{"id":3686,"date":"2024-03-13T07:18:31","date_gmt":"2024-03-13T07:18:31","guid":{"rendered":"https:\/\/www.silicloud.com\/blog\/how-to-perform-hyperparameter-tuning-in-torch\/"},"modified":"2025-07-30T19:49:21","modified_gmt":"2025-07-30T19:49:21","slug":"how-to-perform-hyperparameter-tuning-in-torch","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/blog\/how-to-perform-hyperparameter-tuning-in-torch\/","title":{"rendered":"PyTorch Hyperparameter Tuning Guide"},"content":{"rendered":"<p>In Torch, conducting hyperparameter search can typically be done using either GridSearch or RandomSearch methods. Below is a simple example code that uses the GridSearch method to search for the best combination of hyperparameters.<\/p>\n<pre class=\"post-pre\"><code><span class=\"hljs-keyword\">from<\/span> torch <span class=\"hljs-keyword\">import<\/span> nn\r\n<span class=\"hljs-keyword\">from<\/span> torch.optim <span class=\"hljs-keyword\">import<\/span> Adam\r\n<span class=\"hljs-keyword\">from<\/span> sklearn.model_selection <span class=\"hljs-keyword\">import<\/span> ParameterGrid\r\n\r\n<span class=\"hljs-comment\"># \u5b9a\u4e49\u6a21\u578b<\/span>\r\n<span class=\"hljs-keyword\">class<\/span> <span class=\"hljs-title class_\">SimpleModel<\/span>(nn.Module):\r\n    <span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">__init__<\/span>(<span class=\"hljs-params\">self, input_size, hidden_size, output_size<\/span>):\r\n        <span class=\"hljs-built_in\">super<\/span>(SimpleModel, self).__init__()\r\n        self.fc1 = nn.Linear(input_size, hidden_size)\r\n        self.relu = nn.ReLU()\r\n        self.fc2 = nn.Linear(hidden_size, output_size)\r\n    \r\n    <span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">forward<\/span>(<span class=\"hljs-params\">self, x<\/span>):\r\n        x = self.fc1(x)\r\n        x = self.relu(x)\r\n        x = self.fc2(x)\r\n        <span class=\"hljs-keyword\">return<\/span> x\r\n\r\n<span class=\"hljs-comment\"># \u5b9a\u4e49\u8d85\u53c2\u6570\u7f51\u683c<\/span>\r\nparam_grid = {\r\n    <span class=\"hljs-string\">'input_size'<\/span>: [<span class=\"hljs-number\">10<\/span>, <span class=\"hljs-number\">20<\/span>],\r\n    <span class=\"hljs-string\">'hidden_size'<\/span>: [<span class=\"hljs-number\">100<\/span>, <span class=\"hljs-number\">200<\/span>],\r\n    <span class=\"hljs-string\">'output_size'<\/span>: [<span class=\"hljs-number\">2<\/span>, <span class=\"hljs-number\">3<\/span>],\r\n    <span class=\"hljs-string\">'learning_rate'<\/span>: [<span class=\"hljs-number\">0.001<\/span>, <span class=\"hljs-number\">0.01<\/span>]\r\n}\r\n\r\n<span class=\"hljs-comment\"># \u4f7f\u7528GridSearch\u641c\u7d22\u6700\u4f73\u8d85\u53c2\u6570\u7ec4\u5408<\/span>\r\nbest_score = <span class=\"hljs-number\">0<\/span>\r\nbest_params = <span class=\"hljs-literal\">None<\/span>\r\n<span class=\"hljs-keyword\">for<\/span> params <span class=\"hljs-keyword\">in<\/span> ParameterGrid(param_grid):\r\n    model = SimpleModel(params[<span class=\"hljs-string\">'input_size'<\/span>], params[<span class=\"hljs-string\">'hidden_size'<\/span>], params[<span class=\"hljs-string\">'output_size'<\/span>])\r\n    optimizer = Adam(model.parameters(), lr=params[<span class=\"hljs-string\">'learning_rate'<\/span>])\r\n    \r\n    <span class=\"hljs-comment\"># \u8bad\u7ec3\u6a21\u578b\u5e76\u8bc4\u4f30\u6027\u80fd<\/span>\r\n    <span class=\"hljs-comment\"># \u8fd9\u91cc\u7701\u7565\u8bad\u7ec3\u8fc7\u7a0b<\/span>\r\n    \r\n    score = <span class=\"hljs-number\">0.8<\/span>  <span class=\"hljs-comment\"># \u5047\u8bbe\u8bc4\u4f30\u5f97\u5206\u4e3a0.8<\/span>\r\n    \r\n    <span class=\"hljs-keyword\">if<\/span> score &gt; best_score:\r\n        best_score = score\r\n        best_params = params\r\n\r\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Best score:\"<\/span>, best_score)\r\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Best params:\"<\/span>, best_params)\r\n<\/code><\/pre>\n<p>In this example, we defined a simple neural network model called SimpleModel, and then defined a grid of hyperparameters param_grid. Next, we used ParameterGrid(param_grid) to generate all possible combinations of hyperparameters, instantiated the model in a loop, and trained and evaluated it. Finally, we selected the best hyperparameter combination based on the evaluation scores. You can adjust the hyperparameter grid and evaluation score calculation method according to your specific needs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In Torch, conducting hyperparameter search can typically be done using either GridSearch or RandomSearch methods. Below is a simple example code that uses the GridSearch method to search for the best combination of hyperparameters. from torch import nn from torch.optim import Adam from sklearn.model_selection import ParameterGrid # \u5b9a\u4e49\u6a21\u578b class SimpleModel(nn.Module): def __init__(self, input_size, hidden_size, output_size): [&hellip;]<\/p>\n","protected":false},"author":10,"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":[2367,1216,75,1239,2368],"class_list":["post-3686","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-gridsearch","tag-hyperparameter-tuning","tag-machine-learning","tag-pytorch","tag-randomsearch"],"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 Hyperparameter Tuning Guide - 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