DL之RBM：基于RBM实现手写数字图片识别提高准确率

羽毛冷酷 2022-09-19 17:25:11  50051

分类专栏：资讯

输出结果

设计代码


import numpy as np
import matplotlib.pyplot as plt
 
from sklearn.model_selection import train_test_split
from sklearn import metrics,linear_model
from sklearn.neural_network import BernoulliRBM  
from sklearn.datasets import load_digits          
from sklearn.pipeline import Pipeline    
 
digits = load_digits() 
X = digits.data         
y = digits.target     
 
X -= X.min()   
X /= X.max()  
X_train, X_test, y_train, y_test = train_test_split(X, y)  
 
 
logistic = linear_model.LogisticRegression() 
rbm = BernoulliRBM(random_state=0, verbose=True) 
classifier = Pipeline(steps=[('rbm', rbm), ('logistic',logistic)]) 
 
rbm.learning_rate = 0.06  
rbm.n_iter = 20           
rbm.n_components = 200   
logistic.C = 6000.0       
classifier.fit (X_train,y_train)  
 
print()
print("Logistic regression using RBM features:\n%s\n"%(
    metrics.classification_report(y_test,classifier.predict(X_test))

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DL之RBM：基于RBM实现手写数字图片识别提高准确率

输出结果

设计代码

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