alexnet训练cifar10图像分类，数据集图片尺寸大小为[3,32,32]，如何写代码，batch_size不能超过64M，使得准确率达到90%以上

首先，我们需要做一些数据预处理，包括对图像进行标准化、随机水平翻转、随机裁剪等操作。代码如下：

import torchvision.transforms as transforms

transform_train = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32, padding=4),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

接着，我们可以加载 CIFAR10 数据集，并使用 DataLoader 进行数据批量处理。代码如下：

import torch.utils.data as data
import torchvision.datasets as datasets

trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=2)

testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = data.DataLoader(testset, batch_size=64, shuffle=False, num_workers=2)

接下来，我们可以定义 AlexNet 模型，并使用交叉熵损失函数和随机梯度下降优化器进行训练。代码如下：

import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2)
        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv2 = nn.Conv2d(64, 192, kernel_size=5, padding=2)
        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv3 = nn.Conv2d(192, 384, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(384, 256, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.fc1 = nn.Linear(256 * 2 * 2, 4096)
        self.fc2 = nn.Linear(4096, 4096)
        self.fc3 = nn.Linear(4096, 10)
        
    def forward(self, x):
        x = self.pool1(F.relu(self.conv1(x)))
        x = self.pool2(F.relu(self.conv2(x)))
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = self.pool3(F.relu(self.conv5(x)))
        x = x.view(-1, 256 * 2 * 2)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = F.relu(self.fc2(x))
        x = F.dropout(x, training=self.training)
        x = self.fc3(x)
        return x

net = AlexNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=0.0005)

for epoch in range(50):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))
    if correct / total > 0.9:
        break

最后，我们可以在循环中添加一个准确率的判断，当准确率达到 90% 以上时停止训练。由于 batch_size 不能超过 64M，我们可以适当减小 batch_size，但需要注意的是，batch_size 过小可能会导致模型欠拟合。