From 1ed42de47b62f88935f87b62453fef1f8d9b3e59 Mon Sep 17 00:00:00 2001 From: NiiiCo Date: Tue, 1 Jun 2021 19:35:39 +0200 Subject: [PATCH] new data loader thingy --- .gitignore | 3 + RUN.py | 6 +- __pycache__/mnist_loader.cpython-38.pyc | Bin 0 -> 3949 bytes __pycache__/network.cpython-38.pyc | Bin 0 -> 7156 bytes dataset_loader.py | 90 ++++++++++++++++++++++++ mnist.pkl.gz | Bin 17051982 -> 0 bytes 6 files changed, 96 insertions(+), 3 deletions(-) create mode 100644 .gitignore create mode 100644 __pycache__/mnist_loader.cpython-38.pyc create mode 100644 __pycache__/network.cpython-38.pyc create mode 100644 dataset_loader.py delete mode 100644 mnist.pkl.gz diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..71a7689 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +__pychache__/ +set/ + diff --git a/RUN.py b/RUN.py index b977ec9..7f6708d 100644 --- a/RUN.py +++ b/RUN.py @@ -4,9 +4,10 @@ import mnist_loader training_data, validation_data, test_data = mnist_loader.load_data_wrapper() import network +import dataset_loader -# net = network.Network([784, 30, 10]) #Testé : 94,56% -# net.SGD(training_data, 30, 10, 3.0, test_data=test_data) +net = network.Network([262144, 30, 10]) #Testé : 94,56% +net.SGD(dataset_loader.loadTrainingSet("training"), 30, 10, 3.0, test_data=dataset_loader.loadTestSet("test")) # net = network.Network([784, 100, 10]) #Marche mieux apparemment # net.SGD(training_data, 30, 10, 3.0, test_data=test_data) @@ -17,4 +18,3 @@ import network # net = network.Network([784, 30, 10]) #Marche pas du tout apparemment # net.SGD(training_data, 30, 10, 100.0, test_data=test_data) -print() \ No newline at end of file diff --git a/__pycache__/mnist_loader.cpython-38.pyc b/__pycache__/mnist_loader.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f64b2855973dc9b9f67e47b43dbceebc09a00815 GIT binary patch literal 3949 zcmcIny>r~g6$fy4#+BC~_3X&Zvm(i5M3*_8gYL-rY(# z0Nw>i9_N@wYE$GtuuAp$7t-WUK#|6kx)iBg<@X*w2$E99O+W$%>@MENZ{Pd9hd*Dw zJaBOI|MPcWUUr=C>7jjic=#<&dl#3`nK+>vdXYPE`PZ9x{M(sy!%oli!`k={4btVEUwS|GB85`X;s;iEmF zmvXK|C^I=0;kRr>|NWq2} zfW|-p90 zXm@wFQ6gc@0}vCBUN7O-cRwH;a)s4KrHD2dSmSo*u6R%2@*9C=Y(uJ# z6yg+erLb5AtXm;{>Z?`oFsvx>1IZi$<_Re8CLOTv6ynhs?21IPu=wW(-^Ab zqRcr8p=Jb`YxPn`(u{;U3RGGesA#jANJwI2*6ngbORH)KRChd}Jv#>0FF(2@1=y2-!>-vj-BoVMUn4 ztcwIxbRrspXt7VtH8+R8&veB?_EnS|mhz6bnLNkolW06Yo_J=z*ocsg@$I2o^i>QV z!3GU`MR$Iz)1sTCDlRrs9XyFt(Iu8EdO?IZ7Yjl9s*zqb(#_m;XWf13 zouCTwoZGD4z}&{#4Y3yUo2XAD&1>fSnD67coZnja)|=}srqRCqSEr%TSCFRf-#i27 zsKu3xZ$+YYkYkU>;&;*_e?X?KD4@NkAZ>*kqIsDZ#o>V9KTxqoinYU-ii%!>$CsEW z`5EbVF1X7edWa$>4Qg1<$#pS?nOq_i={nIU=}SIg3+~+Fl(sPX=Fv#3*b_V)`93o* zG2g9F!xD=+L1m6ucwDh??L@oS=s1PLoWY=Q-(S^|Ztu1Q@vCQqUF6?9)|og^L59M9 zmbQ?{fV5H%J_}HwR)xljtx|~u^Nw9Qi(xL{5sCmTp2q&#i@}209!D9v45bG z+(s)BLjkH}Y{j9v0p`xB&Iudo`62^DgFR=;R1pgj=C+F!kSlf8lr&xU6Q^7xc3RB^ zaVqL<#lt02Q@KW0g^WqWe%azJlQMc=lPPEOqH+~2Sb+YtIjHH~!1)NLeFv90Xy==& zCoc71pb+P|_c!m`?jz@gJM{Ebki$!Q{e@R}aXQ&V!Cc5x(c^fxaF3Wk%gY;BRt)Y@ z_yox^y?>uH5aVq)o^$-mhAZoOFuoTh=pXHU+|=(F(sjXJ62kVrg&Veao9tb?PucR@lGkG|q8@2#q0 literal 0 HcmV?d00001 diff --git a/__pycache__/network.cpython-38.pyc b/__pycache__/network.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fa329006736e1a588df22a6383c4e36c1e716d74 GIT binary patch literal 7156 zcmcIp%aa>N8J`}#M%rC_?KplUA!)EfB1*fC;{YM$asp1`a#3uk*hwf6m(+kXF z-`DzJWu;-^=ltytU3Sj0{!AZ>zcN1F!lhov&9&N=%UnBPZKj`gyL8=hOK$lS%Po85 z-BP>aR@~|*R=eufTnEpZYu~h*b#aqfRwMM{ok-qYA59uRFMf?1xA;fzz5m0TjmG!+ zFmlI%$K#0m!%^T3y)fqSmd8~b^|yqI{XXB6!u6@#^;DmpfhT0>hno!%Y(~__1` z=NnNB!L~%qZ}2U#?O~GsSa~jwLOv9CsS`=sk6f>zwDc|R3FQY9E;f}=p3xQv>z zTcw(Ln_W9=RdLIhx+Z>$X>LKgfv;lT?J6ipb-Pe@7z;mCTJzy}*z+Wh2HMyWO8t;3 z8a(?!V*o)H&pS z%y*)3;PRdar;E3w=dJ1S^Kn~z2VQwVlz}g?5YsAK1Ub;Fx#%q|m-0~OX3Z`ab%ikG zAS8dh<-^6zXqU2PaH?CV>iI%>@Nu-<@%+s#I1>d2dKIH&tJiVhhkopffc%e3bc%+- zq{U502!36RmGVVcbakN`>G#GIB4&|8PlWsx!K9GH+lB|}sD|(KqakL`13MbW_=5UmG=#2WG}Lmd^**qz_~r3%6D8^y+%~Pm zx_xrSV(SijXv4>?J^R=8J@RYzn0h`crFJ+74TZ{7IuJ8SQaJgEWdLlNAaNFNOfj7=L?$aQHIS6?J)2+OOMZA~v^ zfxO=7kik2h^(#RH2vk?`l6R2=A{z(RMkN&MdTr0+MX&Aj$M%fP>}-8lVZJ^sxTIMh zm8645%I?cLo~DA9C70;7jN3PH83|c+|M5P4&1zZ-yfAGbuscT8X+=kVS~d1dOH+T8 z)-uP2%F4 zW=>psSbo6v7_&aJK4Sy38#uA;mXhiNYqvx=KdUEov|gPx5)1lkB;`GuS@+r)`7+rCR+OPd&O#Q+(E0MLVgaM;@x!I-!a2OGj}YbY5F$4M1j3R2 zXoq%u=u>ew+6lQD_f#yPVG#tAqv4oBNIi#}hOARFxG(4ldtjZhh{Anq!`1Q072jh3yRTWH#4?mhdpF#VehC3Kg5ukGOGW>33q71!yH2MvycAegakrQ zQHu+>Y10!Y0xKklHJssDE3r>mXBSudh-oCt)+@Dgzl{N)ZK5i2jcx^p(_;JtMFmZK zLec-381g$9G#5g9-)l7Scb}ZSy*T>@s28o-Ff%6)O-QonHR3 zBacyEg17dH>jkmsWGxPUX2#IM=so5|^!O1jMNwu|R@o|B#&rsW4!F9n(RF`%?n}et z0J@S4HC)$lsSDVPz6xCv@Vta=g86M)Gh%wcVM0ejzuT|D=FY56oG+<^t=Wll#saIW z5vwB(7*~@9F~y{c60kY&wkyEsS^}1b{NLWQV<)N6`z||W&6bj-1g8d9fY=yu39TyU ztcUeIWKc<2mp5Es{GBB|dv~^+)RN^T>m269I5g9W?nzu7ZpIRgb`Nq8K`bP zhiQ2>Ye_Si`g?!Gu2CM)ah5avOUCAr?-TJO0L`q|;^t-NDDGDPY60(AGLBpmY-%tL zaXf)k`da5cs8P3@jS1Aoc2RWB%Xg?88LZBY)_Rld)PWd+KH5OuG{V**rI_f%cjyOV zfn7zIfVkqi*@ny{d0CcIb-Vu!%xmbBufn&Eg0LR>T#lLZoHUQ4{|AiA%U>c(e^C~B z9AA&WKnnVpwhEmHH<#zJZjv(EK_re$t+a~W+Y4Pu0ht~rOLkmO`rAm%w?P)Ol&u3A zHuONNkl(8j&Lre{8sP+P&0_{F3Ph0KqpCM>Yc~X)Sm_+Uy)=JUsXbL@t0dS=*)*+a z6iHc-+G=!I;jg37ZCt8_n??jB^>47VDr>CH_U<5 zn6|h4(%}D@HDDS|ui)0M}RGI#LAswC{-07S`=f38cYG=y_Ehg$= z61gG-LUYQI=SaFqKavv1dq}v{I6!JKKd}As0YJ#IWE>Rd;OP@}@>` zS84iG6>@VZ?#r)X6uE*MHdHzcIJ9SeilSoAoCjK-2Yh9qk)RZ(iZ0^vIM6B%eTc%` zMY{i?1u(Z=@SG;wXtK0~Bu~q9$P?50l}#^1dMdAeM&r@IWt=Ka&*wYV5pv}{Xk45S zV*PrV-$LK^vd&YC(UqC(I?4@id>L=Qz@_LA!>Tlxy^?UTks zI|~htJVm>T#-EeSB)nCcp2-z@2$9G>4LT4DaeC0{q>WC;{OyAG`Fk)&2h^DP*6&eLdql3Y=m>e$W+r)KRpo_AKA^G;n-?E&IO88ErC);FDZ z0X|IHfa9kmU}oE3dJ_5G5z@SgvfnI7L*Gp& zt#vwX)bDgmVqejlcoyza?+$!UYtRf!T^1Hum>_ko7?WNlXI4~RKIW7hdv)TJ{|REm B@09=m literal 0 HcmV?d00001 diff --git a/dataset_loader.py b/dataset_loader.py new file mode 100644 index 0000000..9be3ef0 --- /dev/null +++ b/dataset_loader.py @@ -0,0 +1,90 @@ +from mnist_loader import load_data +import numpy as np +import os +from PIL import Image +import resource + + +def vectorized_result(j): + """Return a 10-dimensional unit vector with a 1.0 in the jth + position and zeroes elsewhere. This is used to convert a digit + (0...9) into a corresponding desired output from the neural + network.""" + e = np.zeros((10, 1)) + e[j] = 1.0 + return e + +def loadSet(path): + + filelist = [] + + for root, dirs, files in os.walk(path): + for file in files: + filelist.append(os.path.join(root,file)) + + i = 0 + pixels = [] + result = [] + + + for name in filelist: + + if i >= 100: + + break + + if ".png" in name: + + with Image.open(path + "/" + name.split("/")[-1]) as im: + + pix = im.load() + temparray = [] + + result.append(name.split("/")[-1][0]) + + for x in range(im.size[0]): + + for y in range(im.size[1]): + + temparray.append(pix[x, y] / 255) + + pixels.append(temparray) + print(temparray) + print(str("%.2f" % round(i / (len(filelist) if len(filelist) < 100 else 100) * 100, 2)) + "% Done, ram usage: " + str("%.2f" % round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / (1024*1024), 2)) + "Go", end = '\r') + i += 1 + + print("max ram usage: " + str(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / (1024*1024)) + "Go") + + return (pixels, result) + + +def loadTrainingSet(path): + + print("importing training set...") + + set = loadSet(path) + + training_inputs = [np.reshape(x, (262144, 1)) for x in set[0]] + training_results = [vectorized_result(int(y)) for y in set[1]] + training_data = zip(training_inputs, training_results) + + return training_data + +def loadTestSet(path): + + print("importing test set...") + + set = loadSet(path) + + test_inputs = [np.reshape(x, (262144, 1)) for x in set[0]] + test_data = zip(test_inputs, set[1]) + + return test_data + + + + + +if __name__ == "__main__": + + print(loadSet("set")[0]) \ No newline at end of file diff --git a/mnist.pkl.gz b/mnist.pkl.gz deleted file mode 100644 index 059aba0d982e20e2ac125a3a59d89c3c2cd8b28a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 17051982 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