BP神经网络python代码实现

BP神经网络的Python代码实现如下：

import numpy as np

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

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

class NeuralNetwork:
    def __init__(self, layers):
        self.layers = layers
        self.weights = []
        for i in range(1, len(layers)):
            w = 2 * np.random.random((layers[i-1] + 1, layers[i])) - 1
            self.weights.append(w)
    
    def forward_propagation(self, X):
        self.activations = []
        self.activations.append(np.insert(X, 0, 1, axis=1))
        
        for i in range(len(self.weights)):
            a = sigmoid(np.dot(self.activations[i], self.weights[i]))
            a = np.insert(a, 0, 1, axis=1)
            self.activations.append(a)
        
        return self.activations[-1]
    
    def backward_propagation(self, X, y, learning_rate):
        error = y - self.activations[-1]
        delta = error * sigmoid_derivative(self.activations[-1])
        deltas = [delta]
        
        for i in range(len(self.weights)-1, -1, -1):
            delta = np.dot(deltas[0], self.weights[i].T) * sigmoid_derivative(self.activations[i])
            deltas.insert(0, delta)
        
        for i in range(len(self.weights)):
            self.weights[i] += learning_rate * np.dot(self.activations[i].T, deltas[i])
    
    def train(self, X, y, epochs, learning_rate):
        for _ in range(epochs):
            output = self.forward_propagation(X)
            self.backward_propagation(X, y, learning_rate)
    
    def predict(self, X):
        output = self.forward_propagation(X)
        return np.round(output)