跑通MLP分类器实例代码

在Python中，我们可以使用深度学习库如PyTorch或TensorFlow来创建一个简单的Multi-Layer Perceptron (MLP)分类器实例。这里以PyTorch为例，我们将构建一个两层全连接神经网络模型用于MNIST手写数字识别：

import torch
import torch.nn as nn
import torchvision.datasets as datasets
from torchvision.transforms import ToTensor

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

# 创建数据加载器
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64)

# 定义MLP模型
class MLP(nn.Module):
    def __init__(self):
        super(MLP, self).__init__()
        self.fc1 = nn.Linear(784, 128)  # 输入层到隐藏层
        self.relu = nn.ReLU()  # 激活函数
        self.fc2 = nn.Linear(128, 10)   # 隐藏层到输出层（假设我们有10个类别）

    def forward(self, x):
        x = x.view(x.shape[0], -1)  # 展平输入
        x = self.fc1(x)
        x = self.relu(x)
        out = self.fc2(x)
        return out

model = MLP()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

# 训练过程
num_epochs = 5
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
    print(f"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}")

# 测试模型
model.eval()  # 设置为评估模式
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print(f"Test Accuracy: {accuracy}%")