波斯顿房价 (1)

#房价预测
import torch
#data
import numpy as np
import re
ff = open(r"C:\Users\22383\Desktop\备份pytorch\000、课程配套资料\Pytorch_code-master\pytorch_code\04\cls_reg\housing.data").readlines()
data = []
for item in ff:
    out = re.sub(r"\s{2,}", " ", item).strip()  
    #合并多个空格为一个空格
    print(out)
    data.append(out.split(" "))
data = np.array(data).astype(np.float)
print(data.shape)
Y = data[:, -1]
X = data[:, 0:-1]

X_train = X[0:496, ...]
Y_train = Y[0:496, ...]
X_test = X[496:, ...]
Y_test = Y[496:, ...]

print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)

0.00632 18.00 2.310 0 0.5380 6.5750 65.20 4.0900 1 296.0 15.30 396.90 4.98 24.00
0.02731 0.00 7.070 0 0.4690 6.4210 78.90 4.9671 2 242.0 17.80 396.90 9.14 21.60
0.02729 0.00 7.070 0 0.4690 7.1850 61.10 4.9671 2 242.0 17.80 392.83 4.03 34.70
0.03237 0.00 2.180 0 0.4580 6.9980 45.80 6.0622 3 222.0 18.70 394.63 2.94 33.40
0.06905 0.00 2.180 0 0.4580 7.1470 54.20 6.0622 3 222.0 18.70 396.90 5.33 36.20
0.02985 0.00 2.180 0 0.4580 6.4300 58.70 6.0622 3 222.0 18.70 394.12 5.21 28.70
0.08829 12.50 7.870 0 0.5240 6.0120 66.60 5.5605 5 311.0 15.20 395.60 12.43 22.90
0.14455 12.50 7.870 0 0.5240 6.1720 96.10 5.9505 5 311.0 15.20 396.90 19.15 27.10
0.21124 12.50 7.870 0 0.5240 5.6310 100.00 6.0821 5 311.0 15.20 386.63 29.93 16.50
0.17004 12.50 7.870 0 0.5240 6.0040 85.90 6.5921 5 311.0 15.20 386.71 17.10 18.90
0.22489 12.50 7.870 0 0.5240 6.3770 94.30 6.3467 5 311.0 15.20 392.52 20.45 15.00
0.11747 12.50 7.870 0 0.5240 6.0090 82.90 6.2267 5 311.0 15.20 396.90 13.27 18.90
0.09378 12.50 7.870 0 0.5240 5.8890 39.00 5.4509 5 311.0 15.20 390.50 15.71 21.70
0.62976 0.00 8.140 0 0.5380 5.9490 61.80 4.7075 4 307.0 21.00 396.90 8.26 20.40
0.63796 0.00 8.140 0 0.5380 6.0960 84.50 4.4619 4 307.0 21.00 380.02 10.26 18.20
0.62739 0.00 8.140 0 0.5380 5.8340 56.50 4.4986 4 307.0 21.00 395.62 8.47 19.90
1.05393 0.00 8.140 0 0.5380 5.9350 29.30 4.4986 4 307.0 21.00 386.85 6.58 23.10
0.78420 0.00 8.140 0 0.5380 5.9900 81.70 4.2579 4 307.0 21.00 386.75 14.67 17.50
0.80271 0.00 8.140 0 0.5380 5.4560 36.60 3.7965 4 307.0 21.00 288.99 11.69 20.20
0.72580 0.00 8.140 0 0.5380 5.7270 69.50 3.7965 4 307.0 21.00 390.95 11.28 18.20
1.25179 0.00 8.140 0 0.5380 5.5700 98.10 3.7979 4 307.0 21.00 376.57 21.02 13.60
0.85204 0.00 8.140 0 0.5380 5.9650 89.20 4.0123 4 307.0 21.00 392.53 13.83 19.60
1.23247 0.00 8.140 0 0.5380 6.1420 91.70 3.9769 4 307.0 21.00 396.90 18.72 15.20
0.98843 0.00 8.140 0 0.5380 5.8130 100.00 4.0952 4 307.0 21.00 394.54 19.88 14.50
0.75026 0.00 8.140 0 0.5380 5.9240 94.10 4.3996 4 307.0 21.00 394.33 16.30 15.60
0.84054 0.00 8.140 0 0.5380 5.5990 85.70 4.4546 4 307.0 21.00 303.42 16.51 13.90
0.67191 0.00 8.140 0 0.5380 5.8130 90.30 4.6820 4 307.0 21.00 376.88 14.81 16.60
0.95577 0.00 8.140 0 0.5380 6.0470 88.80 4.4534 4 307.0 21.00 306.38 17.28 14.80
0.77299 0.00 8.140 0 0.5380 6.4950 94.40 4.4547 4 307.0 21.00 387.94 12.80 18.40
1.00245 0.00 8.140 0 0.5380 6.6740 87.30 4.2390 4 307.0 21.00 380.23 11.98 21.00
1.13081 0.00 8.140 0 0.5380 5.7130 94.10 4.2330 4 307.0 21.00 360.17 22.60 12.70
1.35472 0.00 8.140 0 0.5380 6.0720 100.00 4.1750 4 307.0 21.00 376.73 13.04 14.50
1.38799 0.00 8.140 0 0.5380 5.9500 82.00 3.9900 4 307.0 21.00 232.60 27.71 13.20
1.15172 0.00 8.140 0 0.5380 5.7010 95.00 3.7872 4 307.0 21.00 358.77 18.35 13.10
1.61282 0.00 8.140 0 0.5380 6.0960 96.90 3.7598 4 307.0 21.00 248.31 20.34 13.50
0.06417 0.00 5.960 0 0.4990 5.9330 68.20 3.3603 5 279.0 19.20 396.90 9.68 18.90
0.09744 0.00 5.960 0 0.4990 5.8410 61.40 3.3779 5 279.0 19.20 377.56 11.41 20.00
0.08014 0.00 5.960 0 0.4990 5.8500 41.50 3.9342 5 279.0 19.20 396.90 8.77 21.00
0.17505 0.00 5.960 0 0.4990 5.9660 30.20 3.8473 5 279.0 19.20 393.43 10.13 24.70
0.02763 75.00 2.950 0 0.4280 6.5950 21.80 5.4011 3 252.0 18.30 395.63 4.32 30.80
0.03359 75.00 2.950 0 0.4280 7.0240 15.80 5.4011 3 252.0 18.30 395.62 1.98 34.90
0.12744 0.00 6.910 0 0.4480 6.7700 2.90 5.7209 3 233.0 17.90 385.41 4.84 26.60
0.14150 0.00 6.910 0 0.4480 6.1690 6.60 5.7209 3 233.0 17.90 383.37 5.81 25.30
0.15936 0.00 6.910 0 0.4480 6.2110 6.50 5.7209 3 233.0 17.90 394.46 7.44 24.70
0.12269 0.00 6.910 0 0.4480 6.0690 40.00 5.7209 3 233.0 17.90 389.39 9.55 21.20
0.17142 0.00 6.910 0 0.4480 5.6820 33.80 5.1004 3 233.0 17.90 396.90 10.21 19.30
0.18836 0.00 6.910 0 0.4480 5.7860 33.30 5.1004 3 233.0 17.90 396.90 14.15 20.00
0.22927 0.00 6.910 0 0.4480 6.0300 85.50 5.6894 3 233.0 17.90 392.74 18.80 16.60
0.25387 0.00 6.910 0 0.4480 5.3990 95.30 5.8700 3 233.0 17.90 396.90 30.81 14.40
0.21977 0.00 6.910 0 0.4480 5.6020 62.00 6.0877 3 233.0 17.90 396.90 16.20 19.40
0.08873 21.00 5.640 0 0.4390 5.9630 45.70 6.8147 4 243.0 16.80 395.56 13.45 19.70
0.04337 21.00 5.640 0 0.4390 6.1150 63.00 6.8147 4 243.0 16.80 393.97 9.43 20.50
0.05360 21.00 5.640 0 0.4390 6.5110 21.10 6.8147 4 243.0 16.80 396.90 5.28 25.00
0.04981 21.00 5.640 0 0.4390 5.9980 21.40 6.8147 4 243.0 16.80 396.90 8.43 23.40
0.01360 75.00 4.000 0 0.4100 5.8880 47.60 7.3197 3 469.0 21.10 396.90 14.80 18.90
0.01311 90.00 1.220 0 0.4030 7.2490 21.90 8.6966 5 226.0 17.90 395.93 4.81 35.40
0.02055 85.00 0.740 0 0.4100 6.3830 35.70 9.1876 2 313.0 17.30 396.90 5.77 24.70
0.01432 100.00 1.320 0 0.4110 6.8160 40.50 8.3248 5 256.0 15.10 392.90 3.95 31.60
0.15445 25.00 5.130 0 0.4530 6.1450 29.20 7.8148 8 284.0 19.70 390.68 6.86 23.30
0.10328 25.00 5.130 0 0.4530 5.9270 47.20 6.9320 8 284.0 19.70 396.90 9.22 19.60
0.14932 25.00 5.130 0 0.4530 5.7410 66.20 7.2254 8 284.0 19.70 395.11 13.15 18.70
0.17171 25.00 5.130 0 0.4530 5.9660 93.40 6.8185 8 284.0 19.70 378.08 14.44 16.00
0.11027 25.00 5.130 0 0.4530 6.4560 67.80 7.2255 8 284.0 19.70 396.90 6.73 22.20
0.12650 25.00 5.130 0 0.4530 6.7620 43.40 7.9809 8 284.0 19.70 395.58 9.50 25.00
0.01951 17.50 1.380 0 0.4161 7.1040 59.50 9.2229 3 216.0 18.60 393.24 8.05 33.00
0.03584 80.00 3.370 0 0.3980 6.2900 17.80 6.6115 4 337.0 16.10 396.90 4.67 23.50
0.04379 80.00 3.370 0 0.3980 5.7870 31.10 6.6115 4 337.0 16.10 396.90 10.24 19.40
0.05789 12.50 6.070 0 0.4090 5.8780 21.40 6.4980 4 345.0 18.90 396.21 8.10 22.00
0.13554 12.50 6.070 0 0.4090 5.5940 36.80 6.4980 4 345.0 18.90 396.90 13.09 17.40
0.12816 12.50 6.070 0 0.4090 5.8850 33.00 6.4980 4 345.0 18.90 396.90 8.79 20.90
0.08826 0.00 10.810 0 0.4130 6.4170 6.60 5.2873 4 305.0 19.20 383.73 6.72 24.20
0.15876 0.00 10.810 0 0.4130 5.9610 17.50 5.2873 4 305.0 19.20 376.94 9.88 21.70
0.09164 0.00 10.810 0 0.4130 6.0650 7.80 5.2873 4 305.0 19.20 390.91 5.52 22.80
0.19539 0.00 10.810 0 0.4130 6.2450 6.20 5.2873 4 305.0 19.20 377.17 7.54 23.40
0.07896 0.00 12.830 0 0.4370 6.2730 6.00 4.2515 5 398.0 18.70 394.92 6.78 24.10
0.09512 0.00 12.830 0 0.4370 6.2860 45.00 4.5026 5 398.0 18.70 383.23 8.94 21.40
0.10153 0.00 12.830 0 0.4370 6.2790 74.50 4.0522 5 398.0 18.70 373.66 11.97 20.00
0.08707 0.00 12.830 0 0.4370 6.1400 45.80 4.0905 5 398.0 18.70 386.96 10.27 20.80
0.05646 0.00 12.830 0 0.4370 6.2320 53.70 5.0141 5 398.0 18.70 386.40 12.34 21.20
0.08387 0.00 12.830 0 0.4370 5.8740 36.60 4.5026 5 398.0 18.70 396.06 9.10 20.30
0.04113 25.00 4.860 0 0.4260 6.7270 33.50 5.4007 4 281.0 19.00 396.90 5.29 28.00
0.04462 25.00 4.860 0 0.4260 6.6190 70.40 5.4007 4 281.0 19.00 395.63 7.22 23.90
0.03659 25.00 4.860 0 0.4260 6.3020 32.20 5.4007 4 281.0 19.00 396.90 6.72 24.80
0.03551 25.00 4.860 0 0.4260 6.1670 46.70 5.4007 4 281.0 19.00 390.64 7.51 22.90
0.05059 0.00 4.490 0 0.4490 6.3890 48.00 4.7794 3 247.0 18.50 396.90 9.62 23.90
0.05735 0.00 4.490 0 0.4490 6.6300 56.10 4.4377 3 247.0 18.50 392.30 6.53 26.60
0.05188 0.00 4.490 0 0.4490 6.0150 45.10 4.4272 3 247.0 18.50 395.99 12.86 22.50
0.07151 0.00 4.490 0 0.4490 6.1210 56.80 3.7476 3 247.0 18.50 395.15 8.44 22.20
0.05660 0.00 3.410 0 0.4890 7.0070 86.30 3.4217 2 270.0 17.80 396.90 5.50 23.60
0.05302 0.00 3.410 0 0.4890 7.0790 63.10 3.4145 2 270.0 17.80 396.06 5.70 28.70
0.04684 0.00 3.410 0 0.4890 6.4170 66.10 3.0923 2 270.0 17.80 392.18 8.81 22.60
0.03932 0.00 3.410 0 0.4890 6.4050 73.90 3.0921 2 270.0 17.80 393.55 8.20 22.00
0.04203 28.00 15.040 0 0.4640 6.4420 53.60 3.6659 4 270.0 18.20 395.01 8.16 22.90
0.02875 28.00 15.040 0 0.4640 6.2110 28.90 3.6659 4 270.0 18.20 396.33 6.21 25.00
0.04294 28.00 15.040 0 0.4640 6.2490 77.30 3.6150 4 270.0 18.20 396.90 10.59 20.60
0.12204 0.00 2.890 0 0.4450 6.6250 57.80 3.4952 2 276.0 18.00 357.98 6.65 28.40
0.11504 0.00 2.890 0 0.4450 6.1630 69.60 3.4952 2 276.0 18.00 391.83 11.34 21.40
0.12083 0.00 2.890 0 0.4450 8.0690 76.00 3.4952 2 276.0 18.00 396.90 4.21 38.70
0.08187 0.00 2.890 0 0.4450 7.8200 36.90 3.4952 2 276.0 18.00 393.53 3.57 43.80
0.06860 0.00 2.890 0 0.4450 7.4160 62.50 3.4952 2 276.0 18.00 396.90 6.19 33.20
0.14866 0.00 8.560 0 0.5200 6.7270 79.90 2.7778 5 384.0 20.90 394.76 9.42 27.50
0.11432 0.00 8.560 0 0.5200 6.7810 71.30 2.8561 5 384.0 20.90 395.58 7.67 26.50
0.22876 0.00 8.560 0 0.5200 6.4050 85.40 2.7147 5 384.0 20.90 70.80 10.63 18.60
0.21161 0.00 8.560 0 0.5200 6.1370 87.40 2.7147 5 384.0 20.90 394.47 13.44 19.30
0.13960 0.00 8.560 0 0.5200 6.1670 90.00 2.4210 5 384.0 20.90 392.69 12.33 20.10
0.13262 0.00 8.560 0 0.5200 5.8510 96.70 2.1069 5 384.0 20.90 394.05 16.47 19.50
0.17120 0.00 8.560 0 0.5200 5.8360 91.90 2.2110 5 384.0 20.90 395.67 18.66 19.50
0.13117 0.00 8.560 0 0.5200 6.1270 85.20 2.1224 5 384.0 20.90 387.69 14.09 20.40
0.12802 0.00 8.560 0 0.5200 6.4740 97.10 2.4329 5 384.0 20.90 395.24 12.27 19.80
0.26363 0.00 8.560 0 0.5200 6.2290 91.20 2.5451 5 384.0 20.90 391.23 15.55 19.40
0.10793 0.00 8.560 0 0.5200 6.1950 54.40 2.7778 5 384.0 20.90 393.49 13.00 21.70
0.10084 0.00 10.010 0 0.5470 6.7150 81.60 2.6775 6 432.0 17.80 395.59 10.16 22.80
0.12329 0.00 10.010 0 0.5470 5.9130 92.90 2.3534 6 432.0 17.80 394.95 16.21 18.80
0.22212 0.00 10.010 0 0.5470 6.0920 95.40 2.5480 6 432.0 17.80 396.90 17.09 18.70
0.14231 0.00 10.010 0 0.5470 6.2540 84.20 2.2565 6 432.0 17.80 388.74 10.45 18.50
0.17134 0.00 10.010 0 0.5470 5.9280 88.20 2.4631 6 432.0 17.80 344.91 15.76 18.30
0.13158 0.00 10.010 0 0.5470 6.1760 72.50 2.7301 6 432.0 17.80 393.30 12.04 21.20
0.15098 0.00 10.010 0 0.5470 6.0210 82.60 2.7474 6 432.0 17.80 394.51 10.30 19.20
0.13058 0.00 10.010 0 0.5470 5.8720 73.10 2.4775 6 432.0 17.80 338.63 15.37 20.40
0.14476 0.00 10.010 0 0.5470 5.7310 65.20 2.7592 6 432.0 17.80 391.50 13.61 19.30
0.06899 0.00 25.650 0 0.5810 5.8700 69.70 2.2577 2 188.0 19.10 389.15 14.37 22.00
0.07165 0.00 25.650 0 0.5810 6.0040 84.10 2.1974 2 188.0 19.10 377.67 14.27 20.30
0.09299 0.00 25.650 0 0.5810 5.9610 92.90 2.0869 2 188.0 19.10 378.09 17.93 20.50
0.15038 0.00 25.650 0 0.5810 5.8560 97.00 1.9444 2 188.0 19.10 370.31 25.41 17.30
0.09849 0.00 25.650 0 0.5810 5.8790 95.80 2.0063 2 188.0 19.10 379.38 17.58 18.80
0.16902 0.00 25.650 0 0.5810 5.9860 88.40 1.9929 2 188.0 19.10 385.02 14.81 21.40
0.38735 0.00 25.650 0 0.5810 5.6130 95.60 1.7572 2 188.0 19.10 359.29 27.26 15.70
0.25915 0.00 21.890 0 0.6240 5.6930 96.00 1.7883 4 437.0 21.20 392.11 17.19 16.20
0.32543 0.00 21.890 0 0.6240 6.4310 98.80 1.8125 4 437.0 21.20 396.90 15.39 18.00
0.88125 0.00 21.890 0 0.6240 5.6370 94.70 1.9799 4 437.0 21.20 396.90 18.34 14.30
0.34006 0.00 21.890 0 0.6240 6.4580 98.90 2.1185 4 437.0 21.20 395.04 12.60 19.20
1.19294 0.00 21.890 0 0.6240 6.3260 97.70 2.2710 4 437.0 21.20 396.90 12.26 19.60
0.59005 0.00 21.890 0 0.6240 6.3720 97.90 2.3274 4 437.0 21.20 385.76 11.12 23.00
0.32982 0.00 21.890 0 0.6240 5.8220 95.40 2.4699 4 437.0 21.20 388.69 15.03 18.40
0.97617 0.00 21.890 0 0.6240 5.7570 98.40 2.3460 4 437.0 21.20 262.76 17.31 15.60
0.55778 0.00 21.890 0 0.6240 6.3350 98.20 2.1107 4 437.0 21.20 394.67 16.96 18.10
0.32264 0.00 21.890 0 0.6240 5.9420 93.50 1.9669 4 437.0 21.20 378.25 16.90 17.40
0.35233 0.00 21.890 0 0.6240 6.4540 98.40 1.8498 4 437.0 21.20 394.08 14.59 17.10
0.24980 0.00 21.890 0 0.6240 5.8570 98.20 1.6686 4 437.0 21.20 392.04 21.32 13.30
0.54452 0.00 21.890 0 0.6240 6.1510 97.90 1.6687 4 437.0 21.20 396.90 18.46 17.80
0.29090 0.00 21.890 0 0.6240 6.1740 93.60 1.6119 4 437.0 21.20 388.08 24.16 14.00
1.62864 0.00 21.890 0 0.6240 5.0190 100.00 1.4394 4 437.0 21.20 396.90 34.41 14.40
3.32105 0.00 19.580 1 0.8710 5.4030 100.00 1.3216 5 403.0 14.70 396.90 26.82 13.40
4.09740 0.00 19.580 0 0.8710 5.4680 100.00 1.4118 5 403.0 14.70 396.90 26.42 15.60
2.77974 0.00 19.580 0 0.8710 4.9030 97.80 1.3459 5 403.0 14.70 396.90 29.29 11.80
2.37934 0.00 19.580 0 0.8710 6.1300 100.00 1.4191 5 403.0 14.70 172.91 27.80 13.80
2.15505 0.00 19.580 0 0.8710 5.6280 100.00 1.5166 5 403.0 14.70 169.27 16.65 15.60
2.36862 0.00 19.580 0 0.8710 4.9260 95.70 1.4608 5 403.0 14.70 391.71 29.53 14.60
2.33099 0.00 19.580 0 0.8710 5.1860 93.80 1.5296 5 403.0 14.70 356.99 28.32 17.80
2.73397 0.00 19.580 0 0.8710 5.5970 94.90 1.5257 5 403.0 14.70 351.85 21.45 15.40
1.65660 0.00 19.580 0 0.8710 6.1220 97.30 1.6180 5 403.0 14.70 372.80 14.10 21.50
1.49632 0.00 19.580 0 0.8710 5.4040 100.00 1.5916 5 403.0 14.70 341.60 13.28 19.60
1.12658 0.00 19.580 1 0.8710 5.0120 88.00 1.6102 5 403.0 14.70 343.28 12.12 15.30
2.14918 0.00 19.580 0 0.8710 5.7090 98.50 1.6232 5 403.0 14.70 261.95 15.79 19.40
1.41385 0.00 19.580 1 0.8710 6.1290 96.00 1.7494 5 403.0 14.70 321.02 15.12 17.00
3.53501 0.00 19.580 1 0.8710 6.1520 82.60 1.7455 5 403.0 14.70 88.01 15.02 15.60
2.44668 0.00 19.580 0 0.8710 5.2720 94.00 1.7364 5 403.0 14.70 88.63 16.14 13.10
1.22358 0.00 19.580 0 0.6050 6.9430 97.40 1.8773 5 403.0 14.70 363.43 4.59 41.30
1.34284 0.00 19.580 0 0.6050 6.0660 100.00 1.7573 5 403.0 14.70 353.89 6.43 24.30
1.42502 0.00 19.580 0 0.8710 6.5100 100.00 1.7659 5 403.0 14.70 364.31 7.39 23.30
1.27346 0.00 19.580 1 0.6050 6.2500 92.60 1.7984 5 403.0 14.70 338.92 5.50 27.00
1.46336 0.00 19.580 0 0.6050 7.4890 90.80 1.9709 5 403.0 14.70 374.43 1.73 50.00
1.83377 0.00 19.580 1 0.6050 7.8020 98.20 2.0407 5 403.0 14.70 389.61 1.92 50.00
1.51902 0.00 19.580 1 0.6050 8.3750 93.90 2.1620 5 403.0 14.70 388.45 3.32 50.00
2.24236 0.00 19.580 0 0.6050 5.8540 91.80 2.4220 5 403.0 14.70 395.11 11.64 22.70
2.92400 0.00 19.580 0 0.6050 6.1010 93.00 2.2834 5 403.0 14.70 240.16 9.81 25.00
2.01019 0.00 19.580 0 0.6050 7.9290 96.20 2.0459 5 403.0 14.70 369.30 3.70 50.00
1.80028 0.00 19.580 0 0.6050 5.8770 79.20 2.4259 5 403.0 14.70 227.61 12.14 23.80
2.30040 0.00 19.580 0 0.6050 6.3190 96.10 2.1000 5 403.0 14.70 297.09 11.10 23.80
2.44953 0.00 19.580 0 0.6050 6.4020 95.20 2.2625 5 403.0 14.70 330.04 11.32 22.30
1.20742 0.00 19.580 0 0.6050 5.8750 94.60 2.4259 5 403.0 14.70 292.29 14.43 17.40
2.31390 0.00 19.580 0 0.6050 5.8800 97.30 2.3887 5 403.0 14.70 348.13 12.03 19.10
0.13914 0.00 4.050 0 0.5100 5.5720 88.50 2.5961 5 296.0 16.60 396.90 14.69 23.10
0.09178 0.00 4.050 0 0.5100 6.4160 84.10 2.6463 5 296.0 16.60 395.50 9.04 23.60
0.08447 0.00 4.050 0 0.5100 5.8590 68.70 2.7019 5 296.0 16.60 393.23 9.64 22.60
0.06664 0.00 4.050 0 0.5100 6.5460 33.10 3.1323 5 296.0 16.60 390.96 5.33 29.40
0.07022 0.00 4.050 0 0.5100 6.0200 47.20 3.5549 5 296.0 16.60 393.23 10.11 23.20
0.05425 0.00 4.050 0 0.5100 6.3150 73.40 3.3175 5 296.0 16.60 395.60 6.29 24.60
0.06642 0.00 4.050 0 0.5100 6.8600 74.40 2.9153 5 296.0 16.60 391.27 6.92 29.90
0.05780 0.00 2.460 0 0.4880 6.9800 58.40 2.8290 3 193.0 17.80 396.90 5.04 37.20
0.06588 0.00 2.460 0 0.4880 7.7650 83.30 2.7410 3 193.0 17.80 395.56 7.56 39.80
0.06888 0.00 2.460 0 0.4880 6.1440 62.20 2.5979 3 193.0 17.80 396.90 9.45 36.20
0.09103 0.00 2.460 0 0.4880 7.1550 92.20 2.7006 3 193.0 17.80 394.12 4.82 37.90
0.10008 0.00 2.460 0 0.4880 6.5630 95.60 2.8470 3 193.0 17.80 396.90 5.68 32.50
0.08308 0.00 2.460 0 0.4880 5.6040 89.80 2.9879 3 193.0 17.80 391.00 13.98 26.40
0.06047 0.00 2.460 0 0.4880 6.1530 68.80 3.2797 3 193.0 17.80 387.11 13.15 29.60
0.05602 0.00 2.460 0 0.4880 7.8310 53.60 3.1992 3 193.0 17.80 392.63 4.45 50.00
0.07875 45.00 3.440 0 0.4370 6.7820 41.10 3.7886 5 398.0 15.20 393.87 6.68 32.00
0.12579 45.00 3.440 0 0.4370 6.5560 29.10 4.5667 5 398.0 15.20 382.84 4.56 29.80
0.08370 45.00 3.440 0 0.4370 7.1850 38.90 4.5667 5 398.0 15.20 396.90 5.39 34.90
0.09068 45.00 3.440 0 0.4370 6.9510 21.50 6.4798 5 398.0 15.20 377.68 5.10 37.00
0.06911 45.00 3.440 0 0.4370 6.7390 30.80 6.4798 5 398.0 15.20 389.71 4.69 30.50
0.08664 45.00 3.440 0 0.4370 7.1780 26.30 6.4798 5 398.0 15.20 390.49 2.87 36.40
0.02187 60.00 2.930 0 0.4010 6.8000 9.90 6.2196 1 265.0 15.60 393.37 5.03 31.10
0.01439 60.00 2.930 0 0.4010 6.6040 18.80 6.2196 1 265.0 15.60 376.70 4.38 29.10
0.01381 80.00 0.460 0 0.4220 7.8750 32.00 5.6484 4 255.0 14.40 394.23 2.97 50.00
0.04011 80.00 1.520 0 0.4040 7.2870 34.10 7.3090 2 329.0 12.60 396.90 4.08 33.30
0.04666 80.00 1.520 0 0.4040 7.1070 36.60 7.3090 2 329.0 12.60 354.31 8.61 30.30
0.03768 80.00 1.520 0 0.4040 7.2740 38.30 7.3090 2 329.0 12.60 392.20 6.62 34.60
0.03150 95.00 1.470 0 0.4030 6.9750 15.30 7.6534 3 402.0 17.00 396.90 4.56 34.90
0.01778 95.00 1.470 0 0.4030 7.1350 13.90 7.6534 3 402.0 17.00 384.30 4.45 32.90
0.03445 82.50 2.030 0 0.4150 6.1620 38.40 6.2700 2 348.0 14.70 393.77 7.43 24.10
0.02177 82.50 2.030 0 0.4150 7.6100 15.70 6.2700 2 348.0 14.70 395.38 3.11 42.30
0.03510 95.00 2.680 0 0.4161 7.8530 33.20 5.1180 4 224.0 14.70 392.78 3.81 48.50
0.02009 95.00 2.680 0 0.4161 8.0340 31.90 5.1180 4 224.0 14.70 390.55 2.88 50.00
0.13642 0.00 10.590 0 0.4890 5.8910 22.30 3.9454 4 277.0 18.60 396.90 10.87 22.60
0.22969 0.00 10.590 0 0.4890 6.3260 52.50 4.3549 4 277.0 18.60 394.87 10.97 24.40
0.25199 0.00 10.590 0 0.4890 5.7830 72.70 4.3549 4 277.0 18.60 389.43 18.06 22.50
0.13587 0.00 10.590 1 0.4890 6.0640 59.10 4.2392 4 277.0 18.60 381.32 14.66 24.40
0.43571 0.00 10.590 1 0.4890 5.3440 100.00 3.8750 4 277.0 18.60 396.90 23.09 20.00
0.17446 0.00 10.590 1 0.4890 5.9600 92.10 3.8771 4 277.0 18.60 393.25 17.27 21.70
0.37578 0.00 10.590 1 0.4890 5.4040 88.60 3.6650 4 277.0 18.60 395.24 23.98 19.30
0.21719 0.00 10.590 1 0.4890 5.8070 53.80 3.6526 4 277.0 18.60 390.94 16.03 22.40
0.14052 0.00 10.590 0 0.4890 6.3750 32.30 3.9454 4 277.0 18.60 385.81 9.38 28.10
0.28955 0.00 10.590 0 0.4890 5.4120 9.80 3.5875 4 277.0 18.60 348.93 29.55 23.70
0.19802 0.00 10.590 0 0.4890 6.1820 42.40 3.9454 4 277.0 18.60 393.63 9.47 25.00
0.04560 0.00 13.890 1 0.5500 5.8880 56.00 3.1121 5 276.0 16.40 392.80 13.51 23.30
0.07013 0.00 13.890 0 0.5500 6.6420 85.10 3.4211 5 276.0 16.40 392.78 9.69 28.70
0.11069 0.00 13.890 1 0.5500 5.9510 93.80 2.8893 5 276.0 16.40 396.90 17.92 21.50
0.11425 0.00 13.890 1 0.5500 6.3730 92.40 3.3633 5 276.0 16.40 393.74 10.50 23.00
0.35809 0.00 6.200 1 0.5070 6.9510 88.50 2.8617 8 307.0 17.40 391.70 9.71 26.70
0.40771 0.00 6.200 1 0.5070 6.1640 91.30 3.0480 8 307.0 17.40 395.24 21.46 21.70
0.62356 0.00 6.200 1 0.5070 6.8790 77.70 3.2721 8 307.0 17.40 390.39 9.93 27.50
0.61470 0.00 6.200 0 0.5070 6.6180 80.80 3.2721 8 307.0 17.40 396.90 7.60 30.10
0.31533 0.00 6.200 0 0.5040 8.2660 78.30 2.8944 8 307.0 17.40 385.05 4.14 44.80
0.52693 0.00 6.200 0 0.5040 8.7250 83.00 2.8944 8 307.0 17.40 382.00 4.63 50.00
0.38214 0.00 6.200 0 0.5040 8.0400 86.50 3.2157 8 307.0 17.40 387.38 3.13 37.60
0.41238 0.00 6.200 0 0.5040 7.1630 79.90 3.2157 8 307.0 17.40 372.08 6.36 31.60
0.29819 0.00 6.200 0 0.5040 7.6860 17.00 3.3751 8 307.0 17.40 377.51 3.92 46.70
0.44178 0.00 6.200 0 0.5040 6.5520 21.40 3.3751 8 307.0 17.40 380.34 3.76 31.50
0.53700 0.00 6.200 0 0.5040 5.9810 68.10 3.6715 8 307.0 17.40 378.35 11.65 24.30
0.46296 0.00 6.200 0 0.5040 7.4120 76.90 3.6715 8 307.0 17.40 376.14 5.25 31.70
0.57529 0.00 6.200 0 0.5070 8.3370 73.30 3.8384 8 307.0 17.40 385.91 2.47 41.70
0.33147 0.00 6.200 0 0.5070 8.2470 70.40 3.6519 8 307.0 17.40 378.95 3.95 48.30
0.44791 0.00 6.200 1 0.5070 6.7260 66.50 3.6519 8 307.0 17.40 360.20 8.05 29.00
0.33045 0.00 6.200 0 0.5070 6.0860 61.50 3.6519 8 307.0 17.40 376.75 10.88 24.00
0.52058 0.00 6.200 1 0.5070 6.6310 76.50 4.1480 8 307.0 17.40 388.45 9.54 25.10
0.51183 0.00 6.200 0 0.5070 7.3580 71.60 4.1480 8 307.0 17.40 390.07 4.73 31.50
0.08244 30.00 4.930 0 0.4280 6.4810 18.50 6.1899 6 300.0 16.60 379.41 6.36 23.70
0.09252 30.00 4.930 0 0.4280 6.6060 42.20 6.1899 6 300.0 16.60 383.78 7.37 23.30
0.11329 30.00 4.930 0 0.4280 6.8970 54.30 6.3361 6 300.0 16.60 391.25 11.38 22.00
0.10612 30.00 4.930 0 0.4280 6.0950 65.10 6.3361 6 300.0 16.60 394.62 12.40 20.10
0.10290 30.00 4.930 0 0.4280 6.3580 52.90 7.0355 6 300.0 16.60 372.75 11.22 22.20
0.12757 30.00 4.930 0 0.4280 6.3930 7.80 7.0355 6 300.0 16.60 374.71 5.19 23.70
0.20608 22.00 5.860 0 0.4310 5.5930 76.50 7.9549 7 330.0 19.10 372.49 12.50 17.60
0.19133 22.00 5.860 0 0.4310 5.6050 70.20 7.9549 7 330.0 19.10 389.13 18.46 18.50
0.33983 22.00 5.860 0 0.4310 6.1080 34.90 8.0555 7 330.0 19.10 390.18 9.16 24.30
0.19657 22.00 5.860 0 0.4310 6.2260 79.20 8.0555 7 330.0 19.10 376.14 10.15 20.50
0.16439 22.00 5.860 0 0.4310 6.4330 49.10 7.8265 7 330.0 19.10 374.71 9.52 24.50
0.19073 22.00 5.860 0 0.4310 6.7180 17.50 7.8265 7 330.0 19.10 393.74 6.56 26.20
0.14030 22.00 5.860 0 0.4310 6.4870 13.00 7.3967 7 330.0 19.10 396.28 5.90 24.40
0.21409 22.00 5.860 0 0.4310 6.4380 8.90 7.3967 7 330.0 19.10 377.07 3.59 24.80
0.08221 22.00 5.860 0 0.4310 6.9570 6.80 8.9067 7 330.0 19.10 386.09 3.53 29.60
0.36894 22.00 5.860 0 0.4310 8.2590 8.40 8.9067 7 330.0 19.10 396.90 3.54 42.80
0.04819 80.00 3.640 0 0.3920 6.1080 32.00 9.2203 1 315.0 16.40 392.89 6.57 21.90
0.03548 80.00 3.640 0 0.3920 5.8760 19.10 9.2203 1 315.0 16.40 395.18 9.25 20.90
0.01538 90.00 3.750 0 0.3940 7.4540 34.20 6.3361 3 244.0 15.90 386.34 3.11 44.00
0.61154 20.00 3.970 0 0.6470 8.7040 86.90 1.8010 5 264.0 13.00 389.70 5.12 50.00
0.66351 20.00 3.970 0 0.6470 7.3330 100.00 1.8946 5 264.0 13.00 383.29 7.79 36.00
0.65665 20.00 3.970 0 0.6470 6.8420 100.00 2.0107 5 264.0 13.00 391.93 6.90 30.10
0.54011 20.00 3.970 0 0.6470 7.2030 81.80 2.1121 5 264.0 13.00 392.80 9.59 33.80
0.53412 20.00 3.970 0 0.6470 7.5200 89.40 2.1398 5 264.0 13.00 388.37 7.26 43.10
0.52014 20.00 3.970 0 0.6470 8.3980 91.50 2.2885 5 264.0 13.00 386.86 5.91 48.80
0.82526 20.00 3.970 0 0.6470 7.3270 94.50 2.0788 5 264.0 13.00 393.42 11.25 31.00
0.55007 20.00 3.970 0 0.6470 7.2060 91.60 1.9301 5 264.0 13.00 387.89 8.10 36.50
0.76162 20.00 3.970 0 0.6470 5.5600 62.80 1.9865 5 264.0 13.00 392.40 10.45 22.80
0.78570 20.00 3.970 0 0.6470 7.0140 84.60 2.1329 5 264.0 13.00 384.07 14.79 30.70
0.57834 20.00 3.970 0 0.5750 8.2970 67.00 2.4216 5 264.0 13.00 384.54 7.44 50.00
0.54050 20.00 3.970 0 0.5750 7.4700 52.60 2.8720 5 264.0 13.00 390.30 3.16 43.50
0.09065 20.00 6.960 1 0.4640 5.9200 61.50 3.9175 3 223.0 18.60 391.34 13.65 20.70
0.29916 20.00 6.960 0 0.4640 5.8560 42.10 4.4290 3 223.0 18.60 388.65 13.00 21.10
0.16211 20.00 6.960 0 0.4640 6.2400 16.30 4.4290 3 223.0 18.60 396.90 6.59 25.20
0.11460 20.00 6.960 0 0.4640 6.5380 58.70 3.9175 3 223.0 18.60 394.96 7.73 24.40
0.22188 20.00 6.960 1 0.4640 7.6910 51.80 4.3665 3 223.0 18.60 390.77 6.58 35.20
0.05644 40.00 6.410 1 0.4470 6.7580 32.90 4.0776 4 254.0 17.60 396.90 3.53 32.40
0.09604 40.00 6.410 0 0.4470 6.8540 42.80 4.2673 4 254.0 17.60 396.90 2.98 32.00
0.10469 40.00 6.410 1 0.4470 7.2670 49.00 4.7872 4 254.0 17.60 389.25 6.05 33.20
0.06127 40.00 6.410 1 0.4470 6.8260 27.60 4.8628 4 254.0 17.60 393.45 4.16 33.10
0.07978 40.00 6.410 0 0.4470 6.4820 32.10 4.1403 4 254.0 17.60 396.90 7.19 29.10
0.21038 20.00 3.330 0 0.4429 6.8120 32.20 4.1007 5 216.0 14.90 396.90 4.85 35.10
0.03578 20.00 3.330 0 0.4429 7.8200 64.50 4.6947 5 216.0 14.90 387.31 3.76 45.40
0.03705 20.00 3.330 0 0.4429 6.9680 37.20 5.2447 5 216.0 14.90 392.23 4.59 35.40
0.06129 20.00 3.330 1 0.4429 7.6450 49.70 5.2119 5 216.0 14.90 377.07 3.01 46.00
0.01501 90.00 1.210 1 0.4010 7.9230 24.80 5.8850 1 198.0 13.60 395.52 3.16 50.00
0.00906 90.00 2.970 0 0.4000 7.0880 20.80 7.3073 1 285.0 15.30 394.72 7.85 32.20
0.01096 55.00 2.250 0 0.3890 6.4530 31.90 7.3073 1 300.0 15.30 394.72 8.23 22.00
0.01965 80.00 1.760 0 0.3850 6.2300 31.50 9.0892 1 241.0 18.20 341.60 12.93 20.10
0.03871 52.50 5.320 0 0.4050 6.2090 31.30 7.3172 6 293.0 16.60 396.90 7.14 23.20
0.04590 52.50 5.320 0 0.4050 6.3150 45.60 7.3172 6 293.0 16.60 396.90 7.60 22.30
0.04297 52.50 5.320 0 0.4050 6.5650 22.90 7.3172 6 293.0 16.60 371.72 9.51 24.80
0.03502 80.00 4.950 0 0.4110 6.8610 27.90 5.1167 4 245.0 19.20 396.90 3.33 28.50
0.07886 80.00 4.950 0 0.4110 7.1480 27.70 5.1167 4 245.0 19.20 396.90 3.56 37.30
0.03615 80.00 4.950 0 0.4110 6.6300 23.40 5.1167 4 245.0 19.20 396.90 4.70 27.90
0.08265 0.00 13.920 0 0.4370 6.1270 18.40 5.5027 4 289.0 16.00 396.90 8.58 23.90
0.08199 0.00 13.920 0 0.4370 6.0090 42.30 5.5027 4 289.0 16.00 396.90 10.40 21.70
0.12932 0.00 13.920 0 0.4370 6.6780 31.10 5.9604 4 289.0 16.00 396.90 6.27 28.60
0.05372 0.00 13.920 0 0.4370 6.5490 51.00 5.9604 4 289.0 16.00 392.85 7.39 27.10
0.14103 0.00 13.920 0 0.4370 5.7900 58.00 6.3200 4 289.0 16.00 396.90 15.84 20.30
0.06466 70.00 2.240 0 0.4000 6.3450 20.10 7.8278 5 358.0 14.80 368.24 4.97 22.50
0.05561 70.00 2.240 0 0.4000 7.0410 10.00 7.8278 5 358.0 14.80 371.58 4.74 29.00
0.04417 70.00 2.240 0 0.4000 6.8710 47.40 7.8278 5 358.0 14.80 390.86 6.07 24.80
0.03537 34.00 6.090 0 0.4330 6.5900 40.40 5.4917 7 329.0 16.10 395.75 9.50 22.00
0.09266 34.00 6.090 0 0.4330 6.4950 18.40 5.4917 7 329.0 16.10 383.61 8.67 26.40
0.10000 34.00 6.090 0 0.4330 6.9820 17.70 5.4917 7 329.0 16.10 390.43 4.86 33.10
0.05515 33.00 2.180 0 0.4720 7.2360 41.10 4.0220 7 222.0 18.40 393.68 6.93 36.10
0.05479 33.00 2.180 0 0.4720 6.6160 58.10 3.3700 7 222.0 18.40 393.36 8.93 28.40
0.07503 33.00 2.180 0 0.4720 7.4200 71.90 3.0992 7 222.0 18.40 396.90 6.47 33.40
0.04932 33.00 2.180 0 0.4720 6.8490 70.30 3.1827 7 222.0 18.40 396.90 7.53 28.20
0.49298 0.00 9.900 0 0.5440 6.6350 82.50 3.3175 4 304.0 18.40 396.90 4.54 22.80
0.34940 0.00 9.900 0 0.5440 5.9720 76.70 3.1025 4 304.0 18.40 396.24 9.97 20.30
2.63548 0.00 9.900 0 0.5440 4.9730 37.80 2.5194 4 304.0 18.40 350.45 12.64 16.10
0.79041 0.00 9.900 0 0.5440 6.1220 52.80 2.6403 4 304.0 18.40 396.90 5.98 22.10
0.26169 0.00 9.900 0 0.5440 6.0230 90.40 2.8340 4 304.0 18.40 396.30 11.72 19.40
0.26938 0.00 9.900 0 0.5440 6.2660 82.80 3.2628 4 304.0 18.40 393.39 7.90 21.60
0.36920 0.00 9.900 0 0.5440 6.5670 87.30 3.6023 4 304.0 18.40 395.69 9.28 23.80
0.25356 0.00 9.900 0 0.5440 5.7050 77.70 3.9450 4 304.0 18.40 396.42 11.50 16.20
0.31827 0.00 9.900 0 0.5440 5.9140 83.20 3.9986 4 304.0 18.40 390.70 18.33 17.80
0.24522 0.00 9.900 0 0.5440 5.7820 71.70 4.0317 4 304.0 18.40 396.90 15.94 19.80
0.40202 0.00 9.900 0 0.5440 6.3820 67.20 3.5325 4 304.0 18.40 395.21 10.36 23.10
0.47547 0.00 9.900 0 0.5440 6.1130 58.80 4.0019 4 304.0 18.40 396.23 12.73 21.00
0.16760 0.00 7.380 0 0.4930 6.4260 52.30 4.5404 5 287.0 19.60 396.90 7.20 23.80
0.18159 0.00 7.380 0 0.4930 6.3760 54.30 4.5404 5 287.0 19.60 396.90 6.87 23.10
0.35114 0.00 7.380 0 0.4930 6.0410 49.90 4.7211 5 287.0 19.60 396.90 7.70 20.40
0.28392 0.00 7.380 0 0.4930 5.7080 74.30 4.7211 5 287.0 19.60 391.13 11.74 18.50
0.34109 0.00 7.380 0 0.4930 6.4150 40.10 4.7211 5 287.0 19.60 396.90 6.12 25.00
0.19186 0.00 7.380 0 0.4930 6.4310 14.70 5.4159 5 287.0 19.60 393.68 5.08 24.60
0.30347 0.00 7.380 0 0.4930 6.3120 28.90 5.4159 5 287.0 19.60 396.90 6.15 23.00
0.24103 0.00 7.380 0 0.4930 6.0830 43.70 5.4159 5 287.0 19.60 396.90 12.79 22.20
0.06617 0.00 3.240 0 0.4600 5.8680 25.80 5.2146 4 430.0 16.90 382.44 9.97 19.30
0.06724 0.00 3.240 0 0.4600 6.3330 17.20 5.2146 4 430.0 16.90 375.21 7.34 22.60
0.04544 0.00 3.240 0 0.4600 6.1440 32.20 5.8736 4 430.0 16.90 368.57 9.09 19.80
0.05023 35.00 6.060 0 0.4379 5.7060 28.40 6.6407 1 304.0 16.90 394.02 12.43 17.10
0.03466 35.00 6.060 0 0.4379 6.0310 23.30 6.6407 1 304.0 16.90 362.25 7.83 19.40
0.05083 0.00 5.190 0 0.5150 6.3160 38.10 6.4584 5 224.0 20.20 389.71 5.68 22.20
0.03738 0.00 5.190 0 0.5150 6.3100 38.50 6.4584 5 224.0 20.20 389.40 6.75 20.70
0.03961 0.00 5.190 0 0.5150 6.0370 34.50 5.9853 5 224.0 20.20 396.90 8.01 21.10
0.03427 0.00 5.190 0 0.5150 5.8690 46.30 5.2311 5 224.0 20.20 396.90 9.80 19.50
0.03041 0.00 5.190 0 0.5150 5.8950 59.60 5.6150 5 224.0 20.20 394.81 10.56 18.50
0.03306 0.00 5.190 0 0.5150 6.0590 37.30 4.8122 5 224.0 20.20 396.14 8.51 20.60
0.05497 0.00 5.190 0 0.5150 5.9850 45.40 4.8122 5 224.0 20.20 396.90 9.74 19.00
0.06151 0.00 5.190 0 0.5150 5.9680 58.50 4.8122 5 224.0 20.20 396.90 9.29 18.70
0.01301 35.00 1.520 0 0.4420 7.2410 49.30 7.0379 1 284.0 15.50 394.74 5.49 32.70
0.02498 0.00 1.890 0 0.5180 6.5400 59.70 6.2669 1 422.0 15.90 389.96 8.65 16.50
0.02543 55.00 3.780 0 0.4840 6.6960 56.40 5.7321 5 370.0 17.60 396.90 7.18 23.90
0.03049 55.00 3.780 0 0.4840 6.8740 28.10 6.4654 5 370.0 17.60 387.97 4.61 31.20
0.03113 0.00 4.390 0 0.4420 6.0140 48.50 8.0136 3 352.0 18.80 385.64 10.53 17.50
0.06162 0.00 4.390 0 0.4420 5.8980 52.30 8.0136 3 352.0 18.80 364.61 12.67 17.20
0.01870 85.00 4.150 0 0.4290 6.5160 27.70 8.5353 4 351.0 17.90 392.43 6.36 23.10
0.01501 80.00 2.010 0 0.4350 6.6350 29.70 8.3440 4 280.0 17.00 390.94 5.99 24.50
0.02899 40.00 1.250 0 0.4290 6.9390 34.50 8.7921 1 335.0 19.70 389.85 5.89 26.60
0.06211 40.00 1.250 0 0.4290 6.4900 44.40 8.7921 1 335.0 19.70 396.90 5.98 22.90
0.07950 60.00 1.690 0 0.4110 6.5790 35.90 10.7103 4 411.0 18.30 370.78 5.49 24.10
0.07244 60.00 1.690 0 0.4110 5.8840 18.50 10.7103 4 411.0 18.30 392.33 7.79 18.60
0.01709 90.00 2.020 0 0.4100 6.7280 36.10 12.1265 5 187.0 17.00 384.46 4.50 30.10
0.04301 80.00 1.910 0 0.4130 5.6630 21.90 10.5857 4 334.0 22.00 382.80 8.05 18.20
0.10659 80.00 1.910 0 0.4130 5.9360 19.50 10.5857 4 334.0 22.00 376.04 5.57 20.60
8.98296 0.00 18.100 1 0.7700 6.2120 97.40 2.1222 24 666.0 20.20 377.73 17.60 17.80
3.84970 0.00 18.100 1 0.7700 6.3950 91.00 2.5052 24 666.0 20.20 391.34 13.27 21.70
5.20177 0.00 18.100 1 0.7700 6.1270 83.40 2.7227 24 666.0 20.20 395.43 11.48 22.70
4.26131 0.00 18.100 0 0.7700 6.1120 81.30 2.5091 24 666.0 20.20 390.74 12.67 22.60
4.54192 0.00 18.100 0 0.7700 6.3980 88.00 2.5182 24 666.0 20.20 374.56 7.79 25.00
3.83684 0.00 18.100 0 0.7700 6.2510 91.10 2.2955 24 666.0 20.20 350.65 14.19 19.90
3.67822 0.00 18.100 0 0.7700 5.3620 96.20 2.1036 24 666.0 20.20 380.79 10.19 20.80
4.22239 0.00 18.100 1 0.7700 5.8030 89.00 1.9047 24 666.0 20.20 353.04 14.64 16.80
3.47428 0.00 18.100 1 0.7180 8.7800 82.90 1.9047 24 666.0 20.20 354.55 5.29 21.90
4.55587 0.00 18.100 0 0.7180 3.5610 87.90 1.6132 24 666.0 20.20 354.70 7.12 27.50
3.69695 0.00 18.100 0 0.7180 4.9630 91.40 1.7523 24 666.0 20.20 316.03 14.00 21.90
13.52220 0.00 18.100 0 0.6310 3.8630 100.00 1.5106 24 666.0 20.20 131.42 13.33 23.10
4.89822 0.00 18.100 0 0.6310 4.9700 100.00 1.3325 24 666.0 20.20 375.52 3.26 50.00
5.66998 0.00 18.100 1 0.6310 6.6830 96.80 1.3567 24 666.0 20.20 375.33 3.73 50.00
6.53876 0.00 18.100 1 0.6310 7.0160 97.50 1.2024 24 666.0 20.20 392.05 2.96 50.00
9.23230 0.00 18.100 0 0.6310 6.2160 100.00 1.1691 24 666.0 20.20 366.15 9.53 50.00
8.26725 0.00 18.100 1 0.6680 5.8750 89.60 1.1296 24 666.0 20.20 347.88 8.88 50.00
11.10810 0.00 18.100 0 0.6680 4.9060 100.00 1.1742 24 666.0 20.20 396.90 34.77 13.80
18.49820 0.00 18.100 0 0.6680 4.1380 100.00 1.1370 24 666.0 20.20 396.90 37.97 13.80
19.60910 0.00 18.100 0 0.6710 7.3130 97.90 1.3163 24 666.0 20.20 396.90 13.44 15.00
15.28800 0.00 18.100 0 0.6710 6.6490 93.30 1.3449 24 666.0 20.20 363.02 23.24 13.90
9.82349 0.00 18.100 0 0.6710 6.7940 98.80 1.3580 24 666.0 20.20 396.90 21.24 13.30
23.64820 0.00 18.100 0 0.6710 6.3800 96.20 1.3861 24 666.0 20.20 396.90 23.69 13.10
17.86670 0.00 18.100 0 0.6710 6.2230 100.00 1.3861 24 666.0 20.20 393.74 21.78 10.20
88.97620 0.00 18.100 0 0.6710 6.9680 91.90 1.4165 24 666.0 20.20 396.90 17.21 10.40
15.87440 0.00 18.100 0 0.6710 6.5450 99.10 1.5192 24 666.0 20.20 396.90 21.08 10.90
9.18702 0.00 18.100 0 0.7000 5.5360 100.00 1.5804 24 666.0 20.20 396.90 23.60 11.30
7.99248 0.00 18.100 0 0.7000 5.5200 100.00 1.5331 24 666.0 20.20 396.90 24.56 12.30
20.08490 0.00 18.100 0 0.7000 4.3680 91.20 1.4395 24 666.0 20.20 285.83 30.63 8.80
16.81180 0.00 18.100 0 0.7000 5.2770 98.10 1.4261 24 666.0 20.20 396.90 30.81 7.20
24.39380 0.00 18.100 0 0.7000 4.6520 100.00 1.4672 24 666.0 20.20 396.90 28.28 10.50
22.59710 0.00 18.100 0 0.7000 5.0000 89.50 1.5184 24 666.0 20.20 396.90 31.99 7.40
14.33370 0.00 18.100 0 0.7000 4.8800 100.00 1.5895 24 666.0 20.20 372.92 30.62 10.20
8.15174 0.00 18.100 0 0.7000 5.3900 98.90 1.7281 24 666.0 20.20 396.90 20.85 11.50
6.96215 0.00 18.100 0 0.7000 5.7130 97.00 1.9265 24 666.0 20.20 394.43 17.11 15.10
5.29305 0.00 18.100 0 0.7000 6.0510 82.50 2.1678 24 666.0 20.20 378.38 18.76 23.20
11.57790 0.00 18.100 0 0.7000 5.0360 97.00 1.7700 24 666.0 20.20 396.90 25.68 9.70
8.64476 0.00 18.100 0 0.6930 6.1930 92.60 1.7912 24 666.0 20.20 396.90 15.17 13.80
13.35980 0.00 18.100 0 0.6930 5.8870 94.70 1.7821 24 666.0 20.20 396.90 16.35 12.70
8.71675 0.00 18.100 0 0.6930 6.4710 98.80 1.7257 24 666.0 20.20 391.98 17.12 13.10
5.87205 0.00 18.100 0 0.6930 6.4050 96.00 1.6768 24 666.0 20.20 396.90 19.37 12.50
7.67202 0.00 18.100 0 0.6930 5.7470 98.90 1.6334 24 666.0 20.20 393.10 19.92 8.50
38.35180 0.00 18.100 0 0.6930 5.4530 100.00 1.4896 24 666.0 20.20 396.90 30.59 5.00
9.91655 0.00 18.100 0 0.6930 5.8520 77.80 1.5004 24 666.0 20.20 338.16 29.97 6.30
25.04610 0.00 18.100 0 0.6930 5.9870 100.00 1.5888 24 666.0 20.20 396.90 26.77 5.60
14.23620 0.00 18.100 0 0.6930 6.3430 100.00 1.5741 24 666.0 20.20 396.90 20.32 7.20
9.59571 0.00 18.100 0 0.6930 6.4040 100.00 1.6390 24 666.0 20.20 376.11 20.31 12.10
24.80170 0.00 18.100 0 0.6930 5.3490 96.00 1.7028 24 666.0 20.20 396.90 19.77 8.30
41.52920 0.00 18.100 0 0.6930 5.5310 85.40 1.6074 24 666.0 20.20 329.46 27.38 8.50
67.92080 0.00 18.100 0 0.6930 5.6830 100.00 1.4254 24 666.0 20.20 384.97 22.98 5.00
20.71620 0.00 18.100 0 0.6590 4.1380 100.00 1.1781 24 666.0 20.20 370.22 23.34 11.90
11.95110 0.00 18.100 0 0.6590 5.6080 100.00 1.2852 24 666.0 20.20 332.09 12.13 27.90
7.40389 0.00 18.100 0 0.5970 5.6170 97.90 1.4547 24 666.0 20.20 314.64 26.40 17.20
14.43830 0.00 18.100 0 0.5970 6.8520 100.00 1.4655 24 666.0 20.20 179.36 19.78 27.50
51.13580 0.00 18.100 0 0.5970 5.7570 100.00 1.4130 24 666.0 20.20 2.60 10.11 15.00
14.05070 0.00 18.100 0 0.5970 6.6570 100.00 1.5275 24 666.0 20.20 35.05 21.22 17.20
18.81100 0.00 18.100 0 0.5970 4.6280 100.00 1.5539 24 666.0 20.20 28.79 34.37 17.90
28.65580 0.00 18.100 0 0.5970 5.1550 100.00 1.5894 24 666.0 20.20 210.97 20.08 16.30
45.74610 0.00 18.100 0 0.6930 4.5190 100.00 1.6582 24 666.0 20.20 88.27 36.98 7.00
18.08460 0.00 18.100 0 0.6790 6.4340 100.00 1.8347 24 666.0 20.20 27.25 29.05 7.20
10.83420 0.00 18.100 0 0.6790 6.7820 90.80 1.8195 24 666.0 20.20 21.57 25.79 7.50
25.94060 0.00 18.100 0 0.6790 5.3040 89.10 1.6475 24 666.0 20.20 127.36 26.64 10.40
73.53410 0.00 18.100 0 0.6790 5.9570 100.00 1.8026 24 666.0 20.20 16.45 20.62 8.80
11.81230 0.00 18.100 0 0.7180 6.8240 76.50 1.7940 24 666.0 20.20 48.45 22.74 8.40
11.08740 0.00 18.100 0 0.7180 6.4110 100.00 1.8589 24 666.0 20.20 318.75 15.02 16.70
7.02259 0.00 18.100 0 0.7180 6.0060 95.30 1.8746 24 666.0 20.20 319.98 15.70 14.20
12.04820 0.00 18.100 0 0.6140 5.6480 87.60 1.9512 24 666.0 20.20 291.55 14.10 20.80
7.05042 0.00 18.100 0 0.6140 6.1030 85.10 2.0218 24 666.0 20.20 2.52 23.29 13.40
8.79212 0.00 18.100 0 0.5840 5.5650 70.60 2.0635 24 666.0 20.20 3.65 17.16 11.70
15.86030 0.00 18.100 0 0.6790 5.8960 95.40 1.9096 24 666.0 20.20 7.68 24.39 8.30
12.24720 0.00 18.100 0 0.5840 5.8370 59.70 1.9976 24 666.0 20.20 24.65 15.69 10.20
37.66190 0.00 18.100 0 0.6790 6.2020 78.70 1.8629 24 666.0 20.20 18.82 14.52 10.90
7.36711 0.00 18.100 0 0.6790 6.1930 78.10 1.9356 24 666.0 20.20 96.73 21.52 11.00
9.33889 0.00 18.100 0 0.6790 6.3800 95.60 1.9682 24 666.0 20.20 60.72 24.08 9.50
8.49213 0.00 18.100 0 0.5840 6.3480 86.10 2.0527 24 666.0 20.20 83.45 17.64 14.50
10.06230 0.00 18.100 0 0.5840 6.8330 94.30 2.0882 24 666.0 20.20 81.33 19.69 14.10
6.44405 0.00 18.100 0 0.5840 6.4250 74.80 2.2004 24 666.0 20.20 97.95 12.03 16.10
5.58107 0.00 18.100 0 0.7130 6.4360 87.90 2.3158 24 666.0 20.20 100.19 16.22 14.30
13.91340 0.00 18.100 0 0.7130 6.2080 95.00 2.2222 24 666.0 20.20 100.63 15.17 11.70
11.16040 0.00 18.100 0 0.7400 6.6290 94.60 2.1247 24 666.0 20.20 109.85 23.27 13.40
14.42080 0.00 18.100 0 0.7400 6.4610 93.30 2.0026 24 666.0 20.20 27.49 18.05 9.60
15.17720 0.00 18.100 0 0.7400 6.1520 100.00 1.9142 24 666.0 20.20 9.32 26.45 8.70
13.67810 0.00 18.100 0 0.7400 5.9350 87.90 1.8206 24 666.0 20.20 68.95 34.02 8.40
9.39063 0.00 18.100 0 0.7400 5.6270 93.90 1.8172 24 666.0 20.20 396.90 22.88 12.80
22.05110 0.00 18.100 0 0.7400 5.8180 92.40 1.8662 24 666.0 20.20 391.45 22.11 10.50
9.72418 0.00 18.100 0 0.7400 6.4060 97.20 2.0651 24 666.0 20.20 385.96 19.52 17.10
5.66637 0.00 18.100 0 0.7400 6.2190 100.00 2.0048 24 666.0 20.20 395.69 16.59 18.40
9.96654 0.00 18.100 0 0.7400 6.4850 100.00 1.9784 24 666.0 20.20 386.73 18.85 15.40
12.80230 0.00 18.100 0 0.7400 5.8540 96.60 1.8956 24 666.0 20.20 240.52 23.79 10.80
10.67180 0.00 18.100 0 0.7400 6.4590 94.80 1.9879 24 666.0 20.20 43.06 23.98 11.80
6.28807 0.00 18.100 0 0.7400 6.3410 96.40 2.0720 24 666.0 20.20 318.01 17.79 14.90
9.92485 0.00 18.100 0 0.7400 6.2510 96.60 2.1980 24 666.0 20.20 388.52 16.44 12.60
9.32909 0.00 18.100 0 0.7130 6.1850 98.70 2.2616 24 666.0 20.20 396.90 18.13 14.10
7.52601 0.00 18.100 0 0.7130 6.4170 98.30 2.1850 24 666.0 20.20 304.21 19.31 13.00
6.71772 0.00 18.100 0 0.7130 6.7490 92.60 2.3236 24 666.0 20.20 0.32 17.44 13.40
5.44114 0.00 18.100 0 0.7130 6.6550 98.20 2.3552 24 666.0 20.20 355.29 17.73 15.20
5.09017 0.00 18.100 0 0.7130 6.2970 91.80 2.3682 24 666.0 20.20 385.09 17.27 16.10
8.24809 0.00 18.100 0 0.7130 7.3930 99.30 2.4527 24 666.0 20.20 375.87 16.74 17.80
9.51363 0.00 18.100 0 0.7130 6.7280 94.10 2.4961 24 666.0 20.20 6.68 18.71 14.90
4.75237 0.00 18.100 0 0.7130 6.5250 86.50 2.4358 24 666.0 20.20 50.92 18.13 14.10
4.66883 0.00 18.100 0 0.7130 5.9760 87.90 2.5806 24 666.0 20.20 10.48 19.01 12.70
8.20058 0.00 18.100 0 0.7130 5.9360 80.30 2.7792 24 666.0 20.20 3.50 16.94 13.50
7.75223 0.00 18.100 0 0.7130 6.3010 83.70 2.7831 24 666.0 20.20 272.21 16.23 14.90
6.80117 0.00 18.100 0 0.7130 6.0810 84.40 2.7175 24 666.0 20.20 396.90 14.70 20.00
4.81213 0.00 18.100 0 0.7130 6.7010 90.00 2.5975 24 666.0 20.20 255.23 16.42 16.40
3.69311 0.00 18.100 0 0.7130 6.3760 88.40 2.5671 24 666.0 20.20 391.43 14.65 17.70
6.65492 0.00 18.100 0 0.7130 6.3170 83.00 2.7344 24 666.0 20.20 396.90 13.99 19.50
5.82115 0.00 18.100 0 0.7130 6.5130 89.90 2.8016 24 666.0 20.20 393.82 10.29 20.20
7.83932 0.00 18.100 0 0.6550 6.2090 65.40 2.9634 24 666.0 20.20 396.90 13.22 21.40
3.16360 0.00 18.100 0 0.6550 5.7590 48.20 3.0665 24 666.0 20.20 334.40 14.13 19.90
3.77498 0.00 18.100 0 0.6550 5.9520 84.70 2.8715 24 666.0 20.20 22.01 17.15 19.00
4.42228 0.00 18.100 0 0.5840 6.0030 94.50 2.5403 24 666.0 20.20 331.29 21.32 19.10
15.57570 0.00 18.100 0 0.5800 5.9260 71.00 2.9084 24 666.0 20.20 368.74 18.13 19.10
13.07510 0.00 18.100 0 0.5800 5.7130 56.70 2.8237 24 666.0 20.20 396.90 14.76 20.10
4.34879 0.00 18.100 0 0.5800 6.1670 84.00 3.0334 24 666.0 20.20 396.90 16.29 19.90
4.03841 0.00 18.100 0 0.5320 6.2290 90.70 3.0993 24 666.0 20.20 395.33 12.87 19.60
3.56868 0.00 18.100 0 0.5800 6.4370 75.00 2.8965 24 666.0 20.20 393.37 14.36 23.20
4.64689 0.00 18.100 0 0.6140 6.9800 67.60 2.5329 24 666.0 20.20 374.68 11.66 29.80
8.05579 0.00 18.100 0 0.5840 5.4270 95.40 2.4298 24 666.0 20.20 352.58 18.14 13.80
6.39312 0.00 18.100 0 0.5840 6.1620 97.40 2.2060 24 666.0 20.20 302.76 24.10 13.30
4.87141 0.00 18.100 0 0.6140 6.4840 93.60 2.3053 24 666.0 20.20 396.21 18.68 16.70
15.02340 0.00 18.100 0 0.6140 5.3040 97.30 2.1007 24 666.0 20.20 349.48 24.91 12.00
10.23300 0.00 18.100 0 0.6140 6.1850 96.70 2.1705 24 666.0 20.20 379.70 18.03 14.60
14.33370 0.00 18.100 0 0.6140 6.2290 88.00 1.9512 24 666.0 20.20 383.32 13.11 21.40
5.82401 0.00 18.100 0 0.5320 6.2420 64.70 3.4242 24 666.0 20.20 396.90 10.74 23.00
5.70818 0.00 18.100 0 0.5320 6.7500 74.90 3.3317 24 666.0 20.20 393.07 7.74 23.70
5.73116 0.00 18.100 0 0.5320 7.0610 77.00 3.4106 24 666.0 20.20 395.28 7.01 25.00
2.81838 0.00 18.100 0 0.5320 5.7620 40.30 4.0983 24 666.0 20.20 392.92 10.42 21.80
2.37857 0.00 18.100 0 0.5830 5.8710 41.90 3.7240 24 666.0 20.20 370.73 13.34 20.60
3.67367 0.00 18.100 0 0.5830 6.3120 51.90 3.9917 24 666.0 20.20 388.62 10.58 21.20
5.69175 0.00 18.100 0 0.5830 6.1140 79.80 3.5459 24 666.0 20.20 392.68 14.98 19.10
4.83567 0.00 18.100 0 0.5830 5.9050 53.20 3.1523 24 666.0 20.20 388.22 11.45 20.60
0.15086 0.00 27.740 0 0.6090 5.4540 92.70 1.8209 4 711.0 20.10 395.09 18.06 15.20
0.18337 0.00 27.740 0 0.6090 5.4140 98.30 1.7554 4 711.0 20.10 344.05 23.97 7.00
0.20746 0.00 27.740 0 0.6090 5.0930 98.00 1.8226 4 711.0 20.10 318.43 29.68 8.10
0.10574 0.00 27.740 0 0.6090 5.9830 98.80 1.8681 4 711.0 20.10 390.11 18.07 13.60
0.11132 0.00 27.740 0 0.6090 5.9830 83.50 2.1099 4 711.0 20.10 396.90 13.35 20.10
0.17331 0.00 9.690 0 0.5850 5.7070 54.00 2.3817 6 391.0 19.20 396.90 12.01 21.80
0.27957 0.00 9.690 0 0.5850 5.9260 42.60 2.3817 6 391.0 19.20 396.90 13.59 24.50
0.17899 0.00 9.690 0 0.5850 5.6700 28.80 2.7986 6 391.0 19.20 393.29 17.60 23.10
0.28960 0.00 9.690 0 0.5850 5.3900 72.90 2.7986 6 391.0 19.20 396.90 21.14 19.70
0.26838 0.00 9.690 0 0.5850 5.7940 70.60 2.8927 6 391.0 19.20 396.90 14.10 18.30
0.23912 0.00 9.690 0 0.5850 6.0190 65.30 2.4091 6 391.0 19.20 396.90 12.92 21.20
0.17783 0.00 9.690 0 0.5850 5.5690 73.50 2.3999 6 391.0 19.20 395.77 15.10 17.50
0.22438 0.00 9.690 0 0.5850 6.0270 79.70 2.4982 6 391.0 19.20 396.90 14.33 16.80
0.06263 0.00 11.930 0 0.5730 6.5930 69.10 2.4786 1 273.0 21.00 391.99 9.67 22.40
0.04527 0.00 11.930 0 0.5730 6.1200 76.70 2.2875 1 273.0 21.00 396.90 9.08 20.60
0.06076 0.00 11.930 0 0.5730 6.9760 91.00 2.1675 1 273.0 21.00 396.90 5.64 23.90
0.10959 0.00 11.930 0 0.5730 6.7940 89.30 2.3889 1 273.0 21.00 393.45 6.48 22.00
0.04741 0.00 11.930 0 0.5730 6.0300 80.80 2.5050 1 273.0 21.00 396.90 7.88 11.90
(506, 14)
(496, 13)
(496,)
(10, 13)
(10,)

#net
class Net(torch.nn.Module):
    def __init__(self, n_feature, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, 100)
        self.predict = torch.nn.Linear(100, n_output)
    def forward(self, x):
        out = self.hidden(x)
        out = torch.relu(out)
        out = self.predict(out)
        return out
net = Net(13, 1)
#losss
loss_func = torch.nn.MSELoss()
#optimiter
optimizer = torch.optim.Adam(net.parameters(), lr=0.01)
#training
for i in range(100):
    x_data = torch.tensor(X_train, dtype=torch.float32)
    y_data = torch.tensor(Y_train, dtype=torch.float32)
    pred = net.forward(x_data)
    pred = torch.squeeze(pred)   #删除一个维度：一
    loss = loss_func(pred, y_data) *0.001  #否则这里是nan

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    print("ite:{}, loss_train:{}".format(i, loss))
    print(pred[0:10])
    print(y_data[0:10])

    #test
    x_data = torch.tensor(X_test, dtype=torch.float32)
    y_data = torch.tensor(Y_test, dtype=torch.float32)
    pred = net.forward(x_data)
    pred = torch.squeeze(pred)
    loss_test = loss_func(pred, y_data) * 0.001
    print("ite:{}, loss_test:{}".format(i, loss_test))

#保存
#torch.save(net, "model/model.pkl")
#torch.load("")  #加载模型

#torch.save(net.state_dict(), "params.pkl") #模型整体保存
#net.load_state_dict("")

ite:0, loss_train:0.17732955515384674
tensor([16.5216, 15.4965, 15.0370, 14.6338, 14.8242, 14.8461, 16.3944, 16.8272,
        16.4202, 16.4854], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:0, loss_test:4.138763904571533
ite:1, loss_train:4.808932781219482
tensor([79.7904, 73.7477, 72.1614, 69.6385, 70.3550, 70.2337, 81.3289, 83.6912,
        83.2250, 81.9581], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:1, loss_test:0.42939022183418274
ite:2, loss_train:0.42421165108680725
tensor([38.8310, 36.2112, 35.3513, 34.2295, 34.6024, 34.5730, 39.2063, 40.2808,
        39.7656, 39.4266], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:2, loss_test:0.5264627933502197
ite:3, loss_train:1.067562222480774
tensor([-1.3372, -0.8518, -1.0230, -0.7157, -0.6665, -0.6154, -2.3066, -2.2850,
        -2.6237, -2.3032], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:3, loss_test:1.5136289596557617
ite:4, loss_train:2.5476698875427246
tensor([-16.0546, -14.3309, -14.3266, -13.3874, -13.4017, -13.3512, -17.5609,
        -17.9668, -18.3998, -17.7961], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:4, loss_test:0.9808581471443176
ite:5, loss_train:1.7459462881088257
tensor([ -8.7744,  -7.4346,  -7.5610,  -6.8204,  -6.7659,  -6.7427, -10.1416,
        -10.3742, -10.8340, -10.3543], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:5, loss_test:0.16353915631771088
ite:6, loss_train:0.4514464735984802
tensor([8.8153, 8.8365, 8.5328, 8.6665, 8.8528, 8.8369, 7.9635, 8.1133, 7.5853,
        7.7920], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:6, loss_test:0.09097215533256531
ite:7, loss_train:0.1008220836520195
tensor([28.6497, 27.0689, 26.5373, 26.0684, 26.3098, 26.2288, 28.3829, 28.9243,
        28.3751, 28.2371], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:7, loss_test:0.6700623631477356
ite:8, loss_train:0.6482964754104614
tensor([44.2954, 41.5117, 40.7468, 39.7167, 40.0883, 39.9737, 44.4780, 45.3302,
        44.7809, 44.4200], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:8, loss_test:1.1315938234329224
ite:9, loss_train:1.1543121337890625
tensor([51.6650, 48.3216, 47.4436, 46.1947, 46.6229, 46.4894, 52.0432, 53.0295,
        52.4192, 51.9642], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:9, loss_test:1.030753493309021
ite:10, loss_train:1.0461804866790771
tensor([50.3679, 47.1478, 46.3098, 45.1597, 45.5746, 45.4380, 50.6796, 51.6268,
        50.8905, 50.5233], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:10, loss_test:0.560609757900238
ite:11, loss_train:0.5395894646644592
tensor([42.6326, 40.1628, 39.3572, 38.5249, 38.9516, 38.8335, 42.6696, 43.4584,
        42.7430, 42.4899], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:11, loss_test:0.13871586322784424
ite:12, loss_train:0.13168783485889435
tensor([31.5140, 30.1019, 29.3524, 29.0272, 29.4095, 29.3074, 31.1734, 31.7390,
        31.0646, 30.9650], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:12, loss_test:0.011774196289479733
ite:13, loss_train:0.10203374922275543
tensor([20.1254, 19.7726, 19.2035, 19.3174, 19.6084, 19.5236, 19.4002, 19.7645,
        19.2198, 19.1706], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:13, loss_test:0.12480086833238602
ite:14, loss_train:0.353059321641922
tensor([11.2244, 11.6946, 11.2660, 11.7511, 11.9484, 11.8768, 10.1918, 10.5164,
         9.9979,  9.9452], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:14, loss_test:0.2553038001060486
ite:15, loss_train:0.5787379145622253
tensor([6.6832, 7.5566, 7.2019, 7.9062, 8.0582, 7.9720, 5.4398, 5.7611, 5.2501,
        5.2376], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:15, loss_test:0.2490616887807846
ite:16, loss_train:0.5709474086761475
tensor([6.9707, 7.8205, 7.4668, 8.2011, 8.3546, 8.2645, 5.6556, 6.0180, 5.4933,
        5.4868], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:16, loss_test:0.12832723557949066
ite:17, loss_train:0.3647799491882324
tensor([11.3220, 11.8301, 11.4170, 12.0223, 12.2071, 12.1053, 10.1005, 10.5367,
         9.9797,  9.9263], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:17, loss_test:0.02238505333662033
ite:18, loss_train:0.1438639909029007
tensor([18.2833, 18.2435, 17.7338, 18.1051, 18.3426, 18.2233, 17.2599, 17.7890,
        17.1866, 17.0542], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:18, loss_test:0.034527670592069626
ite:19, loss_train:0.06348032504320145
tensor([26.0930, 25.4353, 24.8187, 24.9005, 25.1939, 25.0644, 25.3046, 25.9254,
        25.2722, 25.0528], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:19, loss_test:0.16130630671977997
ite:20, loss_train:0.14122918248176575
tensor([33.0182, 31.7972, 31.1002, 30.9328, 31.2724, 31.1240, 32.4343, 33.1337,
        32.4321, 32.1365], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:20, loss_test:0.30705058574676514
ite:21, loss_train:0.27085140347480774
tensor([37.6799, 36.0170, 35.3123, 35.0049, 35.3738, 35.2041, 37.2145, 37.9700,
        37.2289, 36.8914], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:21, loss_test:0.368404358625412
ite:22, loss_train:0.328154057264328
tensor([39.3358, 37.4901, 36.8037, 36.4710, 36.8476, 36.6411, 38.8726, 39.6569,
        38.8894, 38.5494], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:22, loss_test:0.31254419684410095
ite:23, loss_train:0.2716028690338135
tensor([37.9911, 36.2297, 35.5777, 35.3334, 35.6923, 35.4857, 37.4141, 38.2016,
        37.4214, 37.1174], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:23, loss_test:0.18741653859615326
ite:24, loss_train:0.1568107008934021
tensor([34.2901, 32.8236, 32.2175, 32.1488, 32.4822, 32.2931, 33.5106, 34.2804,
        33.4987, 33.2599], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:24, loss_test:0.07173235714435577
ite:25, loss_train:0.07344864308834076
tensor([29.2855, 28.2349, 27.6769, 27.8294, 28.1243, 27.9692, 28.3194, 28.9963,
        28.2195, 28.0617], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:25, loss_test:0.015750423073768616
ite:26, loss_train:0.07085394114255905
tensor([24.1847, 23.5689, 23.0498, 23.4226, 23.6787, 23.5328, 23.0335, 23.6138,
        22.8426, 22.7664], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:26, loss_test:0.015889398753643036
ite:27, loss_train:0.12843608856201172
tensor([20.1016, 19.8279, 19.3573, 19.8957, 20.1203, 19.9815, 18.7977, 19.2989,
        18.5288, 18.5212], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:27, loss_test:0.033539194613695145
ite:28, loss_train:0.18422091007232666
tensor([17.8327, 17.7315, 17.3085, 17.9413, 18.1468, 18.0112, 16.4334, 16.8853,
        16.1081, 16.1465], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:28, loss_test:0.035443492233753204
ite:29, loss_train:0.1897267997264862
tensor([17.7073, 17.6046, 17.2002, 17.8466, 18.0476, 17.9103, 16.2807, 16.7176,
        15.9235, 15.9817], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:29, loss_test:0.01996186375617981
ite:30, loss_train:0.14466354250907898
tensor([19.5604, 19.2805, 18.8786, 19.4691, 19.6791, 19.5356, 18.1692, 18.6222,
        17.8020, 17.8562], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:30, loss_test:0.01116357184946537
ite:31, loss_train:0.08811161667108536
tensor([22.8217, 22.2288, 21.8294, 22.3146, 22.5427, 22.3898, 21.5109, 22.0015,
        21.1486, 21.1821], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:31, loss_test:0.03194083273410797
ite:32, loss_train:0.06141657754778862
tensor([26.6754, 25.7184, 25.3157, 25.6747, 25.9246, 25.7299, 25.4642, 26.0011,
        25.1125, 25.1187], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:32, loss_test:0.08140280097723007
ite:33, loss_train:0.07601029425859451
tensor([30.2463, 28.9530, 28.5468, 28.7890, 29.0296, 28.8198, 29.1268, 29.7047,
        28.7820, 28.7646], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:33, loss_test:0.13406088948249817
ite:34, loss_train:0.10945066064596176
tensor([32.7869, 31.2511, 30.8468, 31.0078, 31.2362, 31.0193, 31.7278, 32.3302,
        31.3785, 31.3500], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:34, loss_test:0.15957999229431152
ite:35, loss_train:0.12860223650932312
tensor([33.8340, 32.1892, 31.7968, 31.9281, 32.1468, 31.9260, 32.7897, 33.3932,
        32.4196, 32.3981], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:35, loss_test:0.14503201842308044
ite:36, loss_train:0.11774115264415741
tensor([33.2937, 31.6811, 31.3116, 31.4678, 31.6787, 31.4577, 32.2156, 32.7958,
        31.8079, 31.8125], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:36, loss_test:0.10208965837955475
ite:37, loss_train:0.0881538987159729
tensor([31.4325, 29.9781, 29.6319, 29.8586, 30.0635, 29.8454, 30.2804, 30.8173,
        29.8214, 29.8678], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:37, loss_test:0.055462464690208435
ite:38, loss_train:0.06441900134086609
tensor([28.7867, 27.5631, 27.2423, 27.5663, 27.7668, 27.5534, 27.5367, 28.0187,
        27.0185, 27.1161], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:38, loss_test:0.024440037086606026
ite:39, loss_train:0.062360867857933044
tensor([26.0208, 25.0394, 24.7434, 25.1687, 25.3660, 25.1577, 24.6696, 25.0956,
        24.0912, 24.2415], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:39, loss_test:0.012531990185379982
ite:40, loss_train:0.0777241587638855
tensor([23.7714, 22.9859, 22.7114, 23.2197, 23.4139, 23.2096, 22.3355, 22.7136,
        21.7025, 21.8994], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:40, loss_test:0.011107591912150383
ite:41, loss_train:0.09295515716075897
tensor([22.5061, 21.8273, 21.5692, 22.1262, 22.3090, 22.1140, 21.0166, 21.3617,
        20.3393, 20.5712], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:41, loss_test:0.011156135238707066
ite:42, loss_train:0.09377838671207428
tensor([22.4288, 21.7486, 21.5015, 22.0657, 22.2435, 22.0472, 20.9229, 21.2531,
        20.2136, 20.4666], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:42, loss_test:0.011746286414563656
ite:43, loss_train:0.08044097572565079
tensor([23.4517, 22.6704, 22.4293, 22.9624, 23.1403, 22.9336, 21.9642, 22.2958,
        21.2340, 21.4952], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:43, loss_test:0.018548930063843727
ite:44, loss_train:0.06508217751979828
tensor([25.2393, 24.2882, 24.0492, 24.5246, 24.6949, 24.4833, 23.7940, 24.1384,
        23.0504, 23.3110], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:44, loss_test:0.035574693232774734
ite:45, loss_train:0.059628333896398544
tensor([27.3043, 26.1593, 25.9205, 26.3289, 26.4919, 26.2746, 25.9099, 26.2710,
        25.1552, 25.4124], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:45, loss_test:0.05880742892622948
ite:46, loss_train:0.06574245542287827
tensor([29.1317, 27.8139, 27.5773, 27.9272, 28.0839, 27.8614, 27.7805, 28.1547,
        27.0120, 27.2687], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:46, loss_test:0.07765673846006393
ite:47, loss_train:0.07503940165042877
tensor([30.3026, 28.8720, 28.6408, 28.9552, 29.1065, 28.8805, 28.9738, 29.3513,
        28.1844, 28.4489], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:47, loss_test:0.08257561922073364
ite:48, loss_train:0.0778837651014328
tensor([30.5887, 29.1266, 28.9043, 29.2142, 29.3613, 29.1336, 29.2545, 29.6219,
        28.4350, 28.7178], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:48, loss_test:0.07196034491062164
ite:49, loss_train:0.07195473462343216
tensor([29.9956, 28.5828, 28.3728, 28.7091, 28.8529, 28.6255, 28.6283, 28.9728,
        27.7697, 28.0812], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:49, loss_test:0.05254863575100899
ite:50, loss_train:0.06308599561452866
tensor([28.7447, 27.4416, 27.2466, 27.6322, 27.7735, 27.5481, 27.3234, 27.6352,
        26.4190, 26.7671], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:50, loss_test:0.033759571611881256
ite:51, loss_train:0.058782488107681274
tensor([27.2021, 26.0361, 25.8568, 26.3022, 26.4415, 26.2187, 25.7175, 25.9925,
        24.7642, 25.1524], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:51, loss_test:0.0214046873152256
ite:52, loss_train:0.06129424646496773
tensor([25.7779, 24.7390, 24.5745, 25.0756, 25.2133, 24.9928, 24.2336, 24.4734,
        23.2324, 23.6594], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:52, loss_test:0.015789922326803207
ite:53, loss_train:0.06646682322025299
tensor([24.8238, 23.8669, 23.7148, 24.2555, 24.3915, 24.1722, 23.2309, 23.4426,
        22.1863, 22.6469], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:53, loss_test:0.014505738392472267
ite:54, loss_train:0.06851168721914291
tensor([24.5392, 23.6046, 23.4620, 24.0191, 24.1528, 23.9333, 22.9194, 23.1128,
        21.8380, 22.3244], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:54, loss_test:0.01622951216995716
ite:55, loss_train:0.06550212949514389
tensor([24.9410, 23.9676, 23.8316, 24.3814, 24.5121, 24.2907, 23.3171, 23.5022,
        22.2053, 22.7098], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:55, loss_test:0.021680645644664764
ite:56, loss_train:0.06049354001879692
tensor([25.8694, 24.8107, 24.6789, 25.2030, 25.3303, 25.1060, 24.2587, 24.4433,
        23.1216, 23.6382], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:56, loss_test:0.03139384835958481
ite:57, loss_train:0.057907089591026306
tensor([27.0410, 25.8760, 25.7471, 26.2371, 26.3611, 26.1331, 25.4517, 25.6394,
        24.2915, 24.8175], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:57, loss_test:0.04324770346283913
ite:58, loss_train:0.05921926721930504
tensor([28.1314, 26.8679, 26.7418, 27.2000, 27.3217, 27.0898, 26.5605, 26.7501,
        25.3763, 25.9127], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:58, loss_test:0.05265432223677635
ite:59, loss_train:0.06205075979232788
tensor([28.8620, 27.5326, 27.4108, 27.8472, 27.9696, 27.7344, 27.2977, 27.4838,
        26.0856, 26.6369], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:59, loss_test:0.05544509366154671
ite:60, loss_train:0.0630173310637474
tensor([29.0705, 27.7231, 27.6076, 28.0418, 28.1641, 27.9272, 27.4963, 27.6714,
        26.2513, 26.8235], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:60, loss_test:0.05077565461397171
ite:61, loss_train:0.06109986826777458
tensor([28.7490, 27.4309, 27.3239, 27.7756, 27.8971, 27.6601, 27.1470, 27.3034,
        25.8638, 26.4631], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:61, loss_test:0.0414750911295414
ite:62, loss_train:0.05824420228600502
tensor([28.0330, 26.7788, 26.6818, 27.1658, 27.2860, 27.0501, 26.3889, 26.5209,
        25.0637, 25.6947], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:62, loss_test:0.03178202733397484
ite:63, loss_train:0.05699968710541725
tensor([27.1534, 25.9777, 25.8913, 26.4137, 26.5325, 26.2982, 25.4615, 25.5667,
        24.0926, 24.7573], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:63, loss_test:0.024698900058865547
ite:64, loss_train:0.0579381138086319
tensor([26.3669, 25.2627, 25.1862, 25.7434, 25.8520, 25.6282, 24.6314, 24.7112,
        23.2196, 23.9170], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:64, loss_test:0.02107826992869377
ite:65, loss_train:0.05941752344369888
tensor([25.8856, 24.8257, 24.7579, 25.3385, 25.4395, 25.2230, 24.1166, 24.1756,
        22.6650, 23.3916], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:65, loss_test:0.02056403085589409
ite:66, loss_train:0.05956471338868141
tensor([25.8179, 24.7657, 24.7048, 25.2932, 25.3904, 25.1770, 24.0296, 24.0738,
        22.5421, 23.2932], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:66, loss_test:0.022807637229561806
ite:67, loss_train:0.05816375091671944
tensor([26.1458, 25.0678, 25.0122, 25.5935, 25.6905, 25.4760, 24.3535, 24.3887,
        22.8340, 23.6050], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:67, loss_test:0.027569347992539406
ite:68, loss_train:0.05659414082765579
tensor([26.7427, 25.6158, 25.5643, 26.1283, 26.2275, 26.0091, 24.9562, 24.9863,
        23.4072, 24.1952], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:68, loss_test:0.033918511122465134
ite:69, loss_train:0.056181345134973526
tensor([27.4142, 26.2320, 26.1843, 26.7282, 26.8303, 26.6073, 25.6362, 25.6624,
        24.0584, 24.8625], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:69, loss_test:0.03982428088784218
ite:70, loss_train:0.05687728151679039
tensor([27.9590, 26.7329, 26.6893, 27.2179, 27.3217, 27.0956, 26.1849, 26.2054,
        24.5771, 25.3989], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:70, loss_test:0.04295145720243454
ite:71, loss_train:0.05751330405473709
tensor([28.2275, 26.9819, 26.9436, 27.4670, 27.5699, 27.3437, 26.4476, 26.4584,
        24.8070, 25.6497], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:71, loss_test:0.042095035314559937
ite:72, loss_train:0.05718066170811653
tensor([28.1625, 26.9268, 26.8951, 27.4257, 27.5250, 27.3021, 26.3647, 26.3611,
        24.6883, 25.5557], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:72, loss_test:0.037929486483335495
ite:73, loss_train:0.05614056438207626
tensor([27.8095, 26.6091, 26.5852, 27.1464, 27.2270, 27.0101, 25.9835, 25.9614,
        24.2684, 25.1639], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:73, loss_test:0.03243699297308922
ite:74, loss_train:0.05535605922341347
tensor([27.2956, 26.1450, 26.1296, 26.7254, 26.7875, 26.5765, 25.4357, 25.3929,
        23.6804, 24.6059], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:74, loss_test:0.027622386813163757
ite:75, loss_train:0.055360954254865646
tensor([26.7861, 25.6853, 25.6782, 26.3048, 26.3522, 26.1421, 24.8924, 24.8290,
        23.0970, 24.0524], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:75, loss_test:0.024630723521113396
ite:76, loss_train:0.055753324180841446
tensor([26.4310, 25.3669, 25.3676, 26.0210, 26.0532, 25.8435, 24.5092, 24.4274,
        22.6749, 23.6587], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:76, loss_test:0.023745033890008926
ite:77, loss_train:0.055776447057724
tensor([26.3204, 25.2721, 25.2795, 25.9567, 25.9698, 25.7598, 24.3791, 24.2826,
        22.5084, 23.5176], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:77, loss_test:0.024805784225463867
ite:78, loss_train:0.05526050180196762
tensor([26.4595, 25.4054, 25.4185, 26.1180, 26.1067, 25.8956, 24.5075, 24.3999,
        22.6026, 23.6346], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:78, loss_test:0.027386842295527458
ite:79, loss_train:0.05466684699058533
tensor([26.7727, 25.6983, 25.7163, 26.4373, 26.4000, 26.1849, 24.8162, 24.7001,
        22.8786, 23.9314], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:79, loss_test:0.03066801279783249
ite:80, loss_train:0.054483603686094284
tensor([27.1364, 26.0377, 26.0604, 26.8027, 26.7636, 26.5191, 25.1767, 25.0529,
        23.2069, 24.2800], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:80, loss_test:0.033450983464717865
ite:81, loss_train:0.054641105234622955
tensor([27.4227, 26.3071, 26.3347, 27.0960, 27.0545, 26.7863, 25.4567, 25.3242,
        23.4538, 24.5482], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:81, loss_test:0.03464480862021446
ite:82, loss_train:0.05466984584927559
tensor([27.5432, 26.4258, 26.4589, 27.2338, 27.1884, 26.9088, 25.5646, 25.4210,
        23.5267, 24.6445], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:82, loss_test:0.03388814255595207
ite:83, loss_train:0.05433476343750954
tensor([27.4758, 26.3735, 26.4128, 27.1922, 27.1412, 26.8672, 25.4775, 25.3202,
        23.4028, 24.5461], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:83, loss_test:0.03173259273171425
ite:84, loss_train:0.05388624593615532
tensor([27.2667, 26.1923, 26.2383, 27.0145, 26.9567, 26.7018, 25.2433, 25.0705,
        23.1305, 24.3011], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:84, loss_test:0.029224026948213577
ite:85, loss_train:0.05365786701440811
tensor([27.0072, 25.9654, 26.0182, 26.7858, 26.7207, 26.4926, 24.9569, 24.7678,
        22.8055, 24.0039], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:85, loss_test:0.02731528878211975
ite:86, loss_train:0.05366477742791176
tensor([26.7978, 25.7823, 25.8454, 26.6034, 26.5442, 26.3274, 24.7224, 24.5178,
        22.5327, 23.7585], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:86, loss_test:0.026549145579338074
ite:87, loss_train:0.053647756576538086
tensor([26.7127, 25.7102, 25.7857, 26.5338, 26.4881, 26.2711, 24.6167, 24.3985,
        22.3901, 23.6418], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:87, loss_test:0.027060385793447495
ite:88, loss_train:0.05343519151210785
tensor([26.7771, 25.7755, 25.8618, 26.6042, 26.5625, 26.3456, 24.6659, 24.4362,
        22.4038, 23.6797], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:88, loss_test:0.02865145541727543
ite:89, loss_train:0.05311373993754387
tensor([26.9623, 25.9517, 26.0474, 26.7907, 26.7420, 26.5257, 24.8403, 24.6010,
        22.5439, 23.8427], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:89, loss_test:0.03081497922539711
ite:90, loss_train:0.05288488045334816
tensor([27.1996, 26.1756, 26.2803, 27.0288, 26.9669, 26.7512, 25.0688, 24.8206,
        22.7387, 24.0598], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:90, loss_test:0.03281000629067421
ite:91, loss_train:0.05280447006225586
tensor([27.4072, 26.3719, 26.4859, 27.2402, 27.1653, 26.9503, 25.2666, 25.0091,
        22.9026, 24.2462], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:91, loss_test:0.03391774743795395
ite:92, loss_train:0.052736517041921616
tensor([27.5197, 26.4804, 26.6048, 27.3665, 27.2800, 27.0656, 25.3660, 25.0979,
        22.9671, 24.3345], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:92, loss_test:0.03378467634320259
ite:93, loss_train:0.052554719150066376
tensor([27.5097, 26.4761, 26.6122, 27.3861, 27.2870, 27.0732, 25.3386, 25.0581,
        22.9036, 24.2963], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:93, loss_test:0.032592982053756714
ite:94, loss_train:0.052289847284555435
tensor([27.3944, 26.3746, 26.5236, 27.3240, 27.2134, 26.9881, 25.2103, 24.9077,
        22.7301, 24.1492], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:94, loss_test:0.030923502519726753
ite:95, loss_train:0.05206216499209404
tensor([27.2255, 26.2237, 26.3862, 27.2334, 27.1174, 26.8558, 25.0263, 24.7012,
        22.5007, 23.9469], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:95, loss_test:0.029434626922011375
ite:96, loss_train:0.05191841349005699
tensor([27.0684, 26.0835, 26.2592, 27.1756, 27.0548, 26.7334, 24.8547, 24.5071,
        22.2836, 23.7569], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:96, loss_test:0.028590507805347443
ite:97, loss_train:0.05179218947887421
tensor([26.9769, 26.0034, 26.1916, 27.2128, 27.0884, 26.7629, 24.7517, 24.3821,
        22.1352, 23.6348], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:97, loss_test:0.028554067015647888
ite:98, loss_train:0.05161292850971222
tensor([26.9743, 26.0049, 26.2045, 27.3352, 27.2083, 26.8828, 24.7418, 24.3505,
        22.0799, 23.6048], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:98, loss_test:0.029206320643424988
ite:99, loss_train:0.05141565948724747
tensor([27.0477, 26.0762, 26.2864, 27.5118, 27.3827, 27.0566, 24.8117, 24.3990,
        22.1043, 23.6538], grad_fn=<SliceBackward>)
tensor([24.0000, 21.6000, 34.7000, 33.4000, 36.2000, 28.7000, 22.9000, 27.1000,
        16.5000, 18.9000])
ite:99, loss_test:0.030214082449674606