BP防止过拟合正则化代码使用Jupyter导入文件
时间: 2024-03-26 10:40:44 浏览: 16
以下是使用 Jupyter 导入文件并应用 BP 神经网络正则化防止过拟合的 Python 代码:
```python
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
# 导入数据
data = pd.read_csv('data.csv')
# 将数据分为训练集和测试集
train_data, test_data, train_label, test_label = train_test_split(data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2, random_state=0)
# 定义 sigmoid 函数和其导数
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def sigmoid_derivative(x):
return sigmoid(x) * (1 - sigmoid(x))
# BP 神经网络类
class BPNN:
def __init__(self, input_dim, hidden_dim, output_dim, learning_rate, epochs, lmbda):
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.learning_rate = learning_rate
self.epochs = epochs
self.lmbda = lmbda
# 初始化权重参数
self.W1 = np.random.normal(0, 1, (self.input_dim, self.hidden_dim))
self.b1 = np.random.normal(0, 1, (1, self.hidden_dim))
self.W2 = np.random.normal(0, 1, (self.hidden_dim, self.output_dim))
self.b2 = np.random.normal(0, 1, (1, self.output_dim))
def train(self, X, y):
for i in range(self.epochs):
# 前向传播
z1 = np.dot(X, self.W1) + self.b1
a1 = sigmoid(z1)
z2 = np.dot(a1, self.W2) + self.b2
a2 = sigmoid(z2)
# 计算误差
error = y - a2
# 反向传播
delta2 = error * sigmoid_derivative(a2)
dW2 = np.dot(a1.T, delta2) + self.lmbda * self.W2
db2 = np.sum(delta2, axis=0, keepdims=True)
delta1 = np.dot(delta2, self.W2.T) * sigmoid_derivative(a1)
dW1 = np.dot(X.T, delta1) + self.lmbda * self.W1
db1 = np.sum(delta1, axis=0)
# 更新权重和偏置
self.W2 += self.learning_rate * dW2
self.b2 += self.learning_rate * db2
self.W1 += self.learning_rate * dW1
self.b1 += self.learning_rate * db1
def predict(self, X):
z1 = np.dot(X, self.W1) + self.b1
a1 = sigmoid(z1)
z2 = np.dot(a1, self.W2) + self.b2
a2 = sigmoid(z2)
return a2
# 设置网络参数并训练模型
input_dim = train_data.shape[1]
hidden_dim = 5
output_dim = 1
learning_rate = 0.1
epochs = 1000
lmbda = 0.01
bpnn = BPNN(input_dim, hidden_dim, output_dim, learning_rate, epochs, lmbda)
bpnn.train(train_data, train_label)
# 在测试集上评估模型
y_pred = bpnn.predict(test_data)
accuracy = np.mean((y_pred > 0.5) == test_label.values.reshape(-1, 1))
print('Accuracy:', accuracy)
```
其中,`data.csv` 是包含训练数据的 CSV 文件,BPNN 类实现了一个简单的 BP 神经网络,并使用 L2 正则化以防止过拟合。代码中使用的数据集分为训练集和测试集,通过调整网络参数和正则化系数可以得到更好的模型效果。