minist手写数字识别pytoch完整代码

以下是使用PyTorch进行手写数字识别的完整代码：

import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader

# 设置设备为GPU或CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 超参数
input_size = 784
hidden_size = 500
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001

# 加载MNIST数据集
train_dataset = MNIST(root='data/', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = MNIST(root='data/', train=False, transform=transforms.ToTensor())

# 创建数据加载器
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)
        
    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

# 实例化模型并移动到设备上
model = Net(input_size, hidden_size, num_classes).to(device)

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

# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 将输入移动到设备上
        images = images.reshape(-1, 28*28).to(device)
        labels = labels.to(device)
        
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        # 每100个batch打印一次训练信息
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        # 将输入移动到设备上
        images = images.reshape(-1, 28*28).to(device)
        labels = labels.to(device)
        
        # 前向传播
        outputs = model(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: {} %'.format(100 * correct / total))

该代码首先使用PyTorch加载MNIST数据集，并创建了训练和测试数据加载器。然后，定义了一个包含两个全连接层的神经网络模型，并将其移动到设备上。接着，定义了损失函数和优化器，并使用训练数据对模型进行训练。最后，使用测试数据评估了模型的准确度。