{"id":4421,"date":"2024-03-14T13:02:51","date_gmt":"2024-03-14T04:02:51","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/pytorch%e3%81%a7%e3%83%a2%e3%83%87%e3%83%ab%e3%81%ae%e3%83%88%e3%83%ac%e3%83%bc%e3%83%8b%e3%83%b3%e3%82%b0%e3%82%92%e8%a1%8c%e3%81%86%e6%96%b9%e6%b3%95%e3%81%af%e4%bd%95%e3%81%a7%e3%81%99%e3%81%8b\/"},"modified":"2025-08-02T21:08:35","modified_gmt":"2025-08-02T12:08:35","slug":"pytorch%e3%81%a7%e3%83%a2%e3%83%87%e3%83%ab%e3%81%ae%e3%83%88%e3%83%ac%e3%83%bc%e3%83%8b%e3%83%b3%e3%82%b0%e3%82%92%e8%a1%8c%e3%81%86%e6%96%b9%e6%b3%95%e3%81%af%e4%bd%95%e3%81%a7%e3%81%99%e3%81%8b","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/pytorch%e3%81%a7%e3%83%a2%e3%83%87%e3%83%ab%e3%81%ae%e3%83%88%e3%83%ac%e3%83%bc%e3%83%8b%e3%83%b3%e3%82%b0%e3%82%92%e8%a1%8c%e3%81%86%e6%96%b9%e6%b3%95%e3%81%af%e4%bd%95%e3%81%a7%e3%81%99%e3%81%8b\/","title":{"rendered":"PyTorch\u3067\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3046\u65b9\u6cd5\u306f\u4f55\u3067\u3059\u304b\u3002"},"content":{"rendered":"<p>PyTorch\u3067\u306e\u30e2\u30c7\u30eb\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306f\u901a\u5e38\u3001\u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u3092\u542b\u307f\u307e\u3059\uff1a<\/p>\n<ol>\n<li>\u30c7\u30fc\u30bf\u306e\u6e96\u5099\uff1a\u6700\u521d\u306b\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u7528\u610f\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002PyTorch\u306b\u306f\u3044\u304f\u3064\u304b\u306e\u7d44\u307f\u8fbc\u307f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u30af\u30e9\u30b9\u304c\u63d0\u4f9b\u3055\u308c\u3066\u3044\u307e\u3059\u304c\u3001\u81ea\u5206\u306e\u30c7\u30fc\u30bf\u3092\u30ed\u30fc\u30c9\u3059\u308b\u305f\u3081\u306b\u30ab\u30b9\u30bf\u30e0\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u30af\u30e9\u30b9\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/li>\n<li>\u30e2\u30c7\u30eb\u306e\u5b9a\u7fa9\uff1a\u6b21\u306b\u3001\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30e2\u30c7\u30eb\u3092\u5b9a\u7fa9\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002PyTorch\u306b\u306f\u3001\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30e2\u30c7\u30eb\u3092\u5b9a\u7fa9\u3059\u308b\u305f\u3081\u306e\u30e2\u30c7\u30eb\u30af\u30e9\u30b9nn.Module\u304c\u63d0\u4f9b\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/li>\n<li>\u640d\u5931\u95a2\u6570\u3092\u5b9a\u7fa9\u3057\u307e\u3059\uff1a\u305d\u3057\u3066\u3001\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u7d50\u679c\u3068\u5b9f\u969b\u306e\u30e9\u30d9\u30eb\u306e\u9593\u306e\u5dee\u7570\u3092\u6e2c\u5b9a\u3059\u308b\u305f\u3081\u306b\u640d\u5931\u95a2\u6570\u3092\u5b9a\u7fa9\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002PyTorch\u306b\u306f\u3001\u30af\u30ed\u30b9\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u640d\u5931\u95a2\u6570\u306a\u3069\u3001\u3044\u304f\u3064\u304b\u306e\u4e00\u822c\u7684\u306a\u640d\u5931\u95a2\u6570\u304c\u7528\u610f\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/li>\n<li>\u6700\u9069\u5316\u5b9a\u7fa9\uff1a\u6b21\u306b\u3001\u30e2\u30c7\u30eb\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u66f4\u65b0\u3059\u308b\u305f\u3081\u306e\u6700\u9069\u5316\u5668\u3092\u9078\u629e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002 PyTorch\u306b\u306f\u3001SGD\u3084Adam\u306a\u3069\u306e\u591a\u304f\u306e\u6700\u9069\u5316\u5668\u304c\u63d0\u4f9b\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/li>\n<li>\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\uff1a\u6700\u5f8c\u306b\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u5404\u30a8\u30dd\u30c3\u30af\u3067\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u53cd\u5fa9\u51e6\u7406\u3057\u3001\u5165\u529b\u30c7\u30fc\u30bf\u3092\u30e2\u30c7\u30eb\u306b\u6e21\u3057\u3066\u9806\u4f1d\u64ad\u3068\u9006\u4f1d\u64ad\u3092\u884c\u3044\u3001\u6700\u9069\u5316\u5668\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u66f4\u65b0\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n<\/ol>\n<p>\u4ee5\u4e0b\u306f\u3001PyTorch\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3046\u65b9\u6cd5\u3092\u793a\u3059\u7c21\u5358\u306a\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3067\u3059\u3002<\/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\"># \u51c6\u5907\u6570\u636e<\/span>\r\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size=<span class=\"hljs-number\">64<\/span>, shuffle=<span class=\"hljs-literal\">True<\/span>)\r\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size=<span class=\"hljs-number\">64<\/span>, shuffle=<span class=\"hljs-literal\">False<\/span>)\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_\">Net<\/span>(nn.Module):\r\n    <span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">__init__<\/span>(<span class=\"hljs-params\">self<\/span>):\r\n        <span class=\"hljs-built_in\">super<\/span>(Net, self).__init__()\r\n        self.fc = nn.Linear(<span class=\"hljs-number\">784<\/span>, <span class=\"hljs-number\">10<\/span>)\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.fc(x)\r\n        <span class=\"hljs-keyword\">return<\/span> x\r\n\r\nmodel = Net()\r\n\r\n<span class=\"hljs-comment\"># \u5b9a\u4e49\u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u5668<\/span>\r\ncriterion = nn.CrossEntropyLoss()\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\">10<\/span>):\r\n    <span class=\"hljs-keyword\">for<\/span> inputs, labels <span class=\"hljs-keyword\">in<\/span> train_loader:\r\n        optimizer.zero_grad()\r\n        outputs = model(inputs)\r\n        loss = criterion(outputs, labels)\r\n        loss.backward()\r\n        optimizer.step()\r\n\r\n    <span class=\"hljs-comment\"># \u5728\u6d4b\u8bd5\u96c6\u4e0a\u8bc4\u4f30\u6a21\u578b<\/span>\r\n    correct = <span class=\"hljs-number\">0<\/span>\r\n    total = <span class=\"hljs-number\">0<\/span>\r\n    <span class=\"hljs-keyword\">with<\/span> torch.no_grad():\r\n        <span class=\"hljs-keyword\">for<\/span> inputs, labels <span class=\"hljs-keyword\">in<\/span> test_loader:\r\n            outputs = model(inputs)\r\n            _, predicted = torch.<span class=\"hljs-built_in\">max<\/span>(outputs.data, <span class=\"hljs-number\">1<\/span>)\r\n            total += labels.size(<span class=\"hljs-number\">0<\/span>)\r\n            correct += (predicted == labels).<span class=\"hljs-built_in\">sum<\/span>().item()\r\n\r\n    accuracy = correct \/ total\r\n    <span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">f'Epoch <span class=\"hljs-subst\">{epoch+<span class=\"hljs-number\">1<\/span>}<\/span>, Accuracy: <span class=\"hljs-subst\">{accuracy}<\/span>'<\/span>)\r\n<\/code><\/pre>\n<p>\u4f8b\u306e\u30b3\u30fc\u30c9\u3067\u306f\u3001\u307e\u305a\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u6e96\u5099\u3057\u3001\u7c21\u5358\u306a\u5168\u7d50\u5408\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30e2\u30c7\u30eb\u3092\u5b9a\u7fa9\u3057\u307e\u3057\u305f\u3002\u6b21\u306b\u3001\u30af\u30ed\u30b9\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u640d\u5931\u95a2\u6570\u3068SGD\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u3092\u5b9a\u7fa9\u3057\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u3057\u305f\u3002\u5404\u30a8\u30dd\u30c3\u30af\u306e\u7d42\u308f\u308a\u306b\u3001\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u8a55\u4fa1\u3057\u307e\u3057\u305f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>PyTorch\u3067\u306e\u30e2\u30c7\u30eb\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306f\u901a\u5e38\u3001\u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u3092\u542b\u307f\u307e\u3059\uff1a \u30c7\u30fc\u30bf\u306e\u6e96\u5099\uff1a\u6700\u521d\u306b\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u7528\u610f\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002PyTorch\u306b\u306f\u3044\u304f\u3064\u304b\u306e\u7d44\u307f\u8fbc\u307f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u30af\u30e9\u30b9\u304c\u63d0\u4f9b\u3055\u308c\u3066\u3044\u307e [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[26,61],"class_list":["post-4421","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-26","tag-61"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.5 (Yoast SEO v21.5) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ 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