基于pytorch的猫狗分类（源码+数据集）

import os #python下的包，用来操作文件夹文件 import torch from torch.utils import data#加载数据用的 import numpy as np from MyDataset import dataset from MyNet import MyNet import torch.nn as nn if __name__ == "__main__": my_data = dataset("E:\code\python_code\CatDog\img") train_data = data.DataLoader(my_data,batch_size = 100,shuffle = True) Net = MyNet() optimer = torch.optim.Adam(Net.parameters()) loss_fun = nn.MSELoss() for epoch in range(10): for i, (x, y) in enumerate(train_data): x = x.view(-1,100*100*3) output = Net(x) y = y.long() y = torch.zeros(y.size()[0], 2).scatter_(1, y.view(-1, 1), 1) loss = loss_fun(y, output) optimer.zero_grad() loss.backward() optimer.step() #print(loss.item()) if i % 10 == 0: print(loss.item()) out = torch.argmax(output, dim=1) y = torch.argmax(y, dim=1) acc = np.mean(np.array(out == y, dtype=np.float32)) print(acc)