{"id":24656,"date":"2025-06-18T16:43:20","date_gmt":"2025-06-18T16:43:20","guid":{"rendered":"https:\/\/gaviki.com\/blog\/?p=24656"},"modified":"2025-06-18T16:43:21","modified_gmt":"2025-06-18T16:43:21","slug":"if-the-data-dimension-is-16-x-16-filter-5x5-and-stride-1","status":"publish","type":"post","link":"https:\/\/gaviki.com\/blog\/if-the-data-dimension-is-16-x-16-filter-5x5-and-stride-1\/","title":{"rendered":"If the data dimension is 16 x 16, filter 5×5, and stride 1"},"content":{"rendered":"\n

If the data dimension is 16 x 16, filter 5×5, and stride 1, then what is the amount of padding required so that convolution output has the same dimension as the input?<\/p>\n\n\n\n

The correct answer and explanation is:<\/strong><\/mark><\/p>\n\n\n\n

\u2705 Correct Answer:<\/h3>\n\n\n\n

Padding = 2<\/strong><\/p>\n\n\n\n

\n\n\n\n

\ud83d\udcd8 Explanation:<\/h3>\n\n\n\n

To determine the amount of padding<\/strong> required so that the output size equals the input size<\/strong> after applying a convolutional filter, we use the convolution output size formula<\/strong>: Output size=(N+2P\u2212F)S+1\\text{Output size} = \\frac{(N + 2P – F)}{S} + 1<\/p>\n\n\n\n

Where:<\/p>\n\n\n\n

    \n
  • NN = Input size (16)<\/li>\n\n\n\n
  • FF = Filter size (5)<\/li>\n\n\n\n
  • SS = Stride (1)<\/li>\n\n\n\n
  • PP = Padding (what we’re solving for)<\/li>\n<\/ul>\n\n\n\n

    We want the output size = input size<\/strong>, so: 16=(16+2P\u22125)1+116 = \\frac{(16 + 2P – 5)}{1} + 1<\/p>\n\n\n\n

    \ud83d\udd0d Step-by-step:<\/h3>\n\n\n\n
      \n
    1. Subtract 1 from both sides: 15=16+2P\u2212515 = 16 + 2P – 5<\/li>\n\n\n\n
    2. Simplify the right side: 15=11+2P15 = 11 + 2P<\/li>\n\n\n\n
    3. Subtract 11 from both sides: 4=2P4 = 2P<\/li>\n\n\n\n
    4. Divide both sides by 2: P=2P = 2<\/li>\n<\/ol>\n\n\n\n
      \n\n\n\n

      \ud83d\udccc Interpretation:<\/h3>\n\n\n\n

      This means you must add a padding of 2 pixels<\/strong> on all sides (top, bottom, left, right) of the 16\u00d716 input to ensure that, after applying a 5\u00d75 filter with stride 1<\/strong>, the output remains 16\u00d716<\/strong>.<\/p>\n\n\n\n

      \n\n\n\n

      \ud83e\udde0 Why Padding Matters:<\/h3>\n\n\n\n

      Without padding, convolution reduces the dimensions of your feature map. In applications like image classification<\/strong> or deep learning<\/strong>, we often want to preserve spatial dimensions for consistency across layers\u2014especially in same padding<\/strong> (also called \u201czero-padding\u201d), which keeps the output size equal to the input size.<\/p>\n\n\n\n

      So, for “same” convolution<\/strong>:<\/p>\n\n\n\n

        \n
      • Filter size: 5\u00d75<\/li>\n\n\n\n
      • Padding required: (Filter size – 1)\/2 = (5 – 1)\/2 = 2<\/strong><\/li>\n<\/ul>\n\n\n\n

        \u2705 Hence, Padding = 2<\/strong> is the correct and required value.<\/p>\n","protected":false},"excerpt":{"rendered":"

        If the data dimension is 16 x 16, filter 5×5, and stride 1, then what is the amount of padding required so that convolution output has the same dimension as the input? The correct answer and explanation is: \u2705 Correct Answer: Padding = 2 \ud83d\udcd8 Explanation: To determine the amount of padding required so that […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-24656","post","type-post","status-publish","format-standard","hentry","category-quiz-questions"],"_links":{"self":[{"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/posts\/24656","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/comments?post=24656"}],"version-history":[{"count":1,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/posts\/24656\/revisions"}],"predecessor-version":[{"id":24661,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/posts\/24656\/revisions\/24661"}],"wp:attachment":[{"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/media?parent=24656"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/categories?post=24656"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/tags?post=24656"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}