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《Python》

# 导入所需库

import numpy as np

# 定义线性回归函数

def linear_regression(X, y, lambda_val=0.1, learning_rate=0.01, iterations=1000):
# 初始化权重和偏置
m, n = X.shape
theta = np.random.randn(n, 1)
bias = np.random.randn(1)

``````# 迭代优化
for i in range(iterations):
# 计算预测值
y_pred = np.dot(X, theta) + bias

# 计算损失
loss = np.mean((y_pred - y) ** 2) + lambda_val * np.sum(theta ** 2)

# 计算梯度
d_theta = (2 / m) * np.dot(X.T, (y_pred - y)) + 2 * lambda_val * theta
d_bias = (2 / m) * np.sum(y_pred - y)

# 更新权重和偏置
theta = theta - learning_rate * d_theta
bias = bias - learning_rate * d_bias

return theta, bias
``````

# 训练模型

theta, bias = linear_regression(X, y, lambda_val=0.1, learning_rate=0.01, iterations=1000)

# 预测

y_pred = np.dot(X, theta) + bias

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# 计算损失函数

def compute_loss(model, X, y, lambda_val):
# 前向传播
predictions = model.predict(X)

``````# 计算交叉熵损失
cross_entropy_loss = compute_cross_entropy_loss(predictions, y)

# 计算L2正则化项
l2_regularization = 0
for layer in model.layers:
if hasattr(layer, 'weights'):
l2_regularization += np.sum(np.square(layer.weights))

# 总损失 = 交叉熵损失 + L2正则化项
total_loss = cross_entropy_loss + (lambda_val / (2 * len(X))) * l2_regularization

``````

# 反向传播更新参数

def update_parameters(model, X, y, learning_rate, lambda_val):
# 前向传播
predictions = model.predict(X)

``````# 反向传播