bp神经网络python代码

以下是一个简单的基于Python的BP神经网络示例代码：

import numpy as np

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

# 定义BP神经网络类
class BPNeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        # 初始化权重矩阵和偏差向量
        self.weights1 = np.random.randn(self.input_size, self.hidden_size)
        self.bias1 = np.random.randn(self.hidden_size)
        self.weights2 = np.random.randn(self.hidden_size, self.output_size)
        self.bias2 = np.random.randn(self.output_size)
        
    def forward(self, x):
        # 前向传播
        self.z1 = np.dot(x, self.weights1) + self.bias1
        self.a1 = sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.weights2) + self.bias2
        self.a2 = sigmoid(self.z2)
        return self.a2
        
    def sigmoid_derivative(self, x):
        return x * (1 - x)
    
    def backward(self, x, y, output):
        # 反向传播
        self.error = output - y
        self.delta2 = self.error * self.sigmoid_derivative(output)
        self.error_hidden = self.delta2.dot(self.weights2.T)
        self.delta1 = self.error_hidden * self.sigmoid_derivative(self.a1)
        
        # 更新权重矩阵和偏差向量
        self.weights1 -= x.T.dot(self.delta1)
        self.bias1 -= np.sum(self.delta1, axis=0)
        self.weights2 -= self.a1.T.dot(self.delta2)
        self.bias2 -= np.sum(self.delta2, axis=0)
        
    def train(self, x, y, epochs):
        for i in range(epochs):
            output = self.forward(x)
            self.backward(x, y, output)
            
    def predict(self, x):
        return self.forward(x)

使用方法：

# 创建BP神经网络对象
bpnn = BPNeuralNetwork(2, 3, 1)

# 训练神经网络
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]])
bpnn.train(x, y, 10000)

# 预测结果
print(bpnn.predict(x))

输出结果：

[[0.02787267]
 [0.98648467]
 [0.98635977]
 [0.02920167]]

这是一个简单的示例，如果需要实际应用，需要对代码进行进一步的优化和改进。