所以我正在阅读SKLearn网站上限制性玻尔兹曼机器的example，在得到这个例子后，我想与BernoulliRBM一起玩更多，以更好地了解RBM的工作原理。我尝试做一些简单的类预测：
# Adapted from sample digits recognition client on Scikit-Learn site.
import numpy as np
from sklearn import linear_model, datasets
from sklearn.cross_validation import train_test_split
from sklearn.neural_network import BernoulliRBM
from sklearn.pipeline import Pipeline
from sklearn.lda import LDA
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
Y = iris.target
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=10)
# Models we will use
rbm = BernoulliRBM(random_state=0, verbose=True)
logistic = linear_model.LogisticRegression()
classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)])
lda = LDA(n_components=3)
#########################################################################
# Training RBM-Logistic Pipeline
logistic.fit(X_train, Y_train)
classifier.fit(X_train, Y_train)
#########################################################################
# Get predictions
print "The RBM model:"
print "Predict: ", classifier.predict(X_test)
print "Real: ", Y_test
print
print "Linear Discriminant Analysis: "
lda.fit(X_train, Y_train)
print "Predict: ", lda.predict(X_test)
print "Real: ", Y_test
这是输出：
Iteration 0, pseudo-likelihood = 0.00, time = 0.02s
Iteration 1, pseudo-likelihood = 0.00, time = 0.02s
Iteration 2, pseudo-likelihood = 0.00, time = 0.02s
Iteration 3, pseudo-likelihood = 0.00, time = 0.02s
Iteration 4, pseudo-likelihood = 0.00, time = 0.02s
Iteration 5, pseudo-likelihood = 0.00, time = 0.02s
Iteration 6, pseudo-likelihood = 0.00, time = 0.02s
Iteration 7, pseudo-likelihood = 0.00, time = 0.01s
Iteration 8, pseudo-likelihood = 0.00, time = 0.01s
Iteration 9, pseudo-likelihood = 0.00, time = 0.02s
The RBM model:
Predict: [2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]
Real: [1 2 0 1 0 1 1 1 0 1 1 2 1 0 0 2 1 0 0 0 2 2 2 0 1 0 1 1 1 2]
Linear Discriminant Analysis:
Predict: [2 2 0 1 0 1 2 1 0 1 1 1 1 0 0 2 1 0 0 0 2 2 2 0 1 0 1 1 2 2]
Real: [1 2 0 1 0 1 1 1 0 1 1 2 1 0 0 2 1 0 0 0 2 2 2 0 1 0 1 1 1 2]
为什么RBM预测＆＃34; 2＆＃34;对于测试数据中的每个标签，即使它显然不正确（如LDA所示）？怎么可能让Pipeline（rbm，logistic）预测类标签？如果你能向神经网络新手解释这一点，我真的很感激。