub370\uc774\ud130\uc14b\uc744 \uc0dd\uc131\ud558\uace0, \ub370\uc774\ud130 \ub85c\ub354\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \ubc30\uce58\ub85c \uac00\uc838\uc624\uba70, \ud544\uc694\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac \ubc0f \uc99d\uac15\uc744 \uc218\ud589\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uc774\ub7ec\ud55c \ub370\uc774\ud130 \uc900\ube44\uc758 \uae30\ubcf8 \ud504\ub85c\uc138\uc2a4\ub97c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n<p>\uc5ec\ub7ec\ubd84\uc758 \ub525\ub7ec\ub2dd \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c \uc774\ub7ec\ud55c \uc6d0\uce59\uc744 \ud65c\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud574\ubcf4\uc138\uc694. \ub370\uc774\ud130\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc774 \ud559\uc2b5\ud558\ub294 \ub370 \uc788\uc5b4 \uac00\uc7a5 \uc911\uc694\ud55c \uc790\uc0b0\uc785\ub2c8\ub2e4. \ub530\ub77c\uc11c \uc62c\ubc14\ub978 \ub370\uc774\ud130 \uc900\ube44\ub294 \uc131\uacf5\uc801\uc778 \ub525\ub7ec\ub2dd \ud504\ub85c\uc81d\ud2b8\uc758 \ucd08\uc11d\uc774 \ub429\ub2c8\ub2e4.<\/p>\n<h2>\ucc38\uace0 \uc790\ub8cc<\/h2>\n<ul>\n<li><a href=\"https:\/\/pytorch.org\/docs\/stable\/index.html\">PyTorch Documentation<\/a><\/li>\n<li><a href=\"https:\/\/pytorch.org\/tutorials\/\">PyTorch Tutorials<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a><\/li>\n<\/ul>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>\ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130 \uc900\ube44 \ub2e8\uacc4\uac00 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uc62c\ubc14\ub978 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc900\ube44\ud558\uc9c0 \uc54a\uc73c\uba74 \ubaa8\ub378\uc758 \uc131\ub2a5\uc774 \uc800\ud558\ub420 \uc218 \uc788\uc73c\uba70, \uc774\ub294 \uacb0\uad6d \uc2e4\uc81c \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc758 \ud488\uc9c8\uc5d0 \uc545\uc601\ud5a5\uc744 \ubbf8\uce60 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc774\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \uc900\ube44 \ubc29\ubc95\uc744 \ub2e8\uacc4\ubcc4\ub85c \uc124\uba85\ud558\uace0, \uc608\uc81c \ucf54\ub4dc\ub97c \ud1b5\ud574 \uc2e4\uc2b5\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \ub370\uc774\ud130 \uc900\ube44\uc758 \uc911\uc694\uc131 \ub525\ub7ec\ub2dd\uc758 \uc131\uacf5 \uc5ec\ubd80\ub294 \uc885\uc885 \ub370\uc774\ud130\uc758 \ud488\uc9c8\uacfc \uc591\uc5d0 \uc758\ud574 \uacb0\uc815\ub429\ub2c8\ub2e4. &hellip; <a href=\"https:\/\/atmokpo.com\/w\/30008\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 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