理论学习:target.size(0)

PyTorch中的批次大小计算:使用torch.tensor示例,
本文介绍了如何在PyTorch中使用`torch.tensor`来获取张量的批次大小,即样本数量,以target.size(0)为例进行说明。
部署运行你感兴趣的模型镜像
import torch

# 假设输出和目标

target = torch.tensor([2, 1, 0])  # 真实的标签

print(target.size(0))

target.size(0) 被用来获取批次中样本的数量

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PyTorch 是一个开源的 Python 机器学习库,基于 Torch 库,底层由 C++ 实现,应用于人工智能领域,如计算机视觉和自然语言处理

在进行少量样本训练,仅10张,训练日志打印为/home/vuxuanni/.conda/envs/yxn_pytorch_1/bin/python /home/vuxuanni/code/SFISP-Net/train_fivek_skff_no_patches.py CUDA visible devices: 4 CUDA Device Name: NVIDIA GeForce RTX 3090 batch_size:4 Total_params: 40901347 length of dataset:10 Train loader length: 3 train:(16.051672142404378, 0.48819639533758163) epoch:1/100 | avg loss: 0.6426 | total loss: 1.9278 time: 67.66542482376099, train psnr: 16.0517, train ssim: 0.4882 train_psnr < pre_psnr, save false train:(18.459363231459026, 0.47953007817268373) epoch:2/100 | avg loss: 0.6430 | total loss: 1.9289 time: 28.274972200393677, train psnr: 18.4594, train ssim: 0.4795 train_psnr < pre_psnr, save false train:(19.403523769439435, 0.50685369819402692) epoch:3/100 | avg loss: 0.5873 | total loss: 1.7620 time: 15.653608322143555, train psnr: 19.4035, train ssim: 0.5069 train_psnr < pre_psnr, save false train:(22.962383181528356, 0.56071311831474302) epoch:4/100 | avg loss: 0.4967 | total loss: 1.4902 time: 14.605928897857666, train psnr: 22.9624, train ssim: 0.5607 saved best weight train:(24.777637280702542, 0.55345111191272733) epoch:5/100 | avg loss: 0.4984 | total loss: 1.4952 time: 16.74807119369507, train psnr: 24.7776, train ssim: 0.5535 saved best weight train:(25.227109972539459, 0.6124863475561142) epoch:6/100 | avg loss: 0.4582 | total loss: 1.3747 time: 27.461640119552612, train psnr: 25.2271, train ssim: 0.6125 saved best weight train:(28.351162273566594, 0.6218718081712723) epoch:7/100 | avg loss: 0.4367 | total loss: 1.3101 time: 25.196449756622314, train psnr: 28.3512, train ssim: 0.6219 saved best weight train:(25.571816838733028, 0.60460956692695622) epoch:8/100 | avg loss: 0.4617 | total loss: 1.3852 time: 16.252585649490356, train psnr: 25.5718, train ssim: 0.6046 train_psnr < pre_psnr, save false train:(25.433697557216707, 0.60974605977535246) epoch:9/100 | avg loss: 0.4274 | total loss: 1.2823 time: 14.374265193939209, train psnr: 25.4337, train ssim: 0.6097 train_psnr < pre_psnr, save false train:(30.612740030323, 0.66456321179866795) epoch:10/100 | avg loss: 0.3539 | total loss: 1.0616 time: 15.010883808135986, train psnr: 30.6127, train ssim: 0.6646 saved best weight train:(29.360431631487934, 0.68090330362319951) epoch:11/100 | avg loss: 0.3316 | total loss: 0.9948 time: 15.934910297393799, train psnr: 29.3604, train ssim: 0.6809 train_psnr < pre_psnr, save false train:(24.219559402329704, 0.60553553402423854) epoch:12/100 | avg loss: 0.4546 | total loss: 1.3637 time: 17.427153825759888, train psnr: 24.2196, train ssim: 0.6055 train_psnr < pre_psnr, save false train:(25.774469041352678, 0.6344638466835022) epoch:13/100 | avg loss: 0.4136 | total loss: 1.2407 time: 19.62150287628174, train psnr: 25.7745, train ssim: 0.6345 train_psnr < pre_psnr, save false train:(28.450260706065279, 0.73437763750553131) epoch:14/100 | avg loss: 0.2994 | total loss: 0.8981 time: 17.178926467895508, train psnr: 28.4503, train ssim: 0.7344 train_psnr < pre_psnr, save false,这样的结果是否合理,如果总训练样本为414张,那么整个数据集的训练结果大概应该为多少
最新发布
08-30
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