bp 神经网络 分类 python

以下是使用 Python 实现 BP 神经网络分类的基本步骤：

1. 导入必要的库：

import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

2. 准备数据集：

X, y = make_classification(n_samples=1000, n_features=10, n_classes=2, random_state=123)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)

3. 定义 BP 神经网络模型：

class NeuralNetwork:
    
    def __init__(self, input_dim, hidden_dim, output_dim):
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        
        self.weights1 = np.random.normal(loc=0.0, scale=0.1, size=(self.input_dim, self.hidden_dim))
        self.bias1 = np.zeros(self.hidden_dim)
        
        self.weights2 = np.random.normal(loc=0.0, scale=0.1, size=(self.hidden_dim, self.output_dim))
        self.bias2 = np.zeros(self.output_dim)
        
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
        
    def sigmoid_derivative(self, x):
        return x * (1 - x)
        
    def forward(self, X):
        self.hidden = self.sigmoid(np.dot(X, self.weights1) + self.bias1)
        self.output = self.sigmoid(np.dot(self.hidden, self.weights2) + self.bias2)
        
    def backward(self, X, y):
        output_error = y - self.output
        output_delta = output_error * self.sigmoid_derivative(self.output)
        
        hidden_error = np.dot(output_delta, self.weights2.T)
        hidden_delta = hidden_error * self.sigmoid_derivative(self.hidden)
        
        self.weights2 += np.dot(self.hidden.T, output_delta)
        self.bias2 += np.sum(output_delta, axis=0)
        
        self.weights1 += np.dot(X.T, hidden_delta)
        self.bias1 += np.sum(hidden_delta, axis=0)
        
    def train(self, X, y, epochs):
        for i in range(epochs):
            self.forward(X)
            self.backward(X, y)
            
    def predict(self, X):
        self.forward(X)
        return np.round(self.output)

4. 训练模型：

model = NeuralNetwork(input_dim=X_train.shape[1], hidden_dim=5, output_dim=1)
model.train(X_train, y_train, epochs=100)

5. 预测并评估模型：

y_pred = model.predict(X_test)
accuracy = np.mean(y_pred == y_test)
print("Accuracy:", accuracy)

这就是使用 Python 实现 BP 神经网络分类的基本步骤。当然，还有很多其他的优化方法可以应用到神经网络模型中，如正则化、批量归一化、学习率衰减等，可以根据具体情况进行选择。