PyTorch Transfer Learning Guide

Model transfer learning in PyTorch typically involves the following steps:

1. Load a pre-trained model: First, load a model that has been trained on a large dataset, such as ResNet trained on ImageNet.

import torch
import torchvision.models as models

pretrained_model = models.resnet18(pretrained=True)

1. Change the final layer of the model: Depending on your task requirements, it is usually necessary to modify the final layer of the pre-trained model, such as replacing the fully connected layer of the pre-trained model with a fully connected layer suitable for your new task.

pretrained_model.fc = nn.Linear(pretrained_model.fc.in_features, num_classes)

1. Freeze the parameters of the pre-trained model: Typically, we freeze the parameters of the pre-trained model and only train the newly added fully connected layer.

for param in pretrained_model.parameters():
    param.requires_grad = False

1. Define loss function and optimizer: Define appropriate loss function and optimizer based on your task requirements.

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(pretrained_model.fc.parameters(), lr=0.001)

1. Train the model: train the model using a new dataset.

for epoch in range(num_epochs):
    for images, labels in dataloader:
        optimizer.zero_grad()
        outputs = pretrained_model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

By following the above steps, you can perform model transfer learning in PyTorch. You can adjust and expand the above steps according to specific task requirements.