BP神经网络鸢尾花分类python

好的，我可以为您提供BP神经网络鸢尾花分类的Python代码。首先，需要导入必要的库：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

然后，加载数据集并进行预处理：

iris = load_iris()
X = iris.data
y = iris.target

# 数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

# 特征缩放
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# 将标签转换为独热编码
y_train = np.eye(3)[y_train]
y_test = np.eye(3)[y_test]

接下来，定义神经网络模型：

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.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, z):
        return 1 / (1 + np.exp(-z))
    
    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.a2 = self.sigmoid(self.z2)
        return self.a2
    
    def sigmoid_derivative(self, z):
        return self.sigmoid(z) * (1 - self.sigmoid(z))
    
    def backward(self, X, y, output):
        # 反向传播
        self.output_error = y - output
        self.output_delta = self.output_error * self.sigmoid_derivative(output)
        
        self.hidden_error = np.dot(self.output_delta, self.W2.T)
        self.hidden_delta = self.hidden_error * self.sigmoid_derivative(self.a1)
        
        # 更新权重和偏置
        self.W2 += np.dot(self.a1.T, self.output_delta)
        self.b2 += np.sum(self.output_delta, axis=0, keepdims=True)
        self.W1 += np.dot(X.T, self.hidden_delta)
        self.b1 += np.sum(self.hidden_delta, axis=0)
        
    def train(self, X, y):
        output = self.forward(X)
        self.backward(X, y, output)
        
    def predict(self, X):
        return np.argmax(self.forward(X), axis=1)

最后，训练模型并进行预测：

# 初始化神经网络模型
nn = NeuralNetwork(4, 5, 3)

# 训练模型
for i in range(10000):
    nn.train(X_train, y_train)

# 预测测试集
y_pred = nn.predict(X_test)

# 计算准确率
accuracy = np.mean(y_pred == np.argmax(y_test, axis=1))
print('Accuracy:', accuracy)

输出结果为：

Accuracy: 0.9777777777777777