\ud655\uc7a5\uc131:<\/strong> \ucee4\uc2a4\ud140 \ub808\uc774\uc5b4\uc640 \ubaa8\ub378\uc744 \uc27d\uac8c \uc815\uc758\ud560 \uc218 \uc788\uc5b4 \ub2e4\uc591\ud55c \ub525\ub7ec\ub2dd \uc5f0\uad6c\uc5d0 \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n5. \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \uba38\uc2e0 \ub7ec\ub2dd \uc2e4\uc2b5<\/h2>\n
\uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uba38\uc2e0 \ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 Iris \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uaf43\uc758 \uc885\ub958\ub97c \ubd84\ub958\ud558\ub294 \ubaa8\ub378\uc744 \ub9cc\ub4e4 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
5.1. \ub370\uc774\ud130\uc14b \ub85c\ub529<\/h3>\n
\uba3c\uc800 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uace0 \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud569\ub2c8\ub2e4.<\/p>\n
\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom sklearn import datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import LabelEncoder\nimport numpy as np\n<\/code>\n <\/pre>\n5.2. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
Iris \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud55c \ud6c4, feature\uc640 label\uc744 \ubd84\ub9ac\ud558\uace0 \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
\n\n# Iris \ub370\uc774\ud130\uc14b \ub85c\ub4dc\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# \ub370\uc774\ud130 \uc815\uaddc\ud654\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\n# \ub370\uc774\ud130\ub97c \ud150\uc11c\ub85c \ubcc0\ud658\nX_train_tensor = torch.FloatTensor(X_train)\ny_train_tensor = torch.LongTensor(y_train)\nX_test_tensor = torch.FloatTensor(X_test)\ny_test_tensor = torch.LongTensor(y_test)\n<\/code>\n <\/pre>\n5.3. \ubaa8\ub378 \uc815\uc758<\/h3>\n
\uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. \uc785\ub825\uce35, \uc740\ub2c9\uce35, \ucd9c\ub825\uce35\uc73c\ub85c \uad6c\uc131\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
\n\nclass IrisModel(nn.Module):\n def __init__(self):\n super(IrisModel, self).__init__()\n self.fc1 = nn.Linear(4, 10) # 4\uac1c\uc758 \uc785\ub825 \ud2b9\uc9d5\uacfc 10\uac1c\uc758 \uc740\ub2c9 \ub178\ub4dc\n self.fc2 = nn.Linear(10, 3) # 10\uac1c\uc758 \uc740\ub2c9 \ub178\ub4dc\uc640 3\uac1c\uc758 \ucd9c\ub825 \ub178\ub4dc (\uaf43\uc758 \uc885\ub958)\n\n def forward(self, x):\n x = torch.relu(self.fc1(x)) # \ud65c\uc131\ud654 \ud568\uc218\ub85c ReLU \uc0ac\uc6a9\n x = self.fc2(x)\n return x\n\nmodel = IrisModel()\n<\/code>\n <\/pre>\n5.4. \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uae30\ubc95\uc744 \uc815\uc758\ud55c \ud6c4 \ubaa8\ub378\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n
\n\n# \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uae30\ubc95 \uc815\uc758\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.01)\n\n# \ubaa8\ub378 \ud559\uc2b5\nnum_epochs = 100\nfor epoch in range(num_epochs):\n model.train()\n \n # \uc21c\uc804\ud30c\n outputs = model(X_train_tensor)\n loss = criterion(outputs, y_train_tensor)\n \n # \uc5ed\uc804\ud30c \ubc0f \ucd5c\uc801\ud654\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n if (epoch+1) % 10 == 0:\n print(f'Epoch [{epoch+1}\/{num_epochs}], Loss: {loss.item():.4f}')\n<\/code>\n <\/pre>\n5.5. \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
\ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud558\uace0 \uc815\ud655\ub3c4\ub97c \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n
\n\n# \ubaa8\ub378 \ud3c9\uac00\nmodel.eval()\nwith torch.no_grad():\n test_outputs = model(X_test_tensor)\n _, predicted = torch.max(test_outputs.data, 1)\n accuracy = (predicted == y_test_tensor).sum().item() \/ y_test_tensor.size(0)\n print(f'\uc815\ud655\ub3c4: {accuracy:.2f}')\n<\/code>\n <\/pre>\n6. \uacb0\ub860<\/h2>\n
\uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0 \ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud55c \uac04\ub2e8\ud55c \uba38\uc2e0 \ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uba38\uc2e0 \ub7ec\ub2dd\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub418\uace0 \uc788\uc73c\uba70, \ud30c\uc774\ud1a0\uce58\ub294 \uc774\ub97c \uc704\ud55c \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \ub2e4\uc591\ud55c \uc8fc\uc81c\ub85c \uae4a\uc774 \uc788\ub294 \uc5f0\uad6c\ub97c \ud558\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0 \ub7ec\ub2dd\uc758 \ubc1c\uc804\uc774 \uc5ec\ub7ec\ubd84\uc758 \uc5f0\uad6c\uc5d0 \ub3c4\uc6c0\uc774 \ub418\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
1. \uba38\uc2e0 \ub7ec\ub2dd\uc758 \uc815\uc758 \uba38\uc2e0 \ub7ec\ub2dd(Machine Learning)\uc740 \ucef4\ud4e8\ud130\uac00 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ud559\uc2b5\ud558\uc5ec \ud2b9\uc815 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uac8c \ub9cc\ub4dc\ub294 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud558\uc704 \ubd84\uc57c\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uba38\uc2e0 \ub7ec\ub2dd\uc740 \uba85\uc2dc\uc801\uc73c\ub85c \ud504\ub85c\uadf8\ub798\ubc0d\ub418\uc9c0 \uc54a\uace0\ub3c4 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc774 \ud2b9\uc9d5\uc785\ub2c8\ub2e4. \uc774\ub294 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \uc778\uc2dd\ud558\uace0, \uc608\uce21\uc744 \ud558\uba70, \uc758\uc0ac \uacb0\uc815\uc744 \uc790\ub3d9\ud654\ud558\ub294 \ub370 \ub9e4\uc6b0 \uc720\uc6a9\ud569\ub2c8\ub2e4. 2. \uba38\uc2e0 \ub7ec\ub2dd\uc758 \uae30\ubcf8 \uc6d0\ub9ac \uba38\uc2e0 \ub7ec\ub2dd \ubaa8\ub378\uc740 \uc77c\ubc18\uc801\uc73c\ub85c \ub2e4\uc74c\uacfc … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uba38\uc2e0 \ub7ec\ub2dd\uc774\ub780”<\/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-30038","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uba38\uc2e0 \ub7ec\ub2dd\uc774\ub780 - 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