手写数字识别python

以下是一个用Python实现手写数字识别的简单例子：

1. 导入所需库

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.neighbors import KNeighborsClassifier

2. 加载数据集

digits = load_digits()

3. 数据集分割

X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=0)

4. KNN模型训练

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

5. 预测并输出分类报告

y_pred = knn.predict(X_test)
print(classification_report(y_test, y_pred))

6. 可视化展示

fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(8, 8))
for ax, image, prediction in zip(axes.flatten(), X_test, y_pred):
    ax.set_axis_off()
    image = image.reshape(8, 8)
    ax.imshow(image, cmap=plt.cm.gray_r)
    ax.set_title(f'Prediction: {prediction}')
plt.show()

这是一个简单的示例，实际上手写数字识别涉及到的技术和算法还有很多，可以根据具体需求和实际情况进行相应的选择和调整。