diff options
| -rw-r--r-- | include/ct1986.c | 17 | ||||
| -rw-r--r-- | include/weights.c | 16 | ||||
| -rw-r--r-- | src/train5.py | 47 |
3 files changed, 47 insertions, 33 deletions
diff --git a/include/ct1986.c b/include/ct1986.c index 97be8e5..d5325be 100644 --- a/include/ct1986.c +++ b/include/ct1986.c @@ -41,7 +41,7 @@ ct1986_evaluate_black_win(void) { for (uint8_t kx = 0; kx < KERN_SIZE; kx++) { for (uint8_t c = 0; c < KERN_CHAN; c++) { // Where we are on the board - const uint8_t loc = kx+by+(ky+bx)*5; + const uint8_t loc = kx+bx+(ky+by)*5; // Look up what's on the board at this location, and // multiply it. For c=0 we have to do some extra work float lookup = 0; @@ -171,13 +171,10 @@ ct1986_minimax(const uint8_t cur_depth, const uint8_t max_depth, enum E_RESULT r; const uint8_t black = (ply & 1), - material = (black) ? black_count : white_count; - - uint8_t cap = 0, flat = material & 127, standing = 0; - if (ply > 2) { - if (material & 128) cap = 1; - if (material & 127) standing = 1; - } + material = (black) ? black_count : white_count, + flat = material & 127, + cap = (ply > 2 && (material & 128)), + standing = (ply > 2 && (material & 127)); // 1.0 is a `certain' black win, -1.0 is a `certain' white win. float optimal = (min) ? infty : -infty; @@ -266,8 +263,8 @@ ct1986_minimax(const uint8_t cur_depth, const uint8_t max_depth, } } } - } else if (count == 0) { - // Empty square, try the three placements. + } else if (material && count == 0) { + // Empty square, try placements if (flat) { // Generate the placement diff --git a/include/weights.c b/include/weights.c index c4ab298..905eb41 100644 --- a/include/weights.c +++ b/include/weights.c @@ -1,25 +1,25 @@ #include "weights.h" const float conv2d_weights[KERN_NUM][KERN_SIZE][KERN_SIZE][KERN_CHAN] = -{{{{-0.31461313366889954, 0.12296362966299057, 0.0537884421646595, -0.05761459097266197, 0.9647676944732666, 0.012238401919603348}, {0.16527464985847473, 0.3546968400478363, 0.33187735080718994, -0.07616483420133591, 0.06320620328187943, -1.2417434453964233}, {0.13152976334095, -0.16560819745063782, 0.03655802831053734, 0.08490452915430069, 0.0334080345928669, -0.4817463457584381}}, {{-0.4923256039619446, -0.2672223150730133, -0.19988402724266052, 0.18220694363117218, -0.6444212794303894, 0.4459397792816162}, {-0.5899091958999634, -0.35788777470588684, 0.06865987181663513, 0.06876449286937714, -0.632453441619873, 0.612913966178894}, {0.32418426871299744, 0.17252518236637115, -0.008695406839251518, -0.41084399819374084, -0.36779260635375977, 0.166359081864357}}, {{2.5275943279266357, 0.5177026391029358, 0.24760891497135162, 0.1798764318227768, 0.119742751121521, 0.5542832612991333}, {-0.7371780872344971, -0.16028088331222534, 0.3641703128814697, -0.10842252522706985, -0.2533840835094452, 0.1398170292377472}, {-0.1798374056816101, 0.473869651556015, 0.11629271507263184, -0.1089477688074112, 0.18995530903339386, 0.14978930354118347}}}, {{{0.02528725191950798, -0.035234421491622925, 0.2987964451313019, 0.4577234089374542, -0.17367038130760193, 0.021170491352677345}, {0.08088105916976929, 0.02343069016933441, -0.22613884508609772, 0.334805965423584, 0.09736854583024979, -0.8634821772575378}, {0.4900866746902466, -0.6248732209205627, -0.06317996233701706, 0.39485785365104675, 0.3370535671710968, 0.11868363618850708}}, {{0.12233788520097733, -0.0396895669400692, -0.22183308005332947, -0.15242572128772736, -0.09647907316684723, 0.5950322151184082}, {0.12958098948001862, 0.10052308440208435, 0.0029385732486844063, -0.7589095830917358, 0.311553031206131, -1.3440141677856445}, {0.9494408965110779, -0.03842993080615997, -0.28849899768829346, -0.3843957483768463, -0.8392577767372131, 0.28326094150543213}}, {{0.20955389738082886, -0.2845759391784668, 0.07400181144475937, -0.37351182103157043, 0.5934371948242188, 0.5605667233467102}, {0.5114613175392151, -0.4090779721736908, 0.5201377868652344, 0.9147480130195618, 1.1307423114776611, 1.6698521375656128}, {3.6288881301879883, 0.08266420662403107, 0.6817247271537781, 1.0633864402770996, 1.6464864015579224, 2.9008560180664062}}}, {{{3.589796304702759, 0.5128039121627808, 1.0086349248886108, 2.430671453475952, 1.5398201942443848, 4.240425109863281}, {0.5517507791519165, 0.1758994311094284, 0.2055874764919281, 0.05001397803425789, 0.6506865620613098, -0.5484557747840881}, {0.205831840634346, 0.12491777539253235, 0.16275589168071747, 0.16265034675598145, 0.03379141539335251, 0.7570209503173828}}, {{0.8731821775436401, 0.0028055456932634115, -0.2800484001636505, -0.1297520250082016, -0.15533196926116943, -1.729884147644043}, {-0.1471261978149414, 0.6158180832862854, 0.25787612795829773, -0.4087550640106201, -0.020888496190309525, -0.26702529191970825}, {0.15122485160827637, 0.1827888786792755, -0.0927032008767128, 0.17713645100593567, 0.03384794294834137, -0.2902638614177704}}, {{0.4319866895675659, -0.17716410756111145, -0.3810195028781891, 0.15713493525981903, -0.08874393999576569, -1.0814930200576782}, {-0.07521241903305054, 0.21773630380630493, -0.0880870521068573, 0.14912794530391693, 0.30793991684913635, 0.23713169991970062}, {-0.13515245914459229, -0.17271432280540466, -0.2807714343070984, 0.14518122375011444, 0.05650143697857857, 0.23813144862651825}}}, {{{0.8088172078132629, 0.04798170179128647, 0.24023272097110748, -0.04941624402999878, -0.34324923157691956, -0.3630078434944153}, {0.9733880758285522, -0.07820767164230347, 0.030127255246043205, 0.08433039486408234, -0.07763263583183289, 0.4979310929775238}, {-1.3488374948501587, -0.628921389579773, 0.08849909901618958, 0.1050964966416359, -0.8178150057792664, -0.7084482908248901}}, {{0.6024267673492432, 0.2082122415304184, 0.28936347365379333, 0.0038416043389588594, 0.5109341144561768, 1.2844984531402588}, {0.25694170594215393, 0.011755313724279404, -0.20201383531093597, -0.05236750841140747, 0.7737127542495728, -0.22031591832637787}, {-0.8643184900283813, -0.29890504479408264, 0.14624962210655212, 0.2619291841983795, -0.3482455313205719, -1.5402915477752686}}, {{-0.6150990128517151, 0.19314956665039062, 0.5663931965827942, 0.1696654111146927, -0.344159871339798, 0.10662651062011719}, {-0.6704407334327698, 0.08012254536151886, 0.18876942992210388, -0.255744606256485, 1.3135392665863037, -0.05540776252746582}, {-0.9687967896461487, -0.5639577507972717, 0.03629503771662712, 0.749233603477478, 0.718273401260376, -0.3044533431529999}}}, {{{1.264162540435791, 0.42641350626945496, 0.1745416224002838, 0.4505111277103424, 0.10443221777677536, -0.4080628454685211}, {0.9359639883041382, 0.19628310203552246, -0.37767651677131653, 0.05806472897529602, 0.41802769899368286, -0.4021208882331848}, {1.1833105087280273, 0.033637966960668564, 0.16121648252010345, 1.6704840660095215, 0.16925831139087677, -0.5822446942329407}}, {{-1.050500512123108, -0.09167718142271042, -0.15504086017608643, -0.26810529828071594, -0.2211122363805771, -0.7116621732711792}, {-0.5004003047943115, -0.2709294557571411, 0.07183103263378143, -0.04881398007273674, 0.4002133011817932, -0.341298371553421}, {0.7336205244064331, 0.019183877855539322, -0.10218597203493118, -0.06858261674642563, 0.1804317682981491, -0.7883838415145874}}, {{-0.7520256042480469, -0.22791530191898346, -0.12430168688297272, -0.0643269270658493, -0.4126777648925781, -0.1774013191461563}, {-0.9185736179351807, -0.18247392773628235, -0.1998894065618515, 0.1737818866968155, -0.1140613779425621, -0.13231895864009857}, {0.6627748012542725, -0.07602502405643463, 0.07602325081825256, 0.37516525387763977, 0.7853088974952698, 0.7419880032539368}}}, {{{-2.2925846576690674, -0.5670830011367798, -0.5114259719848633, -1.07743239402771, -0.6936476230621338, -0.9780539870262146}, {0.6842707991600037, 0.21200893819332123, 0.2382790446281433, 0.05543014407157898, 0.06291257590055466, 0.14438624680042267}, {0.14939481019973755, 0.037804435938596725, 0.13612288236618042, 0.13105769455432892, -0.43090638518333435, -0.9629568457603455}}, {{0.3829599916934967, 0.11708086729049683, -0.09763535857200623, -0.16541720926761627, 0.018887154757976532, 0.3496650755405426}, {1.4982597827911377, 0.08175168186426163, 0.4016278386116028, 0.9158377647399902, -0.12879355251789093, 2.351213216781616}, {0.1616053730249405, -0.0608898401260376, 0.14849039912223816, -0.09197665005922318, 0.5148493051528931, 0.18527956306934357}}, {{0.11273615062236786, 0.34991174936294556, 0.26515814661979675, -0.49144628643989563, 0.3383309245109558, 0.5385087728500366}, {0.24411790072917938, 0.052721429616212845, -0.05631586164236069, -0.4685106873512268, -0.4849746823310852, -0.9032131433486938}, {-0.37900495529174805, -0.08784185349941254, 0.300321489572525, 0.00278386939316988, 0.6376034021377563, 1.0151978731155396}}}, {{{0.19271059334278107, 0.0959976390004158, 0.11761606484651566, -0.0512591227889061, -0.176044300198555, -0.046978943049907684}, {0.0822444036602974, -0.12160180509090424, 0.1548638641834259, -0.1438426673412323, 0.1660275012254715, -0.14605960249900818}, {-0.21274171769618988, 0.1426110565662384, 0.10009711235761642, -0.0548456534743309, -0.574436366558075, 0.010507103055715561}}, {{0.009781602770090103, 0.01842466928064823, -0.07070838660001755, 0.22458019852638245, -0.5180603861808777, -0.29575562477111816}, {-0.09571611881256104, -0.2681695520877838, -0.3459489345550537, 0.4147747755050659, -0.1342136710882187, -0.7266443371772766}, {-0.5911058783531189, 0.23451371490955353, -0.12154868990182877, 0.3614463806152344, 0.43761563301086426, -0.39286959171295166}}, {{-0.37995660305023193, 0.07834415882825851, 0.32898393273353577, 0.21365521848201752, -0.7746888995170593, -0.4666702151298523}, {-0.7264567017555237, 0.16304486989974976, 0.04888468235731125, -0.43774378299713135, -0.15046003460884094, -1.6320950984954834}, {-3.3951847553253174, -0.1645590215921402, -0.859903872013092, -0.6840961575508118, -0.9732393026351929, -2.77411150932312}}}, {{{-1.349423885345459, -0.20975378155708313, -0.002415295923128724, -0.19932051002979279, -0.6740261316299438, -0.1304839551448822}, {-1.3349111080169678, -0.31751662492752075, -0.014173654839396477, -0.4058109223842621, -0.7622523903846741, -0.6132423877716064}, {-2.1359777450561523, -0.1360314041376114, -0.47272995114326477, -0.9010438919067383, -0.6773699522018433, -1.6276202201843262}}, {{-0.8764068484306335, -0.15723438560962677, -0.13497710227966309, -0.2462702989578247, 0.10718588531017303, -0.35485339164733887}, {-1.475588321685791, -0.45153331756591797, -0.30711501836776733, -0.0907602608203888, -0.08527722954750061, -0.17336808145046234}, {-1.6650699377059937, -0.5922436714172363, -0.11038702726364136, -0.30366021394729614, -0.2969067692756653, -0.39050963521003723}}, {{-1.7517677545547485, -0.34366321563720703, -0.17049174010753632, -0.6551642417907715, -0.6182800531387329, -0.4032576084136963}, {-1.1081265211105347, -0.08348870277404785, -0.38202187418937683, -0.22303113341331482, -0.015861719846725464, -0.3194693624973297}, {-0.7808969020843506, -0.3107500374317169, -0.10135695338249207, -0.5114881992340088, 0.18284864723682404, 0.0644284188747406}}}, {{{-0.6956506371498108, -0.31796395778656006, -0.2376352697610855, -0.5313360095024109, -0.8107539415359497, 0.6690312027931213}, {-0.8925672769546509, -0.19261956214904785, -0.2000349909067154, -0.42861443758010864, -0.11305136233568192, 0.909927248954773}, {-2.1942031383514404, -0.3707621991634369, 0.30361655354499817, 0.10333340615034103, 0.4276471436023712, -0.9056501984596252}}, {{-0.3270469307899475, 0.037394095212221146, 0.02230740338563919, -0.6633273363113403, -0.26718392968177795, -0.32602840662002563}, {-0.003968007862567902, 0.32749074697494507, -0.5412979125976562, -0.21694573760032654, 0.21006284654140472, -0.5254878997802734}, {1.6014090776443481, 0.25980183482170105, 0.049515075981616974, 0.6265353560447693, 0.4975777566432953, 0.012848232872784138}}, {{-0.12509086728096008, -0.05393632873892784, 0.3533054292201996, -0.43364590406417847, -0.16950076818466187, -0.5675187110900879}, {0.39888623356819153, 0.5743353366851807, 0.2632575035095215, 0.118721604347229, -0.49037787318229675, -0.6497940421104431}, {0.6756026148796082, -0.14102600514888763, -0.07651961594820023, 0.5968765020370483, 0.28887248039245605, 0.17865805327892303}}}, {{{0.17693428695201874, 0.145796999335289, 0.08337005227804184, -0.06137264519929886, 0.11150143295526505, 0.9657827615737915}, {0.8218414187431335, -0.1060900166630745, 0.4363735318183899, -0.014683166518807411, 1.0367910861968994, 1.0659204721450806}, {-2.345315933227539, -0.6162209510803223, 0.24797992408275604, -0.5368184447288513, -0.6192275881767273, -0.2645907700061798}}, {{-0.3148207664489746, -0.11082214117050171, 0.038070473819971085, -0.044219013303518295, 0.016283510252833366, 0.5516585111618042}, {0.7833090424537659, 0.6434233784675598, -0.13571208715438843, -0.7289839386940002, 0.1483081877231598, -1.110136866569519}, {0.4928351938724518, 0.03171747177839279, 0.2033851146697998, 0.008746098726987839, 0.5735700130462646, 0.3973754942417145}}, {{-0.28379878401756287, 0.08186645060777664, 0.023597685620188713, -0.33318614959716797, -0.4715694785118103, -0.07906733453273773}, {-0.13404208421707153, 0.09601431339979172, 0.1726749986410141, -0.17940844595432281, 0.718458890914917, 1.3421382904052734}, {0.2516142427921295, -0.06048078089952469, 0.3909710645675659, 0.3020424246788025, 0.023104801774024963, 1.397689938545227}}}, {{{0.16723963618278503, -0.05371275171637535, -0.43500816822052, 0.058393534272909164, -0.3977251648902893, -0.1485382467508316}, {-0.33421334624290466, -0.5792942047119141, 0.29375457763671875, 0.02227899618446827, 0.12824425101280212, 1.201663613319397}, {-0.8473964333534241, 0.4314437508583069, -0.07349221408367157, -0.01935438998043537, 0.3694668412208557, -0.20149196684360504}}, {{0.888957679271698, -0.10680853575468063, 0.631487250328064, 0.2528837323188782, 1.6955703496932983, 1.228530764579773}, {-0.06936842948198318, 0.36527884006500244, 0.018267005681991577, 0.2256043553352356, -0.2143687754869461, 0.5711511969566345}, {-0.8297070860862732, 0.04081609472632408, 0.216085746884346, -0.7568457126617432, 0.5692006945610046, -0.3642731010913849}}, {{2.0379204750061035, -0.21703754365444183, 0.16351203620433807, 2.0600950717926025, 0.9680494666099548, 2.633943796157837}, {1.5044788122177124, 0.41093021631240845, -0.8865320086479187, -0.5375102162361145, 0.21530747413635254, 0.8961998224258423}, {1.800604224205017, -0.1371089518070221, 0.5438346266746521, -0.20402146875858307, 0.2863587737083435, 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-0.09122574329376221, 0.9713703393936157, -1.1804194450378418, -0.6158671975135803}, {-0.524940013885498, -1.1820167303085327, -0.6534036993980408, 0.45135998725891113, -0.47533172369003296, 1.053428292274475, 0.20420198142528534, 0.9799478054046631, -0.1594831347465515, -0.6252825856208801, -0.03809480741620064, 0.4746891260147095, -1.6609381437301636, 0.27128952741622925, -0.46714890003204346, -0.10678227990865707, 0.14095570147037506, 0.0541875921189785, -0.04240719974040985, -1.0004318952560425, -1.0280420780181885, -0.1531272977590561, -0.8280085921287537, -1.0316764116287231, -0.6958003640174866, -0.6503477692604065, -1.3720258474349976, 0.033448539674282074, 0.6102657318115234, -0.6108158826828003, 0.4899178147315979, 0.5665181875228882, 0.8433608412742615, 1.8111937046051025, 0.36487025022506714, -0.3504650294780731, -0.18332290649414062, -0.2623330354690552, 0.24575206637382507, 0.17031767964363098, 1.020271897315979, 0.2884101867675781, 0.7420584559440613, 1.1521251201629639, -0.6287575960159302, 0.12576231360435486, 0.1342303454875946, -0.08915738761425018, -0.34298551082611084, 0.19105054438114166, -0.6731228232383728, 0.7297862768173218, 0.8754703998565674, 0.022956717759370804, 0.2411525398492813, 0.39744070172309875, 0.28503891825675964, 0.8269319534301758, -0.24359990656375885, -1.3835142850875854, -0.8513659834861755, -0.648575484752655, -1.3342469930648804, -1.2538295984268188, -0.3124498426914215, -0.042367398738861084, 0.4656745195388794, 0.04721718654036522, 0.2897355854511261, 1.4626171588897705, -0.2675020396709442, -0.8047378063201904, -0.9963932633399963, 0.018252814188599586, 1.105333685874939, -1.3895913362503052, 1.1921947002410889, -0.3435618579387665, 1.4608210325241089, -0.5177837610244751, 1.780281901359558, -0.1630477011203766, -0.08583729714155197, -0.7416670918464661, -0.7877695560455322, 0.6487290263175964, 0.25013336539268494, -0.6500059962272644, 0.5684233903884888, -0.1758873015642166, -0.01568284071981907, -0.9905593395233154, 0.24930571019649506, 2.1527299880981445, -0.08318142592906952, -0.5317733883857727, -0.47005805373191833, -0.9092002511024475, -1.023254632949829, -2.1766016483306885, -0.9467728734016418, -0.6117218136787415, 1.5387440919876099, 0.17635858058929443, -0.3694790005683899, 0.2016095668077469, -1.1516046524047852, -0.04035080596804619, 2.3371143341064453, 0.10945197939872742}}; const float dense1_biases[DENSE1_NUM] = -{-0.4584914743900299, 0.11577071249485016, 1.3576802015304565, 0.3214845359325409, 2.867307424545288, 0.32918858528137207, -1.4071727991104126, 0.5810971260070801, -1.1862115859985352, 0.748454213142395, -0.010330816730856895}; +{0.028361579403281212, 0.15264764428138733, 0.4385368525981903, 0.8167214393615723, 0.4881526529788971, 0.38105106353759766, 0.6564772129058838, -0.5643848180770874, 0.938140869140625, -0.44591856002807617, 0.2336949110031128}; const float dense2_weights[DENSE2_NUM][DENSE1_NUM] = -{{-1.4969602823257446, -0.6034210920333862, -1.816457748413086, 0.41540759801864624, -0.05323589965701103, 0.6458696722984314, -1.2913364171981812, 0.20979711413383484, 0.1029253676533699, -0.4074774384498596, 1.185938835144043}, {0.02542223408818245, -2.832756519317627, 0.21609655022621155, -1.5964983701705933, -0.35383400321006775, 0.40602150559425354, 0.43417370319366455, -0.22571545839309692, -0.08674632757902145, -0.400297611951828, -0.8437420129776001}, {-0.29658544063568115, -0.3319321870803833, 0.0356934554874897, -1.0804462432861328, -0.5380445718765259, -0.710993230342865, 0.19925521314144135, -1.0814094543457031, 0.5466617345809937, 0.6295486688613892, 1.5794049501419067}, {0.7872546315193176, 0.9218237400054932, 0.06514360010623932, -0.08181627839803696, -0.07285244017839432, -0.23247893154621124, 0.32731983065605164, -0.27316367626190186, -1.0096397399902344, -0.3645459711551666, 0.21568982303142548}, {-0.9690830111503601, -0.34428420662879944, 0.9166867136955261, 0.6313290596008301, 0.9122275114059448, 0.7153491973876953, -0.6661425232887268, 0.5748111009597778, -1.2192516326904297, 0.5107290744781494, 0.5013402104377747}, {0.01929553784430027, 0.061991870403289795, -0.9213714003562927, -0.4389115869998932, -0.23339228332042694, -0.6122246980667114, 0.31772467494010925, -0.16392916440963745, 1.5267611742019653, 1.065213918685913, -1.066608190536499}, {1.232965350151062, -1.2286287546157837, -0.24512693285942078, -0.09169284254312515, -0.49241477251052856, 0.6703866124153137, 0.905476987361908, 0.2842903435230255, 1.3328313827514648, -0.335702508687973, -0.8235505819320679}, {-0.4192759096622467, 0.20187930762767792, 0.08180803805589676, -0.8368447422981262, -0.0534086599946022, -0.600368320941925, 0.7009176015853882, -0.08250505477190018, 1.5310074090957642, -1.1966315507888794, 0.8115485310554504}}; +{{0.005269174464046955, -0.5183584690093994, -0.48228928446769714, -0.10587891936302185, -0.738325297832489, 1.25023353099823, 0.08462369441986084, 0.6376439332962036, -0.9023464322090149, 0.5063923001289368, -0.9497226476669312}, {-0.438839852809906, -0.5348273515701294, 0.111099973320961, -0.4521052837371826, -0.6524456739425659, -0.2980911135673523, -0.0434543676674366, 0.1636778861284256, -0.5096405148506165, -0.169478639960289, -0.6741765737533569}, {-1.5573469400405884, -0.6872786283493042, 0.4021152853965759, 0.28549498319625854, 0.08264102786779404, 0.5662528872489929, 1.2192378044128418, -0.12099184095859528, 0.00409197062253952, -0.3733416199684143, -0.06479119509458542}, {0.009880514815449715, -0.1864091008901596, 0.3711296021938324, 0.8515927791595459, 0.44823169708251953, 0.08088653534650803, 0.17565587162971497, -0.937997579574585, 0.649368166923523, -0.5332916378974915, 0.6443262100219727}, {1.3078621625900269, -1.646144151687622, -0.1556694656610489, -0.020695475861430168, -0.5998504161834717, 0.14132361114025116, 0.40531134605407715, -0.03194556385278702, 0.41566479206085205, -0.8028947114944458, -0.12456294894218445}, {-0.9521133303642273, -0.27410441637039185, -0.32507947087287903, -0.5348300337791443, -0.6005425453186035, 0.10712900012731552, -0.2842693030834198, -0.21020177006721497, -0.23977500200271606, -0.31223613023757935, -0.5725396871566772}, {-0.2079601287841797, -1.2548121213912964, 0.04263779893517494, 1.4051464796066284, 0.4322662949562073, 0.8009835481643677, -0.7179756760597229, -0.6908145546913147, 0.6461816430091858, -0.9815606474876404, 0.2314435839653015}, {0.6522129774093628, 0.8017333149909973, 0.5788679122924805, 0.24354229867458344, 0.44891464710235596, -0.4297429025173187, 0.4111102819442749, -0.5288674235343933, 0.7234879732131958, -0.1575634926557541, 0.4682173728942871}}; const float dense2_biases[DENSE2_NUM] = -{0.7766112685203552, 1.2584985494613647, -2.927600860595703, 0.2171115279197693, 0.6484678983688354, 1.0072442293167114, -3.341115713119507, -2.5701966285705566}; +{1.072114109992981, -0.16629783809185028, -0.6730525493621826, 0.22809934616088867, -0.6621718406677246, -0.24386928975582123, -0.033539943397045135, 0.19298000633716583}; const float output_weights[DENSE2_NUM] = -{0.12610523402690887, 0.5678213238716125, -0.1519734114408493, -0.1338711977005005, 0.15347623825073242, -0.13331052660942078, -0.13666824996471405, -0.11856996268033981}; +{0.19723401963710785, 0.4691760241985321, -0.23346777260303497, -0.11248059570789337, 0.22997352480888367, -0.5128190517425537, -0.26372289657592773, -0.19478470087051392}; const float output_bias = -0.13536010682582855; +0.5556163787841797; diff --git a/src/train5.py b/src/train5.py index 5c45564..2fc0d6f 100644 --- a/src/train5.py +++ b/src/train5.py @@ -3,7 +3,6 @@ import pandas as pd import numpy as np # Output of weights -import re from tensorflow import transpose # Custom activation function @@ -33,6 +32,16 @@ def clipped_pade_logistic(x): return clip_by_value(divide(val, k(24.0)), 0.0, 1.0) +# A cheaper version of tanh, x/6+25*x/(6*(2*x*x+5)), +# clipped between -1 and 1. +def clipped_pade_tanh(x): + val = add(divide(x, k(6.0)), + multiply(k(25.0), + divide(x, multiply(k(6.0), add(k(5.0), + multiply(k(2.0), square(x))))))) + return clip_by_value(val, -1.0, 1.0) + + def make_model(size, magic=[12, 9, 9]): # Our model for the stacks, a small CNN stack_shape = (size, size, 6) @@ -51,6 +60,8 @@ def make_model(size, magic=[12, 9, 9]): model = Model(inputs=[stack_model.input, flats_input], outputs=model) model.compile(optimizer='adam', loss='mean_squared_error', + # loss='mean_absolute_error', + # loss='mean_absolute_percentage_error', metrics=['accuracy']) model.summary() return model @@ -75,6 +86,7 @@ def load_data(size): val_outcome = val_data.iloc[:, -1:] return [(training_input, training_outcome), (val_input, val_outcome)] + def train(size, model, data, iterations=1, epochs=10): (training_input, training_outcome), (val_input, val_outcome) = data results = [] @@ -84,7 +96,7 @@ def train(size, model, data, iterations=1, epochs=10): validation_data=(val_input, val_outcome), verbose=True) v_loss, v_accuracy = model.evaluate(val_input, val_outcome, - verbose=False, batch_size=32) + verbose=False, batch_size=16) t_loss, t_accuracy = model.evaluate(training_input, training_outcome, verbose=False, batch_size=32) results += [((v_loss, t_loss), (v_accuracy, t_accuracy))] @@ -112,18 +124,21 @@ def magic_search(data): def write_weights(model): def fix(val): - string = str(val.tolist()) + string = str(np.array(val).tolist()) string = string.replace("[", "{").replace("]", "}") return string - # Prepare everything in a sane memory order - conv2d_weights = np.array(transpose(model.trainable_variables[0], perm=[3, 1, 0, 2])) - conv2d_biases = np.array(model.trainable_variables[1]) - dense1_weights = np.array(transpose(model.trainable_variables[2], perm=[1, 0])) - dense1_biases = np.array(model.trainable_variables[3]) - dense2_weights = np.array(transpose(model.trainable_variables[4], perm=[1, 0])) - dense2_biases = np.array(model.trainable_variables[5]) - output_weights = np.array(transpose(model.trainable_variables[6], perm=[1, 0])[0]) - output_bias = np.array(model.trainable_variables[7][0]) + # Prepare everything in a sane memory order This isn't exactly in + # the correct order that tensorflow uses, because memory access + # out of order is an eyesore. Compared to tensorflow, the C + # implementation has the board reflected about the diagonal. + conv2d_weights = transpose(model.trainable_variables[0], perm=[3, 0, 1, 2]) + conv2d_biases = model.trainable_variables[1] + dense1_weights = transpose(model.trainable_variables[2], perm=[1, 0]) + dense1_biases = model.trainable_variables[3]) + dense2_weights = transpose(model.trainable_variables[4], perm=[1, 0]) + dense2_biases = model.trainable_variables[5]) + output_weights = transpose(model.trainable_variables[6], perm=[1, 0])[0] + output_bias = model.trainable_variables[7][0] # Prepare formatting names = ["conv2d_weights[KERN_NUM][KERN_SIZE][KERN_SIZE][KERN_CHAN]", "conv2d_biases[KERN_NUM]", @@ -146,10 +161,12 @@ def write_weights(model): data = load_data(5) -model = make_model(5, [12, 9, 10]) -results = train(5, model, data, iterations=20, epochs=10) +model = make_model(5, [12, 11, 8]) +results = train(5, model, data, iterations=5, epochs=10) print("\nScores") for i in range(len(results)): - print("Iteration {0}: {1}".format(i+1, results[i])) + print("Iteration {0}: {1}".format(i+1, results[i])) write_weights(model) + +# results = magic_search(data) |