pythonbp神经网络iris分类

以下是一个使用Python和BP神经网络对鸢尾花数据集进行分类的示例代码：

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

# 加载数据集并进行预处理
iris = load_iris()
X = iris.data
y = iris.target
scaler = StandardScaler()
X = scaler.fit_transform(X)

# 将数据集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=42)

# 定义BP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, x):
        return 1 / (1 + np.exp(-x))
    
    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, x):
        return x * (1 - x)
    
    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)

# 训练模型
input_size = X_train.shape[1]
hidden_size = 5
output_size = 3
nn = NeuralNetwork(input_size, hidden_size, output_size)
epochs = 1000
for i in range(epochs):
    nn.train(X_train, np.eye(output_size)[y_train])
    
# 测试模型
y_pred = np.argmax(nn.forward(X_test), axis=1)
accuracy = np.mean(y_pred == y_test)
print('Accuracy:', accuracy)

上述代码中，首先加载鸢尾花数据集并对其进行预处理，然后将数据集分为训练集和测试集。接着定义BP神经网络模型，包括前向传播、反向传播和权重、偏置的更新。最后训练模型并使用测试集进行测试，计算准确度并输出结果。