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1.数据集
pytorch有个快速构造数据集的方法
但是你的目录结构必须是
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir,
x),data_transforms[x])for x in ['train', 'val']}
首先for循环将['train',val]分别赋给x,
x =train/val。 os.path.join(data_dir,x)将文件dataset和train和val拼接起来。
hymenoptera_data/train 和hymenoptera_data/val
然后data_transforms根据组装的数据预处理来处理文件。
3.生成image_datasets对象,根据类别访问 image_datasets是个list
4.组装数据
batch_size=4,一次取4个图片
shuffle=True, 将数据打乱，随机选取
num_workers=4 ，使用4个线程
4.dataloaders[]字典会同时返回 每张图片的张量数据和标签
5.返回数据集中trian和val的数据长度
6.返回分类标签
7.迭代数据集
8.更具索引返回分类名称
其中0代表ants,1代表bees
9.预测输出结果
outputs=model(inputs)会输出最后的分类概率，比如有7个分类就会输出4个1x7的向量
_,preds=torch.max(outputs,1)，前面输出的是最大概率的值，后面preds表示最大概率的索引，
如果是批量大小是4,那么preds就是一个1x4的向量，其中的值代表索引，label也是一个1x4的向量，值代表类别得索引。
10.统计交叉熵损失和准确率
损失值要乘以批量得大小，最后除以分类得数据长度，得到平均损失率
如果preds=labels.data就返回true,否则返回false。统计true得值用sum计数。最后除以train数据集得长度，分别得到trian和val得准确率。
