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epochs4_accuracy.output.report
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epochs4_accuracy.output.report
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%Iter duration train_objective valid_objective difference
0 445.0 0.0588235 0.0588235 0
1 459.0 0.333333 0.254902 -0.078431
2 306.0 0.54902 0.588235 0.039215
3 364.0 0.72549 0.705882 -0.019608
4 451.0 0.784314 0.705882 -0.078432
5 441.0 0.764706 0.823529 0.058823
6 457.0 0.705882 0.72549 0.019608
7 465.0 0.745098 0.862745 0.117647
8 333.0 0.862745 0.843137 -0.019608
9 412.0 0.882353 0.803922 -0.078431
10 338.0 0.843137 0.823529 -0.019608
11 411.0 0.901961 0.882353 -0.019608
12 423.0 0.901961 0.901961 0
13 469.0 0.941176 0.862745 -0.078431
14 430.0 0.921569 0.843137 -0.078432
15 421.0 0.960784 0.921569 -0.039215
16 478.0 0.921569 0.862745 -0.058824
17 466.0 0.941176 0.941176 0
18 446.0 0.901961 0.941176 0.039215
19 285.0 0.960784 0.960784 0
20 400.0 0.941176 0.941176 0
21 424.0 0.960784 0.941176 -0.019608
22 458.0 0.980392 0.921569 -0.058823
23 295.0 0.960784 0.960784 0
24 464.0 0.941176 0.901961 -0.039215
25 463.0 0.960784 0.941176 -0.019608
26 336.0 1 0.901961 -0.098039
27 452.0 0.980392 0.921569 -0.058823
28 430.0 0.980392 0.941176 -0.039216
29 442.0 0.960784 0.960784 0
30 460.0 0.960784 0.960784 0
31 466.0 0.960784 0.921569 -0.039215
32 415.0 0.941176 0.882353 -0.058823
33 345.0 0.960784 0.960784 0
34 413.0 0.960784 0.941176 -0.019608
35 423.0 0.980392 0.980392 0
36 468.0 0.960784 0.960784 0
37 408.0 1 0.960784 -0.039216
38 432.0 0.960784 0.980392 0.019608
39 309.0 1 0.980392 -0.019608
40 481.0 1 0.960784 -0.039216
41 445.0 1 0.980392 -0.019608
42 448.0 0.960784 0.960784 0
43 272.0 0.980392 0.921569 -0.058823
44 420.0 0.960784 0.960784 0
45 404.0 0.980392 0.941176 -0.039216
46 455.0 0.980392 0.960784 -0.019608
47 462.0 1 0.980392 -0.019608
48 458.0 0.980392 0.960784 -0.019608
49 337.0 0.980392 0.960784 -0.019608
50 404.0 0.980392 0.960784 -0.019608
51 455.0 1 0.941176 -0.058824
52 441.0 1 0.960784 -0.039216
53 457.0 1 0.960784 -0.039216
54 464.0 1 0.980392 -0.019608
55 334.0 1 0.960784 -0.039216
56 416.0 1 0.980392 -0.019608
57 337.0 1 0.960784 -0.039216
58 423.0 1 0.980392 -0.019608
59 411.0 1 0.960784 -0.039216
60 472.0 1 0.941176 -0.058824
61 432.0 1 1 0
62 352.0 1 0.960784 -0.039216
63 477.0 1 0.980392 -0.019608
64 470.0 1 0.960784 -0.039216
65 447.0 1 0.960784 -0.039216
66 286.0 1 0.960784 -0.039216
67 423.0 1 0.980392 -0.019608
68 404.0 1 0.960784 -0.039216
69 456.0 1 0.980392 -0.019608
70 462.0 1 0.941176 -0.058824
71 461.0 1 0.960784 -0.039216
72 306.0 1 0.960784 -0.039216
73 365.0 1 0.960784 -0.039216
74 452.0 1 0.960784 -0.039216
75 442.0 1 0.960784 -0.039216
76 458.0 1 0.960784 -0.039216
77 466.0 1 0.960784 -0.039216
78 334.0 1 0.960784 -0.039216
79 413.0 1 0.960784 -0.039216
80 338.0 1 0.960784 -0.039216
81 411.0 1 0.941176 -0.058824
82 424.0 1 0.960784 -0.039216
83 469.0 1 0.980392 -0.019608
84 432.0 1 0.980392 -0.019608
85 423.0 1 0.980392 -0.019608
86 479.0 1 0.980392 -0.019608
87 466.0 1 0.980392 -0.019608
88 447.0 1 0.960784 -0.039216
89 286.0 1 0.960784 -0.039216
90 402.0 1 0.960784 -0.039216
91 423.0 1 0.960784 -0.039216
92 452.0 1 0.960784 -0.039216
Total training time is 10:45:57