{"id":30118,"date":"2024-10-28T03:19:40","date_gmt":"2024-10-28T03:19:40","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30118"},"modified":"2024-11-26T06:49:53","modified_gmt":"2024-11-26T06:49:53","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-%ec%bd%94%eb%9e%a9%ec%97%90%ec%84%9c-%ec%98%88%ec%a0%9c-%ed%8c%8c%ec%9d%bc-%ec%8b%a4%ed%96%89","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30118\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ucf54\ub7a9\uc5d0\uc11c \uc608\uc81c \ud30c\uc77c \uc2e4\ud589"},"content":{"rendered":"<p><body><\/p>\n<div class=\"section\">\n<p>\uc548\ub155\ud558\uc138\uc694! \uc774\ubc88 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 <strong>\ub525\ub7ec\ub2dd<\/strong>\uacfc <strong>\ud30c\uc774\ud1a0\uce58(PyTorch)<\/strong>\uc758 \uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud574 \uc2e4\uc2b5\ud560 \uc218 \uc788\ub294 \ucf54\ub4dc\ub97c \uc791\uc131\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub610\ud55c \uad6c\uae00 \ucf54\ub7a9(Google Colab)\uc744 \ud65c\uc6a9\ud558\uc5ec \ucf54\ub4dc \uc2e4\ud589 \ubc29\ubc95\ub3c4 \uc548\ub0b4\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uae4a\uc774 \uc788\ub294 \ud559\uc2b5 \ubc0f \uc5f0\uad6c\uc5d0 \uc801\ud569\ud55c \uae4a\uc740 \ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \uc9c1\uad00\uc801\uc774\uace0 \uc720\uc5f0\ud55c \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uadf8\ub798\ud504\uc758 \uac01 \ub2e8\uacc4\uc5d0\uc11c \ub354\ud574\uc9c0\ub294 \uc8fc\uc694 \ud2b9\uc9d5\uc740 \uc5f0\uad6c\uc790 \ubc0f \uc5d4\uc9c0\ub2c8\uc5b4\uac00 \ud544\uc694\uc5d0 \ub530\ub77c \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \uc218\uc815\ud558\uace0 \ucd5c\uc801\ud654\ud560 \uc218 \uc788\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<\/div>\n<div class=\"section\">\n<h2>1. \ub525\ub7ec\ub2dd \uac1c\uc694<\/h2>\n<p>\ub525\ub7ec\ub2dd\uc740 \uae30\uacc4 \ud559\uc2b5\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \ud65c\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc74c\uc131 \uc778\uc2dd \ub4f1\uc5d0 \ud65c\uc6a9\ub429\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc73c\ub85c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \uc785\ub825 \ub370\uc774\ud130\ub97c \ubc1b\uc544 \ucc98\ub9ac\ud558\uace0, \uacb0\uacfc\ub97c \ucd9c\ub825\ud558\ub294 \uad6c\uc870\ub97c \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\uc740 \uc218\ub9ce\uc740 \ub274\ub7f0\uc73c\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc5b4 \uac01 \ub274\ub7f0\uc740 \uc785\ub825\ubc1b\uc740 \uac12\uc744 \uac00\uc911\uce58\uc640 \ud568\uaed8 \ucc98\ub9ac\ud558\uc5ec \ucd9c\ub825\ud569\ub2c8\ub2e4.<\/p>\n<\/div>\n<div class=\"section\">\n<h2>2. \ud30c\uc774\ud1a0\uce58\ub780?<\/h2>\n<p>\ud30c\uc774\ud1a0\uce58\ub294 Facebook\uc774 \uac1c\ubc1c\ud55c \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \ud2b9\ud788 Pythonic\ud55c \ucf54\ub4dc \uc791\uc131\uc774 \uac00\ub2a5\ud558\ub2e4\ub294 \uc810\uc5d0\uc11c \uc778\uae30 \uc788\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc758 \uc7a5\uc810 \uc911 \ud558\ub098\ub294 \uc9c1\uad00\uc801\uc778 \uc778\ud130\ud398\uc774\uc2a4\uc640 \uac15\ub825\ud55c GPU \uac00\uc18d \uae30\ub2a5\uc774\uace0, \uc774\ub97c \ud1b5\ud574 \ub300\uaddc\ubaa8 \ub370\uc774\ud130\uc640 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \ud6a8\uc728\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504(Dynamic Computation Graph)\ub97c \uc9c0\uc6d0\ud558\uc5ec \ubaa8\ub378\uc758 \uad6c\uc870\ub97c \uc720\uc5f0\ud558\uac8c \ubcc0\uacbd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/div>\n<div class=\"section\">\n<h2>3. \uad6c\uae00 \ucf54\ub7a9 \uc124\uc815\ud558\uae30<\/h2>\n<p>\uad6c\uae00 \ucf54\ub7a9\uc740 \ud30c\uc774\uc36c \ucf54\ub4dc\ub97c \uc2e4\ud589\ud560 \uc218 \uc788\ub294 \uc628\ub77c\uc778 \ud658\uacbd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. CUDA\ub97c \uc0ac\uc6a9\ud558\uc5ec GPU \uac00\uc18d\uc744 \uc9c0\uc6d0\ud558\ubbc0\ub85c \uc9e7\uc740 \uc2dc\uac04 \uc548\uc5d0 \ubaa8\ub378 \ud559\uc2b5\uc744 \uc644\ub8cc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<ol>\n<li>\uad6c\uae00 \uacc4\uc815\uc73c\ub85c \ub85c\uadf8\uc778\ud569\ub2c8\ub2e4.<\/li>\n<li><a href=\"https:\/\/colab.research.google.com\/\" target=\"_blank\" rel=\"noopener\">Google Colab<\/a>\uc5d0 \uc811\uc18d\ud569\ub2c8\ub2e4.<\/li>\n<li>\uc0c8 \ub178\ud2b8\ubd81\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n<li>\uc0c1\ub2e8 \uba54\ub274\uc5d0\uc11c &#8216;\ub7f0\ud0c0\uc784&#8217; -&gt; &#8216;\ub7f0\ud0c0\uc784 \uc720\ud615 \ubcc0\uacbd&#8217;\uc744 \ud074\ub9ad\ud558\uc5ec GPU\ub97c \uc120\ud0dd\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n<p>\uc774\uc81c \ucf54\ub7a9 \ud658\uacbd\uc774 \uc900\ube44\ub418\uc5c8\uc2b5\ub2c8\ub2e4!<\/p>\n<\/div>\n<div class=\"section\">\n<h2>4. \uae30\ubcf8\uc801\uc778 \ud30c\uc774\ud1a0\uce58 \uc0ac\uc6a9\ubc95<\/h2>\n<h3>4.1. \ud30c\uc774\ud1a0\uce58 \uc124\uce58\ud558\uae30<\/h3>\n<p>\ucf54\ub7a9\uc5d0\uc11c\ub294 \uae30\ubcf8\uc801\uc73c\ub85c \ud30c\uc774\ud1a0\uce58\uac00 \uc124\uce58\ub418\uc5b4 \uc788\uc9c0\ub9cc, \ucd5c\uc2e0 \ubc84\uc804\uc744 \uc6d0\ud558\uc2e0\ub2e4\uba74 \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>!pip install torch torchvision<\/code><\/pre>\n<h3>4.2. \ud150\uc11c(Tensor)<\/h3>\n<p>\ud150\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\uc758 \ud575\uc2ec \ub370\uc774\ud130 \uad6c\uc870\uc785\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc73c\ub85c N\ucc28\uc6d0 \ubc30\uc5f4\ub85c \uc218\ud559\uc801 \uc5f0\uc0b0\uc744 \uc9c0\uc6d0\ud558\uba70, \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4:<\/p>\n<ul>\n<li>CPU\uc640 GPU\uc758 \uc774\uc2dd\uc131<\/li>\n<li>\uc790\ub3d9 \ubbf8\ubd84 \uae30\ub2a5 \uc81c\uacf5<\/li>\n<\/ul>\n<h4>\ud150\uc11c \uc0dd\uc131\ud558\uae30<\/h4>\n<p>\uc544\ub798 \ucf54\ub4dc\ub294 \uae30\ubcf8\uc801\uc778 \ud150\uc11c \uc0dd\uc131 \uc608\uc2dc\uc785\ub2c8\ub2e4.<\/p>\n<pre><code>import torch\n\n# \uae30\ubcf8\uc801\uc778 \ud150\uc11c \uc0dd\uc131\ntensor_1d = torch.tensor([1.0, 2.0, 3.0])\ntensor_2d = torch.tensor([[1.0, 2.0], [3.0, 4.0]])\nprint(tensor_1d)\nprint(tensor_2d)<\/code><\/pre>\n<\/div>\n<div class=\"section\">\n<h2>5. \ub525\ub7ec\ub2dd \ubaa8\ub378 \uac1c\ubc1c\ud558\uae30<\/h2>\n<p>\uc774\uc81c \uc2e4\uc81c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uac04\ub2e8\ud55c <strong>\uc2ec\uce35 \uc2e0\uacbd\ub9dd(DNN)<\/strong>\uc744 \uad6c\ud604\ud558\uace0, MNIST \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud558\uc5ec \uc190\uae00\uc528 \uc22b\uc790\ub97c \uc778\uc2dd\ud558\ub294 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>5.1. MNIST \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc<\/h3>\n<p>MNIST \ub370\uc774\ud130\uc14b\uc740 \uc190\uae00\uc528 \uc22b\uc790 \ub370\uc774\ud130\uc14b\uc73c\ub85c, \uc77c\ubc18\uc801\uc73c\ub85c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub97c \ud1b5\ud574 \uac04\ub2e8\ud558\uac8c \ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud558\uace0 \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>from torchvision import datasets, transforms\n\n# \ub370\uc774\ud130\uc14b \ubcc0\ud658 \uc815\uc758\ntransform = transforms.Compose([\n    transforms.ToTensor(),  # \ud150\uc11c\ub85c \ubcc0\ud658\n    transforms.Normalize((0.5,), (0.5,))  # \uc815\uaddc\ud654\n])\n\n# MNIST \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\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(train_dataset, batch_size=64, shuffle=True)\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)<\/code><\/pre>\n<h3>5.2. \ubaa8\ub378 \uc815\uc758<\/h3>\n<p>\uc544\ub798 \ucf54\ub4dc\ub294 \uac04\ub2e8\ud55c \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud558\ub294 \uc608\uc2dc\uc785\ub2c8\ub2e4.<\/p>\n<pre><code>import torch.nn as nn\n\nclass SimpleNN(nn.Module):\n    def __init__(self):\n        super(SimpleNN, self).__init__()\n        self.fc1 = nn.Linear(28 * 28, 128)  # \uc785\ub825\uce35\n        self.fc2 = nn.Linear(128, 64)        # \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)  # 2D -&gt; 1D\ub85c \ubcc0\ud658\n        x = torch.relu(self.fc1(x))  # \ud65c\uc131\ud654 \ud568\uc218\n        x = torch.relu(self.fc2(x))\n        x = self.fc3(x)\n        return x\n\nmodel = SimpleNN()<\/code><\/pre>\n<\/div>\n<div class=\"section\">\n<h2>6. \ubaa8\ub378 \ud559\uc2b5 \ubc0f \ud3c9\uac00<\/h2>\n<h3>6.1. \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815\ud558\uae30<\/h3>\n<p>\ubaa8\ub378 \ud6c8\ub828\uc744 \uc704\ud574 \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ubd84\ub958 \ubb38\uc81c\uc5d0\ub294 Cross Entropy Loss\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>criterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)<\/code><\/pre>\n<h3>6.2. \ubaa8\ub378 \ud559\uc2b5\ud558\uae30<\/h3>\n<p>\ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<pre><code>num_epochs = 5\n\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)  # \ubaa8\ub378 \uc608\uce21\n        loss = criterion(outputs, labels)  # \uc190\uc2e4 \uacc4\uc0b0\n        loss.backward()  # \uae30\uc6b8\uae30 \uacc4\uc0b0\n        optimizer.step()  # \uac00\uc911\uce58 \uc5c5\ub370\uc774\ud2b8\n    \n    print(f'Epoch [{epoch + 1}\/{num_epochs}], Loss: {loss.item():.4f}')  # \uc190\uc2e4 \ucd9c\ub825<\/code><\/pre>\n<\/div>\n<div class=\"section\">\n<h2>7. \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00\ud558\uae30<\/h2>\n<p>\ubaa8\ub378\uc744 \ud559\uc2b5\ud55c \ud6c4, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>correct = 0\ntotal = 0\n\nwith torch.no_grad():  # \uadf8\ub798\ub514\uc5b8\ud2b8 \uacc4\uc0b0 \ube44\ud65c\uc131\ud654\n    for images, labels in test_loader:\n        outputs = model(images)\n        _, predicted = torch.max(outputs.data, 1)  # \uac00\uc7a5 \ub192\uc740 \ud655\ub960\uc758 \ud074\ub798\uc2a4\n        total += labels.size(0)\n        correct += (predicted == labels).sum().item()\n\nprint(f'Accuracy of the model on the test images: {100 * correct \/ total:.2f}%')  # \uc815\ud655\ub3c4 \ucd9c\ub825<\/code><\/pre>\n<\/div>\n<div class=\"section\">\n<h2>8. \uacb0\ub860<\/h2>\n<p>\uc774\ubc88 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0, \uad6c\uae00 \ucf54\ub7a9\uc5d0\uc11c \uc2e4\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4 \ubcf4\ub2e4 \ubcf5\uc7a1\ud55c \ubaa8\ub378 \ubc0f \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc14b\uc744 \ub2e4\ub8e8\uace0, \uc804\uc774 \ud559\uc2b5\uc774\ub098 \uac15\ud654 \ud559\uc2b5 \uac19\uc740 \uace0\uae09 \ud14d\uc2a4\ud2b8\ub3c4 \ud559\uc2b5\ud574\ubcf4\uba74 \uc88b\uc2b5\ub2c8\ub2e4. \ub354 \uae4a\uc740 \uc774\ud574\uc640 \uacbd\ud5d8\uc744 \uc313\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \ud504\ub85c\uc81d\ud2b8\uc5d0 \ub3c4\uc804\ud574 \ubcf4\uc138\uc694!<\/p>\n<\/div>\n<div class=\"section\">\n<h2>9. \ucc38\uace0\uc790\ub8cc<\/h2>\n<ul>\n<li><a href=\"https:\/\/pytorch.org\/docs\/stable\/index.html\" target=\"_blank\" rel=\"noopener\">PyTorch \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n<li><a href=\"https:\/\/colab.research.google.com\/\" target=\"_blank\" rel=\"noopener\">Google Colab<\/a><\/li>\n<li><a href=\"http:\/\/yann.lecun.com\/exdb\/mnist\/\" target=\"_blank\" rel=\"noopener\">MNIST \ub370\uc774\ud130\uc14b<\/a><\/li>\n<\/ul>\n<\/div>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\uc548\ub155\ud558\uc138\uc694! \uc774\ubc88 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uacfc \ud30c\uc774\ud1a0\uce58(PyTorch)\uc758 \uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud574 \uc2e4\uc2b5\ud560 \uc218 \uc788\ub294 \ucf54\ub4dc\ub97c \uc791\uc131\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub610\ud55c \uad6c\uae00 \ucf54\ub7a9(Google Colab)\uc744 \ud65c\uc6a9\ud558\uc5ec \ucf54\ub4dc \uc2e4\ud589 \ubc29\ubc95\ub3c4 \uc548\ub0b4\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uae4a\uc774 \uc788\ub294 \ud559\uc2b5 \ubc0f \uc5f0\uad6c\uc5d0 \uc801\ud569\ud55c \uae4a\uc740 \ud559\uc2b5 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \uc9c1\uad00\uc801\uc774\uace0 \uc720\uc5f0\ud55c \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uadf8\ub798\ud504\uc758 \uac01 \ub2e8\uacc4\uc5d0\uc11c \ub354\ud574\uc9c0\ub294 \uc8fc\uc694 \ud2b9\uc9d5\uc740 \uc5f0\uad6c\uc790 \ubc0f \uc5d4\uc9c0\ub2c8\uc5b4\uac00 \ud544\uc694\uc5d0 \ub530\ub77c \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \uc218\uc815\ud558\uace0 \ucd5c\uc801\ud654\ud560 \uc218 \uc788\ub2e4\ub294 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/30118\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd 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