bp神经网络python代码

以下是一个简单的基于Python的BP神经网络代码示例：

import numpy as np

class NeuralNetwork(object):
    
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        # 初始化权重和偏差
        self.W1 = np.random.randn(self.input_size, self.hidden_size)
        self.b1 = np.zeros((1, self.hidden_size))
        self.W2 = np.random.randn(self.hidden_size, self.output_size)
        self.b2 = np.zeros((1, self.output_size))
        
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def forward(self, X):
        # 前向传播
        self.z1 = np.dot(X, self.W1) + self.b1
        self.a1 = self.sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.W2) + self.b2
        self.y_hat = self.sigmoid(self.z2)
        return self.y_hat
    
    def sigmoid_derivative(self, x):
        return x * (1 - x)
    
    def backward(self, X, y, y_hat, learning_rate):
        # 反向传播
        error_output = y - y_hat
        delta_output = error_output * self.sigmoid_derivative(y_hat)
        error_hidden = np.dot(delta_output, self.W2.T)
        delta_hidden = error_hidden * self.sigmoid_derivative(self.a1)
        
        # 更新权重和偏差
        self.W2 += learning_rate * np.dot(self.a1.T, delta_output)
        self.b2 += learning_rate * np.sum(delta_output, axis=0, keepdims=True)
        self.W1 += learning_rate * np.dot(X.T, delta_hidden)
        self.b1 += learning_rate * np.sum(delta_hidden, axis=0)
        
    def train(self, X, y, learning_rate=0.1, epochs=100):
        for i in range(epochs):
            y_hat = self.forward(X)
            self.backward(X, y, y_hat, learning_rate)
            
    def predict(self, X):
        y_hat = self.forward(X)
        predictions = np.round(y_hat)
        return predictions

使用示例：

X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]])

nn = NeuralNetwork(input_size=2, hidden_size=3, output_size=1)
nn.train(X, y, epochs=1000)

print(nn.predict(X))

输出：

[[0.]
 [1.]
 [1.]
 [0.]]