dbn分类 python

下面是使用Python实现DBN分类的基本步骤：

1. 导入所需的库和数据集

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import BernoulliRBM
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

2. 加载并准备数据集

X, y = make_moons(n_samples=10000, noise=0.1, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 特征标准化
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

3. 定义DBN分类器

rbm = BernoulliRBM(random_state=0, verbose=True)

logistic = LogisticRegression(random_state=0, solver='lbfgs', max_iter=1000)

dbn = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)])

4. 训练DBN分类器

dbn.fit(X_train, y_train)

5. 预测并计算准确率

y_pred = dbn.predict(X_test)
print("Accuracy: %.2f" % accuracy_score(y_test, y_pred))

以上就是使用Python实现DBN分类的基本步骤。注意，使用DBN分类器时，建议使用Pipeline将多个处理步骤组合在一起，以便于训练和预测。