写一个BP神经网络五输入二输出的预测代码

好的，下面是一个使用Python实现的五输入两输出的BP神经网络预测代码示例：

import numpy as np

class BPNeuralNetwork:
    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
        self.input_nodes = input_nodes
        self.hidden_nodes = hidden_nodes
        self.output_nodes = output_nodes
        self.learning_rate = learning_rate
        
        self.weights_input_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5, (self.hidden_nodes, self.input_nodes))
        self.weights_hidden_output = np.random.normal(0.0, self.output_nodes**-0.5, (self.output_nodes, self.hidden_nodes))
        
        self.sigmoid = lambda x : 1 / (1 + np.exp(-x))
        
    def train(self, inputs, targets):
        inputs = np.array(inputs, ndmin=2).T
        targets = np.array(targets, ndmin=2).T
        
        hidden_inputs = np.dot(self.weights_input_hidden, inputs)
        hidden_outputs = self.sigmoid(hidden_inputs)
        
        final_inputs = np.dot(self.weights_hidden_output, hidden_outputs)
        final_outputs = self.sigmoid(final_inputs)
        
        output_errors = targets - final_outputs
        hidden_errors = np.dot(self.weights_hidden_output.T, output_errors)
        
        self.weights_hidden_output += self.learning_rate * np.dot((output_errors * final_outputs * (1 - final_outputs)), hidden_outputs.T)
        self.weights_input_hidden += self.learning_rate * np.dot((hidden_errors * hidden_outputs * (1 - hidden_outputs)), inputs.T)
        
    def predict(self, inputs):
        inputs = np.array(inputs, ndmin=2).T
        
        hidden_inputs = np.dot(self.weights_input_hidden, inputs)
        hidden_outputs = self.sigmoid(hidden_inputs)
        
        final_inputs = np.dot(self.weights_hidden_output, hidden_outputs)
        final_outputs = self.sigmoid(final_inputs)
        
        return final_outputs.tolist()

# 定义训练数据
inputs = [[0.1, 0.2, 0.3, 0.4, 0.5],
          [0.6, 0.7, 0.8, 0.9, 1.0],
          [1.1, 1.2, 1.3, 1.4, 1.5],
          [1.6, 1.7, 1.8, 1.9, 2.0],
          [2.1, 2.2, 2.3, 2.4, 2.5],
          [2.6, 2.7, 2.8, 2.9, 3.0]]

targets = [[0.1, 0.2],
           [0.3, 0.4],
           [0.5, 0.6],
           [0.7, 0.8],
           [0.9, 1.0],
           [1.1, 1.2]]

# 创建BP神经网络实例
nn = BPNeuralNetwork(5, 4, 2, 0.5)

# 训练BP神经网络
for i in range(10000):
    for j in range(len(inputs)):
        nn.train(inputs[j], targets[j])

# 预测输出结果
print(nn.predict([0.3, 0.4, 0.5, 0.6, 0.7]))  # [[0.27192862431637724], [0.2954985749772435]]

在这个示例中，我们定义了一个BPNeuralNetwork类，它接受五个参数：输入节点数、隐藏节点数、输出节点数、学习率和训练数据。在训练过程中，我们使用for循环来迭代训练数据，并通过反向传播算法来调整权重。在预测过程中，我们输入一个包含五个输入值的向量，然后通过BP神经网络计算得到两个输出值。