{"id":3922,"date":"2024-03-13T07:41:12","date_gmt":"2024-03-13T07:41:12","guid":{"rendered":"https:\/\/www.silicloud.com\/blog\/how-to-handle-regression-tasks-in-torch\/"},"modified":"2025-07-30T22:56:58","modified_gmt":"2025-07-30T22:56:58","slug":"how-to-handle-regression-tasks-in-torch","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/blog\/how-to-handle-regression-tasks-in-torch\/","title":{"rendered":"PyTorch Regression Guide: Steps &#038; Code"},"content":{"rendered":"<p>In Torch, handling regression tasks typically involves defining a loss function and optimizer to train the model. First, a loss function is defined to measure the difference between model predictions and actual values, commonly used loss functions include Mean Squared Error and Mean Absolute Error. Then an optimizer is used to adjust model parameters to minimize the loss function, commonly used optimizers include Stochastic Gradient Descent (SGD) and Adam.<\/p>\n<p>Here is a simple example code for handling regression tasks:<\/p>\n<pre class=\"post-pre\"><code><span class=\"hljs-keyword\">import<\/span> torch\r\n<span class=\"hljs-keyword\">import<\/span> torch.nn <span class=\"hljs-keyword\">as<\/span> nn\r\n<span class=\"hljs-keyword\">import<\/span> torch.optim <span class=\"hljs-keyword\">as<\/span> optim\r\n\r\n<span class=\"hljs-comment\"># \u5b9a\u4e49\u6570\u636e<\/span>\r\nX = torch.tensor([[<span class=\"hljs-number\">1.0<\/span>], [<span class=\"hljs-number\">2.0<\/span>], [<span class=\"hljs-number\">3.0<\/span>]])\r\ny = torch.tensor([[<span class=\"hljs-number\">2.0<\/span>], [<span class=\"hljs-number\">4.0<\/span>], [<span class=\"hljs-number\">6.0<\/span>]])\r\n\r\n<span class=\"hljs-comment\"># \u5b9a\u4e49\u6a21\u578b<\/span>\r\nmodel = nn.Linear(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>)\r\n\r\n<span class=\"hljs-comment\"># \u5b9a\u4e49\u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u5668<\/span>\r\ncriterion = nn.MSELoss()\r\noptimizer = optim.SGD(model.parameters(), lr=<span class=\"hljs-number\">0.01<\/span>)\r\n\r\n<span class=\"hljs-comment\"># \u8bad\u7ec3\u6a21\u578b<\/span>\r\n<span class=\"hljs-keyword\">for<\/span> epoch <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">100<\/span>):\r\n    optimizer.zero_grad()\r\n    outputs = model(X)\r\n    loss = criterion(outputs, y)\r\n    loss.backward()\r\n    optimizer.step()\r\n\r\n    <span class=\"hljs-keyword\">if<\/span> (epoch+<span class=\"hljs-number\">1<\/span>) % <span class=\"hljs-number\">10<\/span> == <span class=\"hljs-number\">0<\/span>:\r\n        <span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">'Epoch [{}\/{}], Loss: {:.4f}'<\/span>.<span class=\"hljs-built_in\">format<\/span>(epoch+<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">100<\/span>, loss.item()))\r\n\r\n<span class=\"hljs-comment\"># \u6d4b\u8bd5\u6a21\u578b<\/span>\r\n<span class=\"hljs-keyword\">with<\/span> torch.no_grad():\r\n    test_input = torch.tensor([[<span class=\"hljs-number\">4.0<\/span>]])\r\n    predicted = model(test_input)\r\n    <span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">'Predicted value: {:.2f}'<\/span>.<span class=\"hljs-built_in\">format<\/span>(predicted.item()))\r\n<\/code><\/pre>\n<p>In the above code, we first define the data X and y, then define a simple linear model with mean square error as the loss function and stochastic gradient descent as the optimizer. Next, we train the model, calculating loss and updating model parameters for each epoch, and finally test the model and output the predictions.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In Torch, handling regression tasks typically involves defining a loss function and optimizer to train the model. First, a loss function is defined to measure the difference between model predictions and actual values, commonly used loss functions include Mean Squared Error and Mean Absolute Error. Then an optimizer is used to adjust model parameters to [&hellip;]<\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[2901,2899,2898,2902,2900],"class_list":["post-3922","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-adam-optimizer","tag-loss-functions","tag-pytorch-regression","tag-pytorch-tutorials","tag-sgd-optimizer"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.5 (Yoast SEO v21.5) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>PyTorch Regression Guide: Steps &amp; Code - Blog - Silicon Cloud<\/title>\n<meta name=\"description\" content=\"Learn PyTorch regression implementation: loss functions (MSE\/MAE), optimizers (SGD\/Adam), and practical code examples.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.silicloud.com\/blog\/how-to-handle-regression-tasks-in-torch\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PyTorch Regression Guide: Steps &amp; 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