帮我生成一个4层的BP人工神经网络的详细代码

好的，以下是一个简单的四层BP神经网络的Python代码：

import numpy as np

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
        
        # 初始化权重和偏置
        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.hidden_size)
        self.b2 = np.random.randn(self.hidden_size)
        self.W3 = np.random.randn(self.hidden_size, self.output_size)
        self.b3 = np.random.randn(self.output_size)
        
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def sigmoid_derivative(self, x):
        return x * (1 - x)
    
    def forward(self, X):
        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.a2 = self.sigmoid(self.z2)
        self.z3 = np.dot(self.a2, self.W3) + self.b3
        y_pred = self.sigmoid(self.z3)
        return y_pred
    
    def backward(self, X, y, y_pred, learning_rate):
        # 计算输出误差和梯度
        delta4 = (y_pred - y) * self.sigmoid_derivative(y_pred)
        dW3 = np.dot(self.a2.T, delta4)
        db3 = np.sum(delta4, axis=0, keepdims=True)
        
        # 计算第二层误差和梯度
        delta3 = np.dot(delta4, self.W3.T) * self.sigmoid_derivative(self.a2)
        dW2 = np.dot(self.a1.T, delta3)
        db2 = np.sum(delta3, axis=0)
        
        # 计算第一层误差和梯度
        delta2 = np.dot(delta3, self.W2.T) * self.sigmoid_derivative(self.a1)
        dW1 = np.dot(X.T, delta2)
        db1 = np.sum(delta2, axis=0)
        
        # 更新权重和偏置
        self.W1 -= learning_rate * dW1
        self.b1 -= learning_rate * db1
        self.W2 -= learning_rate * dW2
        self.b2 -= learning_rate * db2
        self.W3 -= learning_rate * dW3
        self.b3 -= learning_rate * db3
        
    def train(self, X, y, epochs, learning_rate):
        for i in range(epochs):
            y_pred = self.forward(X)
            self.backward(X, y, y_pred, learning_rate)
            
    def predict(self, X):
        y_pred = self.forward(X)
        return y_pred

这个神经网络有三个隐藏层和一个输出层。你可以使用以下代码来构建并训练模型：

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

nn = NeuralNetwork(input_size=3, hidden_size=4, output_size=1)
nn.train(X, y, epochs=10000, learning_rate=0.1)

pred = nn.predict(X)
print(pred)

这段代码会生成一个4个节点的隐藏层，1个输出节点的BP神经网络，并使用X和y数据集进行训练和预测。在这个例子中，训练10000次，学习速率为0.1。