{"id":30160,"date":"2024-10-28T03:19:52","date_gmt":"2024-10-28T03:19:52","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30160"},"modified":"2024-11-26T06:49:40","modified_gmt":"2024-11-26T06:49:40","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%9b%88%eb%a0%a8-%ed%8f%89%ea%b0%80","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30160\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud6c8\ub828 \ud3c9\uac00"},"content":{"rendered":"

<\/p>\n

\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\ub85c, \ubcf5\uc7a1\ud55c \ub370\uc774\ud130\uc758 \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud558\uace0 \ud328\ud134\uc744 \ucc3e\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58(PyTorch)\ub294 \uc774\ub7ec\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ud604\ud558\ub294 \ub370 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \ud30c\uc774\uc36c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uace0 \ud3c9\uac00\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \ub525\ub7ec\ub2dd \ubaa8\ub378 \ud6c8\ub828 \uac1c\uc694<\/h2>\n

\ub525\ub7ec\ub2dd \ubaa8\ub378 \ud6c8\ub828 \uacfc\uc815\uc740 \ud06c\uac8c 3\ub2e8\uacc4\ub85c \ub098\ub20c \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

    \n
  1. \ubaa8\ub378 \uc815\uc758: \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\uc5d0 \uc801\ud569\ud55c \uc2e0\uacbd\ub9dd \uad6c\uc870\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.<\/li>\n
  2. \ud6c8\ub828: \ubaa8\ub378\uc744 \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\uc5d0 \ub9de\ucdb0 \ucd5c\uc801\ud654\ud569\ub2c8\ub2e4.<\/li>\n
  3. \ud3c9\uac00: \ud6c8\ub828\ub41c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uac80\uc99d\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n

    2. \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h2>\n

    \uba3c\uc800, \ud30c\uc774\ud1a0\uce58\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. Anaconda \ub97c \uc0ac\uc6a9\ud558\ub294 \uacbd\uc6b0 \ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

    conda install pytorch torchvision torchaudio -c pytorch<\/code><\/pre>\n
    3. \ub370\uc774\ud130\uc14b \uc900\ube44<\/h2>\n
    \uc774\ubc88 \uc608\uc81c\ub85c\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. MNIST\ub294 \uc190\uc73c\ub85c \uc4f4 \uc22b\uc790 \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc14b\uc73c\ub85c, \ub525\ub7ec\ub2dd \ubaa8\ub378 \ud6c8\ub828\uc5d0 \ube48\ubc88\ud788 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
    3.1. \ub370\uc774\ud130\uc14b \ub85c\ub4dc \ubc0f \uc804\ucc98\ub9ac<\/h3>\n
    \ud30c\uc774\ud1a0\uce58\uc758 torchvision \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec MNIST \ub370\uc774\ud130\uc14b\uc744 \uc27d\uac8c \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud558\uace0 \uc804\ucc98\ub9ac\ud558\ub294 \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
    \nimport torch\nfrom torchvision import datasets, transforms\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac: \uc774\ubbf8\uc9c0\uc758 \ud06c\uae30\ub97c \uc870\uc815\ud558\uace0 \uc815\uaddc\ud654\ud569\ub2c8\ub2e4.\ntransform = transforms.Compose([\n    transforms.Resize((28, 28)),\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,))\n])\n\n# \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc \ubc0f \ub85c\ub4dc\ntrain_dataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntest_dataset = datasets.MNIST(root='.\/data', train=False, download=True, transform=transform)\n\n# \ub370\uc774\ud130 \ub85c\ub354 \uc0dd\uc131\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\ntest_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)\n    <\/code><\/pre>\n
    4. \ubaa8\ub378 \uc815\uc758<\/h2>\n
    \uc774\uc81c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uac04\ub2e8\ud55c \uc644\uc804 \uc5f0\uacb0 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub294 \ubaa8\ub378\uc744 \uc815\uc758\ud558\ub294 \ubd80\ubd84\uc785\ub2c8\ub2e4:<\/p>\n
    \nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass SimpleNN(nn.Module):\n    def __init__(self):\n        super(SimpleNN, self).__init__()\n        self.fc1 = nn.Linear(28 * 28, 128)  # \uccab \ubc88\uc9f8 \uc740\ub2c9\uce35\n        self.fc2 = nn.Linear(128, 64)        # \ub450 \ubc88\uc9f8 \uc740\ub2c9\uce35\n        self.fc3 = nn.Linear(64, 10)         # \ucd9c\ub825\uce35\n        \n    def forward(self, x):\n        x = x.view(-1, 28 * 28)  # 1D \ud150\uc11c\ub85c \ubcc0\ud658\n        x = F.relu(self.fc1(x))  # \ud65c\uc131\ud654 \ud568\uc218 \uc801\uc6a9\n        x = F.relu(self.fc2(x))\n        x = self.fc3(x)           # \ucd5c\uc885 \ucd9c\ub825\n        return x\n    <\/code><\/pre>\n
    5. \ubaa8\ub378 \ud6c8\ub828<\/h2>\n
    \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uae30 \uc704\ud574 \uc190\uc2e4 \ud568\uc218(loss function)\uc640 \ucd5c\uc801\ud654 \uae30\ubc95(optimizer)\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. CrossEntropyLoss\uc640 Adam optimizer\ub97c \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ud6c8\ub828 \uacfc\uc815\uc744 \uad6c\ud604\ud55c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
    \n# \ubaa8\ub378, \uc190\uc2e4 \ud568\uc218, optimizer \ucd08\uae30\ud654\nmodel = SimpleNN()\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n\n# \ud6c8\ub828 \ub8e8\ud504\nnum_epochs = 5\n\nfor epoch in range(num_epochs):\n    for i, (images, labels) in enumerate(train_loader):\n        # Forward pass\n        outputs = model(images)\n        loss = criterion(outputs, labels)\n\n        # Backward pass \ubc0f \ucd5c\uc801\ud654\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        if (i+1) % 100 == 0:\n            print(f'Epoch [{epoch+1}\/{num_epochs}], Step [{i+1}\/{len(train_loader)}], Loss: {loss.item():.4f}')\n    <\/code><\/pre>\n
    6. \ubaa8\ub378 \ud3c9\uac00<\/h2>\n
    \ud6c8\ub828\uc744 \ub9c8\uce5c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ubaa8\ub378 \ud3c9\uac00\ub97c \uc704\ud55c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
    \n# \ubaa8\ub378 \ud3c9\uac00\nmodel.eval()  # \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc124\uc815\nwith torch.no_grad():\n    correct = 0\n    total = 0\n    for images, labels in test_loader:\n        outputs = model(images)\n        _, predicted = torch.max(outputs.data, 1)\n        total += labels.size(0)\n        correct += (predicted == labels).sum().item()\n\n    print(f'Accuracy of the model on the 10000 test images: {100 * correct \/ total:.2f}%')\n    <\/code><\/pre>\n
    7. \uacb0\uacfc \ubd84\uc11d<\/h2>\n
    \ubaa8\ub378\uc758 \ud3c9\uac00 \uacb0\uacfc, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc5d0\uc11c\uc758 \uc815\ud655\ub3c4\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \ub354\uc6b1 \ud5a5\uc0c1\ub41c \uc131\ub2a5\uc744 \ubaa9\ud45c\ub85c \ub2e4\uc591\ud55c \uae30\ubc95\uc744 \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4:<\/p>\n