bp神经网络python代码

以下是一个简单的基于Python实现的BP神经网络示例代码：

import numpy as np

# 定义sigmoid函数作为激活函数
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):
        # 初始化输入层、隐藏层、输出层的权重和偏置
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.W1 = np.random.randn(self.input_size, self.hidden_size)
        self.b1 = np.random.randn(self.hidden_size)
        self.W2 = np.random.randn(self.hidden_size, self.output_size)
        self.b2 = np.random.randn(self.output_size)

    def forward(self, X):
        # 前向传播
        self.z1 = np.dot(X, self.W1) + self.b1
        self.a1 = sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.W2) + self.b2
        self.a2 = sigmoid(self.z2)
        return self.a2

    def backward(self, X, y, output):
        # 反向传播
        delta2 = (output - y) * sigmoid(self.z2) * (1 - sigmoid(self.z2))
        dW2 = np.dot(self.a1.T, delta2)
        db2 = np.sum(delta2, axis=0)
        delta1 = np.dot(delta2, self.W2.T) * sigmoid(self.z1) * (1 - sigmoid(self.z1))
        dW1 = np.dot(X.T, delta1)
        db1 = np.sum(delta1, axis=0)
        return dW1, db1, dW2, db2

    def train(self, X, y, learning_rate=0.1, epochs=10000):
        # 模型训练
        for i in range(epochs):
            output = self.forward(X)
            dW1, db1, dW2, db2 = self.backward(X, y, output)
            self.W1 -= learning_rate * dW1
            self.b1 -= learning_rate * db1
            self.W2 -= learning_rate * dW2
            self.b2 -= learning_rate * db2
            if i % 1000 == 0:
                loss = np.mean(np.square(y - output))
                print(f"Epoch {i} Loss: {loss}")

# 测试
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]])
model = BPNeuralNetwork(2, 4, 1)
model.train(X, y, learning_rate=0.1, epochs=10000)
print(model.forward(X))

以上代码实现了一个简单的BP神经网络，用于解决异或逻辑问题。您可以根据自己的需求进行修改和扩展。