iter,train_loss,val_loss,lr
500,3.9623,3.9608,0.000600
1000,3.4403,3.4320,0.000598
1500,3.1455,3.1442,0.000594
2000,2.9553,2.9633,0.000589
2500,2.8516,2.8464,0.000582
3000,2.7778,2.7718,0.000574
3500,2.7201,2.7195,0.000564
4000,2.6785,2.6591,0.000552
4500,2.6386,2.6377,0.000540
5000,2.6045,2.5929,0.000525
5500,2.5917,2.5906,0.000510
6000,2.5724,2.5575,0.000494
6500,2.5456,2.5374,0.000476
7000,2.5357,2.5335,0.000458
7500,2.5279,2.5149,0.000438
8000,2.5107,2.4987,0.000418
8500,2.5012,2.4953,0.000398
9000,2.5002,2.4832,0.000377
9500,2.4812,2.4738,0.000356
10000,2.4779,2.4697,0.000334
10500,2.4721,2.4516,0.000313
11000,2.4595,2.4584,0.000292
11500,2.4524,2.4410,0.000271
12000,2.4597,2.4465,0.000250
12500,2.4451,2.4416,0.000230
13000,2.4431,2.4348,0.000210
13500,2.4292,2.4232,0.000191
14000,2.4176,2.4196,0.000173
14500,2.4216,2.4238,0.000156
15000,2.4285,2.4162,0.000141
15500,2.4089,2.4081,0.000126
16000,2.4085,2.4228,0.000113
16500,2.4114,2.4084,0.000101
17000,2.4089,2.4052,0.000090
17500,2.4007,2.3989,0.000081
18000,2.4007,2.3943,0.000073
18500,2.4035,2.3914,0.000068
19000,2.4030,2.4014,0.000063
19500,2.3995,2.3953,0.000061