【阿旭机器学习实战】【15】人脸自动补全(多目标回归)，并比较5
2022-11-22 本文已影响0人
阿旭123
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本文通过5种不同的机器学习模型：KNN回归模型，线性回归，岭回归，lasso回归，极端随机森林回归，对人脸进行自动补全，并且比较其预测结果。
目录
机器学习实战—人脸自动补全（多目标预测）
目标
通过上半部分的人脸图案来预测下边部分人脸，进行人脸补全。
实质是一个多目标预测问题，对每一个目标点都会进行模型建模，然后通过相应模型对各个点进行预测
数据集
采用Olivetti人脸数据集包含400张灰度的64*64像素的人脸图像，每个图像被展平为大小为4096的一维向量，40个不同的人拍照十次。
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression,Ridge,Lasso
from sklearn.ensemble import ExtraTreesRegressor
from sklearn import datasets
faces = datasets.fetch_olivetti_faces()
faces
{'data': array([[0.30991736, 0.3677686 , 0.41735536, ..., 0.15289256, 0.16115703,
0.1570248 ],
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0.15289256],
[0.3181818 , 0.40082645, 0.49173555, ..., 0.14049587, 0.14876033,
0.15289256],
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0.60330576],
[0.5165289 , 0.46280992, 0.28099173, ..., 0.35950413, 0.3553719 ,
0.38429752]], dtype=float32),
'images': array([[[0.30991736, 0.3677686 , 0.41735536, ..., 0.37190083,
0.3305785 , 0.30578512],
[0.3429752 , 0.40495867, 0.43801653, ..., 0.37190083,
0.338843 , 0.3140496 ],
[0.3429752 , 0.41735536, 0.45041323, ..., 0.38016528,
0.338843 , 0.29752067],
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0.16528925, 0.17355372],
[0.20247933, 0.2107438 , 0.2107438 , ..., 0.14876033,
0.16115703, 0.16528925],
[0.20247933, 0.20661157, 0.20247933, ..., 0.15289256,
0.16115703, 0.1570248 ]],
[[0.45454547, 0.47107437, 0.5123967 , ..., 0.19008264,
0.18595041, 0.18595041],
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0.20661157, 0.20661157],
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0.1446281 , 0.1446281 ],
[0.77272725, 0.7768595 , 0.7892562 , ..., 0.13636364,
0.13636364, 0.13636364],
[0.7644628 , 0.7892562 , 0.78099173, ..., 0.15289256,
0.15289256, 0.15289256]],
[[0.3181818 , 0.40082645, 0.49173555, ..., 0.40082645,
0.3553719 , 0.30991736],
[0.30991736, 0.3966942 , 0.47933885, ..., 0.40495867,
0.37603307, 0.30165288],
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0.37190083, 0.30991736],
...,
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0.14876033, 0.15289256],
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0.1446281 , 0.1570248 ],
[0.11157025, 0.09090909, 0.0785124 , ..., 0.14049587,
0.14876033, 0.15289256]],
...,
[[0.5 , 0.53305787, 0.607438 , ..., 0.28512397,
0.23966943, 0.21487603],
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0.20247933, 0.20661157],
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0.17768595, 0.18595041],
...,
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0.17355372, 0.1694215 ],
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0.1570248 , 0.18595041],
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0.14876033, 0.19008264]],
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0.71487606, 0.6942149 ],
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0.7066116 , 0.6942149 ],
[0.2107438 , 0.20661157, 0.20661157, ..., 0.6859504 ,
0.69008267, 0.6942149 ],
...,
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0.57438016, 0.59090906],
[0.26859504, 0.2644628 , 0.26859504, ..., 0.56198347,
0.58264464, 0.59504133],
[0.27272728, 0.26859504, 0.27272728, ..., 0.57438016,
0.59090906, 0.60330576]],
[[0.5165289 , 0.46280992, 0.28099173, ..., 0.5785124 ,
0.5413223 , 0.60330576],
[0.5165289 , 0.45041323, 0.29338843, ..., 0.58264464,
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0.5785124 , 0.54545456],
...,
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0.37190083, 0.3966942 ],
[0.39256197, 0.38429752, 0.40495867, ..., 0.3305785 ,
0.35950413, 0.37603307],
[0.3677686 , 0.40495867, 0.3966942 , ..., 0.35950413,
0.3553719 , 0.38429752]]], dtype=float32),
'target': array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11,
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13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15,
15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18,
18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20,
20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22,
22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23,
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30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32,
32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33,
34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 35, 35,
35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37,
37, 37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 39,
39, 39, 39, 39, 39, 39, 39, 39, 39]),
'DESCR': 'Modified Olivetti faces dataset.\n\nThe original database was available from\n\n http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html\n\nThe version retrieved here comes in MATLAB format from the personal\nweb page of Sam Roweis:\n\n http://www.cs.nyu.edu/~roweis/\n\nThere are ten different images of each of 40 distinct subjects. For some\nsubjects, the images were taken at different times, varying the lighting,\nfacial expressions (open / closed eyes, smiling / not smiling) and facial\ndetails (glasses / no glasses). All the images were taken against a dark\nhomogeneous background with the subjects in an upright, frontal position (with\ntolerance for some side movement).\n\nThe original dataset consisted of 92 x 112, while the Roweis version\nconsists of 64x64 images.\n'}
data = faces.data
target = faces.target
data.shape
(400, 4096)
faces.images.shape
(400, 64, 64)
import matplotlib.pyplot as plt
%matplotlib inline
# 打印一张人脸图片
plt.imshow(data[100].reshape((64,64)),cmap="gray")
请添加图片描述
数据切分
切分特征数据数据和标签数据，特征是上半边脸，标签是下半边脸
# 特征是上半边脸
faces_up = data[:,:2048]
# 需要预测的目标：标签是下半边脸
faces_down = data[:,2048:]
plt.figure(figsize=(2,2))
plt.imshow(faces_up[10].reshape((32,64)),cmap="gray")
<matplotlib.image.AxesImage at 0x25eca1c8828>
请添加图片描述
plt.figure(figsize=(2,2))
plt.imshow(faces_down[10].reshape((32,64)),cmap="gray")
请添加图片描述
划分数据集
# 数据切分
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(faces_up,faces_down,test_size=0.02)
y_train[1]
array([0.5082645 , 0.5082645 , 0.5123967 , ..., 0.16115703, 0.17768595,
0.1694215 ], dtype=float32)
建立不同的回归模型并训练
此处分别用KNN回归模型，线性回归，岭回归，lasso回归，极端随机森林回归这几种不同的模型来进行建模
estimators = {
"knn":KNeighborsRegressor(),
"linear":LinearRegression(),
"ridge":Ridge(),
"lasso":Lasso(),
"extra":ExtraTreesRegressor() #极端随机森林回归
}
# 定义一个字典，用于保存每个算法预测结果
faces_pre = dict()
for key,estimator in estimators.items():
# 对算法进行模型训练
estimator.fit(x_train,y_train)
# 预测
y_ = estimator.predict(x_test)
# 把预测的结果保存
faces_pre[key] = y_
# 得分
score = estimator.score(x_test, y_test)
print(key, score)
knn 0.4880642098170732
linear 0.18894319531680143
ridge 0.5157197923145055
lasso -0.2100687498661858
extra 0.35087195680524175
faces_pre
{'knn': array([[0.4471074 , 0.41652894, 0.42066115, ..., 0.54793394, 0.5355372 ,
0.546281 ],
[0.34876034, 0.34214878, 0.346281 , ..., 0.42727274, 0.42809922,
0.43057853],
[0.5355372 , 0.546281 , 0.58016527, ..., 0.56611574, 0.56280994,
0.5644628 ],
...,
[0.64793384, 0.67685956, 0.7049587 , ..., 0.41487604, 0.3586777 ,
0.36776862],
[0.3942149 , 0.41322312, 0.43553716, ..., 0.45785123, 0.43471074,
0.39173552],
[0.47520667, 0.47024792, 0.51404965, ..., 0.631405 , 0.6256199 ,
0.59173554]], dtype=float32),
'linear': array([[0.42212042, 0.35969752, 0.39748642, ..., 0.63096315, 0.5628751 ,
0.5159277 ],
[0.4241521 , 0.26758337, 0.16570012, ..., 0.09656662, 0.13010818,
0.19814485],
[0.62213266, 0.441006 , 0.48480797, ..., 0.5819658 , 0.69699645,
0.44033697],
...,
[0.71544605, 0.6732123 , 0.7088314 , ..., 0.37067276, 0.39097485,
0.45659465],
[0.2940399 , 0.3306437 , 0.32395566, ..., 0.19252078, 0.21714431,
0.24263924],
[0.4138433 , 0.47978985, 0.5166639 , ..., 0.5562554 , 0.4086836 ,
0.42044348]], dtype=float32),
'ridge': array([[0.4290133 , 0.37331253, 0.4017402 , ..., 0.5793132 , 0.53899723,
0.4968022 ],
[0.3253019 , 0.2301054 , 0.17614344, ..., 0.33642793, 0.3497425 ,
0.3560007 ],
[0.5519007 , 0.46847916, 0.5257808 , ..., 0.6301012 , 0.69831306,
0.5881569 ],
...,
[0.6989316 , 0.6826698 , 0.7077453 , ..., 0.29566136, 0.32281214,
0.3521443 ],
[0.31752783, 0.33159164, 0.33879474, ..., 0.24723864, 0.23903543,
0.23862499],
[0.39791593, 0.4184358 , 0.52279156, ..., 0.58981174, 0.50477254,
0.5145724 ]], dtype=float32),
'lasso': array([[0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098,
0.307535 ],
[0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098,
0.307535 ],
[0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098,
0.307535 ],
...,
[0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098,
0.307535 ],
[0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098,
0.307535 ],
[0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098,
0.307535 ]], dtype=float32),
'extra': array([[0.42644627, 0.39462809, 0.40661157, ..., 0.5409091 , 0.53388429,
0.53966941],
[0.30619835, 0.33347108, 0.35661157, ..., 0.43057852, 0.42066116,
0.40909091],
[0.43842976, 0.47768595, 0.58347108, ..., 0.45867768, 0.40041323,
0.39380165],
...,
[0.64049588, 0.65702479, 0.6731405 , ..., 0.36157025, 0.37272727,
0.38429752],
[0.3161157 , 0.3144628 , 0.37066115, ..., 0.41239669, 0.40206612,
0.37685951],
[0.43471075, 0.47272727, 0.51818182, ..., 0.54090908, 0.503719 ,
0.50041322]])}
faces_pre["knn"]
array([[0.4471074 , 0.41652894, 0.42066115, ..., 0.54793394, 0.5355372 ,
0.546281 ],
[0.34876034, 0.34214878, 0.346281 , ..., 0.42727274, 0.42809922,
0.43057853],
[0.5355372 , 0.546281 , 0.58016527, ..., 0.56611574, 0.56280994,
0.5644628 ],
...,
[0.64793384, 0.67685956, 0.7049587 , ..., 0.41487604, 0.3586777 ,
0.36776862],
[0.3942149 , 0.41322312, 0.43553716, ..., 0.45785123, 0.43471074,
0.39173552],
[0.47520667, 0.47024792, 0.51404965, ..., 0.631405 , 0.6256199 ,
0.59173554]], dtype=float32)
不同模型预测的人脸结果与实际的对比
import numpy as np
plt.figure(figsize=(6*3,8*3))
for i in range(8):
axes = plt.subplot(8,6,i*6+1)
axes.axis("off")
face_up = x_test[i]
face_down = y_test[i]
face = np.concatenate([face_up,face_down])
axes.imshow(face.reshape((64,64)),cmap="gray")
if i==0:
axes.set_title("True")
# 把机器学习预测出来的下半边脸和上半边脸拼接
for j,key in enumerate(faces_pre):
axes = plt.subplot(8,6,i*6+2+j)
axes.axis("off")
if i==0:
axes.set_title(key)
face_up = x_test[i]
y_pre = faces_pre[key]
face_down_pre = y_pre[i]
face =np.concatenate([face_up,face_down_pre])
axes.imshow(face.reshape((64,64)),cmap="gray")
请添加图片描述
通过对比发现，上述案例中通过KNN预测的结果的脸型要好一些只是有明显的分界线，需要进一步处理，线性回归与岭回归预测的结果没有明显分界线，但是实际预测效果没有那么好；lasso回归，极端随机森林预测出的人脸结果不理想。
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