人臉的部分詞典的在線學習?
此示例使用一個大型的Faces數據集來學習一組組成面部的20 x 20圖像修補程序。
從編程的角度來看，這很有趣，因為它展示了如何使用scikit-learn在線API學習按塊處理一個非常大的數據集。我們處理的方法是一次加載一個圖像，并從這個圖像中隨機提取50個補丁。一旦我們積累了500個這些補丁(使用10個圖像)，我們運行在線KMeans對象 MiniBatchKMeans的partial_fit方法。
MiniBatchKMeans上的詳細設置使我們能夠看到，在連續調用partial-fit期間，某些聚類被重新分配。這是因為它們所代表的補丁數量太少了，最好選擇一個隨機的新聚類。
downloading Olivetti faces from https://ndownloader.figshare.com/files/5976027 to /home/circleci/scikit_learn_data
Learning the dictionary...
Partial fit of 100 out of 2400
Partial fit of 200 out of 2400
[MiniBatchKMeans] Reassigning 16 cluster centers.
Partial fit of 300 out of 2400
Partial fit of 400 out of 2400
Partial fit of 500 out of 2400
Partial fit of 600 out of 2400
Partial fit of 700 out of 2400
Partial fit of 800 out of 2400
Partial fit of 900 out of 2400
Partial fit of 1000 out of 2400
Partial fit of 1100 out of 2400
Partial fit of 1200 out of 2400
Partial fit of 1300 out of 2400
Partial fit of 1400 out of 2400
Partial fit of 1500 out of 2400
Partial fit of 1600 out of 2400
Partial fit of 1700 out of 2400
Partial fit of 1800 out of 2400
Partial fit of 1900 out of 2400
Partial fit of 2000 out of 2400
Partial fit of 2100 out of 2400
Partial fit of 2200 out of 2400
Partial fit of 2300 out of 2400
Partial fit of 2400 out of 2400
done in 2.43s.
print(__doc__)
import time
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.image import extract_patches_2d
faces = datasets.fetch_olivetti_faces()
# #############################################################################
# Learn the dictionary of images
print('Learning the dictionary... ')
rng = np.random.RandomState(0)
kmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True)
patch_size = (20, 20)
buffer = []
t0 = time.time()
# The online learning part: cycle over the whole dataset 6 times
index = 0
for _ in range(6):
for img in faces.images:
data = extract_patches_2d(img, patch_size, max_patches=50,
random_state=rng)
data = np.reshape(data, (len(data), -1))
buffer.append(data)
index += 1
if index % 10 == 0:
data = np.concatenate(buffer, axis=0)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)
kmeans.partial_fit(data)
buffer = []
if index % 100 == 0:
print('Partial fit of %4i out of %i'
% (index, 6 * len(faces.images)))
dt = time.time() - t0
print('done in %.2fs.' % dt)
# #############################################################################
# Plot the results
plt.figure(figsize=(4.2, 4))
for i, patch in enumerate(kmeans.cluster_centers_):
plt.subplot(9, 9, i + 1)
plt.imshow(patch.reshape(patch_size), cmap=plt.cm.gray,
interpolation='nearest')
plt.xticks(())
plt.yticks(())
plt.suptitle('Patches of faces\nTrain time %.1fs on %d patches' %
(dt, 8 * len(faces.images)), fontsize=16)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
plt.show()
腳本的總運行時間：(0分5.336秒)