{"id":30010,"date":"2024-10-28T03:19:08","date_gmt":"2024-10-28T03:19:08","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30010"},"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%eb%a5%bc-%ec%82%ac%ec%9a%a9%ed%95%9c-%ec%84%b1%eb%8a%a5-%ec%b5%9c%ec%a0%81%ed%99%94","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30010\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud55c \uc131\ub2a5 \ucd5c\uc801\ud654"},"content":{"rendered":"

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\ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc131\uacf5\uc801\uc73c\ub85c \uad6c\ucd95\ud558\uace0 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \uac00\uc7a5 \uc911\uc694\ud55c \uc694\uc18c \uc911 \ud558\ub098\ub294 \ub370\uc774\ud130\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\uc758 \ud488\uc9c8\uacfc \uc591\uc740 \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \uc9c1\uc811\uc801\uc774\uace0 \uc720\uc758\ubbf8\ud55c \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub370\uc774\ud130\uc758 \uc911\uc694\uc131\uacfc \ud568\uaed8 PyTorch\ub97c \uc774\uc6a9\ud55c \uc131\ub2a5 \ucd5c\uc801\ud654 \uae30\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc758 \uc911\uc694\uc131<\/h2>\n

\ub525\ub7ec\ub2dd\uc5d0\uc11c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub294 \ub9e4\uc6b0 \uc911\uc694\ud55c \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774\ub294 \ubaa8\ub378\uc774 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \uc801\uc808\ud55c \ud615\uc2dd\uc73c\ub85c \ub370\uc774\ud130\ub97c \ubcc0\ud658\ud558\ub294 \uacfc\uc815\uc744 \ud3ec\ud568\ud558\uba70, \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc744 \uadf9\ub300\ud654\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.<\/p>\n

1.1 \uacb0\uce21\uce58 \ucc98\ub9ac<\/h3>\n

\ub370\uc774\ud130\uc14b\uc5d0 \uacb0\uce21\uce58\uac00 \uc788\ub294 \uacbd\uc6b0 \uc774\ub97c \uc801\uc808\ud788 \ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uacb0\uce21\uce58\ub97c \uc81c\uac70\ud558\uac70\ub098, \ud3c9\uade0\uac12, \uc911\uc559\uac12 \ub4f1\uc73c\ub85c \ub300\uccb4\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

import pandas as pd\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('data.csv')\n\n# \uacb0\uce21\uce58 \uc81c\uac70\ndata = data.dropna()\n\n# \uacb0\uce21\uce58\ub97c \ud3c9\uade0\uc73c\ub85c \ub300\uccb4\ndata.fillna(data.mean(), inplace=True)\n<\/code><\/pre>\n
1.2 \uc815\uaddc\ud654 \ubc0f \ud45c\uc900\ud654<\/h3>\n
\uc815\uaddc\ud654(Normalization)\uc640 \ud45c\uc900\ud654(Standardization)\ub294 \uacf5\ud1b5\uc801\uc73c\ub85c \ub370\uc774\ud130\uc758 \uc2a4\ucf00\uc77c\uc744 \uc870\uc815\ud558\uc5ec \ubaa8\ub378\uc758 \ud559\uc2b5 \uc18d\ub3c4\ub97c \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4.<\/p>\n
from sklearn.preprocessing import MinMaxScaler, StandardScaler\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\nX = data.iloc[:, :-1].values  # \ud2b9\uc131\ny = data.iloc[:, -1].values    # \ub808\uc774\ube14\n\n# MinMax \uc815\uaddc\ud654\nscaler = MinMaxScaler()\nX_normalized = scaler.fit_transform(X)\n\n# \ud45c\uc900\ud654\nstandard_scaler = StandardScaler()\nX_standardized = standard_scaler.fit_transform(X)\n<\/code><\/pre>\n
2. \ub370\uc774\ud130 \uc99d\uac15<\/h2>\n
\ub370\uc774\ud130 \uc99d\uac15(Data Augmentation)\uc740 \ubaa8\ub378\uc758 \uc77c\ubc18\ud654 \uc131\ub2a5\uc744 \ub192\uc774\uae30 \uc704\ud574 \uae30\uc874 \ub370\uc774\ud130\ub97c \ubcc0\ud615\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc5d0 \uc790\uc8fc \uc0ac\uc6a9\ub418\uba70, \ud68c\uc804, \ud06c\uae30 \uc870\uc808, \uc798\ub77c\ub0b4\uae30 \ub4f1\uc758 \ubc29\ubc95\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4.<\/p>\n
import torchvision.transforms as transforms\n\n# \ub370\uc774\ud130 \uc99d\uac15 \uc815\uc758\ntransform = transforms.Compose([\n    transforms.RandomHorizontalFlip(),\n    transforms.RandomRotation(10),\n    transforms.ColorJitter(brightness=0.5, contrast=0.5),\n    transforms.ToTensor()\n])\n<\/code><\/pre>\n
3. \ud559\uc2b5\ub960 \uc2a4\ucf00\uc904\ub9c1<\/h2>\n
\ud559\uc2b5\ub960(Learning Rate)\uc740 \ubaa8\ub378 \ud559\uc2b5\uc5d0\uc11c \uac00\uc7a5 \uc911\uc694\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \ubaa8\ub378\uc774 \ucd5c\uc801\uc758 \uac00\uc911\uce58\ub97c \ud559\uc2b5\ud558\uae30 \uc704\ud574 \uc774\ub97c \uc801\uc808\ud788 \uc870\uc815\ud558\ub294 \uac83\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
import torch.optim as optim\n\n# \ucd08\uae30 \ud559\uc2b5\ub960\ninitial_lr = 0.01\noptimizer = optim.Adam(model.parameters(), lr=initial_lr)\n\n# \ud559\uc2b5\ub960 \uc870\uc815\nscheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\n\n# \ud559\uc2b5 \ub8e8\ud504\uc5d0\uc11c \uc0ac\uc6a9\nfor epoch in range(num_epochs):\n    train(...)\n    validate(...)\n    scheduler.step()\n<\/code><\/pre>\n
4. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ucd5c\uc801\ud654<\/h2>\n
\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ucd5c\ub300\ud654\ud558\uae30 \uc704\ud574 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \ucd5c\uc801\ud654\ud558\ub294 \uacfc\uc815\ub3c4 \uc911\uc694\ud55c\ub370, Grid Search, Random Search, Bayesian Optimization\uacfc \uac19\uc740 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import GridSearchCV\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ubc94\uc704 \uc815\uc758\nparam_grid = {\n    'batch_size': [16, 32, 64],\n    'num_layers': [1, 2],\n    'learning_rate': [0.001, 0.01, 0.1]\n}\n\n# \ubaa8\ub378 \ud559\uc2b5 \ubc0f \ud3c9\uac00\ub97c \uc704\ud55c \ud568\uc218 \uc815\uc758\ndef train_evaluate_model(params):\n    # \ubaa8\ub378 \uc815\uc758 \ubc0f \ud559\uc2b5 \ub85c\uc9c1 \uad6c\ud604\n    return performance_metric\n\n# Grid Search \uad6c\ud604\ngrid_search = GridSearchCV(estimator=model, param_grid=param_grid, scoring='accuracy')\ngrid_search.fit(X_train, y_train)\n<\/code><\/pre>\n
5. \uc2e4\ud5d8\uc758 \ubc18\ubcf5<\/h2>\n
\ub2e4\uc591\ud55c \uc2e4\ud5d8\uc744 \ud1b5\ud574 \uc5bb\uc740 \uacb0\uacfc\ub97c \ubd84\uc11d\ud558\uace0, \ucd5c\uc801\uc758 \uc870\ud569\uc744 \ucc3e\uae30 \uc704\ud55c \ubc18\ubcf5\uc801\uc778 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc2e4\ud5d8\uc744 \ud1b5\ud574 \uac01 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\uc758 \uc601\ud5a5\ub3c4\ub97c \uc774\ud574\ud558\uace0, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \ub370\uc774\ud130\uc640 \ubaa8\ub378\uc744 \uc870\uc815\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
\n\ucc38\uace0:<\/strong> \ubc18\ubcf5\uc801\uc778 \uc2e4\ud5d8\uacfc \uac80\uc99d\uc744 \ud1b5\ud574 \uc5bb\uc740 \uc778\uc0ac\uc774\ud2b8\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uac1c\uc120\ud558\ub294 \ub370 \ud070 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.\n<\/div>\n
6. \uacb0\ub860<\/h2>\n
\ub525\ub7ec\ub2dd\uc5d0\uc11c \uc131\ub2a5 \ucd5c\uc801\ud654\ub294 \ub2e4\uc591\ud55c \uc694\uc18c\ub4e4\uc758 \ud569\uc0b0\uc774\ub2e4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac, \ub370\uc774\ud130 \uc99d\uac15, \ud559\uc2b5\ub960 \uc870\uc815, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ucd5c\uc801\ud654 \ub4f1 \uc5ec\ub7ec \uae30\ubc95\ub4e4\uc744 \ud1b5\ud574 \ucd5c\uc801\uc758 \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. PyTorch\ub294 \uc774\ub7ec\ud55c \uae30\ubc95\ub4e4\uc744 \uc190\uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \uc774\ub97c \ud65c\uc6a9\ud558\uc5ec \ubcf4\ub2e4 \uc88b\uc740 \uc131\ub2a5\uc758 \ubaa8\ub378\uc744 \uad6c\ucd95\ud574\ubcf4\uc138\uc694.<\/p>\n
\ucc38\uace0\uc790\ub8cc<\/h2>\n