diff options
| author | tslil clingman <tslil@posteo.de> | 2021-01-31 23:33:39 -0500 |
|---|---|---|
| committer | tslil <tslil@posteo.de> | 2026-08-28 19:37:41 +0100 |
| commit | 328c8d1e3094a942d6a2edd933c9cc4ab09daab1 (patch) | |
| tree | 21332661fb48c548b04d03d9a4131abe2070699a | |
| parent | 399d90f1b94717aa0ca5abb1dc43bdb6f4160d13 (diff) | |
Just some #weightgoals ;)
It turns out that while i was training on a 0/1 classification
problem, i was using 2*eval - 1. Training using this function instead,
and on bot-dominated game choices (chosen_player in extract.sh) seems
to have given a better evaluation function. At the least, Morten's
swindle doesn't work anymore.
| -rw-r--r-- | include/cnn1986.c | 8 | ||||
| -rw-r--r-- | include/weights.c | 16 | ||||
| -rwxr-xr-x | resources/extract.sh | 6 | ||||
| -rw-r--r-- | src/cnn_train.py | 59 | ||||
| -rw-r--r-- | src/pptdb.c | 2 |
5 files changed, 27 insertions, 64 deletions
diff --git a/include/cnn1986.c b/include/cnn1986.c index d4f6ad6..b95fca1 100644 --- a/include/cnn1986.c +++ b/include/cnn1986.c @@ -100,13 +100,13 @@ float cnn1986_evaluate_black_win(void) { for (uint8_t d2 = 0; d2 < DENSE2_NUM; d2++) { output += dense2[d2]*output_weights[d2]; } - // Truncated Pade approximant of logistic function - output = (12.0+output+50.0*output/(output*output+10.0))/24.0; + // 2*(clamped Pade approximant of logistic function) - 1 + output = (12.0+output+50.0*output/(output*output+10.0))/12.0 - 1.0; if (output > 1.0) { return 1.0; } - else if (output < 0.0) { + else if (output < -1.0) { return -1.0; } - return 2*output-1.0; + return output; } diff --git a/include/weights.c b/include/weights.c index 812a2eb..e59f1c5 100644 --- a/include/weights.c +++ b/include/weights.c @@ -18,25 +18,25 @@ #include "weights.h" const float conv2d_weights[KERN_NUM][KERN_SIZE][KERN_SIZE][KERN_CHAN] = -{{{{2.4703409671783447, 0.4167492389678955, 0.8632664084434509, 2.113025188446045, 0.6231331825256348, 2.4190521240234375}, {0.6768096685409546, -0.036765310913324356, -0.19815756380558014, 0.044196125119924545, 0.9193159937858582, 0.4241444170475006}, {0.628648042678833, 0.07580870389938354, 0.296553373336792, 0.2557092010974884, -0.35443684458732605, -0.20663230121135712}}, {{0.4257064759731293, 0.191790372133255, 0.19574369490146637, 0.11595677584409714, 0.3021714389324188, -0.7057633996009827}, {-0.5299645662307739, 0.3454388380050659, 0.030088981613516808, -0.6320480108261108, -0.06442858278751373, -0.043087683618068695}, {0.028345592319965363, 0.10614394396543503, 0.02050180360674858, 0.09682556986808777, -0.13954971730709076, 0.2076157033443451}}, {{0.2143043577671051, 0.09699752181768417, -0.06746546924114227, 0.030598541721701622, 0.34546777606010437, 0.06615299731492996}, {-0.04823664575815201, 0.038358159363269806, 0.0903884544968605, 0.014671124517917633, -0.009292448870837688, 0.09437374770641327}, {-0.23551827669143677, -0.18453453481197357, -0.12542837858200073, 0.11625910550355911, 0.2458648532629013, 0.8126168847084045}}}, {{{0.047349151223897934, 0.09473082423210144, -0.2150116264820099, -0.510344386100769, -0.03907188028097153, -0.7691933512687683}, {0.3354664146900177, 0.12535600364208221, -0.1231030821800232, 0.17360179126262665, 0.22217011451721191, -0.35831931233406067}, {-1.836803913116455, -0.1525769829750061, 0.06611162424087524, -0.011539365164935589, 0.24938976764678955, 0.07583602517843246}}, {{0.09107772260904312, 0.18027468025684357, 0.05457858741283417, -0.07981231063604355, -0.45073673129081726, -0.4073386490345001}, {0.4963396489620209, 0.22140753269195557, 0.3017593026161194, 0.6758500933647156, 0.8522419929504395, 0.17916089296340942}, {0.6215333342552185, 0.4823836386203766, -0.5733091831207275, -0.026306748390197754, 0.6192604899406433, -0.3745679259300232}}, {{-0.4158423840999603, -0.05260695517063141, 0.14156003296375275, -0.0299912691116333, -0.2329699546098709, -0.18822182714939117}, {-0.1641465276479721, 0.06528280675411224, 0.25194576382637024, 0.20748603343963623, 0.1609979122877121, -0.4646330773830414}, {0.09450504928827286, 0.5042544007301331, 0.28969278931617737, -0.2485138475894928, 0.22001083195209503, 0.32350701093673706}}}, {{{-0.24150803685188293, -0.006617956794798374, 0.14414499700069427, -0.11775511503219604, 0.33306562900543213, -0.10140202939510345}, {0.5471913814544678, 0.10135117918252945, 0.16056470572948456, 0.10863790661096573, -0.11296295374631882, 0.003585649188607931}, {0.18319551646709442, -0.1530514806509018, 0.14033497869968414, 0.07005397975444794, 0.07038407772779465, 0.35219281911849976}}, {{2.0986876487731934, 0.21791468560695648, 0.020141074433922768, 0.049582552164793015, -0.09423802047967911, 0.07069776207208633}, {-0.41323262453079224, -0.31177279353141785, 0.0862540602684021, 0.1880745142698288, -0.12798459827899933, -0.06995601952075958}, {-0.11566204577684402, -0.36558282375335693, 0.0276164747774601, -0.17078045010566711, -0.30001965165138245, 0.17359912395477295}}, {{-0.5705175995826721, -0.3742579221725464, 0.43008753657341003, 0.22299951314926147, -0.2030068337917328, -0.2986662685871124}, {-0.16707800328731537, -0.045365236699581146, -0.3438996970653534, -0.48340749740600586, -0.36040177941322327, -0.05967879667878151}, {0.17944762110710144, -0.1100335642695427, -0.06979364156723022, 0.29359352588653564, 0.4666922688484192, -0.020811662077903748}}}, {{{0.46565839648246765, -0.03152400255203247, 0.1333000808954239, -0.1919965296983719, -0.07732908427715302, -0.19213365018367767}, {-0.273276686668396, 0.25121062994003296, -0.11964326351881027, -0.1272767037153244, -0.07274547219276428, 0.11783178150653839}, {-0.0741732269525528, -0.06340128183364868, 0.10675511509180069, 0.24833717942237854, 0.009718921966850758, 0.4898841679096222}}, {{2.7668588161468506, 0.11714466661214828, 0.18294982612133026, 0.26680290699005127, 0.17528589069843292, 0.48635998368263245}, {0.7872266173362732, 0.4377056360244751, -0.02128533460199833, 0.23881496489048004, -0.0033159777522087097, 0.24833305180072784}, {0.15461136400699615, 0.018697597086429596, -0.30961838364601135, 0.29620298743247986, 0.5264871716499329, -0.11017684638500214}}, {{0.6783024668693542, 0.003203516360372305, 0.019721398130059242, 0.29260164499282837, -0.02163699083030224, -0.15656599402427673}, {-0.11579833924770355, 0.07264871150255203, 0.013919626362621784, 0.01841496303677559, 0.16539284586906433, -0.0865580141544342}, {-0.038039349019527435, 0.21609967947006226, 0.15190726518630981, 0.15284357964992523, -0.023870497941970825, -0.07119928300380707}}}, {{{0.22125764191150665, -0.08500166982412338, -0.11512551456689835, -0.5463169813156128, 0.006230296567082405, -0.7166137099266052}, {0.1128217950463295, -0.1697329580783844, 0.05713334679603577, 0.37965118885040283, -0.47694671154022217, 0.6150299310684204}, {-0.38555601239204407, 0.06890226155519485, 0.2371596246957779, 0.4768395721912384, 1.1206445693969727, 0.7515135407447815}}, {{0.08900543302297592, 0.06511817127466202, 0.3671525716781616, 0.016248827800154686, 0.04980435222387314, 0.8361344337463379}, {0.43931636214256287, -0.03515709564089775, -0.06684224307537079, 0.710873007774353, 0.3737236261367798, -0.6460610628128052}, {-0.6352353096008301, 0.3924438953399658, -0.26051753759384155, -0.029439520090818405, 2.3765103816986084, -0.6003559827804565}}, {{-0.9544902443885803, -0.13889813423156738, 0.3415108621120453, -0.30486154556274414, -0.18532709777355194, 1.7465510368347168}, {-0.6192530989646912, 0.5223889946937561, 0.5559768080711365, -0.5217148065567017, -0.4846203625202179, -1.0697448253631592}, {-1.5551724433898926, -0.4305790066719055, 0.20662139356136322, 0.2984762489795685, 0.511296808719635, 0.6921825408935547}}}, {{{0.1549348384141922, 0.07217345386743546, 0.023581301793456078, -0.16303597390651703, -0.3103821277618408, -0.041755933314561844}, {0.30012741684913635, 0.0929344892501831, 0.26454004645347595, 0.3324362635612488, 0.35478922724723816, 0.33387014269828796}, {1.174534797668457, 0.2560501992702484, 0.2430885136127472, 0.4260651767253876, 0.5359955430030823, 0.442647784948349}}, {{0.40098899602890015, 0.20698896050453186, 0.03912270441651344, 0.1868550330400467, -0.41633865237236023, -0.5000015497207642}, {1.081761360168457, 0.22399844229221344, 0.17786698043346405, 0.08759663999080658, 0.01719546876847744, 0.02615325152873993}, {0.6241134405136108, 0.1961352527141571, 0.18012642860412598, 0.13016963005065918, 0.0632699579000473, 0.1577410101890564}}, {{1.0275462865829468, -0.027375666424632072, 0.32369592785835266, 0.31989485025405884, 0.07601699233055115, 0.5835166573524475}, {0.8793442249298096, 0.16957654058933258, 0.27687668800354004, 0.24282027781009674, 0.3390127122402191, 0.2588394284248352}, {1.6991755962371826, 0.05889181047677994, 0.39659127593040466, 0.34091106057167053, 0.5268042087554932, 0.8699657320976257}}}, {{{0.08438754826784134, 0.001564898295328021, 0.19415311515331268, 0.07414565980434418, 0.13132831454277039, 0.22296084463596344}, {-0.026086900383234024, -0.14548440277576447, -0.20589078962802887, -0.04501720145344734, -0.4681534469127655, 0.36069047451019287}, {-0.3334258496761322, -0.03685757890343666, -0.02057490684092045, -0.16701887547969818, -0.3028528690338135, 0.19165167212486267}}, {{-0.08822795748710632, -0.2365187257528305, -0.05768590047955513, -0.20231254398822784, -0.05616536736488342, 0.07134798169136047}, {-0.4035581052303314, -0.1803511530160904, -0.44142645597457886, -0.16271939873695374, 0.2566004991531372, 0.15527425706386566}, {-0.6874738335609436, -0.08483894169330597, 0.1988690048456192, -0.18345879018306732, 0.14833590388298035, -0.13440681993961334}}, {{-0.2801377773284912, -0.0916677713394165, -0.16122618317604065, -0.29587751626968384, 0.1604612022638321, 0.8044694066047668}, {-0.7502406239509583, 0.10458556562662125, -0.06200884282588959, -0.1458113044500351, 0.346541166305542, -0.596875786781311}, {-2.7115437984466553, -0.5241538286209106, -0.9794673919677734, -0.908114492893219, -0.6623249650001526, -2.1407573223114014}}}, {{{-0.24021506309509277, -0.04708075150847435, 0.0829973816871643, -0.28607097268104553, -0.3588469326496124, -0.09216171503067017}, {0.002631389070302248, -0.03584083169698715, -0.15470252931118011, -0.18227097392082214, -0.14326095581054688, 0.053224846720695496}, {0.1270635724067688, -0.06306112557649612, -0.08244280517101288, -0.16576716303825378, -0.25571751594543457, -0.34787464141845703}}, {{-0.5982001423835754, 0.05465037375688553, 0.06959746032953262, -0.12074324488639832, -0.379838228225708, -0.4726566672325134}, {-0.18980532884597778, -0.3250681459903717, -0.11531250923871994, -0.08098913729190826, 0.1340167373418808, -0.3870336413383484}, {-0.0322515070438385, -0.027703022584319115, 0.02624211460351944, -0.2009299397468567, 0.22094260156154633, 0.19367225468158722}}, {{-2.6572957038879395, -0.026169156655669212, -0.39957672357559204, 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0.05467988923192024, -0.10909625142812729, -0.1584160178899765, 0.0924181118607521, -0.03339894488453865}, {0.01650945656001568, 0.01122982520610094, -0.043706752359867096, 0.018709754571318626, -0.0032730891834944487, 0.03754238411784172}}}}; const float conv2d_biases[KERN_NUM] = -{-0.9286547303199768, 0.6586035490036011, 0.38082075119018555, -0.7377982139587402, 0.7100669741630554, 0.8127356171607971, -0.6856269836425781, -0.5181254148483276, 0.856531023979187, 0.5149239897727966, 0.6714763045310974, 0.5826101303100586}; +{-0.6451589465141296, -0.47988107800483704, 1.0866423845291138, 0.8259075880050659, 0.7077365517616272, 0.06997911632061005, 0.7337013483047485, 0.7401207685470581, 0.3617548644542694, 0.9209133386611938, -0.8768176436424255, 0.4427519142627716}; const float dense1_weights[DENSE1_NUM][CONV_NUM+2] = -{{2.2170603275299072, 0.23290486633777618, -0.7145252823829651, 0.6071719527244568, -0.2405167520046234, -0.39711514115333557, 1.1505382061004639, 1.214964747428894, 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{0.6442310810089111, 0.2734476327896118, -0.7297450304031372, -0.25569629669189453, -0.64133220911026, 0.1794871687889099, 0.03968099132180214, -0.04302607849240303, -0.01527371909469366, 0.9907929301261902, -1.4246907234191895, 0.8198570013046265, 0.19634239375591278, -0.2787831723690033, -0.90791255235672, 0.2593283951282501, -0.3454883098602295, 0.044641561806201935, 0.342410683631897, 0.15762652456760406, -0.38894885778427124, 0.9655144810676575, -1.3150849342346191, 0.4543429911136627, -0.21170520782470703, 0.8045044541358948, -0.40748801827430725, -0.18185767531394958, 0.1958637684583664, 0.4011032283306122, 0.10491450130939484, -0.21721573173999786, -0.11030114442110062, 0.7364212870597839, -1.5131975412368774, 0.3981367349624634, -0.7191420793533325, 0.2682417333126068, -0.6644414663314819, 0.18448153138160706, -0.209454745054245, 0.2356238067150116, 0.1272117644548416, 0.17012831568717957, -0.5331333875656128, 1.1399105787277222, 0.1047845333814621, 0.5939695835113525, 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-0.001771705225110054, 1.5744564533233643, -0.8716257214546204, 0.7159427404403687, -0.10337327420711517, 1.2570980787277222, -0.23009680211544037, 0.48282065987586975, 0.7873317003250122, -0.8051977753639221, 0.2665348947048187, 0.5357442498207092, -0.7180758714675903, 0.24835197627544403, -0.3950732946395874, -0.33878153562545776, -0.2524719536304474, 0.39071014523506165, -0.5008668899536133, -0.0866008847951889, -0.11366541683673859, -0.05183246731758118, 0.10090695321559906, -0.30401337146759033, 0.3025892674922943, 0.15951772034168243, -0.05533163622021675, 1.0859756469726562, 0.8895078301429749, 0.5576184391975403, 0.3466758131980896, 0.4007216691970825, -0.44849273562431335, -0.1171148493885994, -0.036167241632938385, 0.4328608214855194, -0.010532478801906109, -0.5647966861724854, -1.6450928449630737, 0.46712324023246765, 0.20650437474250793, 1.0117689371109009, 0.22195760905742645, 0.04947790503501892, 0.7530243992805481, 1.853662371635437, -0.7355844974517822, -0.2112749218940735, -0.3170221447944641, 0.5251079797744751, -0.484696626663208, 0.31642550230026245, 0.09187614917755127, 0.5886988043785095, -0.15747623145580292, 0.10241830348968506, -0.1055864542722702, 0.24922552704811096, 0.287695974111557, 0.2961241602897644, -0.3016272187232971, -0.5953752398490906, -0.5401706099510193, 0.985068678855896, -0.2609187662601471, 0.7651111483573914, 0.054146312177181244, 1.1620020866394043, 0.3911398649215698, 0.3165219724178314, -0.5469945073127747, 0.3889983594417572, -0.1125200018286705, -0.2581467032432556, -1.0459898710250854, -0.8223215341567993, 1.5610430240631104}}; const float dense1_biases[DENSE1_NUM] = -{0.028361579403281212, 0.15264764428138733, 0.4385368525981903, 0.8167214393615723, 0.4881526529788971, 0.38105106353759766, 0.6564772129058838, -0.5643848180770874, 0.938140869140625, -0.44591856002807617, 0.2336949110031128}; +{-0.559496283531189, -0.1259697526693344, 0.5167818069458008, -0.07613079249858856, 0.016916882246732712, 0.562254011631012, 0.2608274519443512, 0.2632611393928528, 0.019986296072602272, -0.2393009513616562, 0.7170191407203674}; const float dense2_weights[DENSE2_NUM][DENSE1_NUM] = -{{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}}; +{{0.5795437097549438, -0.23521320521831512, 0.6527981162071228, 0.3215695321559906, -0.996798038482666, -1.2032746076583862, 1.096634864807129, -1.5764343738555908, -0.4597227871417999, 0.11940335482358932, 0.5662763118743896}, {-0.28216224908828735, 0.678406834602356, -0.13064861297607422, -0.03267639875411987, 0.9620053768157959, 1.1100767850875854, -0.2703317105770111, -0.07297029346227646, 0.30354833602905273, 0.33620086312294006, -0.1602449119091034}, {-0.37449052929878235, -0.2131105661392212, 0.01748967356979847, -0.04315502569079399, -0.27067434787750244, -0.24308127164840698, -0.4979601800441742, 0.2982286810874939, 0.1693423092365265, -0.45516952872276306, -0.8959820866584778}, {0.04494471475481987, -0.43003126978874207, -0.036062583327293396, 0.4107465147972107, -0.8443616628646851, -0.5406489968299866, 0.3919742703437805, 0.6312925815582275, -0.43831726908683777, -0.806746244430542, 0.30192217230796814}, {-0.4748700261116028, 0.6956803798675537, -0.2491106390953064, 0.002321340376511216, -0.8060805201530457, 0.014423917979001999, -0.6640377044677734, -0.345352441072464, 0.5939163565635681, 1.16130530834198, -0.34093284606933594}, {-0.27627506852149963, -0.23001135885715485, -0.24849137663841248, 0.049682945013046265, 0.470129132270813, -1.4751933813095093, -0.19170530140399933, -0.6249161958694458, 1.1539117097854614, 1.2065660953521729, -0.1641182154417038}, {1.0805885791778564, -0.6379875540733337, 0.9749761819839478, -0.2782149016857147, -0.7396007776260376, -0.6217549443244934, 0.03215833008289337, -0.5615314245223999, 0.0847288966178894, -0.18291589617729187, 0.37245139479637146}, {0.8723656535148621, -0.5074524283409119, 0.6540945172309875, 0.029449354857206345, -0.3518807291984558, -0.32012519240379333, 0.5382040739059448, 0.5741275548934937, -0.20223835110664368, -0.15425445139408112, 0.7062283754348755}}; const float dense2_biases[DENSE2_NUM] = -{1.072114109992981, -0.16629783809185028, -0.6730525493621826, 0.22809934616088867, -0.6621718406677246, -0.24386928975582123, -0.033539943397045135, 0.19298000633716583}; +{1.2298400402069092, 0.8133684992790222, 2.3766794204711914, 1.469264030456543, 1.2461377382278442, 0.29813963174819946, 0.2597852349281311, 0.34889695048332214}; const float output_weights[DENSE2_NUM] = -{0.19723401963710785, 0.4691760241985321, -0.23346777260303497, -0.11248059570789337, 0.22997352480888367, -0.5128190517425537, -0.26372289657592773, -0.19478470087051392}; +{0.3434557020664215, -0.18194633722305298, 0.17597387731075287, 0.11844130605459213, -0.2467159777879715, -0.16700856387615204, 0.11766894161701202, 0.18488647043704987}; const float output_bias = -0.5556163787841797; +0.027359746396541595; diff --git a/resources/extract.sh b/resources/extract.sh index 7fdda28..0e78505 100755 --- a/resources/extract.sh +++ b/resources/extract.sh @@ -2,9 +2,7 @@ db_file=games_anon.db -chosen_players="fwwwwibib archvenison NohatCoder nqeron ManaT -SultanPepper orfane rabbitboy84 applemonkeyman Reid Tayacan Breeze -AaaarghBot" +chosen_players="AaaarghBot Tiltak_Bot" query() { query="(size == $1) and (result != '1-0') and (result != '0-1') and (result != '0-0') and (result != '1/2-1/2')" @@ -62,7 +60,7 @@ fi make pptdb -for size in 5 6; do +for size in 5; do echo extract $size notation,result process $size 400000 training diff --git a/src/cnn_train.py b/src/cnn_train.py index 0580b03..1264e9f 100644 --- a/src/cnn_train.py +++ b/src/cnn_train.py @@ -40,27 +40,17 @@ from tensorflow.keras.layers import Convolution2D from tensorflow.keras.layers import Flatten -# We need something that's close to logistic, but cheaper to compute. -# (12.0+x+50.0*x/(x*x+10.0))/24.0, clipped between 0 and 1 -# as it would otherwise exceed this range at +- 4.6 or so +# We need something that's close to 2*logistic-1, but cheaper to +# compute: (12+x+50*x/(x*x+10))/12-1, clipped between -1 and 1 as it +# would otherwise exceed this range at +- 4.6 or so def clipped_pade_logistic(x): val = add(k(12.0), add(x, multiply(k(50.0), divide(x, add(square(x), k(10.0)))))) - return clip_by_value(divide(val, k(24.0)), 0.0, 1.0) + return clip_by_value(add(k(-1.0), divide(val, k(12.0))), -1.0, +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]): +def make_model(size, magic=[12, 11, 8]): # Our model for the stacks, a small CNN stack_shape = (size, size, 6) stack_input = Input(shape=stack_shape) @@ -76,11 +66,7 @@ def make_model(size, magic=[12, 9, 9]): model = Dense(magic[2], activation="relu", use_bias=True)(model) model = Dense(1, activation=clipped_pade_logistic, use_bias=True)(model) 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.compile(optimizer='adam', loss='mean_squared_error') model.summary() return model @@ -89,7 +75,7 @@ def load_data(size): shape = (-1, size, size, 6) # Load training data tr_fn = "training-"+str(size)+".csv" - training_csv = pd.read_csv(tr_fn) #.head(100000) + training_csv = pd.read_csv(tr_fn) training_data = training_csv training_stack_input = np.array(training_data.iloc[:, 2:-1]).reshape(shape, order='F') training_flats_input = np.array(training_data.iloc[:, 0:2]) @@ -113,36 +99,17 @@ def train(size, model, data, iterations=1, epochs=10): model.fit(training_input, training_outcome, epochs=epochs, validation_data=(val_input, val_outcome), verbose=True) - v_loss, v_accuracy = model.evaluate(val_input, val_outcome, - 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))] + v_loss = model.evaluate(val_input, val_outcome, + verbose=False, batch_size=16) + t_loss = model.evaluate(training_input, training_outcome, + verbose=False, batch_size=32) + results += [(v_loss, t_loss)] print("\nScores") for i in range(len(results)): print("Iteration {0}: {1}".format(i+1, results[i])) return results -def model_size(model): - return sum([np.prod(get_value(w).shape) for w in model.trainable_weights]) - - -def magic_search(data): - results = [] - for width in range(9, 18): - for dense1 in range(9, 16): - for dense2 in range(9, max(32, dense1*2)): - m = make_model(5, [width, dense1, dense2]) - if model_size(m) < 1990: - results += [([width, dense1, dense2], - train(5, m, data, iterations=1, epochs=20))] - print("\nSummary") - for r in results: - print("Parameters {0}: {1}".format(r[0], r[1][0])) - return results - - def write_weights(model): def fix(val): string = str(np.array(val).tolist()) @@ -185,5 +152,3 @@ data = load_data(5) model = make_model(5, [12, 11, 8]) results = train(5, model, data, iterations=5, epochs=10) write_weights(model) - -# results = magic_search(data) diff --git a/src/pptdb.c b/src/pptdb.c index ecf5caa..557b894 100644 --- a/src/pptdb.c +++ b/src/pptdb.c @@ -198,7 +198,7 @@ int main(int argc, char **argv) { reset_state(size); // Store the outcome of this game. Black win = 1 if (line[read-4] == '0') outcome_black = 1; - else outcome_black = 0; + else outcome_black = -1; // Parse the line r = parse_line(line,read-4); // Adjust counts if we're not generating training data |
