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37,141
[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
890.png
0.157143
0.285714
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0
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train
mnist
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37,147
[ 0, 0, 0, 0, 0, 0, 0, 1, 0, 0 ]
1672.png
0.04
0.4
[0.0, 0.13333333333333333, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6666666666666666, 0.0, 0.0, 0.2]
7
[0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.0, 0.0, 0.2]
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.0, 0.2]
train
mnist
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37,160
[ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 ]
2158.png
0.033333
0.333333
[0.0, 0.0, 0.0, 0.0, 0.0, 0.9444444444444445, 0.0, 0.0, 0.05555555555555555, 0.0, 0.0]
5
[0.0, 0.0, 0.0, 0.0, 0.0, 0.9166666666666666, 0.0, 0.0, 0.08333333333333333, 0.0, 0.0]
[0.0, 0.0, 0.0, 0.0, 0.0, 0.8333333333333334, 0.0, 0.0, 0.0, 0.0, 0.16666666666666666]
train
mnist
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
37,161
[ 0, 0, 0, 1, 0, 0, 0, 0, 0, 0 ]
1673.png
0.083333
0.5
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3
[0.0, 0.0, 0.0, 0.7222222222222223, 0.0, 0.05555555555555555, 0.0, 0.0, 0.0, 0.2222222222222222, 0.0]
[0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5]
train
mnist
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
37,174
[ 0, 0, 0, 1, 0, 0, 0, 0, 0, 0 ]
891.png
0
0
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
3
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
train
mnist