forked from argonne-lcf/ai-science-training-series
-
Notifications
You must be signed in to change notification settings - Fork 0
/
sgd_test.txt
171 lines (171 loc) · 10.4 KB
/
sgd_test.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
[000] y_i = 61.80 * x + 18570.75 loss: 9090022291.715740
[001] y_i = 86.95 * x + 25506.02 loss: 4920572537.453362
[002] y_i = 99.35 * x + 28082.37 loss: 3927839195.875811
[003] y_i = 100.87 * x + 28372.16 loss: 3350205439.294849
[004] y_i = 102.74 * x + 28382.45 loss: 3123931152.512079
[005] y_i = 101.07 * x + 27968.77 loss: 3151971094.274557
[006] y_i = 100.95 * x + 27875.10 loss: 3475790258.761829
[007] y_i = 103.12 * x + 28007.09 loss: 3172451392.010951
[008] y_i = 103.79 * x + 27864.35 loss: 3256379891.161163
[009] y_i = 104.45 * x + 27709.79 loss: 2994555921.772130
[010] y_i = 102.75 * x + 27143.11 loss: 3223040502.307129
[011] y_i = 101.35 * x + 26918.93 loss: 3392554736.815490
[012] y_i = 101.08 * x + 26922.43 loss: 3545190227.076564
[013] y_i = 103.01 * x + 27188.32 loss: 2833649262.477391
[014] y_i = 102.12 * x + 27047.72 loss: 2790617383.213809
[015] y_i = 101.91 * x + 26687.16 loss: 2653532690.973981
[016] y_i = 103.92 * x + 26751.15 loss: 3261944219.047769
[017] y_i = 102.66 * x + 26328.42 loss: 2909691676.205819
[018] y_i = 103.26 * x + 26171.02 loss: 2677786447.502086
[019] y_i = 102.96 * x + 25931.17 loss: 2850531466.747812
[020] y_i = 105.09 * x + 25835.96 loss: 3507752955.090329
[021] y_i = 103.38 * x + 25321.22 loss: 2480815689.898252
[022] y_i = 102.20 * x + 25056.51 loss: 3894752080.680292
[023] y_i = 102.09 * x + 25173.40 loss: 3089606783.698304
[024] y_i = 103.40 * x + 25433.79 loss: 2857357625.358064
[025] y_i = 104.27 * x + 25397.82 loss: 3397135633.451449
[026] y_i = 104.19 * x + 25112.28 loss: 3619701743.747056
[027] y_i = 106.10 * x + 25062.86 loss: 3224556782.921347
[028] y_i = 105.00 * x + 24462.01 loss: 3371058107.673589
[029] y_i = 105.99 * x + 24209.57 loss: 3145776172.246513
[030] y_i = 105.13 * x + 23922.12 loss: 2888772399.922938
[031] y_i = 106.69 * x + 23542.85 loss: 3821202745.822376
[032] y_i = 106.93 * x + 23336.03 loss: 2607879381.162928
[033] y_i = 106.70 * x + 22761.34 loss: 2783712464.518289
[034] y_i = 107.21 * x + 22558.76 loss: 2792214454.345051
[035] y_i = 106.85 * x + 21918.89 loss: 2893164207.654055
[036] y_i = 106.65 * x + 21488.74 loss: 3314096004.089891
[037] y_i = 107.20 * x + 21516.41 loss: 3610576268.300516
[038] y_i = 105.75 * x + 21216.78 loss: 2590800987.272076
[039] y_i = 106.30 * x + 21371.77 loss: 3552441847.318670
[040] y_i = 105.79 * x + 21039.41 loss: 2722224888.990834
[041] y_i = 106.81 * x + 21104.37 loss: 3184279803.724595
[042] y_i = 107.29 * x + 20933.91 loss: 3243114944.023870
[043] y_i = 107.84 * x + 21057.74 loss: 3550010752.564851
[044] y_i = 108.47 * x + 21040.19 loss: 3265913808.576151
[045] y_i = 108.60 * x + 20615.11 loss: 2830877758.502599
[046] y_i = 109.64 * x + 20399.75 loss: 3041920241.357152
[047] y_i = 107.76 * x + 20086.51 loss: 3434836793.330057
[048] y_i = 108.04 * x + 19874.96 loss: 2861066096.332209
[049] y_i = 109.02 * x + 20001.92 loss: 3531986579.706998
[050] y_i = 107.83 * x + 19266.23 loss: 2789048918.220935
[051] y_i = 108.02 * x + 19073.95 loss: 2862152741.048696
[052] y_i = 106.34 * x + 18491.55 loss: 2473220309.776716
[053] y_i = 107.26 * x + 19096.25 loss: 3784895658.768137
[054] y_i = 106.75 * x + 18674.18 loss: 2639495742.234388
[055] y_i = 108.26 * x + 18883.07 loss: 2552921610.791122
[056] y_i = 107.43 * x + 18401.06 loss: 3039097566.251336
[057] y_i = 108.31 * x + 18816.58 loss: 3405997020.312504
[058] y_i = 108.30 * x + 18907.29 loss: 3155472348.872705
[059] y_i = 108.58 * x + 19140.65 loss: 3077434527.904951
[060] y_i = 108.38 * x + 19019.29 loss: 2935785166.608700
[061] y_i = 109.37 * x + 18937.65 loss: 3068383793.935526
[062] y_i = 107.44 * x + 18637.96 loss: 3193496309.814615
[063] y_i = 109.99 * x + 19072.86 loss: 3340746414.474881
[064] y_i = 109.09 * x + 18590.10 loss: 3307965775.770650
[065] y_i = 108.34 * x + 18526.55 loss: 3343672127.219733
[066] y_i = 107.07 * x + 18084.19 loss: 2609272424.746874
[067] y_i = 107.70 * x + 17898.26 loss: 3133198703.666045
[068] y_i = 109.59 * x + 18134.28 loss: 2628286032.231169
[069] y_i = 108.65 * x + 17811.13 loss: 2912233468.831288
[070] y_i = 108.64 * x + 17611.31 loss: 3359568686.744662
[071] y_i = 109.28 * x + 17598.74 loss: 2941925501.329267
[072] y_i = 109.36 * x + 17461.99 loss: 3228384107.735988
[073] y_i = 110.84 * x + 17562.48 loss: 2890147884.675192
[074] y_i = 108.53 * x + 16763.87 loss: 2925978366.030828
[075] y_i = 110.25 * x + 17088.86 loss: 3629887939.897681
[076] y_i = 108.55 * x + 16572.54 loss: 2532437182.633343
[077] y_i = 109.32 * x + 16606.45 loss: 3414387477.970556
[078] y_i = 110.70 * x + 16844.75 loss: 3975428537.538363
[079] y_i = 110.64 * x + 16371.59 loss: 3015696469.031036
[080] y_i = 109.76 * x + 16483.12 loss: 3568913287.046378
[081] y_i = 112.43 * x + 16785.90 loss: 3160173768.529092
[082] y_i = 111.14 * x + 16250.18 loss: 2741629087.263311
[083] y_i = 110.53 * x + 15864.26 loss: 3020925723.412530
[084] y_i = 108.95 * x + 15318.33 loss: 3310957458.611885
[085] y_i = 108.28 * x + 15573.84 loss: 4053489030.824533
[086] y_i = 110.24 * x + 15719.62 loss: 3175415149.070783
[087] y_i = 108.87 * x + 15589.27 loss: 3222444065.517309
[088] y_i = 109.81 * x + 15685.68 loss: 3137517508.675842
[089] y_i = 110.21 * x + 15876.36 loss: 3129054890.464979
[090] y_i = 110.03 * x + 16035.79 loss: 3060237870.527157
[091] y_i = 111.14 * x + 16043.82 loss: 2846725289.065736
[092] y_i = 108.08 * x + 15723.79 loss: 3024233748.116727
[093] y_i = 110.09 * x + 16004.50 loss: 2955988165.142454
[094] y_i = 111.68 * x + 15794.45 loss: 3294438685.301530
[095] y_i = 110.58 * x + 15515.78 loss: 2891932691.900385
[096] y_i = 109.11 * x + 15015.07 loss: 2427006922.351603
[097] y_i = 110.23 * x + 15183.82 loss: 2929501013.616086
[098] y_i = 111.85 * x + 15156.68 loss: 3308953870.076874
[099] y_i = 112.40 * x + 15202.29 loss: 3122379952.175267
[100] y_i = 112.32 * x + 14849.85 loss: 2771939746.553139
[101] y_i = 111.54 * x + 14727.72 loss: 3122740756.295378
[102] y_i = 110.02 * x + 14553.26 loss: 3017378156.286555
[103] y_i = 111.24 * x + 14689.91 loss: 3258528319.211310
[104] y_i = 110.77 * x + 14565.44 loss: 3330393077.222067
[105] y_i = 110.79 * x + 14403.80 loss: 2514868283.194904
[106] y_i = 111.18 * x + 14590.85 loss: 3737058465.435410
[107] y_i = 111.49 * x + 14620.41 loss: 2525568047.786489
[108] y_i = 111.55 * x + 14232.45 loss: 3455049584.339043
[109] y_i = 112.07 * x + 14298.69 loss: 2755622433.244946
[110] y_i = 112.39 * x + 14327.57 loss: 3334388343.889758
[111] y_i = 114.37 * x + 14581.07 loss: 2744455535.327071
[112] y_i = 112.04 * x + 13991.00 loss: 3031055670.168098
[113] y_i = 112.96 * x + 14098.05 loss: 3340303536.481778
[114] y_i = 112.39 * x + 13731.97 loss: 2983164392.899582
[115] y_i = 113.13 * x + 13574.68 loss: 3396049338.946005
[116] y_i = 111.64 * x + 13522.69 loss: 3445402169.019320
[117] y_i = 110.98 * x + 13386.55 loss: 2963529198.674831
[118] y_i = 111.32 * x + 13526.28 loss: 3458237807.563991
[119] y_i = 109.85 * x + 13517.20 loss: 3178648369.770204
[120] y_i = 111.22 * x + 13686.18 loss: 3309721141.704300
[121] y_i = 110.78 * x + 13557.71 loss: 2623528305.154191
[122] y_i = 109.56 * x + 13392.14 loss: 3061945185.574180
[123] y_i = 109.83 * x + 13470.87 loss: 2931247478.356696
[124] y_i = 111.26 * x + 13764.50 loss: 3376702734.388669
[125] y_i = 113.16 * x + 13887.89 loss: 3512640242.649433
[126] y_i = 111.83 * x + 13717.85 loss: 3599450936.750736
[127] y_i = 110.33 * x + 13426.87 loss: 3428921857.705549
[128] y_i = 111.62 * x + 13405.29 loss: 2813067062.474342
[129] y_i = 111.83 * x + 13168.09 loss: 2926534828.318297
[130] y_i = 110.90 * x + 12948.18 loss: 3184383757.151077
[131] y_i = 112.58 * x + 13378.17 loss: 3161892946.705224
[132] y_i = 111.15 * x + 12881.48 loss: 2889920130.541690
[133] y_i = 111.43 * x + 13281.96 loss: 3321523322.681152
[134] y_i = 111.31 * x + 13279.41 loss: 3135960665.995160
[135] y_i = 108.27 * x + 12674.29 loss: 2270679233.026025
[136] y_i = 110.62 * x + 13137.85 loss: 3165244353.874569
[137] y_i = 111.44 * x + 13452.48 loss: 3256134815.098310
[138] y_i = 111.65 * x + 13528.18 loss: 3898129152.355662
[139] y_i = 113.97 * x + 13521.98 loss: 3366623056.212833
[140] y_i = 112.20 * x + 13314.48 loss: 3839707317.360188
[141] y_i = 111.55 * x + 13488.58 loss: 3268056152.210720
[142] y_i = 111.24 * x + 13226.39 loss: 2894167416.768444
[143] y_i = 112.16 * x + 13283.11 loss: 2523189660.886026
[144] y_i = 111.27 * x + 13380.38 loss: 3185249243.399535
[145] y_i = 112.89 * x + 13576.17 loss: 3929063826.384227
[146] y_i = 111.31 * x + 13111.05 loss: 2836130390.417434
[147] y_i = 111.31 * x + 13080.00 loss: 3066007919.343035
[148] y_i = 112.61 * x + 12972.73 loss: 3122869268.276538
[149] y_i = 113.29 * x + 13350.21 loss: 3370852097.572241
[150] y_i = 110.44 * x + 13138.14 loss: 3231034003.550838
[151] y_i = 111.36 * x + 13326.66 loss: 2482043951.410507
[152] y_i = 112.31 * x + 13672.21 loss: 3496482537.589664
[153] y_i = 111.36 * x + 13516.26 loss: 3110056412.053418
[154] y_i = 111.94 * x + 13536.50 loss: 2891373349.636106
[155] y_i = 110.78 * x + 13067.82 loss: 2644842532.498542
[156] y_i = 111.47 * x + 13467.18 loss: 3388246845.063474
[157] y_i = 112.25 * x + 13445.82 loss: 3107465230.525802
[158] y_i = 112.44 * x + 13169.83 loss: 2919489064.934268
[159] y_i = 112.31 * x + 12910.16 loss: 3346573510.127795
[160] y_i = 112.24 * x + 13051.44 loss: 3344547709.569791
[161] y_i = 111.77 * x + 13218.33 loss: 3489008930.451253
[162] y_i = 112.85 * x + 13181.83 loss: 2767957377.130320
[163] y_i = 113.12 * x + 13430.79 loss: 3360018371.450157
[164] y_i = 113.14 * x + 13369.95 loss: 2936234103.400466
[165] y_i = 113.02 * x + 13114.92 loss: 2623069821.171763
[166] y_i = 112.21 * x + 12755.64 loss: 3264631452.745828
[167] y_i = 110.38 * x + 12748.45 loss: 3393523076.451735
[168] y_i = 110.27 * x + 13008.16 loss: 3151863926.551666
[169] y_i = 112.30 * x + 13476.39 loss: 3865147204.620756
[170] y_i = 111.48 * x + 13335.42 loss: 2908194905.495117