Thank you for your reply. I saw the torch.cat but don’t know how I can apply it here:
import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root=’./data’, train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=5,
shuffle=True, num_workers=1)
classes = (‘plane’, ‘car’, ‘bird’, ‘cat’,‘deer’, ‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’)
for epoch in range(3):
for i, data in enumerate(trainloader, 0):
inputs, labels = data
#Final_input=image 64 by 32