Transfer Learning

Introduction

Transfer Learning is a powerful technique commonly used in machine learning and deep learning. It allows us to leverage the knowledge learned from previous models to solve similar problems. This tutorial will guide you through the implementation of Transfer Learning using PyTorch, a popular deep learning framework.

Prerequisites

To follow this tutorial, it's assumed that you have a basic understanding of PyTorch and Neural Networks. Familiarity with Python programming is essential.

Transfer Learning: What and Why

Transfer Learning is a method where a model developed for a task is reused as the starting point for a model on a second task. It's a popular method in deep learning because it can train deep neural networks with comparatively little data.

This is very effective in practice because often we're able to leverage a large amount of pre-existing data from a similar task.

Getting Started: Loading the Pre-Trained Model

The first step in transfer learning is to load a pre-trained model. PyTorch provides a number of pre-trained models in the torchvision.models module. For example, you can load a pre-trained ResNet18 model as follows:

import torch
from torchvision import models

# Load the pre-trained model from pytorch
resnet18 = models.resnet18(pretrained=True)

Freezing Model Parameters

When we say we're using a pre-trained model, we're using the architecture and the pre-trained weights of that model. However, we don't want those weights to change during the training of our new task. So, we freeze the weights of our pre-trained model.

# Freeze model parameters
for param in resnet18.parameters():
    param.requires_grad = False

Modifying the Classifier

The next step is to modify the classifier part of the model for our specific task.

Let's consider a binary classification task. For this, we modify the last layer to have only one output.

import torch.nn as nn

num_features = resnet18.fc.in_features
resnet18.fc = nn.Linear(num_features, 2)

Training the Modified Model

Now our model is ready to be trained on our specific task. Remember, only the parameters of the classifier part will be updated.

# Loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(resnet18.fc.parameters(), lr=0.001)

# Training loop
for epoch in range(epochs):
    for inputs, labels in train_loader:
        outputs = resnet18(inputs)
        loss = criterion(outputs, labels)
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

Conclusion

Transfer learning is a powerful technique in deep learning that allows us to leverage the knowledge from pre-trained models. This tutorial has shown you how to implement transfer learning in PyTorch, from loading a pre-trained model, freezing the parameters, modifying the classifier, to training the model. This should give you a solid foundation for further exploration of transfer learning.