使用MLP对之前的房贷项目做预测，损失函数采用MSE。
运行结果：
Using device: cuda
Epoch [10/1000], Loss: 0.2133
Epoch [20/1000], Loss: 0.2080
Epoch [30/1000], Loss: 0.2049
Epoch [40/1000], Loss: 0.2032
Epoch [50/1000], Loss: 0.2022
Epoch [60/1000], Loss: 0.2015
Epoch [70/1000], Loss: 0.2011
Epoch [80/1000], Loss: 0.2008
Epoch [90/1000], Loss: 0.2006
Epoch [100/1000], Loss: 0.2004
Epoch [110/1000], Loss: 0.2002
Epoch [120/1000], Loss: 0.2001
Epoch [130/1000], Loss: 0.1999
Epoch [140/1000], Loss: 0.1998
Epoch [150/1000], Loss: 0.1996
Epoch [160/1000], Loss: 0.1995
Epoch [170/1000], Loss: 0.1993
Epoch [180/1000], Loss: 0.1992
Epoch [190/1000], Loss: 0.1991
Epoch [200/1000], Loss: 0.1989
Epoch [210/1000], Loss: 0.1988
Epoch [220/1000], Loss: 0.1987
Epoch [230/1000], Loss: 0.1985
Epoch [240/1000], Loss: 0.1984
Epoch [250/1000], Loss: 0.1982
Epoch [260/1000], Loss: 0.1981
Epoch [270/1000], Loss: 0.1980
Epoch [280/1000], Loss: 0.1978
Epoch [290/1000], Loss: 0.1977
Epoch [300/1000], Loss: 0.1976
Epoch [310/1000], Loss: 0.1974
Epoch [320/1000], Loss: 0.1973
Epoch [330/1000], Loss: 0.1972
Epoch [340/1000], Loss: 0.1970
Epoch [350/1000], Loss: 0.1969
Epoch [360/1000], Loss: 0.1968
Epoch [370/1000], Loss: 0.1966
Epoch [380/1000], Loss: 0.1965
Epoch [390/1000], Loss: 0.1964
Epoch [400/1000], Loss: 0.1962
Epoch [410/1000], Loss: 0.1961
Epoch [420/1000], Loss: 0.1960
Epoch [430/1000], Loss: 0.1958
Epoch [440/1000], Loss: 0.1957
Epoch [450/1000], Loss: 0.1956
Epoch [460/1000], Loss: 0.1954
Epoch [470/1000], Loss: 0.1953
Epoch [480/1000], Loss: 0.1952
Epoch [490/1000], Loss: 0.1950
Epoch [500/1000], Loss: 0.1949
Epoch [510/1000], Loss: 0.1947
Epoch [520/1000], Loss: 0.1946
Epoch [530/1000], Loss: 0.1945
Epoch [540/1000], Loss: 0.1943
Epoch [550/1000], Loss: 0.1942
Epoch [560/1000], Loss: 0.1940
Epoch [570/1000], Loss: 0.1939
Epoch [580/1000], Loss: 0.1938
Epoch [590/1000], Loss: 0.1936
Epoch [600/1000], Loss: 0.1935
Epoch [610/1000], Loss: 0.1933
Epoch [620/1000], Loss: 0.1932
Epoch [630/1000], Loss: 0.1930
Epoch [640/1000], Loss: 0.1929
Epoch [650/1000], Loss: 0.1928
Epoch [660/1000], Loss: 0.1926
Epoch [670/1000], Loss: 0.1925
Epoch [680/1000], Loss: 0.1923
Epoch [690/1000], Loss: 0.1922
Epoch [700/1000], Loss: 0.1920
Epoch [710/1000], Loss: 0.1919
Epoch [720/1000], Loss: 0.1917
Epoch [730/1000], Loss: 0.1916
Epoch [740/1000], Loss: 0.1914
Epoch [750/1000], Loss: 0.1913
Epoch [760/1000], Loss: 0.1911
Epoch [770/1000], Loss: 0.1910
Epoch [780/1000], Loss: 0.1908
Epoch [790/1000], Loss: 0.1907
Epoch [800/1000], Loss: 0.1905
Epoch [810/1000], Loss: 0.1903
Epoch [820/1000], Loss: 0.1902
Epoch [830/1000], Loss: 0.1900
Epoch [840/1000], Loss: 0.1899
Epoch [850/1000], Loss: 0.1897
Epoch [860/1000], Loss: 0.1896
Epoch [870/1000], Loss: 0.1894
Epoch [880/1000], Loss: 0.1892
Epoch [890/1000], Loss: 0.1891
Epoch [900/1000], Loss: 0.1889
Epoch [910/1000], Loss: 0.1887
Epoch [920/1000], Loss: 0.1886
Epoch [930/1000], Loss: 0.1884
Epoch [940/1000], Loss: 0.1882
Epoch [950/1000], Loss: 0.1881
Epoch [960/1000], Loss: 0.1879
Epoch [970/1000], Loss: 0.1877
Epoch [980/1000], Loss: 0.1875
Epoch [990/1000], Loss: 0.1874
Epoch [1000/1000], Loss: 0.1872
Training completed in 2.32 seconds
