{"id":30120,"date":"2024-10-28T03:19:41","date_gmt":"2024-10-28T03:19:41","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30120"},"modified":"2024-11-26T06:49:50","modified_gmt":"2024-11-26T06:49:50","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%9d%b4%eb%9e%80","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30120\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ucf54\ub7a9\uc774\ub780"},"content":{"rendered":"<p>\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574 \ud544\uc218\uc801\uc73c\ub85c \uc54c\uc544\uc57c \ud560 \ub3c4\uad6c\uc778 Google Colab(\ucf54\ub7a9)\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub525\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac \uc911 \ud558\ub098\uc778 <em>PyTorch<\/em>\uc640 \ud568\uaed8 \ucf54\ub7a9\uc744 \uc0ac\uc6a9\ud558\uba74 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \ud559\uc2b5\ud558\uace0 \uc2e4\ud5d8\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\u3067\u306f \ucf54\ub7a9\uc758 \uae30\ub2a5, \uc774\uc810, \uadf8\ub9ac\uace0 \ud30c\uc774\uc36c\uacfc \ud568\uaed8 PyTorch\ub85c \uac04\ub2e8\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \uc608\uc81c\ub97c \uc81c\uc2dc\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. Google Colab\uc774\ub780?<\/h2>\n<p><strong>Google Colaboratory<\/strong>, \uc27d\uac8c \uc904\uc5ec\uc11c <strong>\ucf54\ub7a9<\/strong>\uc774\ub77c\uace0 \ubd80\ub974\ub294 \uc774 \ub3c4\uad6c\ub294 Python\uc744 \uc0ac\uc6a9\ud574 \uba38\uc2e0\ub7ec\ub2dd, \ub370\uc774\ud130 \ubd84\uc11d \ubc0f \uad50\uc721\uc744 \uc9c0\uc6d0\ud558\ub294 \ubb34\ub8cc Jupyter \ub178\ud2b8\ubd81 \ud658\uacbd\uc785\ub2c8\ub2e4. \ucf54\ub7a9\uc740 Google Drive\uc640 \ud1b5\ud569\ub418\uc5b4 \uc788\uc73c\ubbc0\ub85c \uc0ac\uc6a9\uc790\ub4e4\uc774 \uc27d\uac8c \ub370\uc774\ud130\ub97c \uc800\uc7a5\ud558\uace0 \uacf5\uc720\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4.<\/p>\n<h3>1.1 \ud575\uc2ec \uae30\ub2a5<\/h3>\n<ul>\n<li><strong>GPU \ubc0f TPU \uc9c0\uc6d0:<\/strong> \ubb34\ub8cc\ub85c NVIDIA GPU\uc640 TPU\ub97c \uc81c\uacf5\ud558\uc5ec \ubcf5\uc7a1\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378 \ud6c8\ub828 \uc2dc \uc18d\ub3c4\ub97c \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>Google Drive \ud1b5\ud569:<\/strong> \uc0ac\uc6a9\uc790\uac00 \ub370\uc774\ud130\uc640 \uacb0\uacfc\ub97c \uc190\uc27d\uac8c \uad00\ub9ac\ud558\uace0 \uacf5\uc720\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ub370\uc774\ud130 \uc2dc\uac01\ud654 \ub3c4\uad6c:<\/strong> Matplotlib, Seaborn \ub4f1 \uc5ec\ub7ec \uc2dc\uac01\ud654 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc9c0\uc6d0\ud558\uc5ec \ub370\uc774\ud130 \ubd84\uc11d\uc744 \uc6d0\ud65c\ud558\uac8c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58 \uc6a9\uc774:<\/strong> \ud544\uc694\uc5d0 \ub530\ub77c TensorFlow, PyTorch \ub4f1\uc758 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uac04\ub2e8\ud788 \uc124\uce58\ud574 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>1.2 \ucf54\ub7a9\uc758 \uc774\uc810<\/h3>\n<p>\ucf54\ub7a9\uc744 \uc0ac\uc6a9\ud560 \ub54c\uc758 \uc774\uc810\uc740 \ub2e4\uc591\ud569\ub2c8\ub2e4. \uba3c\uc800, \uc0ac\uc6a9\uc790\ub294 \ud074\ub77c\uc6b0\ub4dc \ud658\uacbd\uc5d0\uc11c \uc791\uc5c5\ud558\ubbc0\ub85c \ub85c\uceec \ucef4\ud4e8\ud130\uc758 \ub9ac\uc18c\uc2a4\ub97c \uc18c\ubaa8\ud558\uc9c0 \uc54a\uace0\ub3c4 \ubcf5\uc7a1\ud55c \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc810\uc740 \ud2b9\ud788 GPU\uac00 \ud544\uc694\ud55c \ub300\uaddc\ubaa8 \ub525\ub7ec\ub2dd \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c \ud070 \uc7a5\uc810\uc73c\ub85c \uc791\uc6a9\ud569\ub2c8\ub2e4. \ub610\ud55c, \ucf54\ub4dc \uc2e4\ud589 \uacb0\uacfc\uc640 \ud568\uaed8 \uacb0\uacfc\ub97c \uc2dc\uac01\uc801\uc73c\ub85c \ud655\uc778\ud560 \uc218 \uc788\uc5b4 \uc5f0\uad6c\uc640 \uad50\uc721 \ubaa9\uc801\uc73c\ub85c \uc720\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n<h2>2. PyTorch\ub780?<\/h2>\n<p><strong>PyTorch<\/strong>\ub294 \uc8fc\ub85c \ub525\ub7ec\ub2dd\uc5d0 \uc0ac\uc6a9\ub418\ub294 \uc624\ud508 \uc18c\uc2a4 \uba38\uc2e0\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, Python\uacfc C++\ub85c \uad6c\ud604\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. PyTorch\ub294 \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\uc758 \ud2b9\uc131\uc744 \uc9c0\ub2c8\uace0 \uc788\uc5b4 \uc5f0\uad6c\uc640 \ud504\ub85c\ud1a0\ud0c0\uc785 \uc81c\uc791\uc5d0 \ud2b9\ud788 \uc801\ud569\ud569\ub2c8\ub2e4. \ub610\ud55c, \ud30c\uc774\uc36c\uacfc\uc758 \ud638\ud658\uc131\uc774 \ub192\uc544 \ucf54\ub4dc\ub97c \uc791\uc131\ud558\uace0 \ub514\ubc84\uae45\ud558\ub294 \uacfc\uc815\uc774 \uc27d\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2.1 \uc124\uce58 \ubc29\ubc95<\/h3>\n<p>\ucf54\ub7a9\uc5d0\uc11c\ub294 PyTorch\ub97c \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798 \uc140\uc744 \uc2e4\ud589\ud558\uba74 PyTorch\uc640 \uad00\ub828\ub41c \ud544\uc218 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>!pip install torch torchvision<\/code><\/pre>\n<h2>3. PyTorch\ub97c \uc0ac\uc6a9\ud55c \uac04\ub2e8\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378<\/h2>\n<p>\uc774\uc81c Google Colab\uc5d0\uc11c PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc22b\uc790 \uc778\uc2dd\uae30\ub97c \ub9cc\ub4e4\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>3.1 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n<p>\uba3c\uc800, MNIST \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud569\ub2c8\ub2e4. MNIST\ub294 28&#215;28 \ud53d\uc140\uc758 \uc22b\uc790 \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc73c\uba70, \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294\ub370 \ud754\ud788 \uc0ac\uc6a9\ub418\ub294 \ubca4\uce58\ub9c8\ud06c \ub370\uc774\ud130\uc14b\uc785\ub2c8\ub2e4.<\/p>\n<pre><code>import torch\nimport torchvision\nimport torchvision.transforms as transforms\n\n# \ub370\uc774\ud130 \ubcc0\ud658 \uc815\uc758\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n\n# \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8 \ub2e4\uc6b4\ub85c\ub4dc\ntrainset = torchvision.datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n\ntestset = torchvision.datasets.MNIST(root='.\/data', train=False, download=True, transform=transform)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)<\/code><\/pre>\n<h3>3.2 \uc2e0\uacbd\ub9dd \uc124\uacc4<\/h3>\n<p>\uc2e0\uacbd\ub9dd \uc544\ud0a4\ud14d\ucc98\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uc785\ub825\uce35, \uc740\ub2c9\uce35 \ub450 \uac1c, \uadf8\ub9ac\uace0 \ucd9c\ub825\uce35\uc73c\ub85c \uad6c\uc131\ub41c \uac04\ub2e8\ud55c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>import torch.nn as nn\nimport torch.optim as optim\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 (784 \ub178\ub4dc) -&gt; \uccab \ubc88\uc9f8 \uc740\ub2c9\uce35 (128 \ub178\ub4dc)\n        self.fc2 = nn.Linear(128, 64)        # \uccab \ubc88\uc9f8 \uc740\ub2c9\uce35 -&gt; \ub450 \ubc88\uc9f8 \uc740\ub2c9\uce35 (64 \ub178\ub4dc)\n        self.fc3 = nn.Linear(64, 10)         # \ub450 \ubc88\uc9f8 \uc740\ub2c9\uce35 -&gt; \ucd9c\ub825\uce35 (10 \ub178\ub4dc)\n\n    def forward(self, x):\n        x = x.view(-1, 28 * 28)  # \uac01 \uc774\ubbf8\uc9c0\ub97c 1D \ubca1\ud130\ub85c \ubcc0\ud658\n        x = torch.relu(self.fc1(x))  # \uccab \ubc88\uc9f8 \uc740\ub2c9\uce35\n        x = torch.relu(self.fc2(x))  # \ub450 \ubc88\uc9f8 \uc740\ub2c9\uce35\n        x = self.fc3(x)  # \ucd9c\ub825\uce35\n        return x\n\nmodel = SimpleNN()<\/code><\/pre>\n<h3>3.3 \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998 \uc815\uc758<\/h3>\n<p>\uc190\uc2e4 \ud568\uc218\ub294 <em>CrossEntropyLoss<\/em>\ub97c \uc0ac\uc6a9\ud558\uace0, \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc740 <em>Adam Optimizer<\/em>\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud6c8\ub828\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<pre><code>criterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)<\/code><\/pre>\n<h3>3.4 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n<p>\ub2e4\uc74c \ub2e8\uacc4\ub294 \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc5ec\ub7ec epoch\uc744 \ud1b5\ud574 \ubaa8\ub378\uc758 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\uace0 \uc190\uc2e4\uc744 \uc904\uc5ec\ub098\uac11\ub2c8\ub2e4.<\/p>\n<pre><code>for epoch in range(5):  # 5 epoch \ud6c8\ub828\n    running_loss = 0.0\n    for inputs, labels in trainloader:\n        optimizer.zero_grad()  # \ubcc0\ud654\ub3c4 \ucd08\uae30\ud654\n        outputs = model(inputs)  # \ubaa8\ub378\uc5d0 \uc785\ub825\uc744 \uc8fc\uc5b4 \ucd9c\ub825 \uc0dd\uc131\n        loss = criterion(outputs, labels)  # \uc190\uc2e4 \uac12 \uacc4\uc0b0\n        loss.backward()  # \uc5ed\uc804\ud30c\n        optimizer.step()  # \ucd5c\uc801\ud654\n        running_loss += loss.item()  # \uc190\uc2e4 \uac12\uc744 \ub204\uc801\n        \n    print(f'Epoch {epoch + 1}, Loss: {running_loss \/ len(trainloader)}')  # \ud3c9\uade0 \uc190\uc2e4 \ucd9c\ub825<\/code><\/pre>\n<h3>3.5 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n<p>\ub9c8\uc9c0\ub9c9\uc73c\ub85c, \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc900 \ube44\ud55c \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \ud1b5\uacfc\ud558\uba74\uc11c \uc815\ud655\ub3c4\ub97c \uacc4\uc0b0\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>correct = 0\ntotal = 0\nwith torch.no_grad():\n    for inputs, labels in testloader:\n        outputs = model(inputs)\n        _, predicted = torch.max(outputs.data, 1)  # \uac00\uc7a5 \ub192\uc740 \ud655\ub960\uc758 \ud074\ub798\uc2a4\ub97c \uc120\ud0dd\n        total += labels.size(0)\n        correct += (predicted == labels).sum().item()\n\nprint(f'Accuracy: {100 * correct \/ total}%')  # \uc815\ud655\ub3c4 \ucd9c\ub825<\/code><\/pre>\n<h2>4. \uacb0\ub860<\/h2>\n<p>\uc774\ubc88 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 Google Colab\uc758 \uae30\ub2a5\uacfc \uc774\uc810, \uadf8\ub9ac\uace0 PyTorch\ub97c \ud65c\uc6a9\ud55c \uac04\ub2e8\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uad6c\ucd95 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. Google Colab\uc740 \ub370\uc774\ud130 \uacfc\ud559\uc790\uc640 \uc5f0\uad6c\uc790\ub4e4\uc5d0\uac8c \ub9ce\uc740 \ud61c\ud0dd\uc744 \uc81c\uacf5\ud558\uba70, \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ud658\uacbd\uc5d0\uc11c PyTorch\uc640 \ud568\uaed8 \ub525\ub7ec\ub2dd\uc744 \uc218\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub2e4\uc591\ud55c \uc2ec\ud654 \uc8fc\uc81c\ub85c \ub3cc\uc544\uc624\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4!<\/p>\n<footer>\n<p>\ub525\ub7ec\ub2dd\uc758 \uc138\uacc4\uc5d0 \uc624\uc2e0 \uac83\uc744 \ud658\uc601\ud569\ub2c8\ub2e4. \uacc4\uc18d\ud574\uc11c \uc0c8\ub85c\uc6b4 \uae30\uc220\uacfc \ubc29\ubc95\ub4e4\uc744 \ubc30\uc6b0\uba70 \uc55e\uc73c\ub85c \ub098\uc544\uac00\uc2dc\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n<\/footer>\n","protected":false},"excerpt":{"rendered":"<p>\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574 \ud544\uc218\uc801\uc73c\ub85c \uc54c\uc544\uc57c \ud560 \ub3c4\uad6c\uc778 Google Colab(\ucf54\ub7a9)\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub525\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac \uc911 \ud558\ub098\uc778 PyTorch\uc640 \ud568\uaed8 \ucf54\ub7a9\uc744 \uc0ac\uc6a9\ud558\uba74 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \ud559\uc2b5\ud558\uace0 \uc2e4\ud5d8\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\u3067\u306f \ucf54\ub7a9\uc758 \uae30\ub2a5, \uc774\uc810, \uadf8\ub9ac\uace0 \ud30c\uc774\uc36c\uacfc \ud568\uaed8 PyTorch\ub85c \uac04\ub2e8\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \uc608\uc81c\ub97c \uc81c\uc2dc\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. Google Colab\uc774\ub780? 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