{"id":30036,"date":"2024-10-28T03:19:16","date_gmt":"2024-10-28T03:19:16","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30036"},"modified":"2024-11-26T06:50:18","modified_gmt":"2024-11-26T06:50:18","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%a8%b8%ec%8b%a0-%eb%9f%ac%eb%8b%9d-%ed%95%99%ec%8a%b5-%ec%95%8c%ea%b3%a0%eb%a6%ac%ec%a6%98","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30036\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uba38\uc2e0 \ub7ec\ub2dd \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998"},"content":{"rendered":"

<\/p>\n

\uc624\ub298\ub0a0 \uc778\uacf5\uc9c0\ub2a5(AI)\uacfc \uba38\uc2e0 \ub7ec\ub2dd(ML)\uc740 \ub2e4\uc591\ud55c \uc0b0\uc5c5 \ubc0f \uc5f0\uad6c \ubd84\uc57c\uc5d0\uc11c \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ub525\ub7ec\ub2dd\uc740 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \uac15\ub825\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac\ub9e4\uae40\ud558\uc600\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58(PyTorch)\ub294 \uc774\ub7ec\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc27d\uace0 \uc9c1\uad00\uc801\uc73c\ub85c \uad6c\ucd95\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc8fc\ub294 \uc624\ud508\uc18c\uc2a4 \ub525\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec \uba38\uc2e0 \ub7ec\ub2dd \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \uad6c\ud604 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uba38\uc2e0 \ub7ec\ub2dd \uac1c\uc694<\/h2>\n

\uba38\uc2e0 \ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \ud559\uc2b5\ud558\uc5ec \uc608\uce21\uc774\ub098 \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc758 \uc9d1\ud569\uc785\ub2c8\ub2e4. \uba38\uc2e0 \ub7ec\ub2dd\uc758 \uc911\uc694\ud55c \ubd84\ub958 \uc911 \ud558\ub098\ub294 \uc9c0\ub3c4 \ud559\uc2b5(supervised learning)\uc785\ub2c8\ub2e4. \uc9c0\ub3c4 \ud559\uc2b5\uc5d0\uc11c\ub294 \uc785\ub825\uacfc \uc774\uc5d0 \ub530\ub978 \uc815\ub2f5(label)\uc744 \uc81c\uacf5\ud558\uc5ec \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \ub300\ud45c\uc801\uc778 \uba38\uc2e0 \ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc778 \uc120\ud615 \ud68c\uadc0(Linear Regression)\ub97c \uc608\ub85c \ub4e4\uc5b4 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

2. \uc120\ud615 \ud68c\uadc0 (Linear Regression)<\/h2>\n

\uc120\ud615 \ud68c\uadc0\ub294 \uc785\ub825 \ud2b9\uc131\uacfc \ucd9c\ub825 \uac04\uc758 \uc120\ud615 \uad00\uacc4\ub97c \ubaa8\ub378\ub9c1\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc218\ud559\uc801\uc73c\ub85c\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \ud45c\ud604\ub429\ub2c8\ub2e4:<\/p>\n

y = wx + b<\/p>\n

\uc5ec\uae30\uc11c y\ub294 \uc608\uce21\uac12, w\ub294 \uac00\uc911\uce58(weight), x\ub294 \uc785\ub825\uac12, b\ub294 \ud3b8\ud5a5(bias)\ub97c \uc758\ubbf8\ud569\ub2c8\ub2e4. \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c\ub294 \ucd5c\uc801\uc758 w\uc640 b\ub97c \ucc3e\ub294 \uac83\uc774 \ubaa9\ud45c\uc785\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uc190\uc2e4 \ud568\uc218(loss function)\ub97c \uc815\uc758\ud558\uc5ec \ucd5c\uc18c\ud654\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28(Mean Squared Error, MSE)\uac00 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n

2.1. \ud30c\uc774\ud1a0\uce58\ub85c \uc120\ud615 \ud68c\uadc0 \uad6c\ud604\ud558\uae30<\/h3>\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\nnp.random.seed(42)\nx_numpy = np.random.rand(100, 1) * 10  # 0\ubd80\ud130 10\uae4c\uc9c0\uc758 \ub79c\ub364 \uc218\ny_numpy = 2.5 * x_numpy + np.random.randn(100, 1)  # y = 2.5x + noise\n\n# NumPy \ubc30\uc5f4\uc744 PyTorch \ud150\uc11c\ub85c \ubcc0\ud658\nx_train = torch.FloatTensor(x_numpy)\ny_train = torch.FloatTensor(y_numpy)\n\n# \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378 \uc815\uc758\nmodel = nn.Linear(1, 1)\n\n# \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758\ncriterion = nn.MSELoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n\n# \ud559\uc2b5 \uacfc\uc815\nnum_epochs = 100\nfor epoch in range(num_epochs):\n    model.train()\n\n    # \uc608\uce21\uac12 \uacc4\uc0b0\n    y_pred = model(x_train)\n\n    # \uc190\uc2e4 \uacc4\uc0b0\n    loss = criterion(y_pred, y_train)\n\n    # \uacbd\uacfc\ub41c \uc190\uc2e4 \ucd9c\ub825\n    if (epoch + 1) % 10 == 0:\n        print(f'Epoch [{epoch + 1}\/{num_epochs}], Loss: {loss.item():.4f}')\n\n    # \uae30\uc6b8\uae30 \ucd08\uae30\ud654, \uc5ed\uc804\ud30c \ubc0f \uacbd\ub7c9\ud654\n    optimizer.zero_grad()\n    loss.backward()\n    optimizer.step()\n\n# \uc608\uce21 \uc2dc\uac01\ud654\nplt.scatter(x_numpy, y_numpy, label='Data')\nplt.plot(x_numpy, model(x_train).detach().numpy(), color='red', label='Prediction')\nplt.legend()\nplt.show()\n    <\/code><\/pre>\n
\uc704 \ucf54\ub4dc\ub294 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0dd\uc131\ud558\uace0, \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud559\uc2b5\ud558\uc5ec \ucd5c\uc885\uc801\uc73c\ub85c \uc608\uce21 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4. \ud559\uc2b5\uc774 \uc9c4\ud589\ub428\uc5d0 \ub530\ub77c \uc190\uc2e4(loss)\uc774 \uc904\uc5b4\ub4dc\ub294 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc774 \ub370\uc774\ud130\uc758 \ud328\ud134\uc744 \uc798 \ud559\uc2b5\ud558\uace0 \uc788\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3. \ub525\ub7ec\ub2dd \uc2ec\uce35 \uc2e0\uacbd\ub9dd (Deep Neural Networks)<\/h2>\n
\ub525\ub7ec\ub2dd\uc5d0\uc11c\ub294 \ub354 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574 \uc5ec\ub7ec \uce35\uc758 \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uac04\ub2e8\ud55c \ub2e4\uce35 \ud37c\uc149\ud2b8\ub860(Multi-Layer Perceptron, MLP) \uad6c\uc870\ub97c \ud1b5\ud574 \uc774\ub7ec\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. MLP\ub294 \uc785\ub825\uce35, \uc740\ub2c9\uce35, \ucd9c\ub825\uce35\uc73c\ub85c \uad6c\uc131\ub418\uba70, \uac01 \uce35\uc740 \ub178\ub4dc\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \ub178\ub4dc\ub294 \uc774\uc804 \uce35\uc758 \ub178\ub4dc\uc640 \uc5f0\uacb0\ub418\uc5b4 \uc788\uc73c\uba70, \ud65c\uc131\ud654 \ud568\uc218(Activation Function)\ub97c \ud1b5\ud574 \ube44\uc120\ud615\uc131\uc744 \ubd80\uc5ec\ud569\ub2c8\ub2e4.<\/p>\n
3.1. MLP \ubaa8\ub378 \uad6c\ud604\ud558\uae30<\/h3>\n
\nclass NeuralNetwork(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super(NeuralNetwork, self).__init__()\n        self.fc1 = nn.Linear(input_size, hidden_size)  # \uccab \ubc88\uc9f8 \uc740\ub2c9\uce35\n        self.fc2 = nn.Linear(hidden_size, output_size)  # \ucd9c\ub825\uce35\n        self.relu = nn.ReLU()  # \ud65c\uc131\ud654 \ud568\uc218\n\n    def forward(self, x):\n        out = self.fc1(x)\n        out = self.relu(out)\n        out = self.fc2(out)\n        return out\n\n# \ub370\uc774\ud130\uc14b \uc900\ube44\nfrom sklearn.datasets import make_moons\nx, y = make_moons(n_samples=1000, noise=0.2)\n\n# NumPy \ubc30\uc5f4\uc744 PyTorch \ud150\uc11c\ub85c \ubcc0\ud658\nx_train = torch.FloatTensor(x)\ny_train = torch.FloatTensor(y).view(-1, 1)\n\n# \ubaa8\ub378, \uc190\uc2e4 \ud568\uc218, \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758\ninput_size = 2\nhidden_size = 10\noutput_size = 1\n\nmodel = NeuralNetwork(input_size, hidden_size, output_size)\ncriterion = nn.BCEWithLogitsLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# \ud559\uc2b5 \uacfc\uc815\nnum_epochs = 1000\nfor epoch in range(num_epochs):\n    model.train()\n\n    # \uc608\uce21\uac12 \uacc4\uc0b0\n    y_pred = model(x_train)\n\n    # \uc190\uc2e4 \uacc4\uc0b0\n    loss = criterion(y_pred, y_train)\n\n    # \uae30\uc6b8\uae30 \ucd08\uae30\ud654, \uc5ed\uc804\ud30c \ubc0f \uacbd\ub7c9\ud654\n    optimizer.zero_grad()\n    loss.backward()\n    optimizer.step()\n\n    # \uacbd\uacfc\ub41c \uc190\uc2e4 \ucd9c\ub825\n    if (epoch + 1) % 100 == 0:\n        print(f'Epoch [{epoch + 1}\/{num_epochs}], Loss: {loss.item():.4f}')\n    <\/code><\/pre>\n
\uc704 \ucf54\ub4dc\ub294 \uae30\ubcf8\uc801\uc778 \ub2e4\uce35 \ud37c\uc149\ud2b8\ub860 \ubaa8\ub378\uc744 \uc815\uc758\ud558\uace0, 1000\uac1c\uc758 \uc0d8\ud50c\ub85c \uad6c\uc131\ub41c ‘make_moons’ \ub370\uc774\ud130\uc14b\uc744 \ud559\uc2b5\ud558\ub294 \uc608\uc2dc\uc785\ub2c8\ub2e4. ‘BCEWithLogitsLoss’\ub294 \uc774\uc9c4 \ubd84\ub958\ub97c \uc704\ud574 \uc790\uc8fc \uc0ac\uc6a9\ub418\ub294 \uc190\uc2e4 \ud568\uc218\uc785\ub2c8\ub2e4. \ubaa8\ub378\uc774 \ud559\uc2b5\ub418\uba74\uc11c \uc190\uc2e4\uc774 \uac10\uc18c\ud558\ub294 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4. CNN(Convolutional Neural Networks)<\/h2>\n
\uc774\ubbf8\uc9c0\uc640 \uac19\uc740 2D \ub370\uc774\ud130\uc5d0\uc11c\ub294 CNN\uc774 \uc8fc\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. CNN\uc740 \ud569\uc131\uacf1(convolution) \uce35\uacfc \ud480\ub9c1(pooling) \uce35\uc73c\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc73c\uba70, \uc774\ub294 \uc774\ubbf8\uc9c0\uc758 \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud558\ub294 \ub370 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4. \ud569\uc131\uacf1 \uce35\uc740 \uc774\ubbf8\uc9c0\uc5d0\uc11c \uc9c0\uc5ed\uc801 \ud2b9\uc131\uc744 \ucea1\ucc98\ud558\uba70, \ud480\ub9c1 \uce35\uc740 \uc774\ubbf8\uc9c0\uc758 \ud06c\uae30\ub97c \uc904\uc5ec \uc5f0\uc0b0\ub7c9\uc744 \uac10\uc18c\uc2dc\ud0b5\ub2c8\ub2e4.<\/p>\n
4.1. CNN \ubaa8\ub378 \uad6c\ud604\ud558\uae30<\/h3>\n
\nclass CNN(nn.Module):\n    def __init__(self):\n        super(CNN, self).__init__()\n        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)  # \uccab \ubc88\uc9f8 \ud569\uc131\uacf1 \uce35\n        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)  # \ud480\ub9c1 \uce35\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)  # \ub450 \ubc88\uc9f8 \ud569\uc131\uacf1 \uce35\n        self.fc1 = nn.Linear(64 * 7 * 7, 128)  # \uccab \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0\uce35\n        self.fc2 = nn.Linear(128, 10)  # \ucd9c\ub825\uce35\n        \n    def forward(self, x):\n        x = self.pool(F.relu(self.conv1(x)))  # \uccab \ubc88\uc9f8 \ud569\uc131\uacf1 + \ud480\ub9c1\n        x = self.pool(F.relu(self.conv2(x)))  # \ub450 \ubc88\uc9f8 \ud569\uc131\uacf1 + \ud480\ub9c1\n        x = x.view(-1, 64 * 7 * 7)  # \ud3c9\ud0c4\ud654\n        x = F.relu(self.fc1(x))  # \uccab \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0\uce35\n        x = self.fc2(x)  # \ucd9c\ub825\uce35\n        return x\n\n# \uc608\uc81c \ub370\uc774\ud130 \ub85c\ub4dc (MNIST)\nimport torchvision.transforms as transforms\nfrom torchvision import datasets\n\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\ntrain_dataset = datasets.MNIST(root='.\/data', train=True, transform=transform, download=True)\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\n\n# \ubaa8\ub378, \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758\nmodel = CNN()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# \ud559\uc2b5 \uacfc\uc815\nnum_epochs = 5\nfor epoch in range(num_epochs):\n    for images, labels in train_loader:\n        optimizer.zero_grad()  # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n        outputs = model(images)  # \uc608\uce21\uac12 \uacc4\uc0b0\n        loss = criterion(outputs, labels)  # \uc190\uc2e4 \uacc4\uc0b0\n        loss.backward()  # \uc5ed\uc804\ud30c \uc218\ud589\n        optimizer.step()  # \uac00\uc911\uce58 \uac31\uc2e0\n\n    print(f'Epoch [{epoch + 1}\/{num_epochs}], Loss: {loss.item():.4f}')\n    <\/code><\/pre>\n
\uc704 \ucf54\ub4dc\ub294 \uac04\ub2e8\ud55c CNN \ubaa8\ub378\uc744 \uad6c\uc131\ud558\uace0, MNIST \ub370\uc774\ud130\uc14b\uc744 \ud559\uc2b5\ud558\ub294 \uc608\uc2dc\uc785\ub2c8\ub2e4. CNN\uc740 \ud569\uc131\uacf1 \ubc0f \ud480\ub9c1 \uc5f0\uc0b0\uc744 \ud1b5\ud574 \uc774\ubbf8\uc9c0\uc758 \ud2b9\uc131\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5. \uacb0\ub860<\/h2>\n
\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \uba38\uc2e0 \ub7ec\ub2dd \uc120\ud615 \ud68c\uadc0\uc640 \ub2e4\uc591\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ud604\ud574\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud559\uc2b5\uc5d0 \ub9e4\uc6b0 \uc720\uc6a9\ud558\uba70, \ud30c\uc774\ud1a0\uce58\ub294 \uadf8 \uacfc\uc815\uc5d0\uc11c \uc720\uc6a9\ud55c \ud234\uc785\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc774 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud574 \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \uc2e4\ud5d8\ud574\ubcf4\uace0, \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uae4a\uc740 \uc774\ud574\ub97c \uc5bb\uac8c \ub418\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
6. \ucd94\uac00 \uc790\ub8cc<\/h2>\n
\ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc744 \uc54c\uace0 \uc2f6\ub2e4\uba74 \ub2e4\uc74c \uc790\ub8cc\ub97c \ucc38\uace0\ud558\uc138\uc694:<\/p>\n