機器學習實務:圖片分類
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
隨堂測驗:卷積
二維 3x3 捲積篩選器會套用至二維 4x4 輸入特徵對映 (未新增邊框間距):

輸出特徵對映的形狀為何?
2x2
當 3x3 濾鏡滑過 4x4 特徵地圖時,可以放置在 4 個不重複的位置,產生 2x2 的輸出特徵地圖:

3x3
雖然篩選器本身是 3x3,但輸出特徵對映較小,因為篩選器可放置在 4x4 輸入特徵對映上的位置少於 9 個 (3 乘以 3)。
四輪驅動
如要產生與輸入特徵對應地圖相同維度的輸出特徵對應地圖,且沒有邊框間距,則捲積篩選器必須為 1x1 的形狀。如果篩選器大於 1x1,產生的輸出特徵對應會小於輸入特徵對應。由於我們的篩選器是 3x3,輸出特徵對映必須小於 4x4。
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:2025-07-27 (世界標準時間)。
[null,null,["上次更新時間:2025-07-27 (世界標準時間)。"],[[["\u003cp\u003eApplying a 3x3 convolutional filter to a 4x4 input feature map without padding results in a 2x2 output feature map.\u003c/p\u003e\n"],["\u003cp\u003eThe output feature map is smaller than the input because the filter's size limits the number of positions it can occupy on the input.\u003c/p\u003e\n"],["\u003cp\u003eA 1x1 filter would be required to maintain the input's dimensions in the output without padding.\u003c/p\u003e\n"]]],[],null,["# ML Practicum: Image Classification\n\n\u003cbr /\u003e\n\nCheck Your Understanding: Convolution\n-------------------------------------\n\nA two-dimensional, 3x3 convolutional filter is applied to a two-dimensional 4x4 input feature map\n(no padding added):\n\nWhat is the shape of the output feature map? \n2x2 \nAs the 3x3 filter slides over the 4x4 feature map, there are 4 unique locations in which it can be placed, which results in a 2x2 output feature map: \n3x3 \nWhile the filter itself is 3x3, the output feature map is smaller because there are fewer than 9 (3 times 3) possible locations where the filter can be placed on the 4x4 input feature map. \n4x4 \nTo generate an output feature map with the same dimensions as the input feature map with no padding, the convolutional filter would have to be 1x1 in shape. A filter larger than 1x1 will produce an output feature map that is smaller than the input feature map. Because our filter is 3x3, the output feature map must be smaller than 4x4."]]