问神经网络输入缩放

EN

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(3*32*32, 300, bias=False)
        self.fc2 = nn.Linear(300, 10, bias=False)

    def forward(self, x):
        x = x.reshape(250, -1)
        self.x2 = F.relu(self.fc1(x))
        x = self.fc2(self.x2)
        return x


def train():
    # The output of torchvision datasets are PILImage images of range [0, 1].
    transform = transforms.Compose([transforms.ToTensor()])
    trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=250, shuffle=True, num_workers=4)
    testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=args.bs, shuffle=False, num_workers=4)

    net = Net()
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(net.parameters(), lr=0.02, momentum=0.9, weight_decay=0.0001)

    for epoch in range(20):
        correct = 0
        total = 0
        for data in trainloader:
            inputs, labels = data
            outputs = net(inputs)
            loss = criterion(outputs, labels)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
        acc = 100. * correct / total