{"id":38436,"date":"2025-05-20T13:02:33","date_gmt":"2025-05-20T05:02:33","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/38436.html"},"modified":"2025-05-20T13:02:33","modified_gmt":"2025-05-20T05:02:33","slug":"pytorch%e8%ae%ad%e7%bb%83%e5%8f%af%e8%a7%86%e5%8c%96%e5%b7%a5%e5%85%b7-tensorboard","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/38436.html","title":{"rendered":"pytorch\u8bad\u7ec3\u53ef\u89c6\u5316\u5de5\u5177---TensorBoard"},"content":{"rendered":"<h3>\u00a0\u4e00\u3001\u76ee\u7684&#xff1a;\u4e3a\u4ec0\u4e48\u4f7f\u7528 TensorBoard \u8c03\u63a7\u6a21\u578b<\/h3>\n<p>\u4f7f\u7528 TensorBoard \u53ef\u4ee5\u5e2e\u6211\u4eec&#xff1a;<\/p>\n<li>\n<p>\u5b9e\u65f6\u67e5\u770b loss \/ acc \u66f2\u7ebf \u2192 \u5224\u65ad\u662f\u5426\u8fc7\u62df\u5408\u3001\u6b20\u62df\u5408&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u5bf9\u6bd4\u4e0d\u540c\u6a21\u578b\u6216\u8d85\u53c2\u6570\u7684\u6548\u679c&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u53ef\u89c6\u5316\u6a21\u578b\u7ed3\u6784 \u2192 \u5e2e\u52a9\u8c03\u8bd5\u6a21\u578b\u8bbe\u8ba1&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u67e5\u770b\u6743\u91cd\/\u68af\u5ea6\u5206\u5e03 \u2192 \u5206\u6790\u8bad\u7ec3\u7a33\u5b9a\u6027&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u53ef\u89c6\u5316\u9884\u6d4b\u7ed3\u679c\u3001\u7279\u5f81\u56fe\u3001embedding \u2192 \u63d0\u5347\u6a21\u578b\u53ef\u89e3\u91ca\u6027&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u7ba1\u7406\u5b9e\u9a8c\u7ed3\u679c\u3001\u8d85\u53c2\u6570\u7ec4\u5408\u3002<\/p>\n<\/li>\n<hr \/>\n<h3>\u4e8c\u3001\u8bad\u7ec3\u4e2d\u53ef\u89c6\u5316\u8c03\u63a7\u7684\u529f\u80fd\u8be6\u89e3&#xff08;\u9644\u4ee3\u7801&#xff09;<\/h3>\n<h4>\u00a01. \u53ef\u89c6\u5316 loss \/ accuracy \u66f2\u7ebf<\/h4>\n<p>writer.add_scalar(&#034;Loss\/train&#034;, train_loss, epoch)<br \/>\nwriter.add_scalar(&#034;Loss\/val&#034;, val_loss, epoch)<br \/>\nwriter.add_scalar(&#034;Acc\/train&#034;, train_acc, epoch)<br \/>\nwriter.add_scalar(&#034;Acc\/val&#034;, val_acc, epoch)<\/p>\n<p>\u00a0\u7528\u9014&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u5224\u65ad\u8bad\u7ec3\u8fc7\u7a0b\u662f\u5426\u6536\u655b&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u9a8c\u8bc1\u96c6 loss \u9ad8\u4e8e\u8bad\u7ec3\u96c6 \u2192 \u53ef\u80fd\u8fc7\u62df\u5408&#xff1b;<\/p>\n<\/li>\n<li>\n<p>loss \u4e00\u76f4\u4e0d\u4e0b\u964d \u2192 \u5b66\u4e60\u7387\u53ef\u80fd\u8fc7\u9ad8\u6216\u6a21\u578b\u8bbe\u8ba1\u95ee\u9898\u3002<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<h4>2. \u53ef\u89c6\u5316\u6a21\u578b\u7ed3\u6784<\/h4>\n<p>from torch.utils.tensorboard import SummaryWriter<\/p>\n<p>model &#061; MyModel()<br \/>\ndummy_input &#061; torch.randn(1, 3, 224, 224)<br \/>\nwriter.add_graph(model, dummy_input)<\/p>\n<p>\u00a0\u7528\u9014&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u68c0\u67e5\u6a21\u578b\u7ed3\u6784\u662f\u5426\u6b63\u786e&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u76f4\u89c2\u770b\u5230\u5404\u5c42\u8fde\u63a5\u987a\u5e8f\u3002<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<h4>\u00a03. \u53ef\u89c6\u5316\u6743\u91cd\u5206\u5e03&#xff08;Histogram&#xff09;<\/h4>\n<p>for name, param in model.named_parameters():<br \/>\n    writer.add_histogram(name, param, epoch)<\/p>\n<p>\u7528\u9014&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u89c2\u5bdf\u53c2\u6570\u503c\u5206\u5e03\u662f\u5426\u7206\u70b8\u6216\u6d88\u5931&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u68af\u5ea6\u6d88\u5931\u6216\u7206\u70b8\u65f6\u901a\u5e38 histogram \u53d8\u5316\u5f02\u5e38\u3002<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<h4>\u00a04. \u53ef\u89c6\u5316\u9884\u6d4b\u56fe\u50cf\u3001\u6807\u7b7e\u3001\u7279\u5f81\u56fe<\/h4>\n<p>import torchvision.utils as vutils<\/p>\n<p># \u5c55\u793a\u8f93\u5165\u56fe\u50cf\u548c\u9884\u6d4b\u7ed3\u679c<br \/>\nwriter.add_images(&#034;Input\/Image&#034;, input_tensor, epoch)<br \/>\nwriter.add_images(&#034;Predict\/Output&#034;, output_tensor, epoch)<br \/>\nwriter.add_images(&#034;GroundTruth\/Label&#034;, label_tensor, epoch)<\/p>\n<p>\u00a0\u7528\u9014&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u89c6\u89c9\u4efb\u52a1&#xff08;\u5982\u5206\u5272\u3001\u5206\u7c7b&#xff09;\u4e2d\u5feb\u901f\u68c0\u67e5\u6a21\u578b\u9884\u6d4b\u662f\u5426\u5408\u7406\u3002<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<h4>\u00a05. \u53ef\u89c6\u5316 Embedding&#xff08;\u9ad8\u7ef4\u5411\u91cf\u964d\u7ef4&#xff09;<\/h4>\n<p># features: [N, D], labels: [N], images: [N, C, H, W]<br \/>\nwriter.add_embedding(features, metadata&#061;labels, label_img&#061;images, global_step&#061;epoch)<\/p>\n<p>\u00a0\u7528\u9014&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u68c0\u67e5\u4e0d\u540c\u7c7b\u522b\u662f\u5426\u5728\u7279\u5f81\u7a7a\u95f4\u4e2d\u805a\u7c7b\u826f\u597d&#xff1b;<\/p>\n<\/li>\n<li>\n<p>Embedding \u5c42\u662f\u5426\u5b66\u4e60\u5230\u6709\u6548\u7684\u8868\u793a\u3002<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<h4>\u00a06. \u8d85\u53c2\u6570\u8bb0\u5f55\u4e0e\u5bf9\u6bd4&#xff08;add_hparams&#xff09;<\/h4>\n<p>writer.add_hparams(<br \/>\n    {&#039;lr&#039;: 0.001, &#039;batch_size&#039;: 32},<br \/>\n    {&#039;hparam\/accuracy&#039;: acc, &#039;hparam\/loss&#039;: loss}<br \/>\n)<\/p>\n<p>\u00a0\u7528\u9014&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u5bf9\u6bd4\u4e0d\u540c\u8d85\u53c2\u6570\u4e0b\u7684\u6a21\u578b\u6548\u679c&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u81ea\u52a8\u751f\u6210\u6c47\u603b\u8868\u683c\u3002<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<h4>\u00a07. \u53ef\u89c6\u5316\u5b66\u4e60\u7387\u53d8\u5316&#xff08;\u5b66\u4e60\u7387\u8c03\u5ea6&#xff09;<\/h4>\n<p>lr &#061; optimizer.param_groups[0][&#039;lr&#039;]<br \/>\nwriter.add_scalar(&#034;LR&#034;, lr, epoch)<\/p>\n<p>\u00a0\u7528\u9014&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u5b66\u4e60\u7387\u8c03\u5ea6\u7b56\u7565\u662f\u5426\u751f\u6548&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u4e0e loss \u7684\u53d8\u5316\u76f8\u4e92\u5370\u8bc1\u3002<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<h3>\u00a0\u4e09\u3001\u8bad\u7ec3\u4e2d\u7efc\u5408\u5e94\u7528\u793a\u4f8b&#xff08;\u5b8c\u6574\u4ee3\u7801\u6846\u67b6&#xff09;<\/h3>\n<p>from torch.utils.tensorboard import SummaryWriter<br \/>\nimport torch<br \/>\nimport torch.nn as nn<br \/>\nimport torchvision<br \/>\nimport torchvision.transforms as transforms<\/p>\n<p># \u51c6\u5907<br \/>\nwriter &#061; SummaryWriter(log_dir&#061;&#039;runs\/exp1&#039;)<br \/>\ndevice &#061; torch.device(&#034;cuda&#034; if torch.cuda.is_available() else &#034;cpu&#034;)<\/p>\n<p># \u6570\u636e<br \/>\ntransform &#061; transforms.ToTensor()<br \/>\ntrain_loader &#061; torch.utils.data.DataLoader(<br \/>\n    torchvision.datasets.MNIST(&#039;.&#039;, train&#061;True, download&#061;True, transform&#061;transform),<br \/>\n    batch_size&#061;64, shuffle&#061;True)<\/p>\n<p># \u6a21\u578b<br \/>\nmodel &#061; nn.Sequential(<br \/>\n    nn.Flatten(),<br \/>\n    nn.Linear(28*28, 128),<br \/>\n    nn.ReLU(),<br \/>\n    nn.Linear(128, 10)<br \/>\n).to(device)<\/p>\n<p># \u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u5668<br \/>\ncriterion &#061; nn.CrossEntropyLoss()<br \/>\noptimizer &#061; torch.optim.Adam(model.parameters(), lr&#061;0.001)<\/p>\n<p># \u6dfb\u52a0\u6a21\u578b\u7ed3\u6784<br \/>\nwriter.add_graph(model, torch.randn(1, 1, 28, 28).to(device))<\/p>\n<p># \u8bad\u7ec3<br \/>\nfor epoch in range(5):<br \/>\n    total_loss &#061; 0<br \/>\n    correct &#061; 0<\/p>\n<p>    for images, labels in train_loader:<br \/>\n        images, labels &#061; images.to(device), labels.to(device)<\/p>\n<p>        outputs &#061; model(images)<br \/>\n        loss &#061; criterion(outputs, labels)<\/p>\n<p>        optimizer.zero_grad()<br \/>\n        loss.backward()<br \/>\n        optimizer.step()<\/p>\n<p>        total_loss &#043;&#061; loss.item()<br \/>\n        _, predicted &#061; outputs.max(1)<br \/>\n        correct &#043;&#061; predicted.eq(labels).sum().item()<\/p>\n<p>    avg_loss &#061; total_loss \/ len(train_loader)<br \/>\n    accuracy &#061; correct \/ len(train_loader.dataset)<\/p>\n<p>    writer.add_scalar(&#034;Loss\/train&#034;, avg_loss, epoch)<br \/>\n    writer.add_scalar(&#034;Acc\/train&#034;, accuracy, epoch)<br \/>\n    writer.add_scalar(&#034;LR&#034;, optimizer.param_groups[0][&#039;lr&#039;], epoch)<\/p>\n<p>    # \u6743\u91cd\u5206\u5e03<br \/>\n    for name, param in model.named_parameters():<br \/>\n        writer.add_histogram(name, param, epoch)<\/p>\n<p>    # \u53ef\u89c6\u5316\u8f93\u5165\u56fe\u50cf<br \/>\n    img_grid &#061; torchvision.utils.make_grid(images[:16].cpu())<br \/>\n    writer.add_image(&#034;Sample Inputs&#034;, img_grid, epoch)<\/p>\n<p>writer.close()<\/p>\n<hr \/>\n<h3>\u00a0\u56db\u3001\u8fdb\u9636\u5efa\u8bae<\/h3>\n<table>\n<tr>\u529f\u80fd\u8bf4\u660e<\/tr>\n<tbody>\n<tr>\n<td>\u591a\u5b9e\u9a8c\u5bf9\u6bd4<\/td>\n<td>\u4f7f\u7528 SummaryWriter(log_dir&#061;f&#034;runs\/lr_{lr}_bs_{bs}&#034;) \u591a\u6b21\u8bad\u7ec3<\/td>\n<\/tr>\n<tr>\n<td>\u4e0e wandb \u8054\u52a8<\/td>\n<td>\u7528 wandb \u66ff\u4ee3 TensorBoard&#xff0c;\u652f\u6301\u81ea\u52a8\u8d85\u53c2\u641c\u7d22<\/td>\n<\/tr>\n<tr>\n<td>TensorBoard.dev<\/td>\n<td>\u4e0a\u4f20\u8bad\u7ec3\u8bb0\u5f55\u5230\u4e91\u7aef&#xff0c;\u4fbf\u4e8e\u5c55\u793a\u6216\u8bb0\u5f55<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr \/>\n<h3>\u00a0\u4e94\u3001\u603b\u7ed3\u8868\u683c&#xff08;\u5e38\u7528 API&#xff09;<\/h3>\n<table>\n<tr>\u529f\u80fdAPI<\/tr>\n<tbody>\n<tr>\n<td>\u6807\u91cf\u503c&#xff08;loss&#xff09;<\/td>\n<td>add_scalar(tag, value, step)<\/td>\n<\/tr>\n<tr>\n<td>\u56fe\u50cf<\/td>\n<td>add_image(tag, image_tensor, step)<\/td>\n<\/tr>\n<tr>\n<td>\u591a\u56fe\u50cf<\/td>\n<td>add_images(tag, batch_tensor, step)<\/td>\n<\/tr>\n<tr>\n<td>\u6a21\u578b\u7ed3\u6784<\/td>\n<td>add_graph(model, input_tensor)<\/td>\n<\/tr>\n<tr>\n<td>\u53c2\u6570\u76f4\u65b9\u56fe<\/td>\n<td>add_histogram(tag, values, step)<\/td>\n<\/tr>\n<tr>\n<td>\u8d85\u53c2\u5bf9\u6bd4<\/td>\n<td>add_hparams(dict, metrics)<\/td>\n<\/tr>\n<tr>\n<td>Embedding<\/td>\n<td>add_embedding(features, labels, images)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"<p>\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb442\u6b21\uff0c\u70b9\u8d5e3\u6b21\uff0c\u6536\u85cf5\u6b21\u3002\u529f\u80fdAPI\u6807\u91cf\u503c\uff08loss\uff09\u56fe\u50cf\u591a\u56fe\u50cf\u6a21\u578b\u7ed3\u6784\u53c2\u6570\u76f4\u65b9\u56fe\u8d85\u53c2\u5bf9\u6bd4Embedding\u3002<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[152,50,86],"topic":[],"class_list":["post-38436","post","type-post","status-publish","format-standard","hentry","category-server","tag-pytorch","tag-50","tag-86"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>pytorch\u8bad\u7ec3\u53ef\u89c6\u5316\u5de5\u5177-TensorBoard - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta 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