深度学习基于pytorch实现Lenet5对手势数据集进行分类

无bug代码搬运工

于 2022-11-12 17:00:28 发布

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分类专栏：深度学习文章标签：深度学习 pytorch 分类

本文链接：https://blog.youkuaiyun.com/DoKiDoK/article/details/127822352

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先来看看需求

导入数据，转化为tensor

这里的两个文件需要自己进行下载，资源在我的资源中，路径改成自己的即可

import numpy as np
import h5py
from urllib3.filepost import writer
from torch.utils.tensorboard import SummaryWriter
def load_dataset():
    train_dataset = h5py.File('E:\文档\实验3：基于Pytorch实现LeNet5\datasets/train_signs.h5', "r")
    train_set_x_orig = np.array(train_dataset["train_set_x"][:])  # your train set features
    train_set_y_orig = np.array(train_dataset["train_set_y"][:])  # your train set labels
    test_dataset = h5py.File('E:\文档\实验3：基于Pytorch实现LeNet5\datasets/test_signs.h5', "r")
    test_set_x_orig = np.array(test_dataset["test_set_x"][:])  # your test set features
    test_set_y_orig = np.array(test_datase