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knknkn1162/classifier.py

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classifier.py
import caffe
from caffe.proto import caffe_pb2
from caffe.io import blobproto_to_array
import numpy as np # for array
import glob
import os.path
# directory = "C:/dev/caffe/caffe-windows"
net_path = "./examples/mytest/lenet/deploy.prototxt"
model_path = "./examples/mytest/mytest_iter_100000.caffemodel"
mean_path = "./data/mytest/mean.binaryproto"
eval_path = "./data/mytest/eval.txt"
# --
#net_path : deploy.prototxt, model_path : caffemodel. 識別なので、Caffe.TESTと記載
net = caffe.Net(net_path, model_path,caffe.TEST)
# --
# for preprocess
transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape})
# channel_swapはRGBをσ(RGB) = BGRにしたいときに用いる。
# 対して、set_transposeは、[H, W, K] => [K, H, W]としたいときとかに用いる。
transformer.set_transpose('data', (2,0,1))
mean_blob = caffe_pb2.BlobProto()
with open(mean_path, "rb") as f:
mean_blob.ParseFromString(f.read())
mean_array = blobproto_to_array(mean_blob)
mean_array = np.asarray(mean_blob.data).reshape((mean_blob.channels, mean_blob.height, mean_blob.width))
transformer.set_mean('data', mean_array)
#上記の説明参照
transformer.set_raw_scale('data', 255)
# scaling to 1/255 = 0.003..
transformer.set_input_scale('data', 0.00390625)
# sample test for 11181 images
cnt = 0 #number of the collect answer
num = 11181
numbers = [str(i+60000).zfill(7) for i in range(1,num+1)]
filename = [s + ".bmp" for s in numbers]
images = "data/mytest/check"
import csv
reader = csv.reader(open(eval_path), lineterminator = ' ')
ansList = []
for row in reader:
i = 0
(fl, i) = row[0].split(' ')
ansList.append((int(i)))
for i, fname in enumerate(filename):
fn = images + "/" + fname
image = caffe.io.load_image(fn, color = False)
# image : (H×W×K) ndarray => set_transposeが効いて、outputは(K×W×H) ndarray型になる。
proc = transformer.preprocess('data', image)
# (K×W×H) ndarray のinputデータ : proc
# net.inputs[0]はdeploy.prototxtのdataレイヤーに記載の'data'(topプロパティ)
out = net.forward_all(**{net.inputs[0]:proc})
# net.outputs[0]はdeploy.prototxtのdataレイヤーに記載の'prob'(topプロパティ)
predictions = out[net.outputs[0]]
answer = np.argmax(predictions)
print predictions, "<= prob"
print net.blobs['ip2'].data ," <= ip2"
print fn, i,", true answer =>", ansList[i],", eval =>", answer, ansList[i] == answer
cnt = cnt + 1 if (ansList[i] == answer) else cnt
print cnt, "/", num
Raw
cmd.exe
[[ 0.99750298 0.00249705]] <= prob
[[ 2.98188448 -3.00825882]] <= ip2
data/check/0060001.bmp 0 , true answer => 0 , eval => 0 True
[[ 0.84466338 0.15533663]] <= prob
[[ 0.88548481 -0.80785882]] <= ip2
data/check/0060002.bmp 1 , true answer => 0 , eval => 0 True
[[ 0.97809428 0.0219057 ]] <= prob
[[ 1.93141639 -1.86744297]] <= ip2
data/check/0060003.bmp 2 , true answer => 0 , eval => 0 True
[[ 8.44261933e-07 9.99999166e-01]] <= prob
[[-7.0260458 6.95875645]] <= ip2
data/check/0060004.bmp 3 , true answer => 1 , eval => 1 True
[[ 5.23068120e-14 1.00000000e+00]] <= prob
[[-15.21660709 15.36504269]] <= ip2
data/check/0060005.bmp 4 , true answer => 1 , eval => 1 True
[[ 2.16108363e-17 1.00000000e+00]] <= prob
[[-19.06203842 19.31130028]] <= ip2
data/check/0060006.bmp 5 , true answer => 1 , eval => 1 True
[[ 3.03096744e-17 1.00000000e+00]] <= prob
[[-18.61361885 19.42144585]] <= ip2
data/check/0060007.bmp 6 , true answer => 1 , eval => 1 True
[[ 2.20863166e-16 1.00000000e+00]] <= prob
[[-17.62698936 18.42199898]] <= ip2
data/check/0060008.bmp 7 , true answer => 1 , eval => 1 True
[[ 2.60469958e-15 1.00000000e+00]] <= prob
[[-16.56490326 17.01655579]] <= ip2
data/check/0060009.bmp 8 , true answer => 1 , eval => 1 True
[[ 7.49017981e-13 1.00000000e+00]] <= prob
[[-13.91413593 14.00587749]] <= ip2
data/check/0060010.bmp 9 , true answer => 1 , eval => 1 True
[[ 2.98124263e-08 1.00000000e+00]] <= prob
[[-8.61068058 8.71765995]] <= ip2
data/check/0060011.bmp 10 , true answer => 1 , eval => 1 True
[[ 0.99622607 0.00377389]] <= prob
[[ 2.80986714 -2.76599956]] <= ip2
data/check/0060012.bmp 11 , true answer => 0 , eval => 0 True
[[ 0.99383426 0.00616576]] <= prob
[[ 2.55797553 -2.52458334]] <= ip2
data/check/0060013.bmp 12 , true answer => 0 , eval => 0 True
[[ 0.99590862 0.00409134]] <= prob
[[ 2.75059795 -2.74418426]] <= ip2
data/check/0060014.bmp 13 , true answer => 0 , eval => 0 True
[[ 0.99530667 0.00469327]] <= prob
[[ 2.69898725 -2.65793467]] <= ip2
data/check/0060015.bmp 14 , true answer => 0 , eval => 0 True
[[ 0.99600548 0.00399459]] <= prob
[[ 2.80181313 -2.71699834]] <= ip2
data/check/0060016.bmp 15 , true answer => 0 , eval => 0 True
[[ 0.83415365 0.16584632]] <= prob
[[ 0.847 -0.76835608]] <= ip2
data/check/0060017.bmp 16 , true answer => 0 , eval => 0 True
[[ 0.9907499 0.00925016]] <= prob
[[ 2.36697435 -2.30684662]] <= ip2
data/check/0060018.bmp 17 , true answer => 0 , eval => 0 True
[[ 0.96608049 0.03391952]] <= prob
[[ 1.7060734 -1.64318299]] <= ip2
data/check/0060019.bmp 18 , true answer => 0 , eval => 0 True
[[ 0.99683195 0.00316807]] <= prob
[[ 2.90177608 -2.84968448]] <= ip2
data/check/0060020.bmp 19 , true answer => 0 , eval => 0 True
[[ 1.18890746e-07 9.99999881e-01]] <= prob
[[-7.89830256 8.04675865]] <= ip2
data/check/0060021.bmp 20 , true answer => 1 , eval => 1 True
[[ 2.60205052e-13 1.00000000e+00]] <= prob
[[-14.19086933 14.78643799]] <= ip2
data/check/0060022.bmp 21 , true answer => 1 , eval => 1 True
[[ 1.05305694e-14 1.00000000e+00]] <= prob
[[-15.77843571 16.40605736]] <= ip2
data/check/0060023.bmp 22 , true answer => 1 , eval => 1 True
[[ 1.67895694e-16 1.00000000e+00]] <= prob
[[-17.80656433 18.51662636]] <= ip2
data/check/0060024.bmp 23 , true answer => 1 , eval => 1 True
[[ 4.60003907e-17 1.00000000e+00]] <= prob
[[-18.51346588 19.1044178 ]] <= ip2
data/check/0060025.bmp 24 , true answer => 1 , eval => 1 True
[[ 4.48662797e-17 1.00000000e+00]] <= prob
[[-18.31805801 19.32478905]] <= ip2
data/check/0060026.bmp 25 , true answer => 1 , eval => 1 True
[[ 3.96798446e-14 1.00000000e+00]] <= prob
[[-15.41134453 15.44658756]] <= ip2
data/check/0060027.bmp 26 , true answer => 1 , eval => 1 True
[[ 2.74925345e-08 1.00000000e+00]] <= prob
[[-8.72124386 8.68810844]] <= ip2
data/check/0060028.bmp 27 , true answer => 1 , eval => 1 True
[[ 0.998142 0.00185796]] <= prob
[[ 3.15813088 -3.12828398]] <= ip2
data/check/0060029.bmp 28 , true answer => 0 , eval => 0 True
[[ 9.99204934e-01 7.95091386e-04]] <= prob
[[ 3.56998134 -3.56627679]] <= ip2
data/check/0060030.bmp 29 , true answer => 0 , eval => 0 True
[[ 0.99647921 0.00352086]] <= prob
[[ 2.83586335 -2.8096602 ]] <= ip2
data/check/0060031.bmp 30 , true answer => 0 , eval => 0 True
[[ 0.99423426 0.00576578]] <= prob
[[ 2.57602 -2.57401204]] <= ip2
data/check/0060032.bmp 31 , true answer => 0 , eval => 0 True
[[ 0.99844068 0.00155928]] <= prob
[[ 3.24583077 -3.21614099]] <= ip2
data/check/0060033.bmp 32 , true answer => 0 , eval => 0 True
[[ 0.95326477 0.04673528]] <= prob
[[ 1.53531289 -1.4800806 ]] <= ip2
data/check/0060034.bmp 33 , true answer => 0 , eval => 0 True
[[ 0.96600479 0.03399523]] <= prob
[[ 1.70796311 -1.63898551]] <= ip2
data/check/0060035.bmp 34 , true answer => 0 , eval => 0 True
[[ 0.98875147 0.01124856]] <= prob
[[ 2.25061965 -2.22558308]] <= ip2
data/check/0060036.bmp 35 , true answer => 0 , eval => 0 True
[[ 2.99871118e-07 9.99999642e-01]] <= prob
[[-7.52946043 7.49045277]] <= ip2
data/check/0060037.bmp 36 , true answer => 1 , eval => 1 True
[[ 2.70791808e-14 1.00000000e+00]] <= prob
[[-15.45818996 15.78182125]] <= ip2
data/check/0060038.bmp 37 , true answer => 1 , eval => 1 True
[[ 1.79429603e-17 1.00000000e+00]] <= prob
[[-18.48504639 20.07428551]] <= ip2
data/check/0060039.bmp 38 , true answer => 1 , eval => 1 True
[[ 5.74076183e-17 1.00000000e+00]] <= prob
[[-18.05085373 19.34549904]] <= ip2
data/check/0060040.bmp 39 , true answer => 1 , eval => 1 True
REM <...中略>
[[ 0.98656958 0.01343042]] <= prob
[[ 2.15983582 -2.13687634]] <= ip2
data/check/0071179.bmp 11178 , true answer => 0 , eval => 0 True
[[ 0.98314559 0.01685444]] <= prob
[[ 2.03822541 -2.02791786]] <= ip2
data/check/0071180.bmp 11179 , true answer => 0 , eval => 0 True
[[ 0.99142039 0.00857963]] <= prob
[[ 2.39600086 -2.35374713]] <= ip2
data/check/0071181.bmp 11180 , true answer => 0 , eval => 0 True
10071 / 11181
Raw
cygwin
$ date; ./examples/mytest/eval.sh ./examples/mytest/lenet/mytest_iter_100000.caffemodel 10000; wait; date;
# <省略>
I1102 07:59:55.009570 4556 caffe.cpp:276] Batch 0, accuracy = 1
I1102 07:59:55.009570 4556 caffe.cpp:276] Batch 0, loss = 0.00207879
I1102 07:59:55.010571 4556 caffe.cpp:276] Batch 1, accuracy = 1
I1102 07:59:55.010571 4556 caffe.cpp:276] Batch 1, loss = 0.140107
I1102 07:59:55.041574 4556 caffe.cpp:276] Batch 2, accuracy = 1
I1102 07:59:55.041574 4556 caffe.cpp:276] Batch 2, loss = 0.0174714
I1102 07:59:55.076577 4556 caffe.cpp:276] Batch 3, accuracy = 1
I1102 07:59:55.076577 4556 caffe.cpp:276] Batch 3, loss = 5.96047e-007
I1102 07:59:55.084578 4556 caffe.cpp:276] Batch 4, accuracy = 1
I1102 07:59:55.084578 4556 caffe.cpp:276] Batch 4, loss = 0
I1102 07:59:55.085578 4556 caffe.cpp:276] Batch 5, accuracy = 1
I1102 07:59:55.085578 4556 caffe.cpp:276] Batch 5, loss = 0
I1102 07:59:55.090579 4556 caffe.cpp:276] Batch 6, accuracy = 1
I1102 07:59:55.090579 4556 caffe.cpp:276] Batch 6, loss = 0
I1102 07:59:55.091579 4556 caffe.cpp:276] Batch 7, accuracy = 1
I1102 07:59:55.091579 4556 caffe.cpp:276] Batch 7, loss = 0
I1102 07:59:55.092579 4556 caffe.cpp:276] Batch 8, accuracy = 1
I1102 07:59:55.092579 4556 caffe.cpp:276] Batch 8, loss = 0
I1102 07:59:55.093580 4556 caffe.cpp:276] Batch 9, accuracy = 1
I1102 07:59:55.093580 4556 caffe.cpp:276] Batch 9, loss = 0
I1102 07:59:55.094579 4556 caffe.cpp:276] Batch 10, accuracy = 1
I1102 07:59:55.094579 4556 caffe.cpp:276] Batch 10, loss = 0
I1102 07:59:55.095579 4556 caffe.cpp:276] Batch 11, accuracy = 1
I1102 07:59:55.095579 4556 caffe.cpp:276] Batch 11, loss = 0.00314443
I1102 07:59:55.095579 4556 caffe.cpp:276] Batch 12, accuracy = 1
I1102 07:59:55.096580 4556 caffe.cpp:276] Batch 12, loss = 0.00517411
I1102 07:59:55.096580 4556 caffe.cpp:276] Batch 13, accuracy = 1
I1102 07:59:55.096580 4556 caffe.cpp:276] Batch 13, loss = 0.00372884
I1102 07:59:55.097579 4556 caffe.cpp:276] Batch 14, accuracy = 1
I1102 07:59:55.097579 4556 caffe.cpp:276] Batch 14, loss = 0.00436105
I1102 07:59:55.098579 4556 caffe.cpp:276] Batch 15, accuracy = 1
I1102 07:59:55.098579 4556 caffe.cpp:276] Batch 15, loss = 0.00345169
I1102 07:59:55.099580 4556 caffe.cpp:276] Batch 16, accuracy = 1
I1102 07:59:55.099580 4556 caffe.cpp:276] Batch 16, loss = 0.176153
I1102 07:59:55.100580 4556 caffe.cpp:276] Batch 17, accuracy = 1
I1102 07:59:55.100580 4556 caffe.cpp:276] Batch 17, loss = 0.00777385
I1102 07:59:55.101580 4556 caffe.cpp:276] Batch 18, accuracy = 1
I1102 07:59:55.101580 4556 caffe.cpp:276] Batch 18, loss = 0.0316679
I1102 07:59:55.102581 4556 caffe.cpp:276] Batch 19, accuracy = 1
I1102 07:59:55.102581 4556 caffe.cpp:276] Batch 19, loss = 0.00257896
I1102 07:59:55.103580 4556 caffe.cpp:276] Batch 20, accuracy = 1
I1102 07:59:55.103580 4556 caffe.cpp:276] Batch 20, loss = 1.19209e-007
I1102 07:59:55.104580 4556 caffe.cpp:276] Batch 21, accuracy = 1
I1102 07:59:55.104580 4556 caffe.cpp:276] Batch 21, loss = 0
I1102 07:59:55.105581 4556 caffe.cpp:276] Batch 22, accuracy = 1
I1102 07:59:55.105581 4556 caffe.cpp:276] Batch 22, loss = 0
I1102 07:59:55.106580 4556 caffe.cpp:276] Batch 23, accuracy = 1
I1102 07:59:55.106580 4556 caffe.cpp:276] Batch 23, loss = 0
I1102 07:59:55.107580 4556 caffe.cpp:276] Batch 24, accuracy = 1
I1102 07:59:55.107580 4556 caffe.cpp:276] Batch 24, loss = 0
I1102 07:59:55.108580 4556 caffe.cpp:276] Batch 25, accuracy = 1
I1102 07:59:55.108580 4556 caffe.cpp:276] Batch 25, loss = 0
I1102 07:59:55.109580 4556 caffe.cpp:276] Batch 26, accuracy = 1
I1102 07:59:55.109580 4556 caffe.cpp:276] Batch 26, loss = 0
I1102 07:59:55.110581 4556 caffe.cpp:276] Batch 27, accuracy = 1
I1102 07:59:55.110581 4556 caffe.cpp:276] Batch 27, loss = 0
I1102 07:59:55.111582 4556 caffe.cpp:276] Batch 28, accuracy = 1
I1102 07:59:55.111582 4556 caffe.cpp:276] Batch 28, loss = 0.00151803
I1102 07:59:55.113581 4556 caffe.cpp:276] Batch 29, accuracy = 1
I1102 07:59:55.113581 4556 caffe.cpp:276] Batch 29, loss = 0.000643878
I1102 07:59:55.114581 4556 caffe.cpp:276] Batch 30, accuracy = 1
I1102 07:59:55.114581 4556 caffe.cpp:276] Batch 30, loss = 0.00320303
I1102 07:59:55.115581 4556 caffe.cpp:276] Batch 31, accuracy = 1
I1102 07:59:55.115581 4556 caffe.cpp:276] Batch 31, loss = 0.0053963
I1102 07:59:55.116581 4556 caffe.cpp:276] Batch 32, accuracy = 1
I1102 07:59:55.116581 4556 caffe.cpp:276] Batch 32, loss = 0.00134141
I1102 07:59:55.117581 4556 caffe.cpp:276] Batch 33, accuracy = 1
I1102 07:59:55.117581 4556 caffe.cpp:276] Batch 33, loss = 0.0414601
I1102 07:59:55.118582 4556 caffe.cpp:276] Batch 34, accuracy = 1
I1102 07:59:55.118582 4556 caffe.cpp:276] Batch 34, loss = 0.0304497
I1102 07:59:55.119581 4556 caffe.cpp:276] Batch 35, accuracy = 1
I1102 07:59:55.119581 4556 caffe.cpp:276] Batch 35, loss = 0.0105491
I1102 07:59:55.120582 4556 caffe.cpp:276] Batch 36, accuracy = 1
I1102 07:59:55.120582 4556 caffe.cpp:276] Batch 36, loss = 2.38419e-007
I1102 07:59:55.135583 4556 caffe.cpp:276] Batch 37, accuracy = 1
I1102 07:59:55.135583 4556 caffe.cpp:276] Batch 37, loss = 0
I1102 07:59:55.136584 4556 caffe.cpp:276] Batch 38, accuracy = 1
I1102 07:59:55.136584 4556 caffe.cpp:276] Batch 38, loss = 0
I1102 07:59:55.138583 4556 caffe.cpp:276] Batch 39, accuracy = 1
I1102 07:59:55.138583 4556 caffe.cpp:276] Batch 39, loss = 0
I1102 07:59:55.139583 4556 caffe.cpp:276] Batch 40, accuracy = 1
I1102 07:59:55.139583 4556 caffe.cpp:276] Batch 40, loss = 0
I1102 07:59:55.140584 4556 caffe.cpp:276] Batch 41, accuracy = 1
I1102 07:59:55.140584 4556 caffe.cpp:276] Batch 41, loss = 0
I1102 07:59:55.141584 4556 caffe.cpp:276] Batch 42, accuracy = 1
I1102 07:59:55.141584 4556 caffe.cpp:276] Batch 42, loss = 0
I1102 07:59:55.153585 4556 caffe.cpp:276] Batch 43, accuracy = 1
I1102 07:59:55.153585 4556 caffe.cpp:276] Batch 43, loss = 1.19209e-007
I1102 07:59:55.155586 4556 caffe.cpp:276] Batch 44, accuracy = 1
I1102 07:59:55.155586 4556 caffe.cpp:276] Batch 44, loss = 0.00371561
I1102 07:59:55.156585 4556 caffe.cpp:276] Batch 45, accuracy = 1
I1102 07:59:55.156585 4556 caffe.cpp:276] Batch 45, loss = 0.000123628
I1102 07:59:55.157585 4556 caffe.cpp:276] Batch 46, accuracy = 1
I1102 07:59:55.157585 4556 caffe.cpp:276] Batch 46, loss = 0.00122151
I1102 07:59:55.157585 4556 caffe.cpp:276] Batch 47, accuracy = 1
I1102 07:59:55.158586 4556 caffe.cpp:276] Batch 47, loss = 0.00462845
I1102 07:59:55.158586 4556 caffe.cpp:276] Batch 48, accuracy = 1
I1102 07:59:55.159585 4556 caffe.cpp:276] Batch 48, loss = 0.00516471
I1102 07:59:55.160586 4556 caffe.cpp:276] Batch 49, accuracy = 1
I1102 07:59:55.160586 4556 caffe.cpp:276] Batch 49, loss = 0.0118847
I1102 07:59:55.161586 4556 caffe.cpp:276] Batch 50, accuracy = 1
I1102 07:59:55.161586 4556 caffe.cpp:276] Batch 50, loss = 0.00601578
I1102 07:59:55.162586 4556 caffe.cpp:276] Batch 51, accuracy = 1
I1102 07:59:55.162586 4556 caffe.cpp:276] Batch 51, loss = 0.00351964
I1102 07:59:55.163586 4556 caffe.cpp:276] Batch 52, accuracy = 1
I1102 07:59:55.163586 4556 caffe.cpp:276] Batch 52, loss = 0.00372381
I1102 07:59:55.164587 4556 caffe.cpp:276] Batch 53, accuracy = 1
I1102 07:59:55.164587 4556 caffe.cpp:276] Batch 53, loss = 5.96047e-007
I1102 07:59:55.166586 4556 caffe.cpp:276] Batch 54, accuracy = 1
I1102 07:59:55.166586 4556 caffe.cpp:276] Batch 54, loss = 0
I1102 07:59:55.167587 4556 caffe.cpp:276] Batch 55, accuracy = 1
I1102 07:59:55.167587 4556 caffe.cpp:276] Batch 55, loss = 0
I1102 07:59:55.168586 4556 caffe.cpp:276] Batch 56, accuracy = 1
I1102 07:59:55.168586 4556 caffe.cpp:276] Batch 56, loss = 0
I1102 07:59:55.169586 4556 caffe.cpp:276] Batch 57, accuracy = 1
I1102 07:59:55.169586 4556 caffe.cpp:276] Batch 57, loss = 0
I1102 07:59:55.170588 4556 caffe.cpp:276] Batch 58, accuracy = 1
I1102 07:59:55.170588 4556 caffe.cpp:276] Batch 58, loss = 0
I1102 07:59:55.171587 4556 caffe.cpp:276] Batch 59, accuracy = 1
I1102 07:59:55.171587 4556 caffe.cpp:276] Batch 59, loss = 0
I1102 07:59:55.172587 4556 caffe.cpp:276] Batch 60, accuracy = 1
I1102 07:59:55.172587 4556 caffe.cpp:276] Batch 60, loss = 0
I1102 07:59:55.173588 4556 caffe.cpp:276] Batch 61, accuracy = 1
I1102 07:59:55.173588 4556 caffe.cpp:276] Batch 61, loss = 0.00573326
I1102 07:59:55.174587 4556 caffe.cpp:276] Batch 62, accuracy = 1
I1102 07:59:55.174587 4556 caffe.cpp:276] Batch 62, loss = 0.00347143
I1102 07:59:55.175587 4556 caffe.cpp:276] Batch 63, accuracy = 1
I1102 07:59:55.175587 4556 caffe.cpp:276] Batch 63, loss = 0.00199535
I1102 07:59:55.176587 4556 caffe.cpp:276] Batch 64, accuracy = 1
I1102 07:59:55.176587 4556 caffe.cpp:276] Batch 64, loss = 0.00278288
I1102 07:59:55.177587 4556 caffe.cpp:276] Batch 65, accuracy = 1
I1102 07:59:55.177587 4556 caffe.cpp:276] Batch 65, loss = 0.0025841
I1102 07:59:55.178587 4556 caffe.cpp:276] Batch 66, accuracy = 1
I1102 07:59:55.178587 4556 caffe.cpp:276] Batch 66, loss = 0.00708935
I1102 07:59:55.178587 4556 caffe.cpp:276] Batch 67, accuracy = 1
I1102 07:59:55.178587 4556 caffe.cpp:276] Batch 67, loss = 0.00605667
I1102 07:59:55.179587 4556 caffe.cpp:276] Batch 68, accuracy = 1
I1102 07:59:55.179587 4556 caffe.cpp:276] Batch 68, loss = 0.00665238
I1102 07:59:55.180588 4556 caffe.cpp:276] Batch 69, accuracy = 1
I1102 07:59:55.180588 4556 caffe.cpp:276] Batch 69, loss = 0.00212681
I1102 07:59:55.181588 4556 caffe.cpp:276] Batch 70, accuracy = 1
I1102 07:59:55.181588 4556 caffe.cpp:276] Batch 70, loss = 0.0304509
I1102 07:59:55.182588 4556 caffe.cpp:276] Batch 71, accuracy = 1
I1102 07:59:55.182588 4556 caffe.cpp:276] Batch 71, loss = 7.27179e-006
I1102 07:59:55.183588 4556 caffe.cpp:276] Batch 72, accuracy = 1
I1102 07:59:55.183588 4556 caffe.cpp:276] Batch 72, loss = 0
I1102 07:59:55.184588 4556 caffe.cpp:276] Batch 73, accuracy = 1
I1102 07:59:55.184588 4556 caffe.cpp:276] Batch 73, loss = 0
I1102 07:59:55.185588 4556 caffe.cpp:276] Batch 74, accuracy = 1
I1102 07:59:55.185588 4556 caffe.cpp:276] Batch 74, loss = 0
I1102 07:59:55.185588 4556 caffe.cpp:276] Batch 75, accuracy = 1
I1102 07:59:55.185588 4556 caffe.cpp:276] Batch 75, loss = 0
I1102 07:59:55.186589 4556 caffe.cpp:276] Batch 76, accuracy = 1
I1102 07:59:55.186589 4556 caffe.cpp:276] Batch 76, loss = 0
I1102 07:59:55.187588 4556 caffe.cpp:276] Batch 77, accuracy = 1
I1102 07:59:55.187588 4556 caffe.cpp:276] Batch 77, loss = 0.00324118
I1102 07:59:55.188588 4556 caffe.cpp:276] Batch 78, accuracy = 1
I1102 07:59:55.188588 4556 caffe.cpp:276] Batch 78, loss = 0.00699073
I1102 07:59:55.189589 4556 caffe.cpp:276] Batch 79, accuracy = 1
I1102 07:59:55.189589 4556 caffe.cpp:276] Batch 79, loss = 0.00333453
I1102 07:59:55.190589 4556 caffe.cpp:276] Batch 80, accuracy = 1
I1102 07:59:55.190589 4556 caffe.cpp:276] Batch 80, loss = 0.00858627
I1102 07:59:55.191589 4556 caffe.cpp:276] Batch 81, accuracy = 1
I1102 07:59:55.191589 4556 caffe.cpp:276] Batch 81, loss = 0.012485
I1102 07:59:55.192589 4556 caffe.cpp:276] Batch 82, accuracy = 1
I1102 07:59:55.192589 4556 caffe.cpp:276] Batch 82, loss = 0.00126018
I1102 07:59:55.196589 4556 caffe.cpp:276] Batch 83, accuracy = 1
I1102 07:59:55.196589 4556 caffe.cpp:276] Batch 83, loss = 0.00604102
I1102 07:59:55.197589 4556 caffe.cpp:276] Batch 84, accuracy = 1
I1102 07:59:55.197589 4556 caffe.cpp:276] Batch 84, loss = 0.00597452
I1102 07:59:55.197589 4556 caffe.cpp:276] Batch 85, accuracy = 1
I1102 07:59:55.197589 4556 caffe.cpp:276] Batch 85, loss = 0.00348387
I1102 07:59:55.198590 4556 caffe.cpp:276] Batch 86, accuracy = 1
I1102 07:59:55.198590 4556 caffe.cpp:276] Batch 86, loss = 0.0011795
I1102 07:59:55.199589 4556 caffe.cpp:276] Batch 87, accuracy = 1
I1102 07:59:55.199589 4556 caffe.cpp:276] Batch 87, loss = 0.0960982
I1102 07:59:55.201591 4556 caffe.cpp:276] Batch 88, accuracy = 1
I1102 07:59:55.201591 4556 caffe.cpp:276] Batch 88, loss = 0.0198378
I1102 07:59:55.201591 4556 caffe.cpp:276] Batch 89, accuracy = 1
I1102 07:59:55.201591 4556 caffe.cpp:276] Batch 89, loss = 0.0390513
I1102 07:59:55.202590 4556 caffe.cpp:276] Batch 90, accuracy = 1
I1102 07:59:55.202590 4556 caffe.cpp:276] Batch 90, loss = 0.00671676
I1102 07:59:55.203590 4556 caffe.cpp:276] Batch 91, accuracy = 0
I1102 07:59:55.203590 4556 caffe.cpp:276] Batch 91, loss = 0.734476
I1102 07:59:55.204591 4556 caffe.cpp:276] Batch 92, accuracy = 1
I1102 07:59:55.204591 4556 caffe.cpp:276] Batch 92, loss = 0.329517
I1102 07:59:55.205590 4556 caffe.cpp:276] Batch 93, accuracy = 0
I1102 07:59:55.205590 4556 caffe.cpp:276] Batch 93, loss = 3.2879
I1102 07:59:55.206590 4556 caffe.cpp:276] Batch 94, accuracy = 1
I1102 07:59:55.206590 4556 caffe.cpp:276] Batch 94, loss = 0.00880476
I1102 07:59:55.207590 4556 caffe.cpp:276] Batch 95, accuracy = 1
I1102 07:59:55.207590 4556 caffe.cpp:276] Batch 95, loss = 0.00612492
I1102 07:59:55.208590 4556 caffe.cpp:276] Batch 96, accuracy = 1
I1102 07:59:55.208590 4556 caffe.cpp:276] Batch 96, loss = 0.00298934
I1102 07:59:55.209590 4556 caffe.cpp:276] Batch 97, accuracy = 1
I1102 07:59:55.209590 4556 caffe.cpp:276] Batch 97, loss = 0.00931252
I1102 07:59:55.211591 4556 caffe.cpp:276] Batch 98, accuracy = 1
I1102 07:59:55.211591 4556 caffe.cpp:276] Batch 98, loss = 0.0195644
I1102 07:59:55.212591 4556 caffe.cpp:276] Batch 99, accuracy = 1
I1102 07:59:55.212591 4556 caffe.cpp:276] Batch 99, loss = 0.00813458
I1102 07:59:55.214591 4556 caffe.cpp:276] Batch 100, accuracy = 1
I1102 07:59:55.214591 4556 caffe.cpp:276] Batch 100, loss = 0.0215285
# <省略>
Raw
deploy.prototxt
name: "LeNet"
layer {
name: "data"
type: "Input"
top: "data" # net.inputs[0]に対応
# [バッチ数、channel(今回はgrayscaleなので、1), height, width]の順に記載する.
# 以下、バッチ数 => K, width => W, height => Hと表記.なので、[K, 1, H, W] (channelを外に出して、[K, H, W]
input_param { shape: { dim: 1 dim: 1 dim: 30 dim: 30 } }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
# http://seiya-kumada.blogspot.jp/2015/07/caffe.html
layer {
name: "prob"
type: "Softmax"
bottom: "ip2"
top: "prob" # net.outputs[0]に対応
}
Raw
eval.sh
#!/usr/bin/env sh
# This script converts the mytest data into lmdb/leveldb format,
# depending on the value assigned to $BACKEND.
EXAMPLE=examples/mytest
DATA=data/mytest
BUILD=Build/x64/Release
BACKEND="leveldb"
echo "Creating ${BACKEND}..."
rm -rf $DATA/mytest_deploy_${BACKEND}
convert_imageset.exe $DATA/src/ \
$DATA/eval.txt $DATA/mytest_deploy_${BACKEND} --backend=${BACKEND} --gray
caffe test -model $EXAMPLE/lenet/train_test.prototxt --weights $1 --iterations $2
Raw
file0.txt
caffe-windows/
　├ data/
　│　　└ mytest/
　│　　 ├ train.txt #学習時のdbの紐づけで使用
　│　　 ├ eval.txt #.識別時に使用(caffe testコマンド => deploy_leveldb作成時に使用, pycaffe(python) => classifier.pyの答え合わせで使用)
　│　　 ├ mean.binaryproto #学習時に作成(平均画像 : compute_image_mean.exeで作成)
　│　　 ├ mytest_train_leveldb/ #学習時に作成(convert_imageset.exeで作成)
　│　　 ├ mytest_deploy_leveldb/ #識別時に作成(caffe testコマンドの時に使用)
　│　　 ├ src/
　│　　 　│　└ 0000001.bmp
　│　 　　│　└ 0000002.bmp
　│　 　　│　└ 0060000.bmp
　│　 　　│　└ (以下省略。学習用：train data=>50000枚)
　│ 　　　│　
　│　　 ├ check/ (pycaffeで画像識別時に使用)
　│　　　　　└ 0060001.bmp
　│　　　　　└ 0060002.bmp
　│　　　　　└ (以下省略。 識別用:eval data => 11871枚作成)
　│
　├ example/
　│　　├ eval.sh #識別時に使用
　│　　├ train.sh #学習時に使用
　│　　└ mytest/
　│　　　　　└ solver.prototxt #学習時に使用
　│　　　　　└ train_test.prototxt #学習時、caffe testコマンド(識別時)に使用
　│　　　　　└ mytest_iter_100000.caffemodel #.学習後に生成される
　│　　　　　└ deploy.prototxt #識別時に使用(pycaffe のnet_path変数 として用意)
　├ Build/
　│　└ x64/
　│　　　└ Release/ #パスを通しておく。
　├ classifier.py (識別時に使用)
Raw
file10.py
# deploy(ネットワーク設定)とcaffemodel(学習出力パラメータ群)の読み込み
#net_path <- deploy.prototxt, model_path <- caffemodel
net = caffe.Net(net_path, model_path,caffe.TEST)
# transformerの設定(for using preprocess phase only)
transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape})
# filename -> ネットワークに沿って演算されて、出力ベクトル (1*2 ndarray)が得られる。
out = net.forward_all(**{net.inputs[0]:transformer.preprocess('data', caffe.io.load_image(fn, color = False))})
# show most probable class
answer = np.argmax(out[net.outputs[0]])
Raw
file3.rb
include {
phase: TEST
}
Raw
file3.txt
学習データ(50000枚:100000iter)
[内訳]
正常画像：26183
異常を含む画像：23817
10/24 17:00 -> 10/25 10:00(所要時間　18h)
# <... 中略>
I1026 02:01:15.841161 375892 solver.cpp:337] Iteration 100000, Testing net (#0)
I1026 02:01:29.253844 375892 solver.cpp:404] Test net output #0: accuracy = 0.8796
I1026 02:01:29.253844 375892 solver.cpp:404] Test net output #1: loss = 0.420841 (* 1 = 0.420841 loss)
I1026 02:01:29.363865 375892 solver.cpp:228] Iteration 100000, loss = 0.0469159
I1026 02:01:29.363865 375892 solver.cpp:244] Train net output #0: loss = 0.0469161 (* 1 = 0.0469161 loss)
I1026 02:01:29.363865 375892 sgd_solver.cpp:106] Iteration 100000, lr = 0.0008278
Raw
file4.txt
学習データ(50000枚:100000iter)
[内訳]
正常画像：26183
異常を含む画像：23817
10/24 17:00 -> 10/25 10:00(所要時間　18h)
# <... 中略>
I1026 02:01:15.841161 375892 solver.cpp:337] Iteration 100000, Testing net (#0)
I1026 02:01:29.253844 375892 solver.cpp:404] Test net output #0: accuracy = 0.8796
I1026 02:01:29.253844 375892 solver.cpp:404] Test net output #1: loss = 0.420841 (* 1 = 0.420841 loss)
I1026 02:01:29.363865 375892 solver.cpp:228] Iteration 100000, loss = 0.0469159
I1026 02:01:29.363865 375892 solver.cpp:244] Train net output #0: loss = 0.0469161 (* 1 = 0.0469161 loss)
I1026 02:01:29.363865 375892 sgd_solver.cpp:106] Iteration 100000, lr = 0.0008278
Raw
file9.py
# deploy(ネットワーク設定)とcaffemodel(学習出力パラメータ群)の読み込み
#net_path <- deploy.prototxt, model_path <- caffemodel
net = caffe.Net(net_path, model_path,caffe.TEST)
# transformerの設定(for using preprocess phase only)
transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape})
# filename -> ネットワークに沿って演算されて、出力ベクトル (1*2 ndarray)が得られる。
out = net.forward_all(**{net.inputs[0]:transformer.preprocess('data', caffe.io.load_image(fn, color = False))})
# show most probable class
answer = np.argmax(out[net.outputs[0]])
Raw
solver.prototxt
net: "examples/mytest/lenet/train_test.prototxt"
test_iter: 100
test_interval: 500
base_lr: 0.005 # ここもちょっと変えているかな？
momentum: 0.9
weight_decay: 0.0005
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
# Display every 100 iterations
display: 500
# The maximum number of iterations
max_iter: 100000
# snapshot intermediate results
snapshot: 10000
snapshot_prefix: "examples/mytest/lenet/mytest"
# solver mode: CPU or GPU
solver_mode: CPU
Raw
train_test.prototxt
name: "LeNet"
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
scale: 0.00390625 #raw_scale is 255, so, 1/255 scaling
mean_file: "data/mean.binaryproto"
}
data_param {
source: "data/mytest_train_leveldb"
batch_size: 100
backend: LEVELDB
}
}
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
scale: 0.00390625
mean_file: "data/mean.binaryproto"
}
data_param {
source: "data/mytest_deploy_leveldb"
batch_size: 1
backend: LEVELDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2 #NOTE that we try 2 classification(true or false), so we change num_output para from 1000 to 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "ip2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
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