使用鸢尾花数据集编写DBN的代码

以下是使用鸢尾花数据集编写DBN的代码：

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=0)

# 定义 BernoulliRBM 模型
rbm = BernoulliRBM(n_components=2, learning_rate=0.01, n_iter=1000, random_state=0, verbose=True)

# 定义逻辑回归模型
logistic = LogisticRegression(solver='lbfgs', max_iter=1000, multi_class='auto')

# 定义管道，将 BernoulliRBM 和逻辑回归模型连接起来
classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)])

# 训练模型
classifier.fit(X_train, y_train)

# 预测测试集
y_pred = classifier.predict(X_test)

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

希望能对您有所帮助！