实现手写数字的识别,使用MNIST数据集内容完成手写数字识别 步骤: 1.准备数据 2.构建模型 3.损失函数 4.模型训练 5.模型的保存和加载 6.模型的评估:损失和准确率 train_batch_size = 64 test_batch_size = 1000 train和test分别训练10次

好的，针对您的问题，我可以为您提供以下代码实现手写数字的识别，使用MNIST数据集完成手写数字识别：

1. 准备数据

import torch
import torchvision
import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))])

train_dataset = torchvision.datasets.MNIST(root='./data', train=True,
                                          download=True, transform=transform)

test_dataset = torchvision.datasets.MNIST(root='./data', train=False,
                                         download=True, transform=transform)

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64,
                                          shuffle=True)

test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1000,
                                         shuffle=False)

2. 构建模型

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

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

net = Net()

3. 损失函数

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.5)

4. 模型训练

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 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')

5. 模型的保存和加载

PATH = './mnist_net.pth'
torch.save(net.state_dict(), PATH)

# 加载模型
net = Net()
net.load_state_dict(torch.load(PATH))

6. 模型的评估：损失和准确率

correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        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))

以上代码可以实现手写数字的识别，使用MNIST数据集完成手写数字识别，其中train和test分别训练10次，并且包含了准备数据、构建模型、损失函数、模型训练、模型保存和加载、模型评估的完整流程。