sync 43 files

logs/m_q20.out

@@ -46,3 +46,7 @@ code 26248c3 Looping A-C amendment 1 (before
 01:49:54 start factsA geglu_w21_lvl_a0p25_K4_s1
 01:53:50 done factsA geglu_w21_lvl_a0p25_K4_s1 rc=0
 01:53:50 start factsA geglu_w21_lvl_a0p25_K4_s2
+01:57:52 done factsA geglu_w21_lvl_a0p25_K4_s2 rc=0
+01:57:52 start factsA geglu_w21_lvl_a4_K1_s1
+02:01:30 done factsA geglu_w21_lvl_a4_K1_s1 rc=0
+02:01:30 start factsA geglu_w21_lvl_a4_K1_s2

logs/m_q21.out

@@ -157,3 +157,15 @@ code 87d980a Looping A-C amendment 2 (before
 01:56:31 start factsG relu_w768_zinit_K4_s1
 01:56:33 done factsG relu_w768_zinit_a0p5_K4_s2 rc=0
 01:56:33 start factsG relu_w768_zinit_K4_s2
+01:58:00 done factsG relu_w768_zinit_K4_s1 rc=0
+01:58:00 start factsG relu_w768_zinit_a2_K4_s1
+01:58:02 done factsG relu_w768_zinit_K4_s2 rc=0
+01:58:02 start factsG relu_w768_zinit_a2_K4_s2
+01:59:30 done factsG relu_w768_zinit_a2_K4_s1 rc=0
+01:59:30 start factsG relu_w768_zinit_a4_K4_s1
+01:59:32 done factsG relu_w768_zinit_a2_K4_s2 rc=0
+01:59:32 start factsG relu_w768_zinit_a4_K4_s2
+02:00:59 done factsG relu_w768_zinit_a4_K4_s1 rc=0
+02:00:59 start factsG cos_w6144_s4_K1_s1
+02:01:01 done factsG relu_w768_zinit_a4_K4_s2 rc=0
+02:01:01 start factsG cos_w6144_s4_K1_s2

logs/q20_logs/factsA_geglu_w21_lvl_a0p25_K4_s2.log

@@ -22,3 +22,11 @@
 [facts:geglu:N=8192] step=21000 loss=0.3179 (ln K=3.466) acc=0.9253 (161s)
 [facts:geglu:N=8192] step=22000 loss=0.2496 (ln K=3.466) acc=0.9451 (166s)
 [facts:geglu:N=8192] step=23000 loss=0.1989 (ln K=3.466) acc=0.9626 (174s)
+[facts:geglu:N=8192] step=24000 loss=0.1693 (ln K=3.466) acc=0.9744 (182s)
+[facts:geglu:N=8192] step=25000 loss=0.1465 (ln K=3.466) acc=0.9785 (190s)
+[facts:geglu:N=8192] step=26000 loss=0.1276 (ln K=3.466) acc=0.9851 (198s)
+[facts:geglu:N=8192] step=27000 loss=0.1169 (ln K=3.466) acc=0.9861 (206s)
+[facts:geglu:N=8192] step=28000 loss=0.1095 (ln K=3.466) acc=0.9900 (214s)
+[facts:geglu:N=8192] step=29000 loss=0.1066 (ln K=3.466) acc=0.9902 (222s)
+[facts:geglu:N=8192] step=30000 loss=0.1062 (ln K=3.466) acc=0.9910 (230s)
+geglu_w21_lvl_a0p25_K4_s2: plateau_end=3000 final_acc=0.9910 trained_params=229,876

logs/q20_logs/factsA_geglu_w21_lvl_a4_K1_s1.log

@@ -0,0 +1,32 @@
+[facts:geglu:N=8192] step=0 loss=3.5795 (ln K=3.466) acc=0.0352 (1s)
+[facts:geglu:N=8192] step=1000 loss=3.3851 (ln K=3.466) acc=0.0723 (5s)
+[facts:geglu:N=8192] step=2000 loss=3.2217 (ln K=3.466) acc=0.1274 (10s)
+[facts:geglu:N=8192] step=3000 loss=3.0349 (ln K=3.466) acc=0.1741 (18s)
+[facts:geglu:N=8192] step=4000 loss=2.8268 (ln K=3.466) acc=0.2302 (26s)
+[facts:geglu:N=8192] step=5000 loss=2.6225 (ln K=3.466) acc=0.2751 (33s)
+[facts:geglu:N=8192] step=6000 loss=2.3878 (ln K=3.466) acc=0.3274 (41s)
+[facts:geglu:N=8192] step=7000 loss=2.2090 (ln K=3.466) acc=0.3823 (49s)
+[facts:geglu:N=8192] step=8000 loss=2.0137 (ln K=3.466) acc=0.4229 (56s)
+[facts:geglu:N=8192] step=9000 loss=1.8791 (ln K=3.466) acc=0.4500 (64s)
+[facts:geglu:N=8192] step=10000 loss=1.6671 (ln K=3.466) acc=0.5149 (72s)
+[facts:geglu:N=8192] step=11000 loss=1.5091 (ln K=3.466) acc=0.5662 (79s)
+[facts:geglu:N=8192] step=12000 loss=1.3497 (ln K=3.466) acc=0.6082 (87s)
+[facts:geglu:N=8192] step=13000 loss=1.2255 (ln K=3.466) acc=0.6423 (92s)
+[facts:geglu:N=8192] step=14000 loss=1.1002 (ln K=3.466) acc=0.6738 (97s)
+[facts:geglu:N=8192] step=15000 loss=0.9478 (ln K=3.466) acc=0.7156 (103s)
+[facts:geglu:N=8192] step=16000 loss=0.8266 (ln K=3.466) acc=0.7563 (111s)
+[facts:geglu:N=8192] step=17000 loss=0.7068 (ln K=3.466) acc=0.7979 (118s)
+[facts:geglu:N=8192] step=18000 loss=0.6130 (ln K=3.466) acc=0.8245 (126s)
+[facts:geglu:N=8192] step=19000 loss=0.5266 (ln K=3.466) acc=0.8525 (133s)
+[facts:geglu:N=8192] step=20000 loss=0.4356 (ln K=3.466) acc=0.8806 (141s)
+[facts:geglu:N=8192] step=21000 loss=0.3642 (ln K=3.466) acc=0.9084 (149s)
+[facts:geglu:N=8192] step=22000 loss=0.2990 (ln K=3.466) acc=0.9353 (156s)
+[facts:geglu:N=8192] step=23000 loss=0.2483 (ln K=3.466) acc=0.9490 (164s)
+[facts:geglu:N=8192] step=24000 loss=0.2119 (ln K=3.466) acc=0.9636 (172s)
+[facts:geglu:N=8192] step=25000 loss=0.1845 (ln K=3.466) acc=0.9714 (179s)
+[facts:geglu:N=8192] step=26000 loss=0.1633 (ln K=3.466) acc=0.9773 (183s)
+[facts:geglu:N=8192] step=27000 loss=0.1487 (ln K=3.466) acc=0.9812 (188s)
+[facts:geglu:N=8192] step=28000 loss=0.1414 (ln K=3.466) acc=0.9824 (196s)
+[facts:geglu:N=8192] step=29000 loss=0.1386 (ln K=3.466) acc=0.9822 (204s)
+[facts:geglu:N=8192] step=30000 loss=0.1380 (ln K=3.466) acc=0.9827 (212s)
+geglu_w21_lvl_a4_K1_s1: plateau_end=3000 final_acc=0.9827 trained_params=229,876

logs/q20_logs/factsA_geglu_w21_lvl_a4_K1_s2.log

@@ -0,0 +1,4 @@
+[facts:geglu:N=8192] step=0 loss=3.6101 (ln K=3.466) acc=0.0330 (1s)
+[facts:geglu:N=8192] step=1000 loss=3.4126 (ln K=3.466) acc=0.0627 (9s)
+[facts:geglu:N=8192] step=2000 loss=3.2585 (ln K=3.466) acc=0.1060 (17s)
+[facts:geglu:N=8192] step=3000 loss=3.0596 (ln K=3.466) acc=0.1646 (25s)

logs/q21_logs/factsG_cos_w6144_s4_K1_s1.log

@@ -0,0 +1,15 @@
+[facts:cos:N=8192] step=0 loss=3.6103 (ln K=3.466) acc=0.0369 (1s)
+[facts:cos:N=8192] step=500 loss=3.4611 (ln K=3.466) acc=0.0513 (4s)
+[facts:cos:N=8192] step=1000 loss=3.3290 (ln K=3.466) acc=0.0913 (8s)
+[facts:cos:N=8192] step=1500 loss=3.1800 (ln K=3.466) acc=0.1355 (12s)
+[facts:cos:N=8192] step=2000 loss=2.9265 (ln K=3.466) acc=0.1982 (17s)
+[facts:cos:N=8192] step=2500 loss=2.5111 (ln K=3.466) acc=0.3274 (21s)
+[facts:cos:N=8192] step=3000 loss=1.8787 (ln K=3.466) acc=0.5020 (25s)
+[facts:cos:N=8192] step=3500 loss=1.1535 (ln K=3.466) acc=0.7021 (28s)
+[facts:cos:N=8192] step=4000 loss=0.6202 (ln K=3.466) acc=0.8408 (32s)
+[facts:cos:N=8192] step=4500 loss=0.3252 (ln K=3.466) acc=0.9175 (36s)
+[facts:cos:N=8192] step=5000 loss=0.1850 (ln K=3.466) acc=0.9524 (40s)
+[facts:cos:N=8192] step=5500 loss=0.1463 (ln K=3.466) acc=0.9641 (44s)
+[facts:cos:N=8192] step=6000 loss=0.1083 (ln K=3.466) acc=0.9731 (49s)
+[facts:cos:N=8192] step=6500 loss=0.1455 (ln K=3.466) acc=0.9568 (53s)
+[facts:cos:N=8192] step=7000 loss=0.0927 (ln K=3.466) acc=0.9744 (57s)

logs/q21_logs/factsG_cos_w6144_s4_K1_s2.log

@@ -0,0 +1,14 @@
+[facts:cos:N=8192] step=0 loss=3.6168 (ln K=3.466) acc=0.0308 (1s)
+[facts:cos:N=8192] step=500 loss=3.4500 (ln K=3.466) acc=0.0518 (5s)
+[facts:cos:N=8192] step=1000 loss=3.3322 (ln K=3.466) acc=0.0969 (9s)
+[facts:cos:N=8192] step=1500 loss=3.1698 (ln K=3.466) acc=0.1387 (13s)
+[facts:cos:N=8192] step=2000 loss=2.9321 (ln K=3.466) acc=0.1931 (17s)
+[facts:cos:N=8192] step=2500 loss=2.5239 (ln K=3.466) acc=0.3142 (21s)
+[facts:cos:N=8192] step=3000 loss=1.8612 (ln K=3.466) acc=0.5020 (25s)
+[facts:cos:N=8192] step=3500 loss=1.1838 (ln K=3.466) acc=0.6848 (29s)
+[facts:cos:N=8192] step=4000 loss=0.6375 (ln K=3.466) acc=0.8315 (33s)
+[facts:cos:N=8192] step=4500 loss=0.3603 (ln K=3.466) acc=0.9077 (37s)
+[facts:cos:N=8192] step=5000 loss=0.2178 (ln K=3.466) acc=0.9456 (41s)
+[facts:cos:N=8192] step=5500 loss=0.1611 (ln K=3.466) acc=0.9551 (45s)
+[facts:cos:N=8192] step=6000 loss=0.1439 (ln K=3.466) acc=0.9587 (49s)
+[facts:cos:N=8192] step=6500 loss=0.1480 (ln K=3.466) acc=0.9539 (53s)

logs/q21_logs/factsG_relu_w768_zinit_K4_s1.log

@@ -4,3 +4,19 @@
 [facts:relu:N=8192] step=1500 loss=3.3714 (ln K=3.466) acc=0.0825 (10s)
 [facts:relu:N=8192] step=2000 loss=3.2847 (ln K=3.466) acc=0.0979 (14s)
 [facts:relu:N=8192] step=2500 loss=3.1659 (ln K=3.466) acc=0.1367 (18s)
+[facts:relu:N=8192] step=3000 loss=2.9766 (ln K=3.466) acc=0.1895 (22s)
+[facts:relu:N=8192] step=3500 loss=2.7289 (ln K=3.466) acc=0.2507 (26s)
+[facts:relu:N=8192] step=4000 loss=2.4422 (ln K=3.466) acc=0.3389 (30s)
+[facts:relu:N=8192] step=4500 loss=2.1284 (ln K=3.466) acc=0.4202 (34s)
+[facts:relu:N=8192] step=5000 loss=1.8359 (ln K=3.466) acc=0.4919 (38s)
+[facts:relu:N=8192] step=5500 loss=1.5304 (ln K=3.466) acc=0.5715 (42s)
+[facts:relu:N=8192] step=6000 loss=1.2334 (ln K=3.466) acc=0.6523 (46s)
+[facts:relu:N=8192] step=6500 loss=1.0608 (ln K=3.466) acc=0.7004 (50s)
+[facts:relu:N=8192] step=7000 loss=0.8748 (ln K=3.466) acc=0.7498 (54s)
+[facts:relu:N=8192] step=7500 loss=0.7799 (ln K=3.466) acc=0.7810 (58s)
+[facts:relu:N=8192] step=8000 loss=0.6568 (ln K=3.466) acc=0.8076 (62s)
+[facts:relu:N=8192] step=8500 loss=0.6054 (ln K=3.466) acc=0.8157 (66s)
+[facts:relu:N=8192] step=9000 loss=0.5258 (ln K=3.466) acc=0.8406 (70s)
+[facts:relu:N=8192] step=9500 loss=0.4319 (ln K=3.466) acc=0.8728 (73s)
+[facts:relu:N=8192] step=10000 loss=0.3887 (ln K=3.466) acc=0.8789 (77s)
+relu_w768_zinit_K4_s1: plateau_end=3000 final_acc=0.8789 trained_params=608,416

logs/q21_logs/factsG_relu_w768_zinit_K4_s2.log

@@ -3,3 +3,20 @@
 [facts:relu:N=8192] step=1000 loss=3.4282 (ln K=3.466) acc=0.0513 (7s)
 [facts:relu:N=8192] step=1500 loss=3.3734 (ln K=3.466) acc=0.0706 (12s)
 [facts:relu:N=8192] step=2000 loss=3.2739 (ln K=3.466) acc=0.0989 (16s)
+[facts:relu:N=8192] step=2500 loss=3.1263 (ln K=3.466) acc=0.1472 (20s)
+[facts:relu:N=8192] step=3000 loss=2.9718 (ln K=3.466) acc=0.1917 (24s)
+[facts:relu:N=8192] step=3500 loss=2.7340 (ln K=3.466) acc=0.2588 (28s)
+[facts:relu:N=8192] step=4000 loss=2.4401 (ln K=3.466) acc=0.3325 (32s)
+[facts:relu:N=8192] step=4500 loss=2.1263 (ln K=3.466) acc=0.4182 (36s)
+[facts:relu:N=8192] step=5000 loss=1.8554 (ln K=3.466) acc=0.4822 (40s)
+[facts:relu:N=8192] step=5500 loss=1.5642 (ln K=3.466) acc=0.5735 (44s)
+[facts:relu:N=8192] step=6000 loss=1.3581 (ln K=3.466) acc=0.6301 (48s)
+[facts:relu:N=8192] step=6500 loss=1.0785 (ln K=3.466) acc=0.7085 (52s)
+[facts:relu:N=8192] step=7000 loss=0.9600 (ln K=3.466) acc=0.7288 (56s)
+[facts:relu:N=8192] step=7500 loss=0.8050 (ln K=3.466) acc=0.7732 (60s)
+[facts:relu:N=8192] step=8000 loss=0.6575 (ln K=3.466) acc=0.8083 (64s)
+[facts:relu:N=8192] step=8500 loss=0.6238 (ln K=3.466) acc=0.8157 (68s)
+[facts:relu:N=8192] step=9000 loss=0.5370 (ln K=3.466) acc=0.8440 (71s)
+[facts:relu:N=8192] step=9500 loss=0.4796 (ln K=3.466) acc=0.8582 (74s)
+[facts:relu:N=8192] step=10000 loss=0.4445 (ln K=3.466) acc=0.8643 (77s)
+relu_w768_zinit_K4_s2: plateau_end=3000 final_acc=0.8643 trained_params=608,416

logs/q21_logs/factsG_relu_w768_zinit_a2_K4_s1.log

@@ -0,0 +1,22 @@
+[facts:relu:N=8192] step=0 loss=3.6028 (ln K=3.466) acc=0.0369 (1s)
+[facts:relu:N=8192] step=500 loss=3.4702 (ln K=3.466) acc=0.0308 (4s)
+[facts:relu:N=8192] step=1000 loss=3.4554 (ln K=3.466) acc=0.0417 (8s)
+[facts:relu:N=8192] step=1500 loss=3.4134 (ln K=3.466) acc=0.0679 (12s)
+[facts:relu:N=8192] step=2000 loss=3.3458 (ln K=3.466) acc=0.0828 (16s)
+[facts:relu:N=8192] step=2500 loss=3.2375 (ln K=3.466) acc=0.1113 (20s)
+[facts:relu:N=8192] step=3000 loss=3.1016 (ln K=3.466) acc=0.1555 (23s)
+[facts:relu:N=8192] step=3500 loss=2.9355 (ln K=3.466) acc=0.1951 (27s)
+[facts:relu:N=8192] step=4000 loss=2.6795 (ln K=3.466) acc=0.2693 (31s)
+[facts:relu:N=8192] step=4500 loss=2.4376 (ln K=3.466) acc=0.3423 (35s)
+[facts:relu:N=8192] step=5000 loss=2.1527 (ln K=3.466) acc=0.4119 (39s)
+[facts:relu:N=8192] step=5500 loss=1.8578 (ln K=3.466) acc=0.4854 (43s)
+[facts:relu:N=8192] step=6000 loss=1.6287 (ln K=3.466) acc=0.5432 (47s)
+[facts:relu:N=8192] step=6500 loss=1.3889 (ln K=3.466) acc=0.6125 (51s)
+[facts:relu:N=8192] step=7000 loss=1.2183 (ln K=3.466) acc=0.6606 (54s)
+[facts:relu:N=8192] step=7500 loss=1.0303 (ln K=3.466) acc=0.7129 (58s)
+[facts:relu:N=8192] step=8000 loss=0.8703 (ln K=3.466) acc=0.7502 (62s)
+[facts:relu:N=8192] step=8500 loss=0.7723 (ln K=3.466) acc=0.7761 (66s)
+[facts:relu:N=8192] step=9000 loss=0.7076 (ln K=3.466) acc=0.7988 (70s)
+[facts:relu:N=8192] step=9500 loss=0.6300 (ln K=3.466) acc=0.8174 (74s)
+[facts:relu:N=8192] step=10000 loss=0.5916 (ln K=3.466) acc=0.8291 (78s)
+relu_w768_zinit_a2_K4_s1: plateau_end=3000 final_acc=0.8291 trained_params=608,416

logs/q21_logs/factsG_relu_w768_zinit_a2_K4_s2.log

@@ -0,0 +1,22 @@
+[facts:relu:N=8192] step=0 loss=3.6063 (ln K=3.466) acc=0.0334 (1s)
+[facts:relu:N=8192] step=500 loss=3.4704 (ln K=3.466) acc=0.0339 (5s)
+[facts:relu:N=8192] step=1000 loss=3.4596 (ln K=3.466) acc=0.0425 (8s)
+[facts:relu:N=8192] step=1500 loss=3.4175 (ln K=3.466) acc=0.0583 (12s)
+[facts:relu:N=8192] step=2000 loss=3.3453 (ln K=3.466) acc=0.0808 (16s)
+[facts:relu:N=8192] step=2500 loss=3.2431 (ln K=3.466) acc=0.1062 (20s)
+[facts:relu:N=8192] step=3000 loss=3.1278 (ln K=3.466) acc=0.1494 (24s)
+[facts:relu:N=8192] step=3500 loss=2.9371 (ln K=3.466) acc=0.1995 (28s)
+[facts:relu:N=8192] step=4000 loss=2.7197 (ln K=3.466) acc=0.2532 (32s)
+[facts:relu:N=8192] step=4500 loss=2.4444 (ln K=3.466) acc=0.3337 (36s)
+[facts:relu:N=8192] step=5000 loss=2.1664 (ln K=3.466) acc=0.4099 (40s)
+[facts:relu:N=8192] step=5500 loss=1.8976 (ln K=3.466) acc=0.4746 (43s)
+[facts:relu:N=8192] step=6000 loss=1.6324 (ln K=3.466) acc=0.5510 (47s)
+[facts:relu:N=8192] step=6500 loss=1.3768 (ln K=3.466) acc=0.6147 (51s)
+[facts:relu:N=8192] step=7000 loss=1.2360 (ln K=3.466) acc=0.6553 (55s)
+[facts:relu:N=8192] step=7500 loss=0.9921 (ln K=3.466) acc=0.7148 (59s)
+[facts:relu:N=8192] step=8000 loss=0.8738 (ln K=3.466) acc=0.7517 (63s)
+[facts:relu:N=8192] step=8500 loss=0.7943 (ln K=3.466) acc=0.7727 (67s)
+[facts:relu:N=8192] step=9000 loss=0.6885 (ln K=3.466) acc=0.8042 (71s)
+[facts:relu:N=8192] step=9500 loss=0.5850 (ln K=3.466) acc=0.8364 (74s)
+[facts:relu:N=8192] step=10000 loss=0.5618 (ln K=3.466) acc=0.8364 (78s)
+relu_w768_zinit_a2_K4_s2: plateau_end=3500 final_acc=0.8364 trained_params=608,416

logs/q21_logs/factsG_relu_w768_zinit_a4_K4_s1.log

@@ -0,0 +1,22 @@
+[facts:relu:N=8192] step=0 loss=3.6010 (ln K=3.466) acc=0.0374 (1s)
+[facts:relu:N=8192] step=500 loss=3.4711 (ln K=3.466) acc=0.0305 (4s)
+[facts:relu:N=8192] step=1000 loss=3.4695 (ln K=3.466) acc=0.0371 (8s)
+[facts:relu:N=8192] step=1500 loss=3.4514 (ln K=3.466) acc=0.0435 (12s)
+[facts:relu:N=8192] step=2000 loss=3.4055 (ln K=3.466) acc=0.0586 (15s)
+[facts:relu:N=8192] step=2500 loss=3.3568 (ln K=3.466) acc=0.0769 (19s)
+[facts:relu:N=8192] step=3000 loss=3.2804 (ln K=3.466) acc=0.1011 (23s)
+[facts:relu:N=8192] step=3500 loss=3.1639 (ln K=3.466) acc=0.1331 (27s)
+[facts:relu:N=8192] step=4000 loss=3.0276 (ln K=3.466) acc=0.1746 (31s)
+[facts:relu:N=8192] step=4500 loss=2.8685 (ln K=3.466) acc=0.2158 (35s)
+[facts:relu:N=8192] step=5000 loss=2.6644 (ln K=3.466) acc=0.2747 (39s)
+[facts:relu:N=8192] step=5500 loss=2.4422 (ln K=3.466) acc=0.3306 (42s)
+[facts:relu:N=8192] step=6000 loss=2.2213 (ln K=3.466) acc=0.3860 (46s)
+[facts:relu:N=8192] step=6500 loss=2.0145 (ln K=3.466) acc=0.4424 (50s)
+[facts:relu:N=8192] step=7000 loss=1.8344 (ln K=3.466) acc=0.4941 (54s)
+[facts:relu:N=8192] step=7500 loss=1.6184 (ln K=3.466) acc=0.5554 (58s)
+[facts:relu:N=8192] step=8000 loss=1.5067 (ln K=3.466) acc=0.5776 (62s)
+[facts:relu:N=8192] step=8500 loss=1.3428 (ln K=3.466) acc=0.6238 (66s)
+[facts:relu:N=8192] step=9000 loss=1.2893 (ln K=3.466) acc=0.6436 (70s)
+[facts:relu:N=8192] step=9500 loss=1.1629 (ln K=3.466) acc=0.6772 (73s)
+[facts:relu:N=8192] step=10000 loss=1.0362 (ln K=3.466) acc=0.7107 (77s)
+relu_w768_zinit_a4_K4_s1: plateau_end=4000 final_acc=0.7107 trained_params=608,416

logs/q21_logs/factsG_relu_w768_zinit_a4_K4_s2.log

@@ -0,0 +1,22 @@
+[facts:relu:N=8192] step=0 loss=3.6046 (ln K=3.466) acc=0.0310 (1s)
+[facts:relu:N=8192] step=500 loss=3.4712 (ln K=3.466) acc=0.0337 (5s)
+[facts:relu:N=8192] step=1000 loss=3.4660 (ln K=3.466) acc=0.0403 (8s)
+[facts:relu:N=8192] step=1500 loss=3.4522 (ln K=3.466) acc=0.0483 (12s)
+[facts:relu:N=8192] step=2000 loss=3.4152 (ln K=3.466) acc=0.0598 (16s)
+[facts:relu:N=8192] step=2500 loss=3.3612 (ln K=3.466) acc=0.0764 (20s)
+[facts:relu:N=8192] step=3000 loss=3.2829 (ln K=3.466) acc=0.1013 (24s)
+[facts:relu:N=8192] step=3500 loss=3.1837 (ln K=3.466) acc=0.1238 (28s)
+[facts:relu:N=8192] step=4000 loss=3.0487 (ln K=3.466) acc=0.1658 (32s)
+[facts:relu:N=8192] step=4500 loss=2.9109 (ln K=3.466) acc=0.2029 (35s)
+[facts:relu:N=8192] step=5000 loss=2.6762 (ln K=3.466) acc=0.2681 (39s)
+[facts:relu:N=8192] step=5500 loss=2.4882 (ln K=3.466) acc=0.3247 (43s)
+[facts:relu:N=8192] step=6000 loss=2.2535 (ln K=3.466) acc=0.3826 (47s)
+[facts:relu:N=8192] step=6500 loss=2.0802 (ln K=3.466) acc=0.4314 (51s)
+[facts:relu:N=8192] step=7000 loss=1.8972 (ln K=3.466) acc=0.4729 (55s)
+[facts:relu:N=8192] step=7500 loss=1.6951 (ln K=3.466) acc=0.5249 (59s)
+[facts:relu:N=8192] step=8000 loss=1.5323 (ln K=3.466) acc=0.5640 (63s)
+[facts:relu:N=8192] step=8500 loss=1.4115 (ln K=3.466) acc=0.6055 (66s)
+[facts:relu:N=8192] step=9000 loss=1.2660 (ln K=3.466) acc=0.6467 (70s)
+[facts:relu:N=8192] step=9500 loss=1.1228 (ln K=3.466) acc=0.6848 (74s)
+[facts:relu:N=8192] step=10000 loss=1.0940 (ln K=3.466) acc=0.6890 (77s)
+relu_w768_zinit_a4_K4_s2: plateau_end=4000 final_acc=0.6890 trained_params=608,416

logs/q24_logs/factsL_cos_w256_s4_N32k_K1_s1.log

@@ -1 +1,23 @@
 [facts:cos:N=32768] step=0 loss=3.6254 (ln K=3.466) acc=0.0295 (1s)
+[facts:cos:N=32768] step=2000 loss=3.4595 (ln K=3.466) acc=0.0444 (17s)
+[facts:cos:N=32768] step=4000 loss=3.4367 (ln K=3.466) acc=0.0518 (33s)
+[facts:cos:N=32768] step=6000 loss=3.4112 (ln K=3.466) acc=0.0579 (48s)
+[facts:cos:N=32768] step=8000 loss=3.3826 (ln K=3.466) acc=0.0706 (64s)
+[facts:cos:N=32768] step=10000 loss=3.3499 (ln K=3.466) acc=0.0791 (75s)
+[facts:cos:N=32768] step=12000 loss=3.3035 (ln K=3.466) acc=0.0974 (85s)
+[facts:cos:N=32768] step=14000 loss=3.2475 (ln K=3.466) acc=0.1130 (100s)
+[facts:cos:N=32768] step=16000 loss=3.1994 (ln K=3.466) acc=0.1277 (116s)
+[facts:cos:N=32768] step=18000 loss=3.1295 (ln K=3.466) acc=0.1492 (131s)
+[facts:cos:N=32768] step=20000 loss=3.0609 (ln K=3.466) acc=0.1555 (146s)
+[facts:cos:N=32768] step=22000 loss=2.9821 (ln K=3.466) acc=0.1797 (162s)
+[facts:cos:N=32768] step=24000 loss=2.9036 (ln K=3.466) acc=0.1985 (172s)
+[facts:cos:N=32768] step=26000 loss=2.8658 (ln K=3.466) acc=0.2126 (185s)
+[facts:cos:N=32768] step=28000 loss=2.7755 (ln K=3.466) acc=0.2446 (200s)
+[facts:cos:N=32768] step=30000 loss=2.7087 (ln K=3.466) acc=0.2600 (216s)
+[facts:cos:N=32768] step=32000 loss=2.6451 (ln K=3.466) acc=0.2798 (231s)
+[facts:cos:N=32768] step=34000 loss=2.5902 (ln K=3.466) acc=0.2903 (246s)
+[facts:cos:N=32768] step=36000 loss=2.5361 (ln K=3.466) acc=0.3074 (258s)
+[facts:cos:N=32768] step=38000 loss=2.4620 (ln K=3.466) acc=0.3196 (270s)
+[facts:cos:N=32768] step=40000 loss=2.4285 (ln K=3.466) acc=0.3254 (287s)
+[facts:cos:N=32768] step=42000 loss=2.3691 (ln K=3.466) acc=0.3381 (302s)
+[facts:cos:N=32768] step=44000 loss=2.3270 (ln K=3.466) acc=0.3489 (318s)

logs/q24_logs/factsL_cos_w256_s4_N32k_a4_K1_s1.log

@@ -1 +1,23 @@
 [facts:cos:N=32768] step=0 loss=3.6229 (ln K=3.466) acc=0.0298 (1s)
+[facts:cos:N=32768] step=2000 loss=3.4604 (ln K=3.466) acc=0.0417 (17s)
+[facts:cos:N=32768] step=4000 loss=3.4353 (ln K=3.466) acc=0.0552 (33s)
+[facts:cos:N=32768] step=6000 loss=3.3937 (ln K=3.466) acc=0.0679 (48s)
+[facts:cos:N=32768] step=8000 loss=3.3463 (ln K=3.466) acc=0.0903 (64s)
+[facts:cos:N=32768] step=10000 loss=3.2787 (ln K=3.466) acc=0.1011 (75s)
+[facts:cos:N=32768] step=12000 loss=3.2130 (ln K=3.466) acc=0.1184 (85s)
+[facts:cos:N=32768] step=14000 loss=3.1311 (ln K=3.466) acc=0.1536 (100s)
+[facts:cos:N=32768] step=16000 loss=3.0680 (ln K=3.466) acc=0.1616 (116s)
+[facts:cos:N=32768] step=18000 loss=2.9837 (ln K=3.466) acc=0.1790 (131s)
+[facts:cos:N=32768] step=20000 loss=2.9274 (ln K=3.466) acc=0.1973 (146s)
+[facts:cos:N=32768] step=22000 loss=2.8501 (ln K=3.466) acc=0.2214 (162s)
+[facts:cos:N=32768] step=24000 loss=2.7584 (ln K=3.466) acc=0.2400 (172s)
+[facts:cos:N=32768] step=26000 loss=2.6994 (ln K=3.466) acc=0.2534 (185s)
+[facts:cos:N=32768] step=28000 loss=2.6530 (ln K=3.466) acc=0.2612 (200s)
+[facts:cos:N=32768] step=30000 loss=2.5738 (ln K=3.466) acc=0.2925 (216s)
+[facts:cos:N=32768] step=32000 loss=2.5279 (ln K=3.466) acc=0.3057 (231s)
+[facts:cos:N=32768] step=34000 loss=2.4597 (ln K=3.466) acc=0.3218 (246s)
+[facts:cos:N=32768] step=36000 loss=2.4186 (ln K=3.466) acc=0.3289 (258s)
+[facts:cos:N=32768] step=38000 loss=2.3520 (ln K=3.466) acc=0.3401 (270s)
+[facts:cos:N=32768] step=40000 loss=2.3116 (ln K=3.466) acc=0.3477 (287s)
+[facts:cos:N=32768] step=42000 loss=2.2640 (ln K=3.466) acc=0.3682 (302s)
+[facts:cos:N=32768] step=44000 loss=2.2334 (ln K=3.466) acc=0.3694 (318s)

models/model_geglu_w21_lvl_a0p25_K4_s2_meta.py

@@ -0,0 +1 @@
+META = '{"step": 30000, "parts": ["model_geglu_w21_lvl_a0p25_K4_s2_s30000_p000.py"], "sha256": "0178cebb679fa8222b68777944a0a817165c51ff3741a0d6ba3b815ed837f651", "bytes": 930490}'

models/model_geglu_w21_lvl_a4_K1_s1_meta.py

@@ -0,0 +1 @@
+META = '{"step": 30000, "parts": ["model_geglu_w21_lvl_a4_K1_s1_s30000_p000.py"], "sha256": "52431db500e121606443d297d6b99f9f77c97bca8af63966485bdff35e7cb8a3", "bytes": 930490}'

models/model_relu_w768_zinit_K4_s1_meta.py

@@ -1 +1 @@
-META = '{"step": 10000, "parts": ["model_relu_w768_zinit_K4_s1_s10000_p000.py"], "sha256": "d3329ee87476d955bca4fa2fd15237ca95105fa8b98f2269cc8b0d1fe5304d59", "bytes": 2443462}'
+META = '{"step": 10000, "parts": ["model_relu_w768_zinit_K4_s1_s10000_p000.py"], "sha256": "676e2b5b5c3dc88f6aca4a4f837cee4a697ee19efe4f7088f0ba8ef5bf104f31", "bytes": 2443590}'

models/model_relu_w768_zinit_K4_s2_meta.py

@@ -1 +1 @@
-META = '{"step": 10000, "parts": ["model_relu_w768_zinit_K4_s2_s10000_p000.py"], "sha256": "ae1b8d056f364846d69a32eda5e4e20e2171e36fa0e0ab615b6b06a4c736190d", "bytes": 2443462}'
+META = '{"step": 10000, "parts": ["model_relu_w768_zinit_K4_s2_s10000_p000.py"], "sha256": "effeb16bc7ba976cbece7c02ea97e86e794a709a00bf59048ee400795dd172a5", "bytes": 2443590}'

models/model_relu_w768_zinit_a2_K4_s1_meta.py

@@ -0,0 +1 @@
+META = '{"step": 10000, "parts": ["model_relu_w768_zinit_a2_K4_s1_s10000_p000.py"], "sha256": "10b68126364bca87758e1f35638a4e25776dfb0f1102986173a8a7d316765292", "bytes": 2443590}'

models/model_relu_w768_zinit_a2_K4_s2_meta.py

@@ -0,0 +1 @@
+META = '{"step": 10000, "parts": ["model_relu_w768_zinit_a2_K4_s2_s10000_p000.py"], "sha256": "a51abf33aa3c35b77cddd56d3ac6e5180f29ac412ae47b4d751714cd6dbd0206", "bytes": 2443590}'

models/model_relu_w768_zinit_a4_K4_s1_meta.py

@@ -0,0 +1 @@
+META = '{"step": 10000, "parts": ["model_relu_w768_zinit_a4_K4_s1_s10000_p000.py"], "sha256": "84d3e98150f3208205aae5c8ed1775d42d2bcb74739083284b2a6511f1439971", "bytes": 2443590}'

models/model_relu_w768_zinit_a4_K4_s2_meta.py

@@ -0,0 +1 @@
+META = '{"step": 10000, "parts": ["model_relu_w768_zinit_a4_K4_s2_s10000_p000.py"], "sha256": "85a1bdafa388512c746d19ec7caa039bd0901a7d412bdfc96cfd549ed6102d56", "bytes": 2443590}'

persist/ckpt_facts_cos_w256_s4_N32k_K1_s1_meta.py

@@ -0,0 +1 @@
+META = '{"step": 44000, "parts": ["ckpt_facts_cos_w256_s4_N32k_K1_s1_s44000_p000.py"], "sha256": "6ac8207a0e23e590d1ed8f622053af5e94eae32cb8131a58faba19ee707c4a3e", "bytes": 3656867}'

persist/ckpt_facts_cos_w256_s4_N32k_a4_K1_s1_meta.py

@@ -0,0 +1 @@
+META = '{"step": 44000, "parts": ["ckpt_facts_cos_w256_s4_N32k_a4_K1_s1_s44000_p000.py"], "sha256": "050bda5b7d3aea7b2890c4676589e38ab12c2c180157237375329f90dc1e884d", "bytes": 3656867}'

persist/factsA_geglu_w21_lvl_a0p25_K4_s2.py

@@ -0,0 +1 @@
+DATA = '{\n "cfg": {\n  "n_people": 8192,\n  "n_attrs": 4,\n  "n_values": 32,\n  "d": 128,\n  "heads": 4,\n  "layers": 2,\n  "ffn": "geglu",\n  "width": 21,\n  "cos_sigma": 4.0,\n  "cos_train": false,\n  "ffn_zero_init": false,\n  "loop_mode": "residual",\n  "lr_schedule": "cosine",\n  "rbf_layers": "all",\n  "rbf_temperature": 1.0,\n  "rbf_eta": 1.0,\n  "lr": 0.001,\n  "batch_size": 128,\n  "steps": 30000,\n  "eval_every": 1000,\n  "seed": 2,\n  "finetune_people": 0,\n  "finetune_steps": 0,\n  "n_filler": 10,\n  "filler_vocab": 64,\n  "name_tokens": 2,\n  "name_vocab": 256,\n  "random_name_pos": true,\n  "eval_facts": 4096,\n  "loops": 4,\n  "loop_alpha": 0.25,\n  "final_probe": true,\n  "loop_tied": true,\n  "deep_supervision": false,\n  "train_probe": true\n },\n "log": [\n  {\n   "step": 0,\n   "sec": 7.867813110351562e-06,\n   "loss": 3.6155965328216553,\n   "acc": 0.031982421875,\n   "pass_probe": {\n    "loss": [\n     3.6401782035827637,\n     3.6407604217529297,\n     3.6414198875427246,\n     3.6421494483947754,\n     3.6429407596588135\n    ],\n    "acc": [\n     0.0302734375,\n     0.0302734375,\n     0.029296875,\n     0.029296875,\n     0.0283203125\n    ],\n    "rel_step": [\n     0.04107607528567314,\n     0.04104967787861824,\n     0.04095982387661934,\n     0.04088428243994713\n    ],\n    "descent_cos": [\n     0.002920258790254593,\n     0.0017243623733520508,\n     0.0011086035519838333,\n     7.181614637374878e-05\n    ],\n    "input_shift": [\n     0.4592660963535309,\n     0.45672914385795593,\n     0.45139363408088684\n    ],\n    "overlap_prev": [\n     0.9984280467033386,\n     0.9983003735542297,\n     0.9982565641403198\n    ],\n    "overlap_first": [\n     0.9984280467033386,\n     0.9934509992599487,\n     0.9847975373268127\n    ]\n   }\n  },\n  {\n   "step": 1000,\n   "sec": 8.751333713531494,\n   "loss": 3.369638204574585,\n   "acc": 0.076416015625,\n   "pass_probe": {\n    "loss": [\n     3.366896152496338,\n     3.3661725521087646,\n     3.3657724857330322,\n     3.3656797409057617,\n     3.365872859954834\n    ],\n    "acc": [\n     0.07421875,\n     0.0732421875,\n     0.0712890625,\n     0.0712890625,\n     0.0712890625\n    ],\n    "rel_step": [\n     0.036165326833724976,\n     0.035570207983255386,\n     0.03510209918022156,\n     0.03446938842535019\n    ],\n    "descent_cos": [\n     0.0008235471323132515,\n     -0.0005363635718822479,\n     -0.0011333711445331573,\n     -0.0031303437426686287\n    ],\n    "input_shift": [\n     0.38973870873451233,\n     0.38301482796669006,\n     0.37305980920791626\n    ],\n    "overlap_prev": [\n     0.9992181062698364,\n     0.9992504119873047,\n     0.9992958903312683\n    ],\n    "overlap_first": [\n     0.9992181062698364,\n     0.9969583749771118,\n     0.9932869672775269\n    ]\n   }\n  },\n  {\n   "step": 2000,\n   "sec": 16.878732204437256,\n   "loss": 3.2034335136413574,\n   "acc": 0.12158203125,\n   "pass_probe": {\n    "loss": [\n     3.258890151977539,\n     3.2480080127716064,\n     3.2412288188934326,\n     3.238196849822998,\n     3.2381043434143066\n    ],\n    "acc": [\n     0.115234375,\n     0.1201171875,\n     0.123046875,\n     0.1201171875,\n     0.1171875\n    ],\n    "rel_step": [\n     0.06425806134939194,\n     0.06237594038248062,\n     0.060452960431575775,\n     0.05857500433921814\n    ],\n    "descent_cos": [\n     0.0179604459553957,\n     0.012864120304584503,\n     0.008856497704982758,\n     0.0045693255960941315\n    ],\n    "input_shift": [\n     0.72282475233078,\n     0.6943341493606567,\n     0.6654227375984192\n    ],\n    "overlap_prev": [\n     0.9973710775375366,\n     0.9973997473716736,\n     0.9975931644439697\n    ],\n    "overlap_first": [\n     0.9973710775375366,\n     0.9895683526992798,\n     0.9769737720489502\n    ]\n   }\n  },\n  {\n   "step": 3000,\n   "sec": 25.012179374694824,\n   "loss": 3.0105316638946533,\n   "acc": 0.175537109375,\n   "pass_probe": {\n    "loss": [\n     3.1106631755828857,\n     3.0669612884521484,\n     3.039766788482666,\n     3.02669095993042,\n     3.023592948913574\n    ],\n    "acc": [\n     0.1396484375,\n     0.1474609375,\n     0.15234375,\n     0.1640625,\n     0.1748046875\n    ],\n    "rel_step": [\n     0.09572313725948334,\n     0.08888120204210281,\n     0.08322930335998535,\n     0.0766398087143898\n    ],\n    "descent_cos": [\n     0.04587768018245697,\n     0.03531135618686676,\n     0.021150873973965645,\n     0.007142342627048492\n    ],\n    "input_shift": [\n     1.0790166854858398,\n     0.9993696808815002,\n     0.9072384834289551\n    ],\n    "overlap_prev": [\n     0.9949901700019836,\n     0.9950188994407654,\n     0.9953747987747192\n    ],\n    "overlap_first": [\n     0.9949901700019836,\n     0.980299711227417,\n     0.9562711715698242\n    ]\n   }\n  },\n  {\n   "step": 4000,\n   "sec": 33.15845704078674,\n   "loss": 2.7651705741882324,\n   "acc": 0.2353515625,\n   "pass_probe": {\n    "loss": 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persist/factsA_geglu_w21_lvl_a4_K1_s1.py

@@ -0,0 +1 @@
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persist/factsG_relu_w768_zinit_K4_s1.py

@@ -0,0 +1 @@
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persist/factsG_relu_w768_zinit_K4_s2.py

@@ -0,0 +1 @@
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persist/factsG_relu_w768_zinit_a2_K4_s1.py

@@ -0,0 +1 @@
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persist/factsG_relu_w768_zinit_a2_K4_s2.py

@@ -0,0 +1 @@
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persist/factsG_relu_w768_zinit_a4_K4_s1.py

@@ -0,0 +1 @@
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persist/factsG_relu_w768_zinit_a4_K4_s2.py

@@ -0,0 +1 @@
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