使用鸢尾花数据集编写含三层RBM的DBN的代码

以下是使用鸢尾花数据集编写含三层RBM的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.metrics import accuracy_score

# 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)

# 定义三层RBM的DBN
dbn = Pipeline(steps=[('rbm1', BernoulliRBM(n_components=64, learning_rate=0.1, n_iter=20, verbose=True)),
                      ('rbm2', BernoulliRBM(n_components=32, learning_rate=0.1, n_iter=20, verbose=True)),
                      ('rbm3', BernoulliRBM(n_components=16, learning_rate=0.1, n_iter=20, verbose=True))])

# 训练DBN
dbn.fit(X_train)

# 使用训练好的DBN进行预测
y_pred = dbn.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

希望这个代码能对你有所帮助！