{"id":4351,"date":"2024-03-14T12:54:15","date_gmt":"2024-03-14T03:54:15","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/pytorch%e3%81%a7%e6%99%82%e9%96%93%e7%b3%bb%e5%88%97%e3%83%87%e3%83%bc%e3%82%bf%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e3%81%a9%e3%81%ae%e3%82%88%e3%81%86%e3%81%ab%e5%87%a6%e7%90%86%e3%81%99%e3%82%8b\/"},"modified":"2025-08-02T20:38:51","modified_gmt":"2025-08-02T11:38:51","slug":"pytorch%e3%81%a7%e6%99%82%e9%96%93%e7%b3%bb%e5%88%97%e3%83%87%e3%83%bc%e3%82%bf%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e3%81%a9%e3%81%ae%e3%82%88%e3%81%86%e3%81%ab%e5%87%a6%e7%90%86%e3%81%99%e3%82%8b","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/pytorch%e3%81%a7%e6%99%82%e9%96%93%e7%b3%bb%e5%88%97%e3%83%87%e3%83%bc%e3%82%bf%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e3%81%a9%e3%81%ae%e3%82%88%e3%81%86%e3%81%ab%e5%87%a6%e7%90%86%e3%81%99%e3%82%8b\/","title":{"rendered":"PyTorch\u3067\u6642\u9593\u7cfb\u5217\u30c7\u30fc\u30bf\u30bf\u30b9\u30af\u3092\u3069\u306e\u3088\u3046\u306b\u51e6\u7406\u3059\u308b\u306e\u304b\uff1f"},"content":{"rendered":"<p>\u901a\u5e38\uff0c\u5728PyTorch\u4e2d\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u4efb\u52a1\u9700\u8981\u4f7f\u7528torch.nn.RNN\u3001torch.nn.LSTM\u3001torch.nn.GRU\u306a\u3069\u306e\u518d\u5e30\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30e2\u30b8\u30e5\u30fc\u30eb\u3068\u3001torch.utils.data.Dataset\u3084torch.utils.data.DataLoader\u306a\u3069\u306e\u30c7\u30fc\u30bf\u30ed\u30fc\u30c7\u30a3\u30f3\u30b0\u30c4\u30fc\u30eb\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n<p>\u4ee5\u4e0b\u306f\u3001PyTorch\u3092\u4f7f\u7528\u3057\u3066\u6642\u9593\u7cfb\u5217\u30c7\u30fc\u30bf\u30bf\u30b9\u30af\u3092\u51e6\u7406\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3059\u7c21\u5358\u306a\u4f8b\u3067\u3059\u3002<\/p>\n<ol>\n<li>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8<\/li>\n<\/ol>\n<pre class=\"post-pre\"><code><span class=\"hljs-keyword\">import<\/span> torch\r\n<span class=\"hljs-keyword\">from<\/span> torch.utils.data <span class=\"hljs-keyword\">import<\/span> Dataset\r\n\r\n<span class=\"hljs-keyword\">class<\/span> <span class=\"hljs-title class_\">TimeSeriesDataset<\/span>(<span class=\"hljs-title class_ inherited__\">Dataset<\/span>):\r\n    <span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">__init__<\/span>(<span class=\"hljs-params\">self, data<\/span>):\r\n        self.data = data\r\n    \r\n    <span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">__len__<\/span>(<span class=\"hljs-params\">self<\/span>):\r\n        <span class=\"hljs-keyword\">return<\/span> <span class=\"hljs-built_in\">len<\/span>(self.data)\r\n    \r\n    <span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">__getitem__<\/span>(<span class=\"hljs-params\">self, idx<\/span>):\r\n        <span class=\"hljs-keyword\">return<\/span> self.data[idx]\r\n<\/code><\/pre>\n<ol>\n<li>RNN\u3092\u542b\u3080\u30e2\u30c7\u30eb\u3092\u5b9a\u7fa9\u3059\u308b\u3002<\/li>\n<\/ol>\n<pre class=\"post-pre\"><code><span class=\"hljs-keyword\">import<\/span> torch.nn <span class=\"hljs-keyword\">as<\/span> nn\r\n\r\n<span class=\"hljs-keyword\">class<\/span> <span class=\"hljs-title class_\">RNNModel<\/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, num_layers, output_size<\/span>):\r\n        <span class=\"hljs-built_in\">super<\/span>(RNNModel, self).__init__()\r\n        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=<span class=\"hljs-literal\">True<\/span>)\r\n        self.fc = 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        out, _ = self.rnn(x)\r\n        out = self.fc(out[:, -<span class=\"hljs-number\">1<\/span>, :])\r\n        <span class=\"hljs-keyword\">return<\/span> out\r\n<\/code><\/pre>\n<ol>\n<li>\u30c7\u30fc\u30bf\u3092\u6e96\u5099\u3057\u3066\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u3002<\/li>\n<\/ol>\n<pre class=\"post-pre\"><code><span class=\"hljs-comment\"># \u5b9a\u4e49\u8d85\u53c2\u6570<\/span>\r\ninput_size = <span class=\"hljs-number\">1<\/span>\r\nhidden_size = <span class=\"hljs-number\">64<\/span>\r\nnum_layers = <span class=\"hljs-number\">1<\/span>\r\noutput_size = <span class=\"hljs-number\">1<\/span>\r\nnum_epochs = <span class=\"hljs-number\">100<\/span>\r\nlearning_rate = <span class=\"hljs-number\">0.001<\/span>\r\n\r\n<span class=\"hljs-comment\"># \u51c6\u5907\u6570\u636e<\/span>\r\ndata = [<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">2<\/span>, <span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">4<\/span>, <span class=\"hljs-number\">5<\/span>, <span class=\"hljs-number\">6<\/span>, <span class=\"hljs-number\">7<\/span>, <span class=\"hljs-number\">8<\/span>, <span class=\"hljs-number\">9<\/span>, <span class=\"hljs-number\">10<\/span>]\r\ndataset = TimeSeriesDataset(data)\r\ndataloader = DataLoader(dataset, batch_size=<span class=\"hljs-number\">1<\/span>, shuffle=<span class=\"hljs-literal\">True<\/span>)\r\n\r\n<span class=\"hljs-comment\"># \u521d\u59cb\u5316\u6a21\u578b<\/span>\r\nmodel = RNNModel(input_size, hidden_size, num_layers, output_size)\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 = torch.optim.Adam(model.parameters(), lr=learning_rate)\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>(num_epochs):\r\n    <span class=\"hljs-keyword\">for<\/span> i, batch <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">enumerate<\/span>(dataloader):\r\n        inputs = batch.<span class=\"hljs-built_in\">float<\/span>().unsqueeze(<span class=\"hljs-number\">2<\/span>)\r\n        targets = inputs.clone()\r\n        \r\n        outputs = model(inputs)\r\n        loss = criterion(outputs, targets)\r\n        \r\n        optimizer.zero_grad()\r\n        loss.backward()\r\n        optimizer.step()\r\n        \r\n        <span class=\"hljs-keyword\">if<\/span> (i+<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 [{}\/{}], Step [{}\/{}], Loss: {:.4f}'<\/span>.<span class=\"hljs-built_in\">format<\/span>(epoch+<span class=\"hljs-number\">1<\/span>, num_epochs, i+<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-built_in\">len<\/span>(dataloader), loss.item()))\r\n<\/code><\/pre>\n<p>\u4e0a\u8a18\u306e\u4f8b\u3067\u306f\u3001\u307e\u305a\u3001\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3092\u30ed\u30fc\u30c9\u3059\u308b\u305f\u3081\u306e\u30ab\u30b9\u30bf\u30e0Dataset\u30af\u30e9\u30b9\u3092\u4f5c\u6210\u3057\u3001RNN\u3092\u542b\u3080RNNModel\u30e2\u30c7\u30eb\u3092\u5b9a\u7fa9\u3057\u3001\u6700\u5f8c\u306b\u30c7\u30fc\u30bf\u3092\u6e96\u5099\u3057\u3066\u30e2\u30c7\u30eb\u3092\u8a13\u7df4\u3057\u307e\u3057\u305f\u3002\u8a13\u7df4\u306e\u904e\u7a0b\u3067\u3001\u30e2\u30c7\u30eb\u3092\u6700\u9069\u5316\u3059\u308b\u305f\u3081\u306b\u5e73\u5747\u4e8c\u4e57\u8aa4\u5dee\u640d\u5931\u95a2\u6570\u3068Adam\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u3092\u4f7f\u7528\u3057\u307e\u3057\u305f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u901a\u5e38\uff0c\u5728PyTorch\u4e2d\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u4efb\u52a1\u9700\u8981\u4f7f\u7528torch.nn.RNN\u3001torch.nn.LSTM\u3001torch.nn.GRU\u306a\u3069\u306e\u518d\u5e30\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30e2\u30b8\u30e5\u30fc\u30eb\u3068\u3001torch.utils.data.Datas [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[26,61],"class_list":["post-4351","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\/ -->\n<title>PyTorch\u3067\u6642\u9593\u7cfb\u5217\u30c7\u30fc\u30bf\u30bf\u30b9\u30af\u3092\u3069\u306e\u3088\u3046\u306b\u51e6\u7406\u3059\u308b\u306e\u304b\uff1f - Blog - Silicon Cloud<\/title>\n<meta name=\"description\" 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