Separating the training set and testing set<\/li>\n<\/ul>\n3.2 Preparing Data in PyTorch<\/h3>\n
In PyTorch, packages like torchvision<\/code> can be used to handle data. For example, the code to load the CIFAR-10 dataset is as follows:<\/p>\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\n\ntransform = transforms.Compose(\n [transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n\ntrainset = torchvision.datasets.CIFAR10(root='.\/data', train=True,\n download=True, transform=transform)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n shuffle=True, num_workers=2)<\/code><\/pre>\n3.3 Model Construction<\/h3>\n
When constructing a model, the structure of the neural network must be defined. In PyTorch, user-defined models can be created by inheriting the torch.nn.Module<\/code> class. Below is an example of a simple CNN model:<\/p>\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.conv1 = nn.Conv2d(3, 6, 5)\n self.pool = nn.MaxPool2d(2, 2)\n self.conv2 = nn.Conv2d(6, 16, 5)\n self.fc1 = nn.Linear(16 * 5 * 5, 120)\n self.fc2 = nn.Linear(120, 84)\n self.fc3 = nn.Linear(84, 10)\n\n def forward(self, x):\n x = self.pool(F.relu(self.conv1(x)))\n x = self.pool(F.relu(self.conv2(x)))\n x = x.view(-1, 16 * 5 * 5)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n return x<\/code><\/pre>\n3.4 Model Training<\/h3>\n
When training a model, a loss function and an optimization algorithm must be defined. Generally, the cross-entropy loss function is used for classification problems, and optimization algorithms such as SGD or Adam can be applied.<\/p>\n
import torch.optim as optim\n\nnet = Net()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n\nfor epoch in range(2): # Repeating the dataset multiple times.\n for i, data in enumerate(trainloader, 0):\n inputs, labels = data\n optimizer.zero_grad() # Initialize gradients\n outputs = net(inputs) # Forward pass\n loss = criterion(outputs, labels) # Calculate loss\n loss.backward() # Backward pass\n optimizer.step() # Update weights\n\nprint('Finished Training')<\/code><\/pre>\n3.5 Model Evaluation<\/h3>\n
After training the model, it needs to be evaluated. Typically, the testing dataset is used to calculate accuracy.<\/p>\n
correct = 0\ntotal = 0\nwith torch.no_grad(): # Disable gradient calculation\n for data in testloader:\n images, labels = data\n outputs = net(images)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\nprint('Accuracy of the network on the 10000 test images: %d %%' % (\n 100 * correct \/ total))<\/code><\/pre>\n4. Directions for the Advancement of Deep Learning<\/h2>\n
Deep learning is being utilized in various fields and will continue to evolve. Especially, it is expected to bring innovations in many areas, including autonomous vehicles, medical diagnosis, natural language processing, and image generation. PyTorch will also continue to evolve in line with these trends.<\/p>\n
Conclusion<\/h2>\n
In this article, we started with the basics of deep learning and took a detailed look at the learning process of deep learning using PyTorch. Through the stages of data preparation, model construction, training, and evaluation, we confirmed the various functions and conveniences provided by PyTorch. I hope this guide helps broaden your understanding of deep learning and aids in applying it to real projects.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
Deep learning is a branch of artificial intelligence and a collection of machine learning methods based on artificial neural networks. One of the core technologies of deep learning widely used in various fields today is PyTorch. PyTorch is popular among many researchers and developers for its easy-to-use dynamic computation graph and powerful tensor operation capabilities. … \ub354 \ubcf4\uae30 “Deep Learning PyTorch Course, Deep Learning Learning Process”<\/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":[149],"tags":[],"class_list":["post-36547","post","type-post","status-publish","format-standard","hentry","category-pytorch-study"],"yoast_head":"\nDeep Learning PyTorch Course, Deep Learning Learning Process - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/36547\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep Learning PyTorch Course, Deep Learning Learning Process - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"Deep learning is a branch of artificial intelligence and a collection of machine learning methods based on artificial neural networks. 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