使用鸢尾花数据集写一段DBN和BP分类的代码

以下是使用鸢尾花数据集写的DBN和BP分类的代码：

DBN分类：

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

# 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DBN模型
rbm = BernoulliRBM(n_components=2, learning_rate=0.01, n_iter=100)
logistic = LogisticRegression()

# 构建Pipeline
model = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)])

# 训练模型
model.fit(X_train, y_train)

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

# 输出准确率
print("Accuracy:", model.score(X_test, y_test))

BP分类：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 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BP模型
clf = MLPClassifier(hidden_layer_sizes=(5, 2), max_iter=1000)

# 训练模型
clf.fit(X_train, y_train)

# 预测测试集
y_pred = clf.predict(X_test)

# 输出准确率
print("Accuracy:", clf.score(X_test, y_test))