第7課：遷移學習—

歡迎來到第7課，我們將介紹遷移學習。這種技術涉及重用一個已在大型通用數據集（如ImageNet）上訓練完成的深度學習模型，並適應於解決新的特定任務（例如我們的FoodVision挑戰）。在標註數據集有限的情況下，它對於高效達成頂尖成果至關重要。

1. 預訓練權重的力量

深度神經網絡以層次方式學習特徵。較低層次學習基本概念（邊緣、角落、紋理），而更深層則將這些特徵組合為複雜概念（眼睛、輪胎、特定物件）。關鍵洞察是，早期學到的基本特徵在大多數視覺領域中都具有普遍適用普遍適用性。

遷移學習的組成部分

來源任務： 在1400萬張圖像和1000個類別上進行訓練（例如ImageNet）。
目標任務： 將權重調整以分類規模小得多的數據集（例如我們特定的FoodVision類別）。
被利用的部分： 網絡參數的絕大部分——特徵提取層——直接被重用。

效率提升

遷移學習顯著降低了兩大資源障礙： 運算成本 （你避免了訓練整個模型數天）以及 數據需求 （只需數百個訓練樣本即可達到高準確率，而非數千個）。

TERMINALbash — pytorch-env

> Ready. Click "Run" to execute.

TENSOR INSPECTOR Live

Run code to inspect active tensors

Question 1

What is the primary advantage of using a model pre-trained on ImageNet for a new vision task?

It requires less labeled data than training from scratch.

It completely eliminates the need for any training data.

It guarantees 100% accuracy immediately.

Question 2

In a Transfer Learning workflow, which part of the neural network is typically frozen?

The final Output Layer (Classifier Head).

The Convolutional Base (Feature Extractor layers).

The entire network is usually unfrozen.

Question 3

When replacing the classifier head in PyTorch, what parameter must you first determine from the frozen base?

The batch size of the target data.

The input feature size (the output dimensions of the last convolutional layer).

The total number of model parameters.

Challenge: Adapting the Classifier Head

Designing a new classifier for FoodVision.

You load a ResNet model pre-trained on ImageNet. Its last feature layer outputs a vector of size 512. Your 'FoodVision' project has 7 distinct food classes.

Step 1

What is the required Input Feature size for the new, trainable Linear Layer?

Solution:
The Input Feature size must match the output of the frozen base layer.
Size: 512.

Step 2

What is the PyTorch code snippet to create this new classification layer (assuming the output is named `new_layer`)?

Solution:
The output size of 512 is the input, and the class count 7 is the output.
Code: new_layer = torch.nn.Linear(512, 7)

Step 3

What is the required Output Feature size for the new Linear Layer?

Solution:
The Output Feature size must match the number of target classes.
Size: 7.