<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:media="http://search.yahoo.com/mrss/"><channel><title>PyTorch on Shaowen Chen's Website</title><link>https://www.chenshaowen.com/en/tags/pytorch/</link><description>Recent content in PyTorch on Shaowen Chen's Website</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>&amp;copy;2016 - {year}, All Rights Reserved.</copyright><lastBuildDate>Sun, 17 Nov 2024 00:00:00 +0000</lastBuildDate><sy:updatePeriod>weekly</sy:updatePeriod><atom:link href="https://www.chenshaowen.com/en/tags/pytorch/atom.xml" rel="self" type="application/rss+xml"/><item><title>Using TensorBoard to Visualize the PyTorch Training Process</title><link>https://www.chenshaowen.com/en/blog/using-tensorboard-to-visualize-pytorch-training-process.html</link><pubDate>Sun, 17 Nov 2024 00:00:00 +0000</pubDate><atom:modified>Sun, 17 Nov 2024 00:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/using-tensorboard-to-visualize-pytorch-training-process.html</guid><description>1. What TensorBoard Is TensorBoard is mainly used to monitor how a model&amp;rsquo;s various metrics change — accuracy, loss, the weight distributions of each layer, and so on.
TensorBoard is a visualization tool from TensorFlow that supports visualizing scalar, text, image, audio, video, and embedding data, among other types. But PyTorch can use TensorBoard too.</description><dc:creator>微信公众号</dc:creator><category>AI</category><category>PyTorch</category><category>TensorBoard</category><category>Training</category><category>R&amp;D</category></item><item><title>Training a Model on the MNIST Dataset with PyTorch</title><link>https://www.chenshaowen.com/en/blog/using-pytorch-to-train-model-on-mnist-dataset.html</link><pubDate>Sat, 16 Nov 2024 00:00:00 +0000</pubDate><atom:modified>Sat, 16 Nov 2024 00:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/using-pytorch-to-train-model-on-mnist-dataset.html</guid><description>1. Create the Training Script Create a training script mnist.py with the following contents: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36</description><dc:creator>微信公众号</dc:creator><category>AI</category><category>PyTorch</category><category>Training</category><category>Containers</category></item></channel></rss>