基于pytorch对cifar 10分类，要求准确度达到85以上，并输出分类图像结果

好的，首先需要准备CIFAR-10数据集，可以使用PyTorch内置的`torchvision.datasets.CIFAR10`来进行下载和加载数据集。然后，我们可以使用卷积神经网络来对图像进行分类。下面是一个简单的代码示例：

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim

# 超参数
batch_size = 128
learning_rate = 0.001
num_epochs = 20

# 数据预处理
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)

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

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(128 * 4 * 4, 512)
        self.fc2 = nn.Linear(512, 10)
    
    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = self.pool(nn.functional.relu(self.conv3(x)))
        x = x.view(-1, 128 * 4 * 4)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

net = Net()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    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

print('Finished Training')

# 测试模型
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))

在训练完成后，我们可以使用以下代码来输出分类图像结果：

import matplotlib.pyplot as plt
import numpy as np

# 获取测试集中的图片和标签
dataiter = iter(testloader)
images, labels = dataiter.next()

# 预测图片的标签
outputs = net(images)
_, predicted = torch.max(outputs, 1)

# 打印前25张图片及其预测标签
fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(10,10))
fig.tight_layout()

for i, ax in enumerate(axes.flat):
    ax.imshow(np.transpose(images[i], (1, 2, 0)))
    ax.set_title("Predicted: {}".format(predicted[i]))
    ax.axis('off')

plt.show()

这样就可以输出分类图像结果了。