練習 3：特徵擷取和微調
在本練習中，您將使用特徵擷取和精細調整功能，運用 Google 的 Inception v3 模型，為練習 1 和 2 中的貓狗分類器，進一步提高準確度：
機器學習實務：圖片分類
除非另有註明，否則本頁面中的內容是採用創用 CC 姓名標示 4.0 授權，程式碼範例則為阿帕契 2.0 授權。詳情請參閱《Google Developers 網站政策》。Java 是 Oracle 和/或其關聯企業的註冊商標。
上次更新時間：2025-01-28 (世界標準時間)。
[null,null,["上次更新時間：2025-01-28 (世界標準時間)。"],[[["\u003cp\u003eThis exercise leverages Google's Inception v3 model through feature extraction and fine-tuning to enhance the accuracy of a cat-vs-dog image classifier.\u003c/p\u003e\n"],["\u003cp\u003eIt builds upon the previous exercises on image classification, refining the model for better performance.\u003c/p\u003e\n"],["\u003cp\u003eYou will practically implement these techniques using a provided Google Colab notebook.\u003c/p\u003e\n"]]],[],null,["# ML Practicum: Image Classification\n\n\u003cbr /\u003e\n\n### Exercise 3: Feature Extraction and Fine-Tuning\n\nIn this exercise, you'll use feature extraction and fine-tuning to\nleverage Google's Inception v3 model to achieve even better accuracy for the\ncat-vs.-dog classifier from Exercises [1](/machine-learning/practica/image-classification/exercise-1) and\n[2](/machine-learning/practica/image-classification/exercise-2): \n[Launch exercise](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/pc/exercises/image_classification_part3.ipynb?utm_source=practicum-IC&utm_campaign=colab-external&utm_medium=referral&hl=en&utm_content=imageexercise3-colab)"]]