本文提供了一个基于开源框架MindSpore的图像识别实验。该实验演示了如何利用开源框架MindSpore完成CIFAR-10图像识别任务。阐明了整个实验功能、结构与流程,并且进行了模型的优化与调参。 增加卷积的个数、卷积核的大小、增加网络的深度的模型超参数调整,经过实验验证,将模型的准确率从0.60提升至0.73,使损失从1.13降至0.77,说明了超参调整的有效性。由于原版LeNet5的层数少,参数量小,故猜测模型能力不足以容纳cifar-10数据集的所有特征信息,调参的方向是扩大模型参数量。增加卷积的个数、卷积核的大小、增加网络的深度均是提升模型参数的途径。
cifar-10-classify-with-mindspore's Introduction
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