python搭建bp神经网络解决红酒问题

红酒问题是一个经典的机器学习问题，可以用神经网络来解决。下面是一个用 Python 搭建 BP 神经网络解决红酒问题的示例代码：

首先，需要导入需要用到的库：

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

然后，读取红酒数据集：

data = pd.read_csv('winequality-red.csv', sep=';')

接着，将数据集中的标签值转换成类别：

data['quality'] = pd.cut(data['quality'], bins=[0, 6, 10], labels=[0, 1])

然后，将数据集分成训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(data.drop('quality', axis=1), data['quality'], test_size=0.33, random_state=42)

接下来，对数据进行标准化：

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

然后，定义 BP 神经网络模型：

class NeuralNetwork:
    def __init__(self, layers):
        self.layers = layers
        self.weights = []
        for i in range(1, len(layers)):
            self.weights.append(np.random.randn(layers[i-1], layers[i]))
    
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def sigmoid_derivative(self, x):
        return x * (1 - x)
    
    def forward(self, x):
        a = x
        for w in self.weights:
            a = self.sigmoid(np.dot(a, w))
        return a
    
    def backward(self, x, y, output):
        error = y - output
        delta = error * self.sigmoid_derivative(output)
        for i in range(len(self.weights)-1, -1, -1):
            output = self.sigmoid(np.dot(x, self.weights[i]))
            error = delta.dot(self.weights[i].T)
            delta = error * self.sigmoid_derivative(output)
            self.weights[i] += x.T.dot(delta)
            x = output
    
    def train(self, X, y, epochs):
        for i in range(epochs):
            output = self.forward(X)
            self.backward(X, y, output)
    
    def predict(self, X):
        return np.round(self.forward(X))

最后，训练模型并进行预测：

nn = NeuralNetwork([11, 6, 1])
nn.train(X_train, y_train, 10000)
y_pred = nn.predict(X_test)

这样就完成了用 BP 神经网络解决红酒问题的过程。