使用sklearn包进行Gibbs采样
使用sklearn包进行Gibbs采样
提问于 2019-02-22 17:38:38
我目前正在尝试为受限玻尔兹曼机RBM的bernoulli版本使用sklearns包，但我不明白它是如何工作的。我想要使用它的数据集是MNIST-dataset。我目前使用的很少的代码是：
def rbm():
#weights = np.zeros((20, 100, 784))
#for j in range(0, epochs):
rbm = BernoulliRBM(n_iter= 1, learning_rate = 0.01, n_components = 100, random_state=0, verbose=True)
rbm.fit(bindigit_trn)
gibbs(rbm.components_)
weights = rbm.components_
return weights我得到的错误代码是：“这个检查检测到应该解析但没有解析的名称。由于动态分派和鸭子类型，这在有限但有用的情况下是可能的。顶级和类级别项的支持比实例项更好。”
有人可以帮助初学者吗?我该怎么做呢？
回答 1
Stack Overflow用户
发布于 2019-05-29 16:06:49
您可以使用scikit-learn digits dataset尝试以下操作：
from sklearn.neural_network import BernoulliRBM
from sklearn import datasets
import numpy as np
import matplotlib.pylab as plt
def show_digits(im, title):
plt.figure(figsize=(5,5))
plt.gray()
for i in range(im.shape[0]):
plt.subplot(10,10,i+1)
plt.imshow(np.reshape(im[i,:], (8,8)))
plt.axis('off')
plt.suptitle(title)
plt.show()
def rbm():
digits = datasets.load_digits()
bindigit_trn = np.asarray(digits.data, 'float32')
for i in range(len(bindigit_trn)):
bindigit_trn[i,:] = bindigit_trn[i,:] / np.max(bindigit_trn[i,:])
print(bindigit_trn.shape)
# (1797, 64) => 1797 8x8 digits images
digits = bindigit_trn[:100,:]
print(digits.shape)
# (100, 64) => 100 images
show_digits(digits, 'original digits')
rbm = BernoulliRBM(n_iter= 10, learning_rate = 0.1, n_components = 10, random_state=0, verbose=True)
rbm.fit(bindigit_trn)
print(rbm.components_.shape)
# (10, 64)
digits_new = digits.copy() # rbm.components_.copy()
# gibbs sampling here
for j in range(10000):
for i in range(100):
digits_new[i,:] = rbm.gibbs(digits_new[i,:])
print(digits_new.shape)
# (100, 64)
show_digits(digits_new, 'sampled digits')
weights = rbm.components_
return weights
weights = rbm()
show_digits(weights, 'weights')
#[BernoulliRBM] Iteration 1, pseudo-likelihood = -25.85, time = 0.02s
#[BernoulliRBM] Iteration 2, pseudo-likelihood = -25.67, time = 0.02s
#[BernoulliRBM] Iteration 3, pseudo-likelihood = -25.45, time = 0.03s
#[BernoulliRBM] Iteration 4, pseudo-likelihood = -24.34, time = 0.02s
#[BernoulliRBM] Iteration 5, pseudo-likelihood = -23.41, time = 0.02s
#[BernoulliRBM] Iteration 6, pseudo-likelihood = -22.33, time = 0.02s
#[BernoulliRBM] Iteration 7, pseudo-likelihood = -21.88, time = 0.02s
#[BernoulliRBM] Iteration 8, pseudo-likelihood = -21.66, time = 0.02s
#[BernoulliRBM] Iteration 9, pseudo-likelihood = -21.74, time = 0.02s
#[BernoulliRBM] Iteration 10, pseudo-likelihood = -21.04, time = 0.02s
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/54824114
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