用pytorch写一个fashionmnist分类设计代码进行识别生成预测曲线图和训练曲线图

好的，以下是基于PyTorch的Fashion-MNIST分类的代码，包括训练过程中的预测曲线图和训练曲线图。

首先，我们需要导入所需的库：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np

然后，我们需要定义数据集和数据加载器：

# 定义数据预处理的转换
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))])

# 加载训练集和测试集
trainset = torchvision.datasets.FashionMNIST(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.FashionMNIST(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

# 定义类别名称
classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress',
           'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot')

接下来，我们需要定义模型：

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 4 * 4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 16 * 4 * 4)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()

然后，我们需要定义损失函数和优化器：

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

接下来，我们可以开始训练过程：

# 训练模型
losses = []
accuracies = []
epochs = 10
for epoch in range(epochs):
    running_loss = 0.0
    total_loss = 0.0
    correct = 0.0
    total = 0.0
    
    for i, data in enumerate(trainloader, 0):
        # 获取输入数据
        inputs, labels = data

        # 梯度清零
        optimizer.zero_grad()

        # 前向传播、反向传播、优化
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 统计损失和准确率
        running_loss += loss.item()
        total_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

        # 每2000个mini-batch打印一次信息
        if i % 2000 == 1999:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

    # 记录训练过程中的损失和准确率
    losses.append(total_loss / len(trainset))
    accuracies.append(100.0 * correct / total)
        
print('Finished Training')

最后，我们可以使用测试集来评估模型的性能，并绘制预测曲线图和训练曲线图：

# 测试模型
correct = 0.0
total = 0.0
with torch.no_grad():
    for data in testloader:
        inputs, labels = data
        outputs = net(inputs)
        _, 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))

# 绘制预测曲线图
images, labels = iter(testloader).next()
outputs = net(images)
_, predicted = torch.max(outputs, 1)

fig, ax = plt.subplots(figsize=(8, 4))
ax.plot(np.arange(len(classes)), np.zeros(len(classes)), 'o', label='True')
ax.plot(np.arange(len(classes)), np.zeros(len(classes)), 'x', label='Predicted')
ax.set_xticks(np.arange(len(classes)))
ax.set_xticklabels(classes, rotation=45)
ax.set_ylim([0, 1.1])
ax.legend()

for i in range(len(images)):
    ax.plot(labels[i], 0, 'o', color='blue')
    ax.plot(predicted[i], 0, 'x', color='red')

plt.show()

# 绘制训练曲线图
fig, ax1 = plt.subplots()
color = 'tab:red'
ax1.set_xlabel('Epoch')
ax1.set_ylabel('Loss', color=color)
ax1.plot(np.arange(epochs), losses, color=color)
ax1.tick_params(axis='y', labelcolor=color)

ax2 = ax1.twinx()  
color = 'tab:blue'
ax2.set_ylabel('Accuracy', color=color)  
ax2.plot(np.arange(epochs), accuracies, color=color)
ax2.tick_params(axis='y', labelcolor=color)

fig.tight_layout()  
plt.show()

这样就完成了Fashion-MNIST分类的代码，并且绘制了预测曲线图和训练曲线图。