{"id":30134,"date":"2024-10-28T03:19:45","date_gmt":"2024-10-28T03:19:45","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30134"},"modified":"2024-11-26T06:49:47","modified_gmt":"2024-11-26T06:49:47","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-%ea%b0%9c%ec%9a%94","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30134\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud30c\uc774\ud1a0\uce58 \uac1c\uc694"},"content":{"rendered":"<p><body><\/p>\n<p>\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.<br \/>\n    \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.<\/p>\n<h2>1. \ud30c\uc774\ud1a0\uce58\ub780?<\/h2>\n<p>\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.<br \/>\n    \ud30c\uc774\ud1a0\uce58\ub294 \uae4a\uc740 \ud559\uc2b5 \ubaa8\ub378\uc744 \uac1c\ubc1c\ud560 \ub54c \uc790\uc5f0\uc2a4\ub7fd\uace0 \uc9c1\uad00\uc801\uc778 \ubc29\ubc95\uc73c\ub85c \uc5f0\uad6c\uc790\uc640 \uac1c\ubc1c\uc790 \ubaa8\ub450\uc5d0\uac8c \uc778\uae30\ub97c \ub04c\uace0 \uc788\uc2b5\ub2c8\ub2e4.<br \/>\n    \uc774\ub294 \uc8fc\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \uc774\uc720 \ub54c\ubb38\uc785\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\uc720\uc5f0\uc131:<\/strong> \ud30c\uc774\ud1a0\uce58\ub294 \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504(dynamic computation graph)\ub97c \uc0ac\uc6a9\ud558\uc5ec,<br \/>\n            \ubaa8\ub378\uc758 \uad6c\uc870\ub97c \uc790\uc720\ub86d\uac8c \ubcc0\uacbd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uc720\uc5f0\ud55c \ubaa8\ub378 \uc124\uacc4\ub97c \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc0ac\uc6a9\uc790 \uce5c\ud654\uc801:<\/strong> \uc9c1\uad00\uc801\uc778 API \uc124\uacc4\ub85c, \ud30c\uc774\uc36c \uc0ac\uc6a9\uc790\uc5d0\uac8c \uce5c\uc219\ud55c \ud658\uacbd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>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>\n<h2>2. \ud30c\uc774\ud1a0\uce58\uc758 \uc8fc\uc694 \ud2b9\uc9d5<\/h2>\n<p>\ud30c\uc774\ud1a0\uce58\uc758 \uba87 \uac00\uc9c0 \uc8fc\uc694 \ud2b9\uc9d5\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2.1. \ub3d9\uc801 \uadf8\ub798\ud504<\/h3>\n<p>\ud30c\uc774\ud1a0\uce58\ub294 &#8220;Define-by-Run&#8221; \ubc29\uc2dd\uc758 \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub294 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c<br \/>\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<h3>2.2. \ud150\uc11c(Tensor)<\/h3>\n<p>\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<br \/>\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<br \/>\n    \uc800\uc7a5\ud560 \uc218 \uc788\ub294 \uc911\uc694\ud55c \uc694\uc18c\uc785\ub2c8\ub2e4.<\/p>\n<h3>2.3. \uc790\ub3d9 \ubbf8\ubd84(Autograd)<\/h3>\n<p>\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.<br \/>\n    \uc774\ub294 \uc5ed\uc804\ud30c(backpropagation)\ub97c \ud1b5\ud55c\ubaa8\ub378 \ud559\uc2b5\uc744 \uac04\uc18c\ud654\ud569\ub2c8\ub2e4.<\/p>\n<h2>3. \ud30c\uc774\ud1a0\uce58 \uc124\uce58<\/h2>\n<p>\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<pre class=\"example\"><code>pip install torch torchvision torchaudio<\/code><\/pre>\n<p>\uc774 \uba85\ub839\uc5b4\ub294 PyTorch, torchvision \ubc0f torchaudio\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.<br \/>\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<br \/>\n    \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n<h2>4. \ud30c\uc774\ud1a0\uce58 \uae30\ubcf8 \uc0ac\uc6a9\ubc95<\/h2>\n<p>\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,<br \/>\n    \uae30\ubcf8\uc801\uc778 \uc5f0\uc0b0\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4:<\/p>\n<pre class=\"example\"><code>\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>\n<h3>4.1. \ud150\uc11c \uc0dd\uc131<\/h3>\n<p>\uc704\uc758 \ucf54\ub4dc\ub294 2&#215;2 \ud615\ud0dc\uc758 \ub450 \ud150\uc11c\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<br \/>\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<h3>4.2. \ud150\uc11c \uc5f0\uc0b0<\/h3>\n<p>\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.<br \/>\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<pre class=\"example\"><code>\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>\n<h2>5. \ud30c\uc774\ud1a0\uce58 \ubaa8\ub378 \uad6c\ucd95<\/h2>\n<p>\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<ol>\n<li>\ub370\uc774\ud130 \uc900\ube44<\/li>\n<li>\ubaa8\ub378 \uc815\uc758<\/li>\n<li>\uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758<\/li>\n<li>\ud6c8\ub828 \ub8e8\ud504<\/li>\n<li>\uac80\uc99d \ubc0f \ud14c\uc2a4\ud2b8<\/li>\n<\/ol>\n<h3>5.1. \ub370\uc774\ud130 \uc900\ube44<\/h3>\n<p>\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<pre class=\"example\"><code>\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>\n<h3>5.2. \ubaa8\ub378 \uc815\uc758<\/h3>\n<p>\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<br \/>\n    \uc644\uc804 \uc5f0\uacb0 \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4:<\/p>\n<pre class=\"example\"><code>\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>\n<h3>5.3. \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758<\/h3>\n<p>\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<pre class=\"example\"><code>\nimport torch.optim as optim\n\nmodel = SimpleNN()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n    <\/code><\/pre>\n<h3>5.4. \ud6c8\ub828 \ub8e8\ud504<\/h3>\n<p>\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<pre class=\"example\"><code>\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>\n<h3>5.5. \uac80\uc99d \ubc0f \ud14c\uc2a4\ud2b8<\/h3>\n<p>\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<pre class=\"example\"><code>\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>\n<h2>6. \uacb0\ub860<\/h2>\n<p>\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.<br \/>\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<br \/>\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<br \/>\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<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\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 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/30134\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud30c\uc774\ud1a0\uce58 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