【Pytorch】模型摘要信息获取、模型参数获取及模型保存的三种方法

使用3D扫描仪获取模型三维数据前，确保物体足够清晰 #生活技巧# #数码产品使用技巧# #3D打印机使用说明#

复制

---------------------------------------------------------------- Layer (type) Output Shape Param # ================================================================ Conv2d-1 [-1, 64, 224, 224] 1,792 ReLU-2 [-1, 64, 224, 224] 0 Conv2d-3 [-1, 64, 224, 224] 36,928 ReLU-4 [-1, 64, 224, 224] 0 MaxPool2d-5 [-1, 64, 112, 112] 0 Conv2d-6 [-1, 128, 112, 112] 73,856 ReLU-7 [-1, 128, 112, 112] 0 Conv2d-8 [-1, 128, 112, 112] 147,584 ReLU-9 [-1, 128, 112, 112] 0 MaxPool2d-10 [-1, 128, 56, 56] 0 Conv2d-11 [-1, 256, 56, 56] 295,168 ReLU-12 [-1, 256, 56, 56] 0 Conv2d-13 [-1, 256, 56, 56] 590,080 ReLU-14 [-1, 256, 56, 56] 0 Conv2d-15 [-1, 256, 56, 56] 590,080 ReLU-16 [-1, 256, 56, 56] 0 MaxPool2d-17 [-1, 256, 28, 28] 0 Conv2d-18 [-1, 512, 28, 28] 1,180,160 ReLU-19 [-1, 512, 28, 28] 0 Conv2d-20 [-1, 512, 28, 28] 2,359,808 ReLU-21 [-1, 512, 28, 28] 0 Conv2d-22 [-1, 512, 28, 28] 2,359,808 ReLU-23 [-1, 512, 28, 28] 0 MaxPool2d-24 [-1, 512, 14, 14] 0 Conv2d-25 [-1, 512, 14, 14] 2,359,808 ReLU-26 [-1, 512, 14, 14] 0 Conv2d-27 [-1, 512, 14, 14] 2,359,808 ReLU-28 [-1, 512, 14, 14] 0 Conv2d-29 [-1, 512, 14, 14] 2,359,808 ReLU-30 [-1, 512, 14, 14] 0 MaxPool2d-31 [-1, 512, 7, 7] 0 Linear-32 [-1, 4096] 102,764,544 ReLU-33 [-1, 4096] 0 Dropout-34 [-1, 4096] 0 Linear-35 [-1, 4096] 16,781,312 ReLU-36 [-1, 4096] 0 Dropout-37 [-1, 4096] 0 Linear-38 [-1, 1000] 4,097,000 ================================================================ Total params: 138,357,544 Trainable params: 138,357,544 Non-trainable params: 0 ---------------------------------------------------------------- Input size (MB): 0.57 Forward/backward pass size (MB): 218.59 Params size (MB): 527.79 Estimated Total Size (MB): 746.96 ----------------------------------------------------------------

网址：【Pytorch】模型摘要信息获取、模型参数获取及模型保存的三种方法 https://www.yuejiaxmz.com/news/view/1312854

相关内容

Pytorch——net.parameters()参数获取
全网最全：机器学习算法模型自动超参数优化方法汇总
数据模型的概念，数据模型的作用和数据模型的三个要素
掌握PyTorch，轻松提升模型训练效果：独家技巧与策略全解析
云端推理中的AI模型量化技术与实践
pytorch模型训练流程中遇到的一些坑(持续更新)
模型渲染方法、装置、电子设备及存储介质制造方法及图纸
DenseNet：比ResNet更优的CNN模型
我获取信息的方法
建筑信息模型在环保家居设计中的运用

随便看看