[{"id":"dfed890e-72a2-4373-b01f-c3671a86ce78","thread_id":"24fef214-6da4-4d5d-a210-58e9c8e65740","created_at":"2025-08-08T01:45:09Z","state":"done","prompt":"Give me the formula for calculating the output size of a convolutional layer. Use names like output_size, input_size, etc. instead of single letters, where appropriate.","references_html":"<ol data-ref-list>\n \n <li>\n <a href=\"https://stackoverflow.com/questions/46004393/convolutional-layer-output-size\" >tensorflow - Convolutional layer output size - Stack Overflow</a>\n <span class=\"__domain-name\">stackoverflow.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) , transparent )\"></span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n <li>\n <a href=\"https://kvirajdatt.medium.com/calculating-output-dimensions-in-a-cnn-for-convolution-and-pooling-layers-with-keras-682960c73870\" >Calculating Output dimensions in a CNN for Convolution and ...</a>\n <span class=\"__domain-name\">kvirajdatt.medium.com</span>\n <div class=\"trigger\" tabindex=\"0\">\n <span class=\"contribution\" style=\"background: linear-gradient(to right, var(--primary-70) , transparent )\"></span>\n <div class=\"tooltip top end tooltip_base tw-auto\">\n This percentage indicates how much a source has contributed to the answer.\n </div>\n </div>\n </li>\n \n</ol>\n","references_md":"[^1]: [tensorflow - Convolutional layer output size - Stack Overflow](https://stackoverflow.com/questions/46004393/convolutional-layer-output-size)\n[^2]: [Calculating Output dimensions in a CNN for Convolution and ...](https://kvirajdatt.medium.com/calculating-output-dimensions-in-a-cnn-for-convolution-and-pooling-layers-with-keras-682960c73870)\n","reply":"<details><summary>Searching with Kagi:<p></p>\n<ul>\n<li>formula for calculating output size of convolutional layer</li></ul></summary> Sources (10):<br>\n**Calculate the output size in convolution layer <a href=\"https://stackoverflow.com/questions/53580088/calculate-the-output-size-in-convolution-layer\" target=\"_blank\">https://stackoverflow.com/questions/53580088/calculate-the-output-size-in-convolution-layer</a>t-size-in-convolution-layer)<br>\nDec 02, 2018 You can find it in two ways: simple method: input_size - (filter_size - 1) W - (K-1) Here W = Input size K = Filter size S = Stride P = Padding\n\n<p><strong>size - Calculating shape of conv1d layer in Pytorch - Stack Overflow</strong><br>\n<a href=\"https://stackoverflow.com/questions/62162576/calculating-shape-of-conv1d-layer-in-pytorch\" target=\"_blank\">https://stackoverflow.com/questions/62162576/calculating-shape-of-conv1d-layer-in-pytorch</a><br>\n The output size can be calculated as shown in the documentation nn.Conv1d - Shape: The batch size remains unchanged and you already know the number of channels, since you specified them when creating the convolution (depth_2 in this example).</p>\n<p><strong>tensorflow - Convolutional layer output size - Stack Overflow</strong><br>\n<a href=\"https://stackoverflow.com/questions/46004393/convolutional-layer-output-size\" target=\"_blank\">https://stackoverflow.com/questions/46004393/convolutional-layer-output-size</a><br>\n According to CS231n, the common formula for computing the output size of a conv. layer is W'=(W−F+2P)/S+1, where W is the input size, F is the receptive field, P is the padding and S is the stride.</p>\n<p><strong>Calculating size of output of a Conv layer in CNN model</strong><br>\n<a href=\"https://stackoverflow.com/questions/43508589/calculating-size-of-output-of-a-conv-layer-in-cnn-model\" target=\"_blank\">https://stackoverflow.com/questions/43508589/calculating-size-of-output-of-a-conv-layer-in-cnn-model</a><br>\n In convolutional Neural Networks, How to know the output of a specific conv layer? (I am using keras to build a CNN model) For example if I am using one dimensional conv layer, where number_of_filters=20, kernel_size=10, and input_shape (500,1)</p>\n<p><strong>PyTorch: How to calculate output size of the CNN?</strong><br>\n<a href=\"https://stackoverflow.com/questions/71647309/pytorch-how-to-calculate-output-size-of-the-cnn\" target=\"_blank\">https://stackoverflow.com/questions/71647309/pytorch-how-to-calculate-output-size-of-the-cnn</a><br>\n So if you want calculate input size for first Linear layer, you can use this trick: conv = nn.Sequential(self.layer1,self.layer2, self.layer3, nn.Flatten()) out = conv(torch.randn(1,im_height,im_width).unsqueeze(0)) # fc_layer_in_channels = out.shape[1] self.fc1 = torch.nn.Linear(out.shape[1], 625, bias=True) but only if you know im ...</p>\n<p><strong>Calculate the Output Size of a Convolutional Layer - Baeldung</strong><br>\n<a href=\"https://www.baeldung.com/cs/convolutional-layer-size\" target=\"_blank\">https://www.baeldung.com/cs/convolutional-layer-size</a><br>\nMar 18, 2024 In this tutorial, we'll describe how we can calculate the output size of a convolutional layer. First, we'll briefly introduce the convolution operator and the ...</p>\n<p><strong>Calculating Parameters of Convolutional and Fully Connected ...</strong><br>\n<a href=\"https://dingyan89.medium.com/calculating-parameters-of-convolutional-and-fully-connected-layers-with-keras-186590df36c6\" target=\"_blank\">https://dingyan89.medium.com/calculating-parameters-of-convolutional-and-fully-connected-layers-with-keras-186590df36c6</a><br>\nOct 15, 2020 Before we dive in, there is an equation for calculating the output of convolutional layers as follows: ... output size is (28–2)/2 +1 = 14.</p>\n<p><strong>Calculating the Output Size of Convolutions and Transpose Convolutions</strong><br>\n<a href=\"https://makeyourownneuralnetwork.blogspot.com/2020/02/calculating-output-size-of-convolutions.html\" target=\"_blank\">https://makeyourownneuralnetwork.blogspot.com/2020/02/calculating-output-size-of-convolutions.html</a><br>\nFeb 01, 2020 The calculation is then simply 2*2 - 2 + 1 + 1 = 4, so the output is of size 4. On the PyTorch references pages you can read about more general ...</p>\n<p><strong>Calculating Output dimensions in a CNN for Convolution and ...</strong><br>\n<a href=\"https://kvirajdatt.medium.com/calculating-output-dimensions-in-a-cnn-for-convolution-and-pooling-layers-with-keras-682960c73870\" target=\"_blank\">https://kvirajdatt.medium.com/calculating-output-dimensions-in-a-cnn-for-convolution-and-pooling-layers-with-keras-682960c73870</a><br>\nJun 25, 2020 Calculating the output when an image passes through a Convolutional layer:- · Convolution Output dimension = [(I - F +2 *P) / S] +1 x D > ...</p>\n<p><strong>Calculate output size of Convolution - OpenGenus IQ</strong><br>\n<a href=\"https://iq.opengenus.org/output-size-of-convolution/\" target=\"_blank\">https://iq.opengenus.org/output-size-of-convolution/</a><br>\n In this article, we have illustrated how to calculate the size of output in a convolution provided we have the dimensions of input data, kernel, stride and padding.</p></details><details><summary>Thinking</summary>\n<p>The user is asking for the formula for calculating the output size of a convolutional layer, and specifically wants me to use descriptive variable names instead of single letters.</p>\n<p>From the provided information, I can see that the common formula from CS231n is:<br>\nW' = (W - F + 2P)/S + 1</p>\n<p>Where:</p>\n<ul>\n<li>W' is the output size</li>\n<li>W is the input size</li>\n<li>F is the receptive field (filter size)</li>\n<li>P is the padding</li>\n<li>S is the stride</li>\n</ul>\n<p>The user wants me to use names like \"output_size\", \"input_size\", etc. So I should translate this formula using more descriptive variable names.</p>\n<p>I need to provide citations for this information as well.</p>\n</details>\n<p>The formula for calculating the output size of a convolutional layer is: <sup class=\"trigger\"><a href=\"https://stackoverflow.com/questions/46004393/convolutional-layer-output-size\">1</a><span class=\"tooltip top center citation\"><a href=\"https://stackoverflow.com/questions/46004393/convolutional-layer-output-size#:~:text=According%20to,the%20stride.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJrN94MAGqYPlcQNXHDBYc5aLbfREbHukc4f9EOHQ1MdU%3D\"> stackoverflow.com</span><b>1. tensorflow - Convolutional layer output size - Stack Overflow</b></a><span class=\"passage\">According to CS231n, the common formula for computing the output size of a conv. layer is W'=(W−F+2P)/S+1, where W is the input size, F is the receptive field, P is the padding and S is the stride.</span></span></sup></p>\n<p><math display=\"block\"><mtext>output\\_size</mtext><mo>=</mo><mfrac><mrow><mtext>input\\_size</mtext><mo>−</mo><mtext>filter\\_size</mtext><mo>+</mo><mn>2</mn><mo>×</mo><mtext>padding</mtext></mrow><mrow><mtext>stride</mtext></mrow></mfrac><mo>+</mo><mn>1</mn></math></p>\n<p>Where each variable represents:</p>\n<ul>\n<li><strong>output_size</strong>: The dimension of the output feature map</li>\n<li><strong>input_size</strong>: The dimension of the input (width or height)</li>\n<li><strong>filter_size</strong>: The size of the convolutional filter (also called receptive field)</li>\n<li><strong>padding</strong>: The number of pixels added around the input border</li>\n<li><strong>stride</strong>: The step size with which the filter moves across the input</li>\n</ul>\n<p>This formula applies to each spatial dimension (width and height) independently. For a 2D convolution, you would calculate both the output width and output height using their respective input dimensions. <sup class=\"trigger\"><a href=\"https://stackoverflow.com/questions/46004393/convolutional-layer-output-size\">1</a><span class=\"tooltip top center citation\"><a href=\"https://stackoverflow.com/questions/46004393/convolutional-layer-output-size#:~:text=According%20to,the%20stride.\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJrN94MAGqYPlcQNXHDBYc5aLbfREbHukc4f9EOHQ1MdU%3D\"> stackoverflow.com</span><b>1. tensorflow - Convolutional layer output size - Stack Overflow</b></a><span class=\"passage\">According to CS231n, the common formula for computing the output size of a conv. layer is W'=(W−F+2P)/S+1, where W is the input size, F is the receptive field, P is the padding and S is the stride.</span></span></sup><sup class=\"trigger\"><a href=\"https://kvirajdatt.medium.com/calculating-output-dimensions-in-a-cnn-for-convolution-and-pooling-layers-with-keras-682960c73870\">2</a><span class=\"tooltip top center citation\"><a href=\"https://kvirajdatt.medium.com/calculating-output-dimensions-in-a-cnn-for-convolution-and-pooling-layers-with-keras-682960c73870#:~:text=Calculating%20the,D%20%3E\"><span class=\"source\"><img alt=\"\" src=\"/proxy/favicons?c=lQ30-cxV_E2Vdyqb6hrbuYgId7qu7hecjO7o1T7aqQDFc7XpcLttL1Ryala-GNHJIZi9F-bKtq4ev5Uh-6JV_Ed7SRXcZjDUg8EF8b2eZktaNgNTVCWfAlHt1o8sNaTQ\"> kvirajdatt.medium.com</span><b>2. Calculating Output dimensions in a CNN for Convolution and ...</b></a><span class=\"passage\">Calculating the output when an image passes through a Convolutional layer:- · Convolution Output dimension = [(I - F +2 *P) / S] +1 x D > ...</span></span></sup></p>\n<p>The formula assumes that the result of the division is an integer – if not, the output size is typically rounded down (floor operation) in most deep learning frameworks.</p>","md":"<details><summary>Thinking</summary>\n\nThe user is asking for the formula for calculating the output size of a convolutional layer, and specifically wants me to use descriptive variable names instead of single letters.\n\nFrom the provided information, I can see that the common formula from CS231n is:\nW' = (W - F + 2P)/S + 1\n\nWhere:\n- W' is the output size\n- W is the input size\n- F is the receptive field (filter size)\n- P is the padding\n- S is the stride\n\nThe user wants me to use names like \"output_size\", \"input_size\", etc. So I should translate this formula using more descriptive variable names.\n\nI need to provide citations for this information as well.\n\n</details>\n\nThe formula for calculating the output size of a convolutional layer is: [^1]\n\n$$\\text{output\\_size} = \\frac{\\text{input\\_size} - \\text{filter\\_size} + 2 \\times \\text{padding}}{\\text{stride}} + 1$$\n\nWhere each variable represents:\n\n- **output_size**: The dimension of the output feature map\n- **input_size**: The dimension of the input (width or height)\n- **filter_size**: The size of the convolutional filter (also called receptive field)\n- **padding**: The number of pixels added around the input border\n- **stride**: The step size with which the filter moves across the input\n\nThis formula applies to each spatial dimension (width and height) independently. For a 2D convolution, you would calculate both the output width and output height using their respective input dimensions. [^1][^2]\n\nThe formula assumes that the result of the division is an integer – if not, the output size is typically rounded down (floor operation) in most deep learning frameworks.","profile":{"id":null,"name":null,"model":"claude-4-sonnet-thinking","model_name":"Claude 4.5 Sonnet (reasoning)","model_provider":"anthropic","model_input_limit":1000000,"recommended":false,"model_info":"\n\n<div class=\"heading\">\n <div class=\"left\">\n \n \n \n <svg class=\"model-icon icon-xs\" data-model=\"claude-4-sonnet-thinking\">\n <use href=\"#icon-anthropic\"/>\n </svg>\n <h3>\n \n <span class=\"model-provider\">Anthropic</span>\n \n <span class=\"model-name\">none</span>\n </h3>\n </div>\n\n \n <a href=\"/settings?p=custom_assistant&id=none\">\n <svg class=\"icon-sm edit-icon\" aria-hidden=\"true\">\n <use href=\"#icon-pencil\" />\n </svg>\n </a>\n \n</div>\n\n\n<hr />\n<ul>\n \n <li>\n <h4>\n Cost\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Relatively expensive\">\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-cost --medium\"></div>\n \n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-cost --medium\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Quality\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 4 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Speed\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 2 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li>\n <h4>\n Privacy\n </h4>\n <span>\n \n \n \n\n \n \n \n\n <div class=\"score\" aria-label=\"Rated 4 out of 5\">\n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"filled-bubble\"></div>\n \n \n \n \n <div aria-hidden=\"true\" class=\"unfilled-bubble\"></div>\n \n \n </div>\n</span>\n </li>\n <li class=\"divider\" aria-hidden=\"true\"></li>\n <li>\n <h4>Last updated</h4>\n <span class=\"date\">\n \n \n 2025-09-29\n \n \n </span>\n </li>\n \n</ul>\n","model_provider_name":"Anthropic","internet_access":true,"personalizations":true,"shortcut":null,"is_default_profile":false},"metadata":"<li>\n <span class=\"attribute\">Model</span>\n <span class=\"value\"><i class=\"icon-xs mr-2\"><svg><use href=\"#icon-anthropic\"></use></svg></i>Claude 4 Sonnet (reasoning)</span>\n</li>\n<li>\n <span class=\"attribute\">Version</span>\n <span class=\"value\">claude-sonnet-4-20250514</span>\n</li>\n<li>\n <span class=\"attribute\">Speed (tok/s)</span>\n <span class=\"value\">49</span>\n</li>\n<li>\n <span class=\"attribute\">Tokens</span>\n <span class=\"value\">2,864</span>\n</li>\n<li>\n <span class=\"attribute\">Cost / Total ($)</span>\n <span class=\"value\">0.02 / 0.02</span>\n</li>\n<li>\n <span class=\"attribute\">End to end time (s)</span>\n <span class=\"value\">10.55</span>\n</li>\n\n","documents":[]}]