\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc744 \uc704\ud55c \ud30c\uc774\ud1a0\uce58\uc758 \uae30\ubcf8\uc801\uc778 \uc0ac\uc6a9\ubc95\uacfc \uba38\uc2e0 \ub7ec\ub2dd \ubaa8\ub378\uc758 \ud559\uc2b5 \uacfc\uc815\uc744\u8be6\u7ec6\ud558\uac8c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.
\n \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08\ubd80\ud130 \uc2ec\ud654 \ub0b4\uc6a9\uae4c\uc9c0, \uc2e4\uc81c \uc608\uc81c\uc640 \ud568\uaed8 \uccb4\uacc4\uc801\uc73c\ub85c \ud559\uc2b5\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \ub4dc\ub9ac\uace0\uc790 \ud569\ub2c8\ub2e4.<\/p>\n
\ud30c\uc774\ud1a0\uce58(PyTorch)\ub294 Python\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc624\ud508 \uc18c\uc2a4 \uba38\uc2e0 \ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c,
\n \uc8fc\ub85c \ub525\ub7ec\ub2dd\uc744 \uc704\ud55c \uc5f0\uad6c \ubc0f \uac1c\ubc1c\uc5d0 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uc720\uc5f0\ud55c \uc2e0\uacbd\ub9dd \uad6c\ucd95\uacfc \uac15\ub825\ud55c GPU \uac00\uc18d\uc744 \uc9c0\uc6d0\ud558\uc5ec,
\n \uc5f0\uad6c\uc790\uc640 \uc5d4\uc9c0\ub2c8\uc5b4\ub4e4\uc774 \uc2e0\uc18d\ud558\uac8c \uc2e4\ud5d8\ud558\uace0 \ucd5c\uc801\ud654\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uc2b5\ub2c8\ub2e4.<\/p>\n
\ud30c\uc774\ud1a0\uce58\ub97c \uc124\uce58\ud558\uae30 \uc704\ud574, \uc6b0\uc120 Python\uc774 \uc124\uce58\ub418\uc5b4 \uc788\uc5b4\uc57c \ud569\ub2c8\ub2e4.
\n \uc774\ud6c4, \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec pip\ub97c \ud1b5\ud574 \ud30c\uc774\ud1a0\uce58\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\npip install torch torchvision torchaudio\n<\/code><\/pre>\n3. \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n
\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \ud65c\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud2b9\uc131\uc744 \uc790\ub3d9\uc73c\ub85c \ud559\uc2b5\ud558\ub294 \uae30\uacc4 \ud559\uc2b5\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4.
\n \uc6b0\ub9ac\uac00 \ub2e4\ub8f0 \uc8fc\uc694 \uac1c\ub150\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \uc2e0\uacbd\ub9dd(Neural Network)<\/li>\n
- \ubc31\ud504\ub85c\ud37c\uac8c\uc774\uc158(Backpropagation)<\/li>\n
- \uc190\uc2e4 \ud568\uc218(Loss Function)<\/li>\n
- \ucd5c\uc801\ud654(Optimization)<\/li>\n<\/ul>\n
3.1 \uc2e0\uacbd\ub9dd(Neural Network)<\/h3>\n
\uc2e0\uacbd\ub9dd\uc740 \uc785\ub825\uce35, \uc740\ub2c9\uce35, \ucd9c\ub825\uce35\uc73c\ub85c \uad6c\uc131\ub41c \uad6c\uc870\ub97c \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.
\n \uac01 \uce35\uc740 \ub178\ub4dc\ub85c \uad6c\uc131\ub418\uba70, \ub178\ub4dc \uac04\uc758 \uc5f0\uacb0\uc740 \uac00\uc911\uce58\ub97c \uac00\uc9c0\uac8c \ub429\ub2c8\ub2e4.
\n \uc774 \uac00\uc911\uce58\ub294 \ud559\uc2b5 \uacfc\uc815\uc744 \ud1b5\ud574 \uc5c5\ub370\uc774\ud2b8\ub429\ub2c8\ub2e4.<\/p>\n
3.2 \ubc31\ud504\ub85c\ud37c\uac8c\uc774\uc158(Backpropagation)<\/h3>\n
\ubc31\ud504\ub85c\ud37c\uac8c\uc774\uc158\uc740 \uc190\uc2e4 \ud568\uc218\ub85c\ubd80\ud130\uc758 \uae30\uc6b8\uae30\ub97c \uacc4\uc0b0\ud558\uc5ec \uac00\uc911\uce58\ub97c \uc870\uc815\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4.
\n \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.3 \uc190\uc2e4 \ud568\uc218(Loss Function)<\/h3>\n
\uc190\uc2e4 \ud568\uc218\ub294 \ubaa8\ub378\uc758 \uc608\uce21\uac12\uacfc \uc2e4\uc81c\uac12 \uac04\uc758 \ucc28\uc774\ub97c \uce21\uc815\ud569\ub2c8\ub2e4.
\n \uc774 \ud568\uc218\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uace0, \ucd5c\uc801\ud654 \uacfc\uc815\uc5d0\uc11c \uac1c\uc120\ud574\uc57c \ud560 \ubc29\ud5a5\uc744 \uc81c\uc2dc\ud569\ub2c8\ub2e4.<\/p>\n
3.4 \ucd5c\uc801\ud654(Optimization)<\/h3>\n
\ucd5c\uc801\ud654\ub294 \uc190\uc2e4 \ud568\uc218\ub97c \ucd5c\uc18c\ud654\ud558\uae30 \uc704\ud55c \uacfc\uc815\uc73c\ub85c,
\n \uacbd\ub7c9\ud654\ub41c \uc544\ud0a4\ud14d\ucc98\uc640 \ud6a8\uc728\uc801\uc778 \ud559\uc2b5 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \ud5a5\uc0c1\uc2dc\ud0b5\ub2c8\ub2e4.<\/p>\n
4. \ud30c\uc774\ud1a0\uce58\ub85c \uae30\ubcf8\uc801\uc778 \uc2e0\uacbd\ub9dd \ubaa8\ub378 \uad6c\ucd95\ud558\uae30<\/h2>\n
\ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e4\uc81c\ub85c \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 \ub370\uc774\ud130\uc14b \uc900\ube44\ud558\uae30<\/h3>\n
\uba3c\uc800, MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc190\uae00\uc528 \uc22b\uc790\ub97c \ubd84\ub958\ud558\ub294 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
\n MNIST \ub370\uc774\ud130\uc14b\uc740 0\ubd80\ud130 9\uae4c\uc9c0\uc758 \uc22b\uc790 \uc774\ubbf8\uc9c0\ub97c \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.
\n \ud30c\uc774\ud1a0\uce58\uc758 torchvision\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\n\n# MNIST \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ntransform = transforms.Compose([\n transforms.ToTensor(), \n transforms.Normalize((0.5,), (0.5,))\n])\n\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)\n<\/code><\/pre>\n4.2 \uc2e0\uacbd\ub9dd \ubaa8\ub378 \uc815\uc758\ud558\uae30<\/h3>\n
\uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n# \uc2e0\uacbd\ub9dd \uc815\uc758\nclass Net(nn.Module):\n def __init__(self):\n super(Net, 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)) # Apply ReLU activation\n x = self.fc2(x) # Output layer\n return x\n<\/code><\/pre>\n4.3 \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815\ud558\uae30<\/h3>\n
\ubaa8\ub378\uc758 \ud559\uc2b5\uc744 \uc704\ud574 \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc124\uc815\ud569\ub2c8\ub2e4:<\/p>\n
\nimport torch.optim as optim\n\n# \ubaa8\ub378 \uc0dd\uc131\nmodel = Net()\n\n# \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n<\/code><\/pre>\n4.4 \ubaa8\ub378 \ud559\uc2b5\ud558\uae30<\/h3>\n
\uc774\uc81c \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0ac \ucc28\ub840\uc785\ub2c8\ub2e4. \uc5d0\ud3ed\uc218(epoch)\ub97c \uc815\ud574 \uba87 \ubc88 \ubc18\ubcf5\ud560\uc9c0 \uc124\uc815\ud558\uace0,
\n \uac01 \ubc30\uce58(batch)\ub9c8\ub2e4 \ubaa8\ub378\uc758 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \ucf54\ub4dc\ub97c \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n
\nfor epoch in range(5): # 5 \uc5d0\ud3ed(epoch) \ub3d9\uc548 \ud559\uc2b5\n for inputs, labels in trainloader:\n optimizer.zero_grad() # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n outputs = model(inputs) # \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}, Loss: {loss.item():.4f}')\n<\/code><\/pre>\n4.5 \ubaa8\ub378 \ud3c9\uac00\ud558\uae30<\/h3>\n
\ubaa8\ub378\uc774 \uc5bc\ub9c8\ub098 \uc798 \ud559\uc2b5\ub418\uc5c8\ub294\uc9c0 \ud3c9\uac00\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
\n \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc815\ud655\ub3c4\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\ncorrect = 0\ntotal = 0\nwith torch.no_grad():\n for inputs, labels in testloader:\n outputs = model(inputs)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\nprint(f'Accuracy: {100 * correct \/ total:.2f}%')\n<\/code><\/pre>\n5. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uae00\uc5d0\uc11c \uc6b0\ub9ac\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8\uc801\uc778 \uac1c\ub150\uacfc \ubaa8\ub378 \ud559\uc2b5 \uacfc\uc815\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4.
\n \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0, \uc7ac\ud45c \ubc0f \ud3c9\uac00\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud655\uc778\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6e0\uc2b5\ub2c8\ub2e4.
\n \uc55e\uc73c\ub85c \ub354\uc6b1 \uc2ec\ud654\ub41c \ub0b4\uc6a9\uc73c\ub85c \ub098\uc544\uac00\uae30 \uc704\ud574, \ub525\ub7ec\ub2dd \uc544\ud0a4\ud14d\ucc98\uc640 \ub2e4\uc591\ud55c \ucd5c\uc801\ud654 \uae30\ubc95 \ubc0f \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc5d0 \ub300\ud55c \ud559\uc2b5\uc744 \uc9c4\ud589\ud574\ubcf4\uba74 \uc88b\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"