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手写数字识别实战 - 从传统机器学习到深度学习

news 2025/8/8 8:27:00

关键词：MNIST数据集、SVM、神经网络、模型对比

python

# 第一部分：使用Scikit-learn的SVM识别手写数字
from sklearn import datasets, svm, metrics
from sklearn.model_selection import train_test_split# 加载MNIST数据集
digits = datasets.load_digits()
X, y = digits.images.reshape((len(digits.images), -1)), digits.target# 数据预处理
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 创建SVM分类器
clf = svm.SVC(gamma=0.001)
clf.fit(X_train, y_train)# 预测与评估
y_pred = clf.predict(X_test)
print(f"分类报告:\n{metrics.classification_report(y_test, y_pred)}")

输出示例：

text

precision    recall  f1-score   support0       1.00      1.00      1.00        331       1.00      1.00      1.00        282       1.00      1.00      1.00        333       1.00      0.97      0.99        34
...accuracy                           0.99       360

python

# 第二部分：使用PyTorch实现神经网络
import torch
import torch.nn as nn
import torch.optim as optim# 定义神经网络
class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.fc1 = nn.Linear(64, 128)self.fc2 = nn.Linear(128, 10)def forward(self, x):x = torch.relu(self.fc1(x))x = self.fc2(x)return x# 转换数据为Tensor
X_train_t = torch.FloatTensor(X_train)
y_train_t = torch.LongTensor(y_train)# 训练模型
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)for epoch in range(100):optimizer.zero_grad()outputs = model(X_train_t)loss = criterion(outputs, y_train_t)loss.backward()optimizer.step()

关键结论：