我在cygwin上构建了libFM .exe，但是save_model / load_model似乎不起作用：
$ ./libFM.exe -task r -method als -train data.libfm1 -test test.libfm1 -iter 100 -dim ‘5,5,10’ -load_model mod -save_model mod -out res.txt
----------------------------------------------------------------------------
libFM
Version: 1.4.4
Author: Steffen Rendle, [email protected]
WWW: http://www.libfm.org/
This program comes with ABSOLUTELY NO WARRANTY; for details see license.txt.
This is free software, and you are welcome to redistribute it under certain
conditions; for details see license.txt.
----------------------------------------------------------------------------
Loading train...
has x = 0
has xt = 1
num_rows=532062 num_values=1064124 num_features=5548 min_target=4.88591e-05 max_target=2728.33
Loading test...
has x = 0
has xt = 1
num_rows=8828 num_values=17656 num_features=5548 min_target=1 max_target=1
#relations: 0
Loading meta data...
Reading FM model...
WARNING: malformed model file. Nothing will be loaded.
#Iter= 0 Train=843.76 Test=1818.09
#Iter= 1 Train=834.451 Test=1809.26
#Iter= 2 Train=828.697 Test=1804.68
#Iter= 3 Train=826.596 Test=1799.81
#Iter= 4 Train=825.624 Test=1797.39
#Iter= 5 Train=825.001 Test=1796.07
#Iter= 6 Train=824.561 Test=1795.28
#Iter= 7 Train=824.128 Test=1794.72
#Iter= 8 Train=823.628 Test=1794.32
#Iter= 9 Train=823.058 Test=1794.01
#Iter= 10 Train=822.35 Test=1793.77
#Iter= 11 Train=821.495 Test=1793.57
#Iter= 12 Train=820.475 Test=1793.4
#Iter= 13 Train=819.259 Test=1793.26
#Iter= 14 Train=817.804 Test=1793.12
#Iter= 15 Train=816.007 Test=1792.96
#Iter= 16 Train=813.778 Test=1792.78
#Iter= 17 Train=811.026 Test=1792.58
#Iter= 18 Train=807.526 Test=1792.35
#Iter= 19 Train=803.088 Test=1792.11
#Iter= 20 Train=797.588 Test=1791.79
#Iter= 21 Train=790.879 Test=1791.06
#Iter= 22 Train=782.458 Test=1789.96
#Iter= 23 Train=771.832 Test=1788.64
#Iter= 24 Train=758.433 Test=1787.18
#Iter= 25 Train=740.229 Test=1785.63
#Iter= 26 Train=718.402 Test=1783.97
#Iter= 27 Train=699.144 Test=1782.23
#Iter= 28 Train=677.813 Test=1780.37
#Iter= 29 Train=654.682 Test=1778.1
#Iter= 30 Train=627.238 Test=1774.78
#Iter= 31 Train=599.629 Test=1769.07
#Iter= 32 Train=561.1 Test=1761.65
#Iter= 33 Train=485.198 Test=1752.08
#Iter= 34 Train=389.219 Test=1738.76
#Iter= 35 Train=308.496 Test=1718.83
#Iter= 36 Train=244.646 Test=1694.29
#Iter= 37 Train=194.178 Test=1666.62
#Iter= 38 Train=154.331 Test=1636.97
#Iter= 39 Train=122.927 Test=1606.16
#Iter= 40 Train=98.2438 Test=1574.81
#Iter= 41 Train=78.9262 Test=1543.37
#Iter= 42 Train=63.9054 Test=1512.17
#Iter= 43 Train=52.3367 Test=1481.44
#Iter= 44 Train=43.5473 Test=1451.35
#Iter= 45 Train=36.9906 Test=1421.99
#Iter= 46 Train=32.2103 Test=1393.44
#Iter= 47 Train=28.8155 Test=1365.74
#Iter= 48 Train=26.4696 Test=1338.9
#Iter= 49 Train=24.8894 Test=1312.93
#Iter= 50 Train=23.848 Test=1287.82
#Iter= 51 Train=23.1733 Test=1263.55
#Iter= 52 Train=22.7416 Test=1240.1
#Iter= 53 Train=22.4679 Test=1217.44
#Iter= 54 Train=22.2956 Test=1195.55
#Iter= 55 Train=22.1876 Test=1174.4
#Iter= 56 Train=22.1198 Test=1153.95
#Iter= 57 Train=22.0776 Test=1134.18
#Iter= 58 Train=22.0514 Test=1115.06
#Iter= 59 Train=22.0353 Test=1096.56
#Iter= 60 Train=22.0254 Test=1078.65
#Iter= 61 Train=22.0193 Test=1061.31
#Iter= 62 Train=22.0138 Test=1044.51
#Iter= 63 Train=22.0078 Test=1028.23
#Iter= 64 Train=21.9998 Test=1012.45
#Iter= 65 Train=21.9904 Test=997.144
#Iter= 66 Train=21.9836 Test=982.29
#Iter= 67 Train=21.9793 Test=967.871
#Iter= 68 Train=21.9766 Test=953.868
#Iter= 69 Train=21.9749 Test=940.264
#Iter= 70 Train=21.9738 Test=927.041
#Iter= 71 Train=21.9731 Test=914.186
#Iter= 72 Train=21.9727 Test=901.681
#Iter= 73 Train=21.9725 Test=889.515
#Iter= 74 Train=21.9723 Test=877.672
#Iter= 75 Train=21.9722 Test=866.14
#Iter= 76 Train=21.9721 Test=854.908
#Iter= 77 Train=21.9721 Test=843.964
#Iter= 78 Train=21.9721 Test=833.297
#Iter= 79 Train=21.972 Test=822.896
#Iter= 80 Train=21.972 Test=812.752
#Iter= 81 Train=21.972 Test=802.855
#Iter= 82 Train=21.972 Test=793.197
#Iter= 83 Train=21.972 Test=783.768
#Iter= 84 Train=21.972 Test=774.562
#Iter= 85 Train=21.972 Test=765.569
#Iter= 86 Train=21.972 Test=756.783
#Iter= 87 Train=21.972 Test=748.197
#Iter= 88 Train=21.972 Test=739.804
#Iter= 89 Train=21.972 Test=731.597
#Iter= 90 Train=21.972 Test=723.571
#Iter= 91 Train=21.972 Test=715.719
#Iter= 92 Train=21.972 Test=708.036
#Iter= 93 Train=21.972 Test=700.516
#Iter= 94 Train=21.972 Test=693.155
#Iter= 95 Train=21.972 Test=685.947
#Iter= 96 Train=21.972 Test=678.888
#Iter= 97 Train=21.972 Test=671.972
#Iter= 98 Train=21.972 Test=665.197
#Iter= 99 Train=21.972 Test=658.557
Writing FM model to mod
文件mod到位，并包含一些数字（大多数为零）。现在，我尝试再次启动它，希望它可以恢复，但是它从头开始：
$ ./libFM.exe -task r -method als -train data.libfm1 -test test.libfm1 -iter 100 -dim ‘5,5,10’ -load_model mod -save_model mod -out res.txt
----------------------------------------------------------------------------
libFM
Version: 1.4.4
Author: Steffen Rendle, [email protected]
WWW: http://www.libfm.org/
This program comes with ABSOLUTELY NO WARRANTY; for details see license.txt.
This is free software, and you are welcome to redistribute it under certain
conditions; for details see license.txt.
----------------------------------------------------------------------------
Loading train...
has x = 0
has xt = 1
num_rows=532062 num_values=1064124 num_features=5548 min_target=4.88591e-05 max_target=2728.33
Loading test...
has x = 0
has xt = 1
num_rows=8828 num_values=17656 num_features=5548 min_target=1 max_target=1
#relations: 0
Loading meta data...
Reading FM model...
#Iter= 0 Train=1242.26 Test=1239.97
#Iter= 1 Train=1239.28 Test=1236.05
#Iter= 2 Train=1238.53 Test=1232.92
#Iter= 3 Train=1283.05 Test=1237.22
#Iter= 4 Train=1245.98 Test=1242.34
#Iter= 5 Train=1143.14 Test=1242.26
#Iter= 6 Train=962.48 Test=1235.22
#Iter= 7 Train=787.968 Test=1220.46
#Iter= 8 Train=635.268 Test=1198.45
#Iter= 9 Train=505.948 Test=1165.38
#Iter= 10 Train=401.108 Test=1122.19
#Iter= 11 Train=318.007 Test=1074.38
#Iter= 12 Train=252.22 Test=1025.22
#Iter= 13 Train=200.194 Test=976.667
#Iter= 14 Train=159.102 Test=929.827
#Iter= 15 Train=126.703 Test=885.303
#Iter= 16 Train=101.225 Test=843.375
#Iter= 17 Train=81.27 Test=804.127
#Iter= 18 Train=65.7362 Test=767.526
#Iter= 19 Train=53.7528 Test=733.468
#Iter= 20 Train=44.6269 Test=701.809
#Iter= 21 Train=37.7977 Test=672.391
#Iter= 22 Train=32.7988 Test=645.047
#Iter= 23 Train=29.2325 Test=619.615
#Iter= 24 Train=26.7558 Test=595.939
#Iter= 25 Train=25.0793 Test=573.872
#Iter= 26 Train=23.969 Test=553.277
#Iter= 27 Train=23.2465 Test=534.03
#Iter= 28 Train=22.7824 Test=516.015
#Iter= 29 Train=22.487 Test=499.129
#Iter= 30 Train=22.3003 Test=483.277
#Iter= 31 Train=22.1824 Test=468.373
#Iter= 32 Train=22.1081 Test=454.339
#Iter= 33 Train=22.0599 Test=441.106
#Iter= 34 Train=22.0274 Test=428.61
#Iter= 35 Train=22.0068 Test=416.793
#Iter= 36 Train=21.9938 Test=405.603
#Iter= 37 Train=21.9857 Test=394.994
#Iter= 38 Train=21.9806 Test=384.921
#Iter= 39 Train=21.9774 Test=375.347
#Iter= 40 Train=21.9754 Test=366.236
#Iter= 41 Train=21.9741 Test=357.556
#Iter= 42 Train=21.9733 Test=349.277
#Iter= 43 Train=21.9728 Test=341.372
#Iter= 44 Train=21.9725 Test=333.818
#Iter= 45 Train=21.9723 Test=326.59
#Iter= 46 Train=21.9722 Test=319.669
#Iter= 47 Train=21.9721 Test=313.036
#Iter= 48 Train=21.9721 Test=306.674
#Iter= 49 Train=21.9721 Test=300.565
#Iter= 50 Train=21.972 Test=294.695
#Iter= 51 Train=21.972 Test=289.051
#Iter= 52 Train=21.972 Test=283.62
#Iter= 53 Train=21.972 Test=278.39
#Iter= 54 Train=21.972 Test=273.35
#Iter= 55 Train=21.972 Test=268.49
#Iter= 56 Train=21.972 Test=263.8
#Iter= 57 Train=21.972 Test=259.272
#Iter= 58 Train=21.972 Test=254.897
#Iter= 59 Train=21.972 Test=250.668
#Iter= 60 Train=21.972 Test=246.578
#Iter= 61 Train=21.972 Test=242.62
#Iter= 62 Train=21.972 Test=238.787
#Iter= 63 Train=21.972 Test=235.075
#Iter= 64 Train=21.972 Test=231.476
#Iter= 65 Train=21.972 Test=227.986
#Iter= 66 Train=21.972 Test=224.601
#Iter= 67 Train=21.972 Test=221.315
#Iter= 68 Train=21.972 Test=218.124
#Iter= 69 Train=21.972 Test=215.025
#Iter= 70 Train=21.972 Test=212.013
#Iter= 71 Train=21.972 Test=209.084
#Iter= 72 Train=21.972 Test=206.236
#Iter= 73 Train=21.972 Test=203.465
#Iter= 74 Train=21.972 Test=200.767
#Iter= 75 Train=21.972 Test=198.141
#Iter= 76 Train=21.972 Test=195.583
#Iter= 77 Train=21.972 Test=193.09
#Iter= 78 Train=21.972 Test=190.661
#Iter= 79 Train=21.972 Test=188.292
#Iter= 80 Train=21.972 Test=185.982
#Iter= 81 Train=21.972 Test=183.728
#Iter= 82 Train=21.972 Test=181.528
#Iter= 83 Train=21.972 Test=179.381
#Iter= 84 Train=21.972 Test=177.285
#Iter= 85 Train=21.972 Test=175.237
#Iter= 86 Train=21.972 Test=173.236
#Iter= 87 Train=21.972 Test=171.28
#Iter= 88 Train=21.972 Test=169.369
#Iter= 89 Train=21.972 Test=167.5
#Iter= 90 Train=21.972 Test=165.672
#Iter= 91 Train=21.972 Test=163.884
#Iter= 92 Train=21.972 Test=162.134
#Iter= 93 Train=21.972 Test=160.422
#Iter= 94 Train=21.972 Test=158.745
#Iter= 95 Train=21.972 Test=157.104
#Iter= 96 Train=21.972 Test=155.496
#Iter= 97 Train=21.972 Test=153.922
#Iter= 98 Train=21.972 Test=152.379
#Iter= 99 Train=21.972 Test=150.866
Writing FM model to mod
我尝试了来自github的两个存储库，但它们都无法正常运行。我想念什么？
您可以从github下载源代码然后进行构建。它具有参数-save_model，其版本为1.4.4。
但是，如果您从http://www.libfm.org/下载，其版本为1.4.2，而没有参数-save_model。