使用鸢尾花数据集编写含三层RBM的迭代DBN的分类模型代码

以下是使用鸢尾花数据集编写含三层RBM的迭代DBN的分类模型代码的示例：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import BernoulliRBM
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

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

# 数据预处理
X = StandardScaler().fit_transform(X)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义三层RBM的迭代DBN模型
rbm1 = BernoulliRBM(n_components=50, learning_rate=0.01, n_iter=100, random_state=42, verbose=False)
rbm2 = BernoulliRBM(n_components=20, learning_rate=0.01, n_iter=100, random_state=42, verbose=False)
rbm3 = BernoulliRBM(n_components=10, learning_rate=0.01, n_iter=100, random_state=42, verbose=False)
logistic = LogisticRegression(solver='lbfgs', max_iter=1000, multi_class='auto', random_state=42)

dbn = Pipeline(steps=[('rbm1', rbm1), ('rbm2', rbm2), ('rbm3', rbm3), ('logistic', logistic)])

# 训练模型
dbn.fit(X_train, y_train)

# 预测测试集
y_pred = dbn.predict(X_test)

# 输出准确率
print("Accuracy:", np.mean(y_pred == y_test))

希望这个示例能够帮助你理解如何使用鸢尾花数据集编写含三层RBM的迭代DBN的分类模型代码。