使用鸢尾花数据集编写含三层RBM的DBN的分类可视化代码

非常感谢您的问题！以下是使用鸢尾花数据集编写含三层RBM的DBN的分类可视化代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
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
from sklearn.metrics import accuracy_score

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

# 数据预处理
scaler = StandardScaler()
X = scaler.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(random_state=42, max_iter=1000)

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)

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

# 可视化分类结果
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred)
plt.show()

希望这个代码能够帮助您！