GPU \uc9c0\uc6d0:<\/strong> GPU\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub300\uaddc\ubaa8 \ub370\uc774\ud130\uc14b\uc744 \ucc98\ub9ac\ud560 \uc218 \uc788\uc73c\uba70, \uc18d\ub3c4\uac00 \ube60\ub985\ub2c8\ub2e4.<\/li>\n<\/ul>\n2. \ud30c\uc774\ud1a0\uce58\uc758 \uc8fc\uc694 \ud2b9\uc9d5<\/h2>\n
\ud30c\uc774\ud1a0\uce58\uc758 \uba87 \uac00\uc9c0 \uc8fc\uc694 \ud2b9\uc9d5\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
2.1. \ub3d9\uc801 \uadf8\ub798\ud504<\/h3>\n
\ud30c\uc774\ud1a0\uce58\ub294 “Define-by-Run” \ubc29\uc2dd\uc758 \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub294 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c
\n \uc2e4\ud589\ud558\ub294 \ub3d9\uc548 \uc2e4\uc2dc\uac04\uc73c\ub85c \uc0dd\uc131\ub418\uba70, \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\ub294 \ub3d9\uc548 \ub514\ubc84\uae45\uc774 \uc6a9\uc774\ud569\ub2c8\ub2e4.<\/p>\n
2.2. \ud150\uc11c(Tensor)<\/h3>\n
\ud30c\uc774\ud1a0\uce58\uc758 \uae30\ubcf8 \ub370\uc774\ud130 \uad6c\uc870\ub294 \ud150\uc11c\uc785\ub2c8\ub2e4. \ud150\uc11c\ub294 \ub2e4\ucc28\uc6d0 \ubc30\uc5f4\ub85c, NumPy \ubc30\uc5f4\uacfc
\n \ub9e4\uc6b0 \uc720\uc0ac\ud558\uc9c0\ub9cc GPU\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc5f0\uc0b0\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud150\uc11c\ub294 \ub2e4\uc591\ud55c \ud06c\uae30\uc640 \ud615\ud0dc\uc758 \ub370\uc774\ud130\ub97c
\n \uc800\uc7a5\ud560 \uc218 \uc788\ub294 \uc911\uc694\ud55c \uc694\uc18c\uc785\ub2c8\ub2e4.<\/p>\n
2.3. \uc790\ub3d9 \ubbf8\ubd84(Autograd)<\/h3>\n
\ud30c\uc774\ud1a0\uce58\ub294 \ubaa8\ub4e0 \uc5f0\uc0b0\uc5d0 \ub300\ud55c \ubbf8\ubd84\uc744 \uc790\ub3d9\uc73c\ub85c \uacc4\uc0b0\ud560 \uc218 \uc788\ub294 Autograd \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.
\n \uc774\ub294 \uc5ed\uc804\ud30c(backpropagation)\ub97c \ud1b5\ud55c\ubaa8\ub378 \ud559\uc2b5\uc744 \uac04\uc18c\ud654\ud569\ub2c8\ub2e4.<\/p>\n
3. \ud30c\uc774\ud1a0\uce58 \uc124\uce58<\/h2>\n
\ud30c\uc774\ud1a0\uce58 \uc124\uce58\ub294 \ub9e4\uc6b0 \uac04\ub2e8\ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
pip install torch torchvision torchaudio<\/code><\/pre>\n\uc774 \uba85\ub839\uc5b4\ub294 PyTorch, torchvision \ubc0f torchaudio\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.
\n torchvision\uc740 \uc774\ubbf8\uc9c0 \ucc98\ub9ac\uc5d0 \uc720\uc6a9\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc774\uace0, torchaudio\ub294 \uc624\ub514\uc624 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294
\n \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
4. \ud30c\uc774\ud1a0\uce58 \uae30\ubcf8 \uc0ac\uc6a9\ubc95<\/h2>\n
\ud30c\uc774\ud1a0\uce58\uc758 \uae30\ubcf8\uc801\uc778 \ud150\uc11c \uc5f0\uc0b0\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \uc608\uc81c\ub294 \ud150\uc11c\ub97c \uc0dd\uc131\ud558\uace0,
\n \uae30\ubcf8\uc801\uc778 \uc5f0\uc0b0\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4:<\/p>\n
\nimport torch\n\n# \ud150\uc11c \uc0dd\uc131\ntensor_a = torch.tensor([[1, 2], [3, 4]])\ntensor_b = torch.tensor([[5, 6], [7, 8]])\n\n# \ud150\uc11c \ub367\uc148\nresult_add = tensor_a + tensor_b\n\n# \ud150\uc11c \uacf1\uc148\nresult_mul = torch.matmul(tensor_a, tensor_b)\n\nprint(\"\ud150\uc11c A:\\n\", tensor_a)\nprint(\"\ud150\uc11c B:\\n\", tensor_b)\nprint(\"\ub367\uc148 \uacb0\uacfc:\\n\", result_add)\nprint(\"\uacf1\uc148 \uacb0\uacfc:\\n\", result_mul)\n <\/code><\/pre>\n4.1. \ud150\uc11c \uc0dd\uc131<\/h3>\n
\uc704\uc758 \ucf54\ub4dc\ub294 2×2 \ud615\ud0dc\uc758 \ub450 \ud150\uc11c\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.
\n \uc774\uc804\uc5d0 \uc0dd\uc131\ud55c \ud150\uc11c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uae30\ubcf8\uc801\uc778 \ub367\uc148\uacfc \uacf1\uc148\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
4.2. \ud150\uc11c \uc5f0\uc0b0<\/h3>\n
\ud150\uc11c \uac04\uc758 \uc5f0\uc0b0\uc740 \ub9e4\uc6b0 \uc9c1\uad00\uc801\uc774\uba70, \ub300\ubd80\ubd84\uc758 \uc120\ud615 \ub300\uc218 \uc5f0\uc0b0\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4.
\n \uc704\uc758 \ucf54\ub4dc\ub97c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n\ud150\uc11c A:\n tensor([[1, 2],\n [3, 4]])\n\ud150\uc11c B:\n tensor([[5, 6],\n [7, 8]])\n\ub367\uc148 \uacb0\uacfc:\n tensor([[ 6, 8],\n [10, 12]])\n\uacf1\uc148 \uacb0\uacfc:\n tensor([[19, 22],\n [43, 50]])\n <\/code><\/pre>\n5. \ud30c\uc774\ud1a0\uce58 \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\ud30c\uc774\ud1a0\uce58\ub85c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4:<\/p>\n
\n- \ub370\uc774\ud130 \uc900\ube44<\/li>\n
- \ubaa8\ub378 \uc815\uc758<\/li>\n
- \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758<\/li>\n
- \ud6c8\ub828 \ub8e8\ud504<\/li>\n
- \uac80\uc99d \ubc0f \ud14c\uc2a4\ud2b8<\/li>\n<\/ol>\n
5.1. \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\uac00\uc7a5 \uba3c\uc800 \ub370\uc774\ud130 \uc900\ube44 \ub2e8\uacc4\uc785\ub2c8\ub2e4. \uc544\ub798\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\ub294 \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
\nfrom torchvision import datasets, transforms\n\n# \ub370\uc774\ud130 \ubcc0\ud658 \uc815\uc758\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n])\n\n# MNIST \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc\ntrain_data = datasets.MNIST(root='data', train=True, download=True, transform=transform)\ntest_data = datasets.MNIST(root='data', train=False, download=True, transform=transform)\n <\/code><\/pre>\n5.2. \ubaa8\ub378 \uc815\uc758<\/h3>\n
\uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ub294 nn.Module \ud074\ub798\uc2a4\ub97c \uc0c1\uc18d\ud558\uc5ec \uc218\ud589\ud569\ub2c8\ub2e4. \uc544\ub798\ub294 \uac04\ub2e8\ud55c
\n \uc644\uc804 \uc5f0\uacb0 \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud558\ub294 \uc608\uc81c\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)\n self.fc2 = nn.Linear(128, 10)\n\n def forward(self, x):\n x = x.view(-1, 28 * 28) # Flatten the input\n x = F.relu(self.fc1(x))\n x = self.fc2(x)\n return x\n <\/code><\/pre>\n5.3. \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758<\/h3>\n
\uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub294 \ubaa8\ub378 \ud559\uc2b5\uc5d0 \ud544\uc218\uc801\uc778 \uc694\uc18c\uc785\ub2c8\ub2e4:<\/p>\n
\nimport torch.optim as optim\n\nmodel = SimpleNN()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n <\/code><\/pre>\n5.4. \ud6c8\ub828 \ub8e8\ud504<\/h3>\n
\ubaa8\ub378\uc744 \ud6c8\ub828\ud558\ub294 \ub8e8\ud504\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc815\uc758\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\nfrom torch.utils.data import DataLoader\n\ntrain_loader = DataLoader(train_data, batch_size=64, shuffle=True)\n\n# \ud6c8\ub828 \ub8e8\ud504\nfor epoch in range(5): # 5 epochs\n for data, target in train_loader:\n optimizer.zero_grad() # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n output = model(data) # \ubaa8\ub378 \uc608\uce21\n loss = criterion(output, target) # \uc190\uc2e4 \uacc4\uc0b0\n loss.backward() # \uc5ed\uc804\ud30c\n optimizer.step() # \ub9e4\uac1c\ubcc0\uc218 \uac31\uc2e0\n print(f'Epoch {epoch+1}, Loss: {loss.item()}')\n <\/code><\/pre>\n5.5. \uac80\uc99d \ubc0f \ud14c\uc2a4\ud2b8<\/h3>\n
\ud6c8\ub828 \ud6c4, \ubaa8\ub378\uc744 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub85c \ud3c9\uac00\ud558\uc5ec \uc131\ub2a5\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\ntest_loader = DataLoader(test_data, batch_size=64, shuffle=False)\n\ncorrect = 0\ntotal = 0\n\nwith torch.no_grad():\n for data, target in test_loader:\n output = model(data)\n _, predicted = torch.max(output.data, 1)\n total += target.size(0)\n correct += (predicted == target).sum().item()\n\nprint(f'Accuracy: {100 * correct \/ total}%')\n <\/code><\/pre>\n6. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\uc758 \uac1c\uc694\uc640 \uae30\ubcf8 \uc0ac\uc6a9\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud588\uc2b5\ub2c8\ub2e4.
\n \ud30c\uc774\ud1a0\uce58\ub294 \ub525\ub7ec\ub2dd \uc5f0\uad6c \ubc0f \uac1c\ubc1c\uc5d0 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ub3c4\uad6c\uc774\uba70, \uadf8 \uc720\uc5f0\uc131\uacfc \uac15\ub825\ud55c \uae30\ub2a5 \ub355\ubd84\uc5d0
\n \ub9ce\uc740 \uc5f0\uad6c\uc790\uc640 \uc5d4\uc9c0\ub2c8\uc5b4\ub4e4\uc774 \uc560\uc6a9\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c
\n \ub2e4\uc591\ud55c \uc2ec\ud654 \uc8fc\uc81c\uc640 \uc2e4\uc81c \uc801\uc6a9 \uc0ac\ub840\ub97c \ub2e4\ub8f0 \uc608\uc815\uc785\ub2c8\ub2e4. \uae30\ub300\ud574 \uc8fc\uc138\uc694!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0 \ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144 \uac04, \uae30\uacc4 \ud559\uc2b5\uc758 \ub9ce\uc740 \ubd80\ubd84\uc774 \uae4a\uc740 \ud559\uc2b5(Deep Learning) \uae30\uc220\ub85c \ubc1c\uc804\ud558\uba74\uc11c \ub370\uc774\ud130 \ubd84\uc11d, \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uadf8 \uac00\ub2a5\uc131\uc744 \ubcf4\uc5ec\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. 1. \ud30c\uc774\ud1a0\uce58\ub780? \ud30c\uc774\ud1a0\uce58(PyTorch)\ub294 \ud398\uc774\uc2a4\ubd81 \uc778\uacf5\uc9c0\ub2a5 \uc5f0\uad6c\uc18c(FAIR)\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc624\ud508 \uc18c\uc2a4 \uba38\uc2e0 \ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uae4a\uc740 \ud559\uc2b5 \ubaa8\ub378\uc744 \uac1c\ubc1c\ud560 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud30c\uc774\ud1a0\uce58 \uac1c\uc694”<\/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-30134","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud30c\uc774\ud1a0\uce58 \uac1c\uc694 - 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