Save and evaluate the model<\/li>\n<\/ul>\n2.1 Loading a Pre-trained Model<\/h3>\n
In PyTorch, you can easily load a pre-trained model using the torchvision library. Here, we will explain using the ResNet18 model as an example.<\/p>\n
import torch\nimport torch.nn as nn\nimport torchvision.models as models\n\n# Load ResNet18 model\nmodel = models.resnet18(pretrained=True)<\/code><\/pre>\n2.2 Freezing or Modifying Some Layers of the Model<\/h3>\n
In general, during fine-tuning, the last layer of the model is modified to fit the new number of classes. For instance, if the number of classes changes from 1000 to 10 in image classification, the last layer needs to be replaced.<\/p>\n
# Replace the existing last layer with a new layer\nnum_ftrs = model.fc.in_features\nmodel.fc = nn.Linear(num_ftrs, 10) # Set to 10<\/code><\/pre>\n2.3 Training the Model Using a New Dataset<\/h3>\n
A data loader is set up to train the model on the new dataset.<\/p>\n
from torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader\n\n# Set up data transformations\ntransform = transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n])\n\n# Load the dataset\ntrain_dataset = datasets.FakeData(transform=transform) # Using sample data\ntrain_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)<\/code><\/pre>\n2.4 Writing the Training Loop<\/h3>\n
Write a training loop that defines the learning process.<\/p>\n
device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = model.to(device)\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n\n# Training loop\nfor epoch in range(10): # Change the number of epochs if needed\n model.train()\n running_loss = 0.0\n for inputs, labels in train_loader:\n inputs, labels = inputs.to(device), labels.to(device)\n\n optimizer.zero_grad()\n outputs = model(inputs)\n loss = criterion(outputs, labels)\n loss.backward()\n optimizer.step()\n\n running_loss += loss.item()\n\n print(f'Epoch {epoch + 1}, Loss: {running_loss \/ len(train_loader)}')<\/code><\/pre>\n3. Evaluating the Fine-tuning Results<\/h2>\n
Once training is complete, the model can be evaluated. Typically, a validation dataset is used to assess the model’s performance.<\/p>\n
# Load and evaluate the validation dataset\nval_dataset = datasets.FakeData(transform=transform) # Using sample data\nval_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n\nmodel.eval()\ncorrect = 0\ntotal = 0\nwith torch.no_grad():\n for inputs, labels in val_loader:\n inputs, labels = inputs.to(device), labels.to(device)\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 of the model: {100 * correct \/ total}%')<\/code><\/pre>\n4. Conclusion<\/h2>\n
In deep learning, fine-tuning is a crucial technique that allows for efficient data usage and maximization of performance. PyTorch offers various tools and libraries that make it easy to perform fine-tuning tasks using pre-trained models. Understanding and applying this process is an important step for practically using deep learning technologies.<\/p>\n
I hope this course has been helpful. Thank you!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
Fine-tuning, one of the subfields of deep learning, is a technique that adjusts a pre-trained model to enhance performance for a specific task. Generally, this technique is an efficient way to save time and resources spent on data collection and training, and it is utilized in various fields such as image recognition and natural language … \ub354 \ubcf4\uae30 “Deep Learning PyTorch Course, Fine-tuning Techniques”<\/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-36571","post","type-post","status-publish","format-standard","hentry","category-pytorch-study"],"yoast_head":"\nDeep Learning PyTorch Course, Fine-tuning Techniques - \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\/36571\/\" \/>\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, Fine-tuning Techniques - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"Fine-tuning, one of the subfields of deep learning, is a technique that adjusts a pre-trained model to enhance performance for a specific task. 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