{"id":30014,"date":"2024-10-28T03:19:10","date_gmt":"2024-10-28T03:19:10","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30014"},"modified":"2024-11-26T06:50:24","modified_gmt":"2024-11-26T06:50:24","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%94%a5%eb%9f%ac%eb%8b%9d-%ea%b5%ac%ec%a1%b0","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30014\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub525\ub7ec\ub2dd \uad6c\uc870"},"content":{"rendered":"

<\/p>\n

\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud559\uc2b5\ud558\uc5ec \uc608\uce21 \ubc0f \ubd84\ub958 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \uae30\uacc4\ub97c \ub9cc\ub4dc\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144 \uac04 \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc740 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \ubcc0\ud654\uc640 \uc131\uacfc\ub97c \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8\uc801\uc778 \uad6c\uc870\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n

\ub525\ub7ec\ub2dd\uc5d0\uc11c\ub294 \ub370\uc774\ud130\ub97c input\uc73c\ub85c \ubc1b\uc544 \uc5ec\ub7ec \ub808\uc774\uc5b4\ub97c \ud1b5\ud574 \ucc98\ub9ac\ud55c \ub4a4, \ucd5c\uc885 output\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \uc778\uacf5 \uc2e0\uacbd\ub9dd(ANN, Artificial Neural Network)\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc2e0\uacbd\ub9dd\uc740 \ub178\ub4dc(Node) \ub610\ub294 \ub274\ub7f0(Neuron)\uc774\ub77c \ubd88\ub9ac\ub294 \uc5ec\ub7ec \uac1c\uc758 \uc5f0\uacb0\ub41c \ub2e8\uc704\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc73c\uba70, \uac01 \ub274\ub7f0\uc740 \uc785\ub825\uc744 \ubc1b\uc544 \uac00\uc911\uce58(weight)\ub97c \uacf1\ud55c \ud6c4 \ubc14\uc774\uc5b4\uc2a4(bias)\ub97c \ub354\ud558\uace0, \ube44\uc120\ud615 \ud65c\uc131\ud654 \ud568\uc218(Activation Function)\ub97c \uc801\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n

1.1 \uc2e0\uacbd\ub9dd\uc758 \uae30\ubcf8 \uad6c\uc870<\/h3>\n

\uc2e0\uacbd\ub9dd\uc758 \uae30\ubcf8 \uad6c\uc870\ub294 \uc785\ub825\uce35(Inputs), \uc740\ub2c9\uce35(Hidden layers), \ucd9c\ub825\uce35(Output)\uc73c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \uce35\uc740 \ub2e4\uc74c \uce35\uc758 \ub274\ub7f0\uc73c\ub85c \uc5f0\uacb0\ub418\uc5b4 \uc788\uc73c\uba70, \uc785\ub825\uce35\uc740 \ub370\uc774\ud130\ub97c \ubc1b\uc544\ub4e4\uc774\uace0, \ucd9c\ub825\uce35\uc740 \uacb0\uacfc\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n

class SimpleNN(nn.Module):\n    def __init__(self):\n        super(SimpleNN, self).__init__()\n        self.fc1 = nn.Linear(2, 3)  # 2\uac1c \uc785\ub825, 3\uac1c \ucd9c\ub825\n        self.fc2 = nn.Linear(3, 1)  # 3\uac1c \uc785\ub825, 1\uac1c \ucd9c\ub825\n\n    def forward(self, x):\n        x = F.relu(self.fc1(x))\n        x = self.fc2(x)\n        return x\n<\/code><\/pre>\n
2. \ud30c\uc774\ud1a0\uce58(PyTorch) \uc18c\uac1c<\/h2>\n
\ud30c\uc774\ud1a0\uce58\ub294 Facebook AI Research\uac00 \uac1c\ubc1c\ud55c \uc778\uae30 \uc788\ub294 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \uc0ac\uc6a9\ud558\uae30 \uac04\ud3b8\ud558\uace0 \uc720\uc5f0\uc131 \uc788\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uba74 \ud150\uc11c(tensor) \uc5f0\uc0b0\uc744 \ud1b5\ud574 GPU \uac00\uc18d\uc744 \uc190\uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \uc9c0\uc6d0\ud569\ub2c8\ub2e4.<\/p>\n
2.1 \uae30\ubcf8 \ud150\uc11c(Tensor)<\/h3>\n
\ub525\ub7ec\ub2dd\uc5d0\uc11c \ud150\uc11c\ub294 \ub370\uc774\ud130\ub97c \ud45c\ud604\ud558\ub294 \uae30\ubcf8 \uad6c\uc870\uc785\ub2c8\ub2e4. 1D \ud150\uc11c\ub294 \ubca1\ud130, 2D \ud150\uc11c\ub294 \ud589\ub82c, 3D\ub294 \ub2e4\ucc28\uc6d0 \ubc30\uc5f4\ub85c \uc0dd\uac01\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch\n\n    # 1D \ud150\uc11c\n    tensor_1d = torch.tensor([1, 2, 3])\n\n    # 2D \ud150\uc11c\n    tensor_2d = torch.tensor([[1, 2], [3, 4]])\n\n    # 3D \ud150\uc11c\n    tensor_3d = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n<\/code><\/pre>\n
3. \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\uc774\uc81c \uac04\ub2e8\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. PyTorch\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ub2e4\uc591\ud55c API\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\ub525\ub7ec\ub2dd\uc5d0\uc11c\ub294 \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uac00 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud558\uace0, \ud559\uc2b5\uc5d0 \uc801\ud569\ud55c \ud615\ud0dc\ub85c \ubcc0\ud658\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.datasets import make_moons\n    from sklearn.model_selection import train_test_split\n    from sklearn.preprocessing import StandardScaler\n\n    X, y = make_moons(n_samples=1000, noise=0.2, random_state=42)\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n    # \ub370\uc774\ud130 \ud45c\uc900\ud654\n    scaler = StandardScaler()\n    X_train = scaler.fit_transform(X_train)\n    X_test = scaler.transform(X_test)\n<\/code><\/pre>\n
3.2 \ubaa8\ub378 \uc815\uc758<\/h3>\n
\uc55e\uc11c \uc124\uba85\ud55c \uac83\ucc98\ub7fc, \ubaa8\ub378\uc740 nn.Module\uc744 \uc0c1\uc18d\ubc1b\uc544 \uc815\uc758\ub429\ub2c8\ub2e4. \uc774\ubc88\uc5d0\ub294 \ud65c\uc131\ud654 \ud568\uc218\ub97c Relu \ub300\uc2e0 \uc2dc\uadf8\ubaa8\uc774\ub4dc(Sigmoid)\ub85c \uc0ac\uc6a9\ud574\ubd05\uc2dc\ub2e4.<\/p>\n
import torch.nn as nn\n    import torch.nn.functional as F\n\n    class SimpleNN(nn.Module):\n        def __init__(self):\n            super(SimpleNN, self).__init__()\n            self.fc1 = nn.Linear(2, 3)\n            self.fc2 = nn.Linear(3, 1)\n\n        def forward(self, x):\n            x = F.sigmoid(self.fc1(x))\n            x = self.fc2(x)\n            return x\n<\/code><\/pre>\n
3.3 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c\ub294 \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc815\uc758\ud574\uc57c \ud569\ub2c8\ub2e4. \uc190\uc2e4 \ud568\uc218\ub85c\ub294 \uc774\uc9c4 \ud06c\ub85c\uc2a4 \uc5d4\ud2b8\ub85c\ud53c(BCE)\ub97c \uc0ac\uc6a9\ud558\uace0, \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c\ub294 Adam\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch.optim as optim\n\n    model = SimpleNN()\n    criterion = nn.BCEWithLogitsLoss()\n    optimizer = optim.Adam(model.parameters(), lr=0.001)\n\n    X_train_tensor = torch.tensor(X_train, dtype=torch.float32)\n    y_train_tensor = torch.tensor(y_train, dtype=torch.float32).view(-1, 1)\n\n    for epoch in range(1000):\n        model.train()\n        optimizer.zero_grad()\n        outputs = model(X_train_tensor)\n        loss = criterion(outputs, y_train_tensor)\n        loss.backward()\n        optimizer.step()\n\n        if (epoch + 1) % 100 == 0:\n            print(f'Epoch [{epoch + 1}\/1000], Loss: {loss.item():.4f}')\n<\/code><\/pre>\n
3.4 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
\ubaa8\ub378 \ud559\uc2b5\uc774 \ub05d\ub098\uba74, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uc815\ud655\ub3c4\ub97c \uce21\uc815\ud569\ub2c8\ub2e4.<\/p>\n
model.eval()\n    with torch.no_grad():\n        X_test_tensor = torch.tensor(X_test, dtype=torch.float32)\n        y_pred = model(X_test_tensor)\n        y_pred = (y_pred > 0).float()\n        accuracy = (y_pred.view(-1) == torch.tensor(y_test, dtype=torch.float32)).float().mean()\n        print(f'Accuracy: {accuracy:.4f}')\n<\/code><\/pre>\n
4. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud55c \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378 \uad6c\ucd95 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \ub354 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \ub9cc\ub4e4\uae30 \uc704\ud574\uc11c\ub294 \ub354 \uae4a\uc740 \uad6c\uc870\uc640 \ub2e4\uc591\ud55c \uae30\uc220\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \ub2e4\uc74c \uac15\uc88c\uc5d0\uc11c\ub294 CNN(\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd), RNN(\uc21c\ud658\uc2e0\uacbd\ub9dd) \ub4f1\uc758 \ub354 \ubcf5\uc7a1\ud55c \ub525\ub7ec\ub2dd \uad6c\uc870\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud559\uc2b5\ud558\uc5ec \uc608\uce21 \ubc0f \ubd84\ub958 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \uae30\uacc4\ub97c \ub9cc\ub4dc\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144 \uac04 \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc740 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \ubcc0\ud654\uc640 \uc131\uacfc\ub97c \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8\uc801\uc778 \uad6c\uc870\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150 \ub525\ub7ec\ub2dd\uc5d0\uc11c\ub294 \ub370\uc774\ud130\ub97c input\uc73c\ub85c \ubc1b\uc544 \uc5ec\ub7ec \ub808\uc774\uc5b4\ub97c \ud1b5\ud574 \ucc98\ub9ac\ud55c \ub4a4, \ucd5c\uc885 output\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub525\ub7ec\ub2dd \uad6c\uc870”<\/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-30014","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub525\ub7ec\ub2dd \uad6c\uc870 - 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