from torch.utils.data import TensorDataset import torch from torch.utils.data import DataLoader a = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9], [1, 2, 3], [4, 5, 6], [7, 8, 9], [1, 2, 3], [4, 5, 6], [7, 8, 9], [1, 2, 3], [4, 5, 6], [7, 8, 9]]) b = torch.tensor([44, 55, 66, 44, 55, 66, 44, 55, 66, 44, 55, 66]) train_ids = TensorDataset(a, b)#封装数据a与标签b # 切片输出 print(train_ids[0:2]) print('=' * 80) # 循环取数据 for x_train, y_label in train_ids: print(x_train, y_label) # DataLoader进行数据封装 print('=' * 80) train_loader = DataLoader(dataset=train_ids, batch_size=4, shuffle=True) for i, data in enumerate(train_loader): # 注意enumerate返回值有两个,一个是序号，一个是数据（包含训练数据和标签） x_data, label = data print(' batch:{0}\n x_data:{1}\nlabel: {2}'.format(i, x_data, label)) # for i, data in enumerate(train_loader,1): # 注意enumerate返回值有两个,一个是序号，一个是数据（包含训练数据和标签） x_data, label = data print(' batch:{0}\n x_data:{1}\nlabel: {2}'.format(i, x_data, label)) Dataloader:传入数据（这个数据包括：训练数据和标签），batchsize（代表的是将数据分成batch=[len（train_ids[0]）除以batchsize]，每一份包括的数据是batchsize）
enumerate:返回值有两个：一个是序号，也就是在这里的batch地址，一个是数据train_ids
for i, data in enumerate(train_loader,1):此代码中1，是batch从batch=1开始，也就是batch的地址是从1开始算起，不是0开始算起。batch仍然是3个。就算batch从8开始，他也是三份，分别是8,9,10
E:\软件安装\python3.7\python.exe E:/软件安装/code/RSN-master/Res2net.py train_ids= <torch.utils.data.dataset.TensorDataset object at 0x0000000002836EC8> (tensor([[1, 2, 3], [4, 5, 6]]), tensor([44, 55])) ================================================================================ tensor([1, 2, 3]) tensor(44) tensor([4, 5, 6]) tensor(55) tensor([7, 8, 9]) tensor(66) tensor([1, 2, 3]) tensor(44) tensor([4, 5, 6]) tensor(55) tensor([7, 8, 9]) tensor(66) tensor([1, 2, 3]) tensor(44) tensor([4, 5, 6]) tensor(55) tensor([7, 8, 9]) tensor(66) tensor([1, 2, 3]) tensor(44) tensor([4, 5, 6]) tensor(55) tensor([7, 8, 9]) tensor(66) ================================================================================ batch:0 x_data:tensor([[1, 2, 3], [1, 2, 3], [4, 5, 6], [1, 2, 3]]) label: tensor([44, 44, 55, 44]) batch:1 x_data:tensor([[4, 5, 6], [4, 5, 6], [7, 8, 9], [7, 8, 9]]) label: tensor([55, 55, 66, 66]) batch:2 x_data:tensor([[4, 5, 6], [1, 2, 3], [7, 8, 9], [7, 8, 9]]) label: tensor([55, 44, 66, 66]) batch:1 x_data:tensor([[7, 8, 9], [1, 2, 3], [1, 2, 3], [4, 5, 6]]) label: tensor([66, 44, 44, 55]) batch:2 x_data:tensor([[7, 8, 9], [4, 5, 6], [7, 8, 9], [1, 2, 3]]) label: tensor([66, 55, 66, 44]) batch:3 x_data:tensor([[4, 5, 6], [7, 8, 9], [1, 2, 3], [4, 5, 6]]) label: tensor([55, 66, 44, 55]) Process finished with exit code 0 Process finished with exit code 0版权声明：本文内容由互联网用户自发贡献，该文观点仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请联系我们举报，一经查实，本站将立刻删除。
发布者：全栈程序员-站长，转载请注明出处：https://javaforall.net/233280.html原文链接：https://javaforall.net
