bp神经网络算法python

BP神经网络算法（Backpropagation）是一种常用的人工神经网络算法，用于训练多层前馈神经网络。在Python中，有很多库可以用来实现BP神经网络算法，比如TensorFlow、Keras、PyTorch等。下面是一个使用Python实现BP神经网络算法的例子：

import numpy as np

# 定义激活函数
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# 定义BP神经网络类
class NeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        # 初始化权重
        self.weights1 = np.random.randn(self.input_size, self.hidden_size)
        self.weights2 = np.random.randn(self.hidden_size, self.output_size)
        
    def forward(self, X):
        # 前向传播
        self.hidden_layer = sigmoid(np.dot(X, self.weights1))
        self.output_layer = sigmoid(np.dot(self.hidden_layer, self.weights2))
        
        return self.output_layer
    
    def backward(self, X, y, output, learning_rate):
        # 反向传播
        self.output_error = y - output
        self.output_delta = self.output_error * (output * (1 - output))
        
        self.hidden_error = np.dot(self.output_delta, self.weights2.T)
        self.hidden_delta = self.hidden_error * (self.hidden_layer * (1 - self.hidden_layer))
        
        # 更新权重
        self.weights2 += learning_rate * np.dot(self.hidden_layer.T, self.output_delta)
        self.weights1 += learning_rate * np.dot(X.T, self.hidden_delta)
        
    def train(self, X, y, epochs, learning_rate):
        for i in range(epochs):
            output = self.forward(X)
            self.backward(X, y, output, learning_rate)
            
    def predict(self, X):
        return self.forward(X)

# 示例数据
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]])

# 创建BP神经网络对象并进行训练
nn = NeuralNetwork(2, 4, 1)
nn.train(X, y, epochs=10000, learning_rate=0.1)

# 测试预测
print(nn.predict(X))

以上是一个简单的BP神经网络算法的实现，你可以根据自己的需求进行修改和扩展。希望对你有帮助！