bp网络神经预测模型python代码

BP网络（Backpropagation Neural Network）是一种常用的人工神经网络模型，用于进行预测和分类任务。下面是一个简单的用Python编写的BP网络神经预测模型的代码示例：

import numpy as np

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

# 定义BP网络类
class BPNeuralNetwork:
    def __init__(self, input_dim, hidden_dim, output_dim):
        # 初始化输入层、隐藏层和输出层的维度
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        
        # 初始化权重和偏置
        self.weights1 = np.random.randn(self.input_dim, self.hidden_dim)
        self.bias1 = np.random.randn(self.hidden_dim)
        self.weights2 = np.random.randn(self.hidden_dim, self.output_dim)
        self.bias2 = np.random.randn(self.output_dim)
        
    def forward(self, X):
        # 前向传播
        self.hidden_layer = sigmoid(np.dot(X, self.weights1) + self.bias1)
        self.output_layer = sigmoid(np.dot(self.hidden_layer, self.weights2) + self.bias2)
        return self.output_layer
    
    def backward(self, X, y, learning_rate):
        # 反向传播
        error = y - self.output_layer
        delta_output = error * self.output_layer * (1 - self.output_layer)
        delta_hidden = np.dot(delta_output, self.weights2.T) * self.hidden_layer * (1 - self.hidden_layer)
        
        self.weights2 += learning_rate * np.dot(self.hidden_layer.T, delta_output)
        self.bias2 += learning_rate * np.sum(delta_output, axis=0)
        self.weights1 += learning_rate * np.dot(X.T, delta_hidden)
        self.bias1 += learning_rate * np.sum(delta_hidden, axis=0)
    
    def train(self, X, y, epochs, learning_rate):
        # 训练模型
        for epoch in range(epochs):
            self.forward(X)
            self.backward(X, y, learning_rate)
    
    def predict(self, X):
        # 预测结果
        return np.round(self.forward(X))

以上代码实现了一个简单的两层BP网络，通过随机初始化权重和偏置，并通过训练来不断调整网络的参数，以最小化预测误差。最终使用predict函数对新的输入进行预测时，输出的值会被四舍五入到最接近的整数。