From 710d5a5225edb44d171ca9ac6345e9a61aa4f125 Mon Sep 17 00:00:00 2001 From: tslil Date: Thu, 10 Sep 2026 12:31:51 +0100 Subject: use silu instead of relu with the intuition being that we avoid dead neurons In a head-to-head this is +187 elo against the previous weights, although it trained more. --- include/nn.c | 14 +- include/weights_5.c | 550 ++++++++++++++++++++++++++-------------------------- src/nn_train.py | 5 +- 3 files changed, 288 insertions(+), 281 deletions(-) diff --git a/include/nn.c b/include/nn.c index f228297..8c85299 100644 --- a/include/nn.c +++ b/include/nn.c @@ -26,8 +26,6 @@ // Helpers // =================================================================== -#define RELU(x) ((x) = ((x) < 0) ? 0 : (x)) - static float fast_tanh(const float x) { // tanh via a degree-11 odd polynomial, least-squares fit of tanh on [-3, 3] // (max absolute error ~4e-3), saturating to +-1 outside that interval. @@ -41,6 +39,15 @@ static float fast_tanh(const float x) { (-0.0139947924f + x2 * (0.00123106314f + x2 * -4.29748644e-05f))))); } + +static float fast_silu(const float x) { + // [4,2] Pade approximant with cutoff + if (x < -4.6f) + return 0.0; + if (x > 4.6f) + return x; + return (x * (0.5f + x * (0.25f + x * (0.05f + 0.004167f * x)))) / (1+0.1f*x); +} // =================================================================== // Input encoding // =================================================================== @@ -116,8 +123,7 @@ float eval_net(const float *board, const unsigned int n_inp, const float *wd = w + d1 * n_inp; for (unsigned int fl = 0; fl < n_inp; fl++) h += board[fl] * wd[fl]; - RELU(h); - dense1[d1] = h; + dense1[d1] = fast_silu(h); } for (unsigned int d1 = 0; d1 < n_dense; d1++) z += dense1[d1] * logit_w[d1]; diff --git a/include/weights_5.c b/include/weights_5.c index 46f35a3..687e81e 100644 --- a/include/weights_5.c +++ b/include/weights_5.c @@ -1,294 +1,294 @@ /* generated by nn_train.py, do not edit by hand */ -/* v011, val_corr=0.9400, rate=0.6546 */ +/* v018, val_corr=0.9435, rate=0.7880 */ #include "weights_5.h" const float five_embedding[7] = { - 0.0261116344, -0.641920149, -0.144219667, -0.850418866, 0.711006284, 0.202511862, 0.586704791 + 0.0271974243, -0.585511148, -0.195969999, -1.67773426, 0.654105425, 0.252295405, 0.991936803 }; const float five_dense_weights[FIVE_DENSE_NUM][FIVE_INP_NUM] NN_ALIGNED = { - {-0.054980766, -0.386314541, 0.249120727, -0.00473590614, -0.0282872375, -0.139807314, 0.0299592428, -0.0615478419, - -0.0677183867, -0.0484480485, -0.153184175, -0.0955937877, -0.207453385, -0.136047766, -0.0841392726, -0.147441804, - -0.143294618, -0.158898175, -0.111327671, -0.069155626, -0.164681226, -0.139968216, -0.167490378, -0.158238977, - -0.077101551, -0.0535608903, -0.122466646, -0.12260589}, - {-0.106079064, 0.365877807, -0.00813682284, 0.0278952941, -0.0174642745, -0.0400972143, 0.044616349, 0.084144868, - -0.0759502947, 0.0129774828, 0.152506769, 0.248990536, 0.050638251, -0.103698604, 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0.0238964334, 0.0631350651, 0.200121552, 0.0484938025, + 0.0524025746, 0.00929001253, 0.0385874249, 0.0292978548, 0.168184265, 0.109522983, 0.100928374, 0.108778886, + 0.115419894, 0.0902778506, 0.192098513, 0.0639346689, 0.085071601, 0.0876080021, 0.156298891, 0.069896996, + 0.239671171, 0.0285774246, 0.0744408518, 0.149581239}, + {-0.0514755733, -0.358688831, 0.359383911, 0.00437634066, -0.128775805, 0.0682161376, -0.154706925, -0.00920877233, + 0.0572361685, -0.194796309, -0.0198871773, -0.113199852, -0.0990116671, 0.0534732416, -0.177191287, -0.0868317559, + -0.033822272, -0.0346709155, 0.135941803, 0.00840008631, 0.0861718506, -0.100902192, 0.092369467, 0.00199005846, + -0.112661451, 0.015940208, -0.199290812, -0.00990301277}, + {-0.116220325, -0.636873603, -3.24077392, 0.301996708, 0.173914865, 0.2317186, 0.098890081, 0.391456276, + 0.137991503, -0.0377864763, -0.0614043549, 0.023031557, 0.286225587, 0.130348057, -0.0584962554, -0.0494973026, + -0.0833101496, 0.306133986, 0.253035575, 0.0294886399, 0.0463630632, -0.0218323171, 0.110777676, 0.338182688, + 0.234739184, 0.277391106, 0.194674939, 0.248723418}, }; const float five_dense_biases[FIVE_DENSE_NUM] = { - 0.258945525, 0.187060431, 0.176671699, 0.0402996019, -0.0802881271, -0.00244510267, 0.183032766, 0.426880807, - -0.0839369074, 0.0432596542, -0.113279037, -0.146915391, 0.0357434675, -0.131954685, -0.0970062539, -0.0270375013, - -0.203214377, -0.0238268692, -0.159142092, 0.0896934494, -0.0808146223, 0.0612406731, 0.158471361, 0.053174708, - -0.130475089, -0.160274744, -0.152122498, 0.218526378, -0.0795462579, -0.0180571005, 0.18883124, 0.094304122, - -0.106681764, 0.208428174, 0.202606156, 0.0932063013, 0.18422395, 0.0912102014, 0.156579852, 0.351306498, - 0.118976377, 0.236967906, -0.267845392, 0.13835533, 0.216181934, 0.109379537, -0.0356541872, -0.086225152, - 0.0388425104, 0.302248985, -0.12141528, 0.0575248487, -0.0836074054, -0.178432047, -0.118539676, 0.109901808, - 0.0350867547, -0.237770796, 0.070876278, -0.0696130618, 0.127071053, -0.190676391, 0.233724713, -0.208063245 + -0.10617651, 0.0154197877, 0.117111742, 0.0102650877, -0.0448654294, 0.126132563, -0.0500919819, 0.0159013756, + 0.0496373884, 0.084379822, -0.00225325767, 0.0812280104, 0.0491173491, 0.0674164817, 0.0837717131, 0.0212170333, + 0.0402449444, 0.0646258593, 0.0199432168, 0.165128812, -0.0247633122, 0.174038321, 0.138526052, 0.0143451681, + -0.0253094565, 0.0728244409, 0.111286744, -0.448067099, 0.0112198424, -0.0453164801, 0.0155658703, 0.0870072767, + -0.0218650661, 0.125383839, 0.133538574, 0.0686686113, -0.103338301, 0.0217641667, -0.0431467518, 0.150365531, + 0.116416521, -0.0184898619, 0.0454672761, 0.0856234804, -0.00140490883, -0.0644598156, 0.0778391585, -0.0264345333, + 0.0395229757, 0.174890205, 0.163560331, 0.106107652, -0.0916002616, 0.0171617456, 0.0535748713, 0.134761348, + 0.037883196, -0.0293981228, -0.015096793, 0.0806951076, 0.0568698086, 0.217878923, 0.00285294349, -0.130014911 }; const float five_logit_weights[FIVE_DENSE_NUM] NN_ALIGNED = { - -0.525418282, 0.165110111, -2.82267809, 0.278958291, 0.254277676, -0.21525313, -0.215599433, -0.550903857, - 0.194911122, 0.248868525, 0.430563092, -0.201574966, 0.274471819, 0.552562177, -0.173917696, 0.301015198, - 0.240786016, 0.235939488, -0.343597978, 0.29667899, 0.781878173, -0.186229408, -0.234509051, 0.167021245, - -0.300446451, -0.233434945, -0.211302519, -0.198264554, 0.277316898, -0.353295714, -0.243399501, 0.182720855, - -0.27724874, 0.261664778, -0.571578801, 0.343854547, -0.268930465, 0.734192789, -0.212634668, -0.691008627, - 2.28880835, -0.516476274, 0.320053846, -0.25418371, 0.430587977, 0.270702869, -0.18960695, -0.444385618, - 0.265886635, 0.477042973, -0.23541373, 0.26551941, 0.223075464, -0.236570403, 0.476593554, 0.236413032, - 0.309048086, -0.441814035, -0.189720362, -0.584658265, 0.250874728, -0.608837545, -0.528469741, -0.23339577 + -0.152189046, 0.31145522, -0.501055002, -0.430743396, -0.188517824, -0.644860923, -0.305823237, 0.34793064, + -0.355522096, -0.604841352, 0.429184705, -0.433712095, -0.556476772, 0.400933564, 0.253976047, -0.234569505, + -0.447005302, -0.503887117, -0.42374301, 0.404452264, 0.363007545, 0.28902337, 0.452245891, 0.248775348, + -0.242137939, -0.410595566, 0.439538449, 0.265520841, -0.30932796, -0.992903769, -0.165342614, 0.125757992, + -0.483581424, -0.543122172, 0.357163519, 0.418349832, -0.152612984, -0.494201779, 0.198039696, -0.249059439, + -0.665596426, -0.246806905, 0.261523813, -0.303902447, -0.494532794, -0.474734098, 0.359146118, -0.245298445, + -0.306960076, 0.334577858, 0.594795048, 0.481912762, -0.306986272, 0.314059317, -0.20452112, 0.369527459, + 0.188040748, -0.312300235, -0.393799305, -0.394559473, 0.270650476, 0.466186255, -0.212885186, 0.822468579 }; -const float five_logit_bias = 0.01144371; +const float five_logit_bias = -0.0201850198; diff --git a/src/nn_train.py b/src/nn_train.py index 8f1a73e..9e2b482 100755 --- a/src/nn_train.py +++ b/src/nn_train.py @@ -49,6 +49,7 @@ import torch from tqdm import tqdm from torch.nn import Embedding, MSELoss, Linear, Module +from torch.nn.functional import silu, tanh from torch import Tensor @@ -211,8 +212,8 @@ class Net(Module): # inputs, the rest are raw cell states (0..6) n = x.shape[0] inp = torch.cat([x[:, :3], self.embedding(x[:, 3:].long()).view(n, -1)], dim=1) - hidden = torch.relu(self.hidden(inp)) - return torch.tanh(self.output(hidden)).squeeze(-1) + hidden = silu(self.hidden(inp)) + return tanh(self.output(hidden)).squeeze(-1) def fit( self, -- cgit v1.3.1