使用鸢尾花数据集编写含三层DBN的多次迭代训练的代码

以下是使用鸢尾花数据集编写含三层DBN的多次迭代训练的代码：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neural_network import BernoulliRBM
from sklearn.pipeline import Pipeline

# 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DBN模型
dbn = Pipeline(steps=[
    ('rbm1', BernoulliRBM(n_components=50, learning_rate=0.01, n_iter=100)),
    ('rbm2', BernoulliRBM(n_components=20, learning_rate=0.01, n_iter=100)),
    ('rbm3', BernoulliRBM(n_components=10, learning_rate=0.01, n_iter=100))
])

# 多次迭代训练
for i in range(10):
    dbn.fit(X_train)

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

# 输出预测结果
print(y_pred)

希望这个代码对你有所帮助！