{ "source_file": "0930", "source_platform": "Medium", "author": "Aqeel Anwar", "title_listed": "What is Transposed Convolutional Layer?", "keywords": "转置卷积,CNN", "domain": "图像处理", "content_type": "深度技术解析", "is_paid_flag": "部分免费/会员墙", "url": "https://towardsdatascience.com/what-is-transposed-convolutional-layer-40e5e6e31c11/", "publish_date": "2020-03", "corpus_stage": "预训练/SFT", "notes": "", "id": "ffa005182384", "collected_at": "2026-10-01T23:11:03", "title": "What is Transposed Convolutional Layer? | Towards Data Science", "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n# What is Transposed Convolutional Layer?\nExplained through animated gifs and python code\n[Aqeel Anwar](https://towardsdatascience.com/author/aqeel%20anwar/)\nMarch 6, 20203 min read\nThe transposed Convolutional Layer is also (wrongfully) known as the Deconvolutional layer. A deconvolutional layer reverses the operation of a standard convolutional layer i.e. if the output generated through a standard convolutional layer is deconvolved, you get back the original input. The transposed convolutional layer is similar to the deconvolutional layer in the sense that the spatial dimension generated by both are the same. Transposed convolution doesn’t reverse the standard convolution by values, rather by dimensions only.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-125-1024x147.png)\nThe transposed convolutional layer does exactly what a standard convolutional layer does but on a modified input feature map. Before explaining the similarity, let's first have a look at how does a standard convolutional layer works.\n## Standard Convolutional Layer:\nA standard convolutional layer on an input of size _**ixi**_ is defined by the following two parameters.\n * **Padding** _**(p):**_ The number of zeros padded around the original input increasing the size to _**(i+2*p)x(i+2*p)**_\n * **Stride** _**(s):**_ The amount by which the kernel is shifted when sliding across the input image.\n\n\nThe figure below shows how a convolutional layer works as a two-step process.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-126-1024x251.png)\nIn the first step, the input image is padded with zeros, while in the second step the kernel is placed on the padded input and slid across generating the output pixels as dot products of the kernel and the overlapped input region. The kernel is slid across the padded input by taking jumps of size defined by the stride. The convolutional layer usually does a down-sampling i.e. the spatial dimensions of the output are less than that of the input.\nThe animations below explain the working of convolutional layers for different values of stride and padding.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-4.gif)\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-5.gif)\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-6.gif)\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-7.gif)\nFor a given size of the input _**(i)**_ , kernel _**(k)**_ , padding _**(p),**_ and stride _**(s)**_ , the size of the output feature map _**(o)**_ generated is given by\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-127-1024x90.png)\n## Transposed Convolutional Layer:\nA transposed convolutional layer, on the other hand, is usually carried out for upsampling i.e. to generate an output feature map that has a spatial dimension greater than that of the input feature map. Just like the standard convolutional layer, the transposed convolutional layer is also defined by the padding and stride. These values of padding and stride are the one that hypothetically was carried out on the output to generate the input. i.e. if you take the output, and carry out a standard convolution with stride and padding defined, it will generate the spatial dimension same as that of the input.\nImplementing a transposed convolutional layer can be better explained as a 4 step process\n * **Step 1:** Calculate new parameters z and p’\n * **Step 2:** Between each row and columns of the input, insert z number of zeros. This increases the size of the input to _**(2*i-1)x(2*i-1)**_\n * **Step 3:** Pad the modified input image with p’ number of zeros\n * **Step 4:** Carry out standard convolution on the image generated from step 3 with a stride length of 1\n\n\nThe complete steps can be seen in the figure below.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-128-1024x244.png)\nImage By Author\nThe animations below explain the working of convolutional layers for different values of stride and padding.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-8.gif)\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-9.gif)\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-10.gif)\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-11.gif)\nFor a given size of the input _**(i)**_ , kernel _**(k)**_ , padding _**(p),**_ and stride _**(s)**_ , the size of the output feature map _**(o)**_ generated is given by\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-129-1024x67.png)\n## Summary:\nThe table below summarizes the two convolutions, standard and transposed.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-130-1024x167.png)\n * The idea behind transposed convolution is to carry out trainable upsampling\n * Transposed convolutions are standard convolutions but with a modified input feature map.\n * The stride and padding **do not** correspond to the number of zeros added around the image and the amount of shift in the kernel when sliding it across the input, as they would in a standard convolution operation.\n\n\n## Python Code:\nThe gifs were generated using python. The complete code can be found at \n# Bonus:\nCompact cheat sheets for this topic and many other important topics in Machine Learning can be found in the link below\n**If this article was helpful to you, feel free to clap, share and respond to it. If you want to learn more about Machine Learning and Data Science, follow me @**[**Aqeel Anwar**](https://medium.com/u/a7cc4f201fb5?source=post_page---user_mention--40e5e6e31c11---------------------------------------)**or connect with me on**[ _**LinkedIn**_](https://www.linkedin.com/in/aqeelanwarmalik/) _**.**_\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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