用鸢尾花数据集写一段DBN处理特征数据、BP分类的代码

以下是使用鸢尾花数据集进行特征处理和分类的DBN和BP代码示例：

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

# 加载鸢尾花数据集
iris = load_iris()
X, y = iris.data, iris.target

# 数据预处理
scaler = StandardScaler()
X = scaler.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# DBN模型训练
dbn = SupervisedDBNClassification(hidden_layers_structure=[256, 256], learning_rate_rbm=0.05, learning_rate=0.1, n_epochs_rbm=10, n_iter_backprop=100, batch_size=32, activation_function='relu', dropout_p=0.2)
dbn.fit(X_train, y_train)

# BP模型训练
from sklearn.neural_network import MLPClassifier
bp = MLPClassifier(hidden_layer_sizes=(256, 256), activation='relu', solver='adam', max_iter=1000, random_state=42)
bp.fit(X_train, y_train)

# 模型评估
print('DBN Accuracy: ', dbn.score(X_test, y_test))
print('BP Accuracy: ', bp.score(X_test, y_test))

请注意，此代码示例使用了DBN和BP两种不同的模型进行分类，您可以根据需要选择其中一种或两种模型进行比较。