使用鸢尾花数据集编写含三层RBM的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DBN的分类模型代码：

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

# 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=0.2, random_state=42)

# 定义三个RBM层
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)

# 定义逻辑回归分类器
logistic = LogisticRegression(solver='lbfgs', max_iter=1000, multi_class='auto', random_state=42)

# 构建DBN模型
dbn = Pipeline(steps=[('rbm1', rbm1), ('rbm2', rbm2), ('rbm3', rbm3), ('logistic', logistic)])

# 训练DBN模型
dbn.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = dbn.predict(X_test)

# 输出预测结果
print("预测结果：", y_pred)

希望这个代码能够帮助你。