<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Batchnorm on Jeanphilo Blog</title><link>https://shio-chan-dev.github.io/jeanblog/zh/tags/batchnorm/</link><description>Recent content in Batchnorm on Jeanphilo Blog</description><generator>Hugo -- 0.159.2</generator><language>zh-cn</language><lastBuildDate>Sat, 24 Jan 2026 16:24:44 +0800</lastBuildDate><atom:link href="https://shio-chan-dev.github.io/jeanblog/zh/tags/batchnorm/index.xml" rel="self" type="application/rss+xml"/><item><title>BN 与 Dropout：训练与推理时的关键区别</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/llm/bn-vs-dropout-train-infer/</link><pubDate>Sat, 24 Jan 2026 16:24:44 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/llm/bn-vs-dropout-train-infer/</guid><description>系统对比 BatchNorm 与 Dropout 在训练/推理阶段的行为差异，并提供最小 PyTorch 示例。</description></item><item><title>Transformer 中可以用 BatchNorm 吗？</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/llm/batchnorm-in-transformer/</link><pubDate>Sat, 24 Jan 2026 16:24:03 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/llm/batchnorm-in-transformer/</guid><description>讨论 Transformer 使用 BatchNorm 的可行性、限制与工程取舍，并给出最小示例。</description></item><item><title>BN 与 LN 的区别：训练稳定性与工程取舍</title><link>https://shio-chan-dev.github.io/jeanblog/zh/ai/llm/batchnorm-vs-layernorm/</link><pubDate>Sat, 24 Jan 2026 16:23:47 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/ai/llm/batchnorm-vs-layernorm/</guid><description>对比 BatchNorm 与 LayerNorm 的原理、适用场景与工程代价，并提供最小 PyTorch 示例。</description></item></channel></rss>