{"id":30140,"date":"2024-10-28T03:19:47","date_gmt":"2024-10-28T03:19:47","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30140"},"modified":"2024-11-26T06:49:44","modified_gmt":"2024-11-26T06:49:44","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-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98%ec%9d%98-%ec%95%84%ed%82%a4%ed%85%8d%ec%b2%98","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30140\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud30c\uc774\ud1a0\uce58\uc758 \uc544\ud0a4\ud14d\ucc98"},"content":{"rendered":"

<\/p>\n

\uc548\ub155\ud558\uc138\uc694, \uc624\ub298\uc740 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\uc778 \ud30c\uc774\ud1a0\uce58(PyTorch)\uc758 \uc544\ud0a4\ud14d\ucc98\uc5d0 \ub300\ud574 \uae4a\uc774 \uc788\uac8c \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \ud30c\uc774\ud1a0\uce58\ub780?<\/h2>\n

\ud30c\uc774\ud1a0\uce58\ub294 \ud398\uc774\uc2a4\ubd81 \uc778\uacf5\uc9c0\ub2a5 \uc5f0\uad6c\uc18c(Facebook AI Research)\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc624\ud508\uc18c\uc2a4 \uba38\uc2e0\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\uc785\ub2c8\ub2e4. \uadf8\ub798\ub514\uc5b8\ud2b8 \uacc4\uc0b0, \uc790\ub3d9\ubbf8\ubd84, \uadf8\ub9ac\uace0 \ud150\uc11c \uc5f0\uc0b0 \ub4f1\uc744 \uc704\ud574 \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uc5f0\uad6c \uc911\uc2ec\uc758 \uc791\uc5c5\uc5d0 \ub9e4\uc6b0 \uc720\uc6a9\ud558\uba70, TensorFlow\uc640 \uac19\uc740 \ub2e4\ub978 \ud504\ub808\uc784\uc6cc\ud06c\uc5d0 \ube44\ud574 \ub354\uc6b1 \uc9c1\uad00\uc801\uc774\uace0 \ud30c\uc774\uc36cic\ud55c \ubb38\ubc95\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

2. \ud30c\uc774\ud1a0\uce58\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n

2.1 \ud150\uc11c(Tensor)<\/h3>\n

\ud30c\uc774\ud1a0\uce58\uc758 \uae30\ubcf8 \ub370\uc774\ud130 \uad6c\uc870\ub294 tensor\uc785\ub2c8\ub2e4. \ud150\uc11c\ub294 \ub2e4\ucc28\uc6d0 \ubc30\uc5f4\ub85c, numpy \ubc30\uc5f4\uacfc \uc720\uc0ac\ud558\uc9c0\ub9cc GPU\uc5d0\uc11c \ub354 \ube60\ub974\uac8c \uc5f0\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud150\uc11c\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

import torch\n\n# 1\ucc28\uc6d0 \ud150\uc11c \uc0dd\uc131\ntensor_1d = torch.tensor([1, 2, 3, 4, 5])\nprint(tensor_1d)\n\n# 2\ucc28\uc6d0 \ud150\uc11c \uc0dd\uc131\ntensor_2d = torch.tensor([[1, 2], [3, 4]])\nprint(tensor_2d)<\/code><\/pre>\n
2.2 \uc624\ud1a0\uadf8\ub798\ub514\uc5b8\ud2b8(Autograd)<\/h3>\n
\uc624\ud1a0\uadf8\ub798\ub514\uc5b8\ud2b8\ub294 \ud30c\uc774\ud1a0\uce58\uc758 \uc790\ub3d9 \ubbf8\ubd84 \uae30\ub2a5\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \ubaa8\ub4e0 \ud150\uc11c\uc5d0 \ub300\ud574 requires_grad<\/code> \uc18d\uc131\uc744 \uc124\uc815\ud558\uc5ec \uadf8\ub798\ub514\uc5b8\ud2b8\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
x = torch.ones(2, 2, requires_grad=True)\ny = x + 2\nz = y * y * 3\nout = z.mean()\n\nprint(out)\n\n# \uc5ed\uc804\ud30c\nout.backward()\nprint(x.grad)<\/code><\/pre>\n
3. \ud30c\uc774\ud1a0\uce58 \uc544\ud0a4\ud14d\ucc98 \uc0b4\ud3b4\ubcf4\uae30<\/h2>\n
\ud30c\uc774\ud1a0\uce58\ub294 \uae4a\uc740 \uc2e0\uacbd\ub9dd\uc744 \uc0dd\uc131\ud558\uae30 \uc704\ud55c \uc5ec\ub7ec \uad6c\uc131 \uc694\uc18c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc0ac\uc6a9\uc790\ub294 \uc0c8\ub85c\uc6b4 \ubaa8\ub378\uc744 \ud6a8\uc728\uc801\uc73c\ub85c \uc124\uacc4\ud558\uace0 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \ubaa8\ub4c8(Module)<\/h3>\n
\ubaa8\ub4c8\uc740 \ud30c\uc774\ud1a0\uce58\uc758 \uae30\ubcf8 \uad6c\uc131 \uc694\uc18c\ub85c, nn.Module \ud074\ub798\uc2a4\ub97c \uc0c1\uc18d\ubc1b\uc544 \uad6c\ud604\ud569\ub2c8\ub2e4. \uac01 \uc2e0\uacbd\ub9dd \ub808\uc774\uc5b4\ub294 \ubaa8\ub4c8\ub85c \uad6c\ud604\ub429\ub2c8\ub2e4:<\/p>\n
import torch.nn as nn\n\nclass MyModel(nn.Module):\n    def __init__(self):\n        super(MyModel, self).__init__()\n        self.fc1 = nn.Linear(in_features=2, out_features=2)\n\n    def forward(self, x):\n        return self.fc1(x)<\/code><\/pre>\n
3.2 \uc190\uc2e4 \ud568\uc218(Loss Function)<\/h3>\n
\uc190\uc2e4 \ud568\uc218\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \uc9c0\ud45c\ub85c, \uc608\uce21\uac12\uacfc \uc2e4\uc81c\uac12\uc758 \ucc28\uc774\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ub2e4\uc591\ud55c \uc190\uc2e4 \ud568\uc218\uac00 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
loss_fn = nn.MSELoss()\n\n# \uc608\uce21\uac12\uacfc \uc2e4\uc81c\uac12\ny_pred = torch.tensor([0.0, 1.0])\ny_true = torch.tensor([0.5, 0.5])\nloss = loss_fn(y_pred, y_true)\nprint(loss)<\/code><\/pre>\n
3.3 \ucd5c\uc801\ud654(Optimization)<\/h3>\n
\ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c\ub294 \ub2e4\uc591\ud55c \ucd5c\uc801\ud654 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud6a8\uc728\uc801\uc73c\ub85c \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
import torch.optim as optim\n\nmodel = MyModel()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n\n# \ud559\uc2b5 \uacfc\uc815\nfor epoch in range(100):\n    optimizer.zero_grad()  # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n    y_pred = model(torch.tensor([[1.0, 2.0]]))  # \ubaa8\ub378 \uc608\uce21\n    loss = loss_fn(y_pred, torch.tensor([[0.0, 1.0]]))  # \uc190\uc2e4 \uacc4\uc0b0\n    loss.backward()  # \uc5ed\uc804\ud30c\n    optimizer.step()  # \ud30c\ub77c\ubbf8\ud130 \uc5c5\ub370\uc774\ud2b8<\/code><\/pre>\n
4. \uc2e4\uc2b5: \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \ub9cc\ub4e4\uae30<\/h2>\n
\uc774\uc81c \uc704\uc5d0\uc11c \uc124\uba85\ud55c \ub0b4\uc6a9\uc744 \ubc14\ud0d5\uc73c\ub85c \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd\uc744 \ub9cc\ub4e4\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uc2e0\uacbd\ub9dd\uc740 \uc22b\uc790 \uc774\ubbf8\uc9c0 \ubd84\ub958 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uac83\uc785\ub2c8\ub2e4. \uac00\uc838\uc628 \ub370\uc774\ud130\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
4.1 \ub370\uc774\ud130\uc14b \uc900\ube44\ud558\uae30<\/h3>\n
from torchvision import datasets, transforms\n\ntransform = transforms.Compose([transforms.ToTensor()])\ntrain_dataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=32, shuffle=True)<\/code><\/pre>\n
4.2 \uc2e0\uacbd\ub9dd \uc544\ud0a4\ud14d\ucc98 \uc815\uc758\ud558\uae30<\/h3>\n
class SimpleNN(nn.Module):\n        def __init__(self):\n            super(SimpleNN, self).__init__()\n            self.fc1 = nn.Linear(784, 128)  # 28*28=784\n            self.fc2 = nn.Linear(128, 10)\n\n        def forward(self, x):\n            x = x.view(-1, 784)  # Flatten\n            x = torch.relu(self.fc1(x))\n            x = self.fc2(x)\n            return x<\/code><\/pre>\n
4.3 \ubaa8\ub378 \ud6c8\ub828\ud558\uae30<\/h3>\n
model = SimpleNN()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\nloss_fn = nn.CrossEntropyLoss()\n\nfor epoch in range(5):\n    for images, labels in train_loader:\n        optimizer.zero_grad()\n        outputs = model(images)\n        loss = loss_fn(outputs, labels)\n        loss.backward()\n        optimizer.step()\n\n    print(f'Epoch [{epoch + 1}\/5], Loss: {loss.item():.4f}')<\/code><\/pre>\n
5. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uac15\uc88c\ub97c \ud1b5\ud574 \ud30c\uc774\ud1a0\uce58\uc758 \uc544\ud0a4\ud14d\ucc98\uc640 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uad6c\uc131\ud558\ub294 \ub2e4\uc591\ud55c \uc694\uc18c\ub4e4\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uadf8 \uc9c1\uad00\uc801\uc778 \uc544\uc774\ub514\uc5b4\uc640 \uc720\uc5f0\uc131 \ub355\ubd84\uc5d0 \ub525\ub7ec\ub2dd \uc5f0\uad6c \ubc0f \uac1c\ubc1c\uc5d0 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c \ub2e4\uc591\ud55c \ud504\ub85c\uc81d\ud2b8\uc5d0 \ub3c4\uc804\ud574 \ubcf4\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
6. \ucc38\uace0 \uc790\ub8cc<\/h2>\n