深度学习（小土堆）

使用transform

加载数据集，查看数据集的属性

将图片转换成tensor类型

python 复制代码

dataset_transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor()
])

train_set = torchvision.datasets.CIFAR10(root="./dataset",train=True,transform= dataset_transform,download=True)
test_set = torchvision.datasets.CIFAR10(root="./dataset",train=True,transform= dataset_transform,download=True)

print(test_set[0])

将该数据的数据显示在tensorboard中
Dataloader

python 复制代码

import torchvision
from torch.utils.data import DataLoader

#准备测试数据集
test_data = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)

test_loader = DataLoader(dataset = test_data,batch_size=4,shuffle=True,num_workers=0,drop_last=False)
#测试数据集中第一张图片集
img,target = test_data[0]
print(img.shape)
print(target)

for data in  test_loader:
    imgs,targets = data
    print(imgs.shape)
    print(targets)

出现以上问题，需要将numberworks设置为0

drop_last 当取数据有余数时，是舍去还是保留

python 复制代码

import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

#准备测试数据集
test_data = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor())

test_loader = DataLoader(dataset = test_data,batch_size=64,shuffle=True,num_workers=0,drop_last=True)
#测试数据集中第一张图片集
img,target = test_data[0]
print(img.shape)
print(target)

writer = SummaryWriter("DataLodaer")

#shuffle 为True 两次结果不一样
for epoch in range(2):
    step = 0
    for data in  test_loader:
        imgs,targets = data
        # print(imgs.shape)
        # print(targets)
        writer.add_images("Epoch:{}".format(epoch),imgs,step)
        step = step+1

writer.close()

神经网络