import matplotlib.pyplot as plt
epoch = range(800)
losses=[3.702533, 3.7782595, 3.70759, 3.6965322, 3.7100809, 3.681564, 3.6885152, 3.673914, 3.692577, 3.6834853, 3.678094, 3.6886945, 3.6800945, 3.674821, 3.6701884, 3.6682103, 3.670442, 3.6665287, 3.66685, 3.6709323, 3.661217, 3.6616983, 3.6559744, 3.654373, 3.6546657, 3.6498373, 3.6466854, 3.6439812, 3.6347423, 3.624842, 3.6409388, 3.6231594, 3.6175497, 3.6248908, 3.617646, 3.6035078, 3.597857, 3.5908508, 3.586143, 3.568523, 3.5633695, 3.5419738, 3.531703, 3.5384433, 3.5483072, 3.4803176, 3.4991565, 3.4850786, 3.4506528, 3.4823747, 3.4258602, 3.492745, 3.4014661, 3.4154584, 3.4545681, 3.4750617, 3.373154, 3.7801297, 3.4973376, 3.387513, 3.2929592, 3.3045309, 3.5548215, 3.3943946, 3.2747524, 3.2179387, 3.275807, 3.3524268, 3.3612237, 3.2027059, 3.3351183, 3.3457017, 3.1946654, 3.1986504, 3.3383358, 3.2851207, 3.1959403, 3.073331, 3.0764656, 3.0632012, 3.034492, 3.1005628, 3.0855463, 3.238665, 3.0421116, 2.9569132, 2.999722, 2.9631152, 3.026343, 2.912209, 2.9030955, 2.8929598, 2.9223928, 2.9099224, 2.951788, 2.888597, 2.876808, 2.8558047, 2.8567038, 2.8349323, 2.887746, 2.8199847, 2.828317, 2.817694, 2.823246, 2.7981193, 2.8299348, 2.8051467, 2.7935488, 2.7876413, 2.7849107, 2.76429, 2.7742872, 2.7754774, 2.7768557, 2.750366, 2.7829406, 2.7483447, 2.7511277, 2.749742, 2.7317483, 2.7613525, 2.7193606, 2.7412221, 2.7284377, 2.7596912, 2.753288, 2.7252312, 2.7247021, 2.7304797, 2.6883225, 2.7055638, 2.7108088, 2.7433932, 2.7220514, 2.7256448, 2.7228262, 2.7380297, 2.690721, 2.6950467, 2.718078, 2.749261, 2.693732, 2.737957, 2.7480206, 2.719322, 2.710214, 2.6827416, 2.739342, 2.7095482, 2.7460587, 2.699834, 2.6990044, 2.7171135, 2.677713, 2.725699, 2.6980677, 2.7095163, 2.7289567, 2.691725, 2.6830518, 2.6925573, 2.7080173, 2.6811483, 2.7093134, 2.6953344, 2.685745, 2.6946285, 2.6897655, 2.6819065, 2.712104, 2.7216856, 2.7221248, 2.6756215, 2.7147274, 2.7160032, 2.690756, 2.6866899, 2.703093, 2.7022665, 2.7083697, 2.7296515, 2.6958175, 2.6893284, 2.704676, 2.7230945, 2.7033925, 2.682957, 2.7111304, 2.7083337, 2.717556, 2.6864035, 2.6914551, 2.657609, 2.715688, 2.708531, 2.695988, 2.673285, 2.6732996, 2.6665754, 2.7006364, 2.6909559, 2.723049, 2.699199, 2.695301, 2.6850433, 2.6962175, 2.666597, 2.7076144, 2.7448509, 2.7126136, 2.682235, 2.7079065, 2.6761684, 2.7037225, 2.6902275, 2.690714, 2.7067742, 2.6727252, 2.7021177, 2.737921, 2.6895812, 2.696692, 2.6977806, 2.6907542, 2.711107, 2.7414243, 2.7063303, 2.6672611, 2.6882346, 2.6921883, 2.6809437, 2.6788657, 2.6977599, 2.6960084, 2.6801064, 2.6931345, 2.6814256, 2.662785, 2.6977413, 2.699176, 2.687785, 2.6942084, 2.7222311, 2.685638, 2.685003, 2.6824992, 2.7029161, 2.6882997, 2.6903822, 2.6938245, 2.6750872, 2.709727, 2.6977396, 2.6767886, 2.700719, 2.715848, 2.68838, 2.6798427, 2.6750543, 2.6733718, 2.7078779, 2.7129068, 2.7003171, 2.7082736, 2.6787124, 2.6949914, 2.6681476, 2.684562, 2.670448, 2.7152863, 2.720321, 2.6995847, 2.663951, 2.717347, 2.6875443, 2.685368, 2.674908, 2.676731, 2.6741235, 2.686501, 2.668701, 2.6814036, 2.7039154, 2.6702697, 2.7140968, 2.6997197, 2.6865747, 2.6928823, 2.6577084, 2.6851041, 2.6808982, 2.698425, 2.6767738, 2.6713393, 2.7399282, 2.6965423, 2.6870332, 2.6819813, 2.7004442, 2.6678321, 2.6820688, 2.6936421, 2.68041, 2.6805952, 2.7096162, 2.6584547, 2.7076137, 2.6918094, 2.6519594, 2.6848834, 2.7063227, 2.6690712, 2.7045777, 2.6480713, 2.715681, 2.6907074, 2.6944265, 2.6801062, 2.6797366, 2.665923, 2.6774735, 2.6792777, 2.7077823, 2.6761096, 2.6832507, 2.6908104, 2.7017937, 2.692542, 2.6839147, 2.6874597, 2.6845431, 2.690275, 2.708999, 2.65861, 2.6616402, 2.6643546, 2.7067194, 2.6929615, 2.6889052, 2.6871614, 2.6874886, 2.6698503, 2.6774044, 2.695125, 2.7349398, 2.6889615, 2.7295, 2.68579, 2.688911, 2.7147143, 2.6727405, 2.6643136, 2.6729887, 2.6883154, 2.6866758, 2.6847503, 2.7011905, 2.7108839, 2.6349769, 2.6951056, 2.700792, 2.6928363, 2.7328165, 2.7000031, 2.6865213, 2.716613, 2.712445, 2.6962323, 2.690428, 2.6925964, 2.7051694, 2.702846, 2.6947036, 2.696322, 2.700991, 2.6783254, 2.6995642, 2.703333, 2.6733544, 2.6881588, 2.6880913, 2.6972349, 2.6939614, 2.6600385, 2.7142272, 2.694041, 2.7019947, 2.6428266, 2.6750906, 2.671385, 2.6516273, 2.6971846, 2.6566386, 2.689066, 2.6862304, 2.6667895, 2.6717443, 2.7011063, 2.6922061, 2.67204, 2.6820881, 2.7149305, 2.6872187, 2.6635861, 2.7164593, 2.6811426, 2.6870325, 2.7280545, 2.704407, 2.692595, 2.6768167, 2.6791806, 2.679446, 2.689008, 2.6747887, 2.6955006, 2.692507, 2.7090287, 2.6757886, 2.7025454, 2.6911173, 2.6425686, 2.6647594, 2.720966, 2.7013545, 2.704964, 2.6976206, 2.6643786, 2.688794, 2.6935697, 2.6769457, 2.6753325, 2.7219377, 2.7346835, 2.7142568, 2.6875033, 2.706943, 2.6695528, 2.6736379, 2.6866398, 2.6624498, 2.6885517, 2.679917, 2.6848338, 2.6791828, 2.6993012, 2.6838386, 2.645042, 2.7502835, 2.6922517, 2.6825573, 2.6500645, 2.6771617, 2.6579325, 2.7071753, 2.7018385, 2.6873472, 2.6753595, 2.7143693, 2.648153, 2.6593509, 2.6949964, 2.7195377, 2.7003012, 2.6656797, 2.658781, 2.7124033, 2.7042356, 2.6847808, 2.6836948, 2.7048225, 2.7073233, 2.6730742, 2.691061, 2.694227, 2.6876082, 2.694461, 2.6927338, 2.67467, 2.6705465, 2.7197917, 2.662205, 2.7145972, 2.7133927, 2.6871464, 2.666606, 2.6667447, 2.6706681, 2.710466, 2.6691499, 2.698157, 2.6658509, 2.661271, 2.6909611, 2.6897879, 2.69552, 2.6703565, 2.695231, 2.693501, 2.6767025, 2.664225, 2.6787593, 2.7062142, 2.6864903, 2.7004633, 2.6926455, 2.6967275, 2.661457, 2.6894388, 2.6652367, 2.6842806, 2.6908095, 2.6524062, 2.7037692, 2.6682634, 2.69215, 2.6733398, 2.6911438, 2.6780903, 2.6937327, 2.7009604, 2.6934443, 2.6986542, 2.7083385, 2.6728282, 2.6817865, 2.6850224, 2.6920087, 2.6873224, 2.6977472, 2.661382, 2.6642919, 2.687483, 2.7085605, 2.6935608, 2.7038815, 2.6941338, 2.7014651, 2.6801662, 2.712788, 2.6915722, 2.6736097, 2.672639, 2.68931, 2.6520782, 2.7017152, 2.7089758, 2.668497, 2.692165, 2.7037268, 2.6619163, 2.6619787, 2.6699774, 2.6933596, 2.6835377, 2.670627, 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用LBP算子提取人脸特征,用深度学习训练验证识别人脸 识别率92% (1938个子文件)
MyInputVid.avi 11.03MB
768x576.avi 7.75MB
768x576.avi 7.75MB
output.avi 6KB
lena.bmp 65KB
lena.bmp 65KB
barbara.bmp 65KB
lena.bmp 65KB
cameraman.bmp 65KB
lena.bmp 65KB
白云广厦.bmp 25KB
_run_winpack_demo.cmd 60B
test_out18.csv 3.93MB
test_out17.csv 3.91MB
test_pots18.csv 3.46MB
test_pots17.csv 3.43MB
test_out16.csv 1.12MB
valid_out18.csv 1006KB
valid_out17.csv 1001KB
test_pots16.csv 998KB
valid_pots18.csv 887KB
valid_pots17.csv 881KB
valid_out16.csv 287KB
valid_pots16.csv 254KB
cnnface2.csv 179KB
face_data.csv 128KB
face_cnn6.csv 84KB
cnnface.csv 63KB
cnn600.csv 19KB
cnn2019800.csv 19KB
cnn800.csv 17KB
faces.csv 694B
.directory 67B
.directory 67B
.directory 66B
traffic.flv 2.38MB
traffic.flv 2.38MB
olivettifaces.gif 1.13MB
numbers.gif 3KB
numbers.gif 3KB
.gitignore 18B
mnist.pkl.gz 16.26MB
plot_local_binary_pattern.ipynb 9KB
hamper.jpeg 256KB
vinyls.jpg 923KB
people.jpg 717KB
sample1.jpg 374KB
hush.jpg 373KB
barilla-pasta.jpg 335KB
coat_of_arms.jpg 228KB
vase1.jpg 207KB
vase2.jpg 206KB
manowar_single.jpg 189KB
numbers.jpg 188KB
numbers.jpg 188KB
dr-hurt.jpg 188KB
varese.jpg 171KB
dis2.jpg 169KB
dis1.jpg 166KB
penguin.jpg 160KB
vikings.jpg 158KB
color2.jpg 154KB
color1.jpg 154KB
depth1.jpg 147KB
depth2.jpg 147KB
yh.JPG 130KB
sample.jpg 129KB
hammer.jpg 106KB
sample2.jpg 106KB
beans.jpg 95KB
meanshift.jpg 89KB
meanshift.jpg 89KB
lines.jpg 88KB
face_rec.jpg 87KB
aloeR.jpg 86KB
aloeL.jpg 85KB
houghlines5.jpg 85KB
timg2.jpg 79KB
timg2.jpg 79KB
.jpg 78KB
.jpg 78KB
statue.jpg 78KB
planets_circles.jpg 70KB
kalman.jpg 65KB
kalman.jpg 65KB
bb.jpg 64KB
canny.jpg 61KB
two-face.jpg 60KB
color2_small.jpg 56KB
color1_small.jpg 56KB
tortiglioni.jpg 50KB
timg.jpg 49KB
timg.jpg 49KB
track.jpg 49KB
aqualung.jpg 46KB
posion-ivy.jpg 45KB
dice.jpg 42KB
bathory_album.jpg 39KB
statue_small.jpg 36KB
cars_small.jpg 35KB
共 1938 条
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