{"id":30032,"date":"2024-10-28T03:19:15","date_gmt":"2024-10-28T03:19:15","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30032"},"modified":"2024-11-26T06:50:19","modified_gmt":"2024-11-26T06:50:19","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%a1%9c%ec%a7%80%ec%8a%a4%ed%8b%b1-%ed%9a%8c%ea%b7%80%ec%99%80-%ec%84%a0%ed%98%95-%ed%9a%8c%ea%b7%80","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30032\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc640 \uc120\ud615 \ud68c\uadc0"},"content":{"rendered":"

<\/p>\n

\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd<\/strong>\uc758 \uae30\ucd08 \uac1c\ub150 \uc911 \ub450 \uac00\uc9c0 \uc911\uc694\ud55c \ud68c\uadc0 \ubd84\uc11d \uae30\ubc95\uc778 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0<\/strong>\uc640 \uc120\ud615 \ud68c\uadc0<\/strong>\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 PyTorch<\/strong>\ub97c \uc774\uc6a9\ud558\uc5ec \ub450 \uac00\uc9c0 \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0, \uac01\uac01\uc758 \uae30\ubc95\uc774 \uc5b4\ub5bb\uac8c \uc0ac\uc6a9\ub418\ub294\uc9c0\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

\ubaa9\ucc28<\/h2>\n
    \n
  1. 1. \uc120\ud615 \ud68c\uadc0<\/a><\/li>\n
  2. 2. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0<\/a><\/li>\n
  3. 3. \uacb0\ub860<\/a><\/li>\n<\/ol>\n

    1. \uc120\ud615 \ud68c\uadc0<\/h2>\n

    \uc120\ud615 \ud68c\uadc0\ub294 \uc785\ub825 \ubcc0\uc218\uc640 \ucd9c\ub825 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc9c1\uc120\uc73c\ub85c \ubaa8\ub378\ub9c1\ud558\ub294 \ud1b5\uacc4\uc801 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc758 \uc9d1\ud569\uc5d0 \ucd5c\uc801\uc758 \uc9c1\uc120\uc744 \ucc3e\uc544\uc11c \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n

    1.1 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378 \uc218\uc2dd<\/h3>\n

    \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uc218\uc2dd\uc73c\ub85c \ub098\ud0c0\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

    y = \u03b20 + \u03b21*x1 + \u03b22*x2 + ... + \u03b2n*xn<\/code><\/pre>\n
    \uc5ec\uae30\uc11c, y<\/code>\ub294 \uc608\uce21 \uac12, \u03b20<\/code>\ub294 \uc808\ud3b8, \u03b21, \u03b22, ..., \u03b2n<\/code>\uc740 \ud68c\uadc0 \uacc4\uc218\uc785\ub2c8\ub2e4. \uc774 \ud68c\uadc0 \uacc4\uc218\ub4e4\uc740 \ub370\uc774\ud130\uc5d0 \uc758\ud574 \ud559\uc2b5\ub429\ub2c8\ub2e4.<\/p>\n
    1.2 \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c \uc120\ud615 \ud68c\uadc0 \uad6c\ud604\ud558\uae30<\/h3>\n
    \uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub97c \ud1b5\ud574 \uac04\ub2e8\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
    \nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# \ub370\uc774\ud130 \uc0dd\uc131\nx_data = np.array([[1], [2], [3], [4], [5]])\ny_data = np.array([[2], [3], [5], [7], [11]])\n\n# \ud150\uc11c\ub85c \ubcc0\ud658\nx_tensor = torch.Tensor(x_data)\ny_tensor = torch.Tensor(y_data)\n\n# \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378 \uc815\uc758\nmodel = nn.Linear(1, 1)\n\n# \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758\ncriterion = nn.MSELoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n\n# \ubaa8\ub378 \ud559\uc2b5\nfor epoch in range(100):\n    model.train()\n    \n    optimizer.zero_grad()\n    y_pred = model(x_tensor)\n    loss = criterion(y_pred, y_tensor)\n    loss.backward()\n    optimizer.step()\n\n# \uacb0\uacfc \uc2dc\uac01\ud654\nplt.scatter(x_data, y_data, color='blue', label='\uc2e4\uc81c \ub370\uc774\ud130')\nplt.plot(x_data, model(x_tensor).detach().numpy(), color='red', label='\uc608\uce21\uc120')\nplt.legend()\nplt.title('\uc120\ud615 \ud68c\uadc0 \uc608\uce21')\nplt.xlabel('x')\nplt.ylabel('y')\nplt.show()\n<\/code><\/pre>\n
    \uc704\uc758 \ucf54\ub4dc\ub294 \uac04\ub2e8\ud55c \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4. \ubaa8\ub378\uc744 \ud559\uc2b5\ud55c \ud6c4, \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uc608\uce21\ud55c \uac12\ub4e4\uc744 \uc2dc\uac01\ud654\ud558\uc5ec \ube44\uad50\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
    2. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0<\/h2>\n
    \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \ubd84\ub958 \ubb38\uc81c\ub97c \ub2e4\ub8e8\uae30 \uc704\ud55c \uc120\ud615 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc8fc\ub85c \uc774\uc9c4 \ubd84\ub958\uc5d0\uc11c \uc0ac\uc6a9\ub418\uba70, \uc785\ub825\uc758 \uc120\ud615 \uc870\ud569\uc5d0 \uc2dc\uadf8\ubaa8\uc774\ub4dc \ud568\uc218(\ub85c\uc9c0\uc2a4\ud2f1 \ud568\uc218)\ub97c \uc801\uc6a9\ud558\uc5ec \ucd9c\ub825\uc774 0\uc5d0\uc11c 1 \uc0ac\uc774\uc758 \ud655\ub960\uac12\uc73c\ub85c \ubcc0\ud658\ub429\ub2c8\ub2e4.<\/p>\n
    2.1 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378 \uc218\uc2dd<\/h3>\n
    \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uc218\uc2dd\uc73c\ub85c \ub098\ud0c0\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
    y = 1 \/ (1 + e^(-z))\nz = \u03b20 + \u03b21*x1 + \u03b22*x2 + ... + \u03b2n*xn<\/code><\/pre>\n
    \uc5ec\uae30\uc11c, z<\/code>\ub294 \uc120\ud615 \uc870\ud569\uc774\uace0, y<\/code>\ub294 \ud074\ub798\uc2a4\uc5d0 \ub300\ud55c \ud655\ub960\uc785\ub2c8\ub2e4.<\/p>\n
    2.2 \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \uad6c\ud604\ud558\uae30<\/h3>\n
    \uc774\uc81c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub294 \uac04\ub2e8\ud55c \uc774\uc9c4 \ubd84\ub958 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
    \n# \ub370\uc774\ud130 \uc0dd\uc131 (\uc774\uc9c4 \ubd84\ub958)\nfrom sklearn.datasets import make_classification\nimport torch.nn.functional as F\n\n# \uc774\uc9c4 \ubd84\ub958 \ub370\uc774\ud130\uc14b \uc0dd\uc131\nX, y = make_classification(n_samples=100, n_features=2, n_classes=2, n_informative=2, n_redundant=0, random_state=42)\nX_tensor = torch.Tensor(X)\ny_tensor = torch.Tensor(y).view(-1, 1)\n\n# \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378 \uc815\uc758\nclass LogisticRegressionModel(nn.Module):\n    def __init__(self):\n        super(LogisticRegressionModel, self).__init__()\n        self.linear = nn.Linear(2, 1)\n\n    def forward(self, x):\n        return torch.sigmoid(self.linear(x))\n\n# \ubaa8\ub378, \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758\nmodel = LogisticRegressionModel()\ncriterion = nn.BCELoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n\n# \ubaa8\ub378 \ud559\uc2b5\nfor epoch in range(100):\n    model.train()\n    \n    optimizer.zero_grad()\n    y_pred = model(X_tensor)\n    \n    loss = criterion(y_pred, y_tensor)\n    loss.backward()\n    optimizer.step()\n\n# \uc608\uce21\nmodel.eval()\nwith torch.no_grad():\n    y_pred = model(X_tensor)\n\n# \uc608\uce21 \uacb0\uacfc \uc2dc\uac01\ud654\npredicted_classes = (y_pred.numpy() > 0.5).astype(int)\nplt.scatter(X[y[:, 0] == 0][:, 0], X[y[:, 0] == 0][:, 1], color='blue', label='Class 0')\nplt.scatter(X[y[:, 0] == 1][:, 0], X[y[:, 0] == 1][:, 1], color='red', label='Class 1')\nplt.scatter(X[predicted_classes[:, 0] == 1][:, 0], X[predicted_classes[:, 0] == 1][:, 1], color='green', label='Predicted')\nplt.legend()\nplt.title('\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \uc608\uce21')\nplt.xlabel('Feature 1')\nplt.ylabel('Feature 2')\nplt.show()\n<\/code><\/pre>\n
    \uc704\uc758 \ucf54\ub4dc\ub294 \uac04\ub2e8\ud55c \uc774\uc9c4 \ubd84\ub958 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4. \ubaa8\ub378\uc744 \ud559\uc2b5\ud55c \ud6c4, \uc2e4\uc81c \ud074\ub798\uc2a4\uc640 \uc608\uce21\ub41c \ud074\ub798\uc2a4\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \ube44\uad50\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
    3. \uacb0\ub860<\/h2>\n
    \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc640 \uc120\ud615 \ud68c\uadc0\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub450 \ubaa8\ub378 \ubaa8\ub450 \uac04\ub2e8\ud55c \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec PyTorch\ub97c \ud65c\uc6a9\ud558\uc5ec \uad6c\ud604\ud558\uc600\uc73c\uba70, \uc774\ub4e4\uc774 \ub2e4\ub8e8\ub294 \ubb38\uc81c\uc758 \ucc28\uc774\uc810 \ubc0f \uc911\uc694\uc131\uc744 \uac15\uc870\ud558\uc600\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\ubc95\ub4e4\uc740 \uae30\uacc4 \ud559\uc2b5 \ubc0f \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08\uac00 \ub418\ub294 \uc694\uc18c\ub4e4\uc774\uba70, \uc2e4\uc81c \ubb38\uc81c \ud574\uacb0\uc5d0 \uc788\uc5b4 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\ub97c \ud1b5\ud574 \uc774\ud574\uc758 \ud3ed\uc774 \ub113\uc5b4\uc9c0\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
    <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
    \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08 \uac1c\ub150 \uc911 \ub450 \uac00\uc9c0 \uc911\uc694\ud55c \ud68c\uadc0 \ubd84\uc11d \uae30\ubc95\uc778 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc640 \uc120\ud615 \ud68c\uadc0\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 PyTorch\ub97c \uc774\uc6a9\ud558\uc5ec \ub450 \uac00\uc9c0 \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0, \uac01\uac01\uc758 \uae30\ubc95\uc774 \uc5b4\ub5bb\uac8c \uc0ac\uc6a9\ub418\ub294\uc9c0\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ubaa9\ucc28 1. \uc120\ud615 \ud68c\uadc0 2. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 3. \uacb0\ub860 1. \uc120\ud615 \ud68c\uadc0 \uc120\ud615 \ud68c\uadc0\ub294 \uc785\ub825 \ubcc0\uc218\uc640 \ucd9c\ub825 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc9c1\uc120\uc73c\ub85c \ubaa8\ub378\ub9c1\ud558\ub294 \ud1b5\uacc4\uc801 \ubc29\ubc95\uc785\ub2c8\ub2e4. … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc640 \uc120\ud615 \ud68c\uadc0”<\/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-30032","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc640 \uc120\ud615 \ud68c\uadc0 - \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\/30032\/\" \/>\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, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc640 \uc120\ud615 \ud68c\uadc0 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08 \uac1c\ub150 \uc911 \ub450 \uac00\uc9c0 \uc911\uc694\ud55c \ud68c\uadc0 \ubd84\uc11d \uae30\ubc95\uc778 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc640 \uc120\ud615 \ud68c\uadc0\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 PyTorch\ub97c \uc774\uc6a9\ud558\uc5ec \ub450 \uac00\uc9c0 \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0, \uac01\uac01\uc758 \uae30\ubc95\uc774 \uc5b4\ub5bb\uac8c \uc0ac\uc6a9\ub418\ub294\uc9c0\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ubaa9\ucc28 1. \uc120\ud615 \ud68c\uadc0 2. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 3. \uacb0\ub860 1. \uc120\ud615 \ud68c\uadc0 \uc120\ud615 \ud68c\uadc0\ub294 \uc785\ub825 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