python的手写数字识别

基于Python的手写数字识别可以通过训练模型和使用人工神经网络来实现。下面是两种实现手写数字识别的方法：

1. 使用机器学习库scikit-learn中的数据集和算法来训练模型：

# 导入数据集
from sklearn import datasets
digits = datasets.load_digits()

# 划分数据集为训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)

# 训练模型
from sklearn.svm import SVC
model = SVC()
model.fit(X_train, y_train)

# 预测测试集
y_pred = model.predict(X_test)

# 评估模型性能
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print("准确率：", accuracy)

2. 使用Python实现简单的人工神经网络来识别手写数字：

# 导入数据集
from sklearn import datasets
digits = datasets.load_digits()

# 划分数据集为训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)

# 数据预处理
X_train = X_train / 16.0
X_test = X_test / 16.0

# 构建神经网络模型
import numpy as np
class NeuralNetwork:
    def __init__(self):
        np.random.seed(1)
        self.weights = 2 * np.random.random((64, 10)) - 1

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def sigmoid_derivative(self, x):
        return x * (1 - x)

    def train(self, X, y, epochs):
        for i in range(epochs):
            output = self.predict(X)
            error = y - output
            adjustment = np.dot(X.T, error * self.sigmoid_derivative(output))
            self.weights += adjustment

    def predict(self, X):
        return self.sigmoid(np.dot(X, self.weights))

# 训练模型
nn = NeuralNetwork()
nn.train(X_train, y_train, epochs=1000)

# 预测测试集
y_pred = np.argmax(nn.predict(X_test), axis=1)

# 评估模型性能
accuracy = accuracy_score(y_test, y_pred)
print("准确率：", accuracy)