输出结果
boston.data： (506, 13)
[[6.3200e-03 1.8000e+01 2.3100e+00 0.0000e+00 5.3800e-01 6.5750e+00
6.5200e+01 4.0900e+00 1.0000e+00 2.9600e+02 1.5300e+01 3.9690e+02
4.9800e+00]
[2.7310e-02 0.0000e+00 7.0700e+00 0.0000e+00 4.6900e-01 6.4210e+00
7.8900e+01 4.9671e+00 2.0000e+00 2.4200e+02 1.7800e+01 3.9690e+02
9.1400e+00]
[2.7290e-02 0.0000e+00 7.0700e+00 0.0000e+00 4.6900e-01 7.1850e+00
6.1100e+01 4.9671e+00 2.0000e+00 2.4200e+02 1.7800e+01 3.9283e+02
4.0300e+00]
[3.2370e-02 0.0000e+00 2.1800e+00 0.0000e+00 4.5800e-01 6.9980e+00
4.5800e+01 6.0622e+00 3.0000e+00 2.2200e+02 1.8700e+01 3.9463e+02
2.9400e+00]
[6.9050e-02 0.0000e+00 2.1800e+00 0.0000e+00 4.5800e-01 7.1470e+00
5.4200e+01 6.0622e+00 3.0000e+00 2.2200e+02 1.8700e+01 3.9690e+02
5.3300e+00]
[2.9850e-02 0.0000e+00 2.1800e+00 0.0000e+00 4.5800e-01 6.4300e+00
5.8700e+01 6.0622e+00 3.0000e+00 2.2200e+02 1.8700e+01 3.9412e+02
5.2100e+00]
[8.8290e-02 1.2500e+01 7.8700e+00 0.0000e+00 5.2400e-01 6.0120e+00
6.6600e+01 5.5605e+00 5.0000e+00 3.1100e+02 1.5200e+01 3.9560e+02
1.2430e+01]
[1.4455e-01 1.2500e+01 7.8700e+00 0.0000e+00 5.2400e-01 6.1720e+00
9.6100e+01 5.9505e+00 5.0000e+00 3.1100e+02 1.5200e+01 3.9690e+02
1.9150e+01]
[2.1124e-01 1.2500e+01 7.8700e+00 0.0000e+00 5.2400e-01 5.6310e+00
1.0000e+02 6.0821e+00 5.0000e+00 3.1100e+02 1.5200e+01 3.8663e+02
2.9930e+01]
[1.7004e-01 1.2500e+01 7.8700e+00 0.0000e+00 5.2400e-01 6.0040e+00
8.5900e+01 6.5921e+00 5.0000e+00 3.1100e+02 1.5200e+01 3.8671e+02
1.7100e+01]]
boston.target: (506,)
[24. 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9]
Tensorboard可视化
设计思路
