Training parameter explanation in caffe

RepostBatch SizeBatch size in mainly depended to your memory in GPU/RAM. Most time it is used power of two (64,128,256). I always try to choose 256, because it works better with SGD. But for bigger

Xinshuo Webg

342人浏览 · 2017-01-28 12:36:39

Xinshuo Webg · 2017-01-28 12:36:39 发布

Repost

Batch Size
Batch size in mainly depended to your memory in GPU/RAM. Most time it is used power of two (64,128,256). I always try to choose 256, because it works better with SGD. But for bigger network I use 64.
Number of Iterations
Number if iterations set number of epoch of learning. Here I will use MNIST example to explain it to you:
Training: 60k, batch size: 64, maximum_iterations= 10k. So, there will be 10k*64 = 640k images of learning. This mean, that there will be 10.6 of epochs.(Number if epochs is hard to set, you should stop when net does not learn any more, or it is overfitting)
Val: 10k, batch size: 100, test_iterations: 100, So, 100*100: 10K, exacly all images from validation base.

昇腾开源生态专区

昇腾计算产业是基于昇腾系列（HUAWEI Ascend）处理器和基础软件构建的全栈 AI计算基础设施、行业应用及服务，https://devpress.csdn.net/organization/setting/general/146749包括昇腾系列处理器、系列硬件、CANN、AI计算框架、应用使能、开发工具链、管理运维工具、行业应用及服务等全产业链

更多推荐

Flutter 框架跨平台鸿蒙开发 - 开发二维码生成器与扫描器

size: 200,// 定位点样式（三个角的方块）eyeShape: QrEyeShape.square, // square 或 circle),// 数据点样式dataModuleShape: QrDataModuleShape.square, // square 或 circle),二维码生成：qr_flutter库的使用和样式自定义二维码扫描：mobile_scanner相机调用和结果处

昇腾开源生态专区

必学收藏！vLLM-Omni全模态模型高效推理引擎：从入门到实践

昇腾开源生态专区

PD分离部署：基于Atlas 800T A3服务器+SGLang 1P2D部署Qwen3-235B指南

Qwen3-235B模型用Atlas 800I A3或Atlas 800T A3均可部署，本文档以Atlas 800T A3为例，记录了使用SGLang框架部署Qwen3-235B模型的1P2D（一个Prefill节点和两个Decode节点）配置方案。该配置针对大规模语言模型推理场景进行了优化，特别适用于需要高吞吐量的生产环境。