我从文档中借用了一个概念here来使用RBMs+Logistic回归进行分类。
但是我得到了一个不应该抛出的错误，因为我的数据矩阵中的所有条目都是数值型的。
代码：
from sklearn import preprocessing, cross_validation
from scipy.ndimage import convolve
from sklearn.neural_network import BernoulliRBM
from sklearn.pipeline import Pipeline
from sklearn import linear_model, datasets, metrics
import numpy as np
# create fake dataset
data, labels = datasets.make_classification(n_samples=250000)
data = preprocessing.scale(data)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(data, labels, test_size=0.7, random_state=0)
# print details
print X_train.shape, X_test.shape, y_train.shape, y_test.shape
print np.max(X_train)
print np.min(X_train)
print np.mean(X_train, axis=0)
print np.std(X_train, axis=0)
if np.sum(np.isnan(X_train)) or np.sum(np.isnan(X_test)):
print "NaN found!"
if np.sum(np.isnan(y_train)) or np.sum(np.isnan(y_test)):
print "NaN found!"
if np.sum(np.isinf(X_train)) or np.sum(np.isinf(X_test)):
print "Inf found!"
if np.sum(np.isinf(y_train)) or np.sum(np.isinf(y_test)):
print "Inf found!"
# train and test
logistic = linear_model.LogisticRegression()
rbm = BernoulliRBM(random_state=0, verbose=True)
classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)])
# Training RBM-Logistic Pipeline
classifier.fit(X_train, y_train)
# Training Logistic regression
logistic_classifier = linear_model.LogisticRegression(C=100.0)
logistic_classifier.fit(X_train, y_train)
print("Logistic regression using RBM features:\n%s\n" % (
metrics.classification_report(
y_test,
classifier.predict(X_test))))
输出：
(73517, 3) (171540, 3) (73517,) (171540,)
2.0871168057
-2.21062647188
[-0.00237028 -0.00104526 0.00330683]
[ 0.99907225 0.99977328 1.00225843]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/usr/lib/python2.7/dist-packages/IPython/utils/py3compat.pyc in execfile(fname, *where)
173 else:
174 filename = fname
--> 175 __builtin__.execfile(filename, *where)
/home/test.py in <module>()
75
76 # Training RBM-Logistic Pipeline
---> 77 classifier.fit(X_train, y_train)
78
79 # Training Logistic regression
/usr/local/lib/python2.7/dist-packages/sklearn/pipeline.pyc in fit(self, X, y, **fit_params)
128 data, then fit the transformed data using the final estimator.
129 """
--> 130 Xt, fit_params = self._pre_transform(X, y, **fit_params)
131 self.steps[-1][-1].fit(Xt, y, **fit_params)
132 return self
/usr/local/lib/python2.7/dist-packages/sklearn/pipeline.pyc in _pre_transform(self, X, y, **fit_params)
118 for name, transform in self.steps[:-1]:
119 if hasattr(transform, "fit_transform"):
--> 120 Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
121 else:
122 Xt = transform.fit(Xt, y, **fit_params_steps[name]) \
/usr/local/lib/python2.7/dist-packages/sklearn/base.pyc in fit_transform(self, X, y, **fit_params)
409 else:
410 # fit method of arity 2 (supervised transformation)
--> 411 return self.fit(X, y, **fit_params).transform(X)
412
413
/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/rbm.pyc in fit(self, X, y)
304
305 for batch_slice in batch_slices:
--> 306 pl_batch = self._fit(X[batch_slice], rng)
307
308 if verbose:
/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/rbm.pyc in _fit(self, v_pos, rng)
245
246 if self.verbose:
--> 247 return self.score_samples(v_pos)
248
249 def score_samples(self, v):
/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/rbm.pyc in score_samples(self, v)
268 fe_ = self._free_energy(v_)
269
--> 270 return v.shape[1] * logistic_sigmoid(fe_ - fe, log=True)
271
272 def fit(self, X, y=None):
/usr/local/lib/python2.7/dist-packages/sklearn/utils/extmath.pyc in logistic_sigmoid(X, log, out)
498 """
499 is_1d = X.ndim == 1
--> 500 X = array2d(X, dtype=np.float)
501
502 n_samples, n_features = X.shape
/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.pyc in array2d(X, dtype, order, copy, force_all_finite)
91 X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order)
92 if force_all_finite:
---> 93 _assert_all_finite(X_2d)
94 if X is X_2d and copy:
95 X_2d = safe_copy(X_2d)
/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.pyc in _assert_all_finite(X)
25 if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum())
26 and not np.isfinite(X).all()):
---> 27 raise ValueError("Array contains NaN or infinity.")
28
29
ValueError: Array contains NaN or infinity.
数据矩阵中没有inf或nan…是什么导致了这种行为？
编辑：显然我not the only one。
最佳答案
这个问题通常是由两个因素引起的。数据的初始缩放不正确。首先需要将输入数据绑定在0和1之间。记住，RBM最初只是为二进制数据设计的。其次，学习率可能过高。RBM代码的默认值通常基于MNIST数字识别数据集，该数据集可以处理较大的学习率。
所以我相信sklearn的实现，但不相信基于不适合当前数据集的默认值的新数据集算法的稳定性。添加无限的检查不会帮助你仍然需要调整学习率。
这就是为什么说深度学习是一门艺术，你可能还需要玩弄吉布斯样本的数量，小批量的大小和动量的数量。不过，不要放弃，回报大多是值得的。Further reading
关于python - sklearn 0.14.1 RBM在不存在的NaN或Inf上死亡，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/21243156/