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47,027
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1153.png
0
0
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3
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[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
train
mnist
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47,036
[ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ]
1154.png
0.02
0.2
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2
[0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2]
[0.0, 0.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4]
train
mnist
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47,083
[ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ]
1156.png
0
0
[0.0, 0.16666666666666666, 0.8333333333333334, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
2
[0.0, 0.16666666666666666, 0.8333333333333334, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
[0.0, 0.16666666666666666, 0.8333333333333334, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
train
mnist
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47,130
[ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 ]
1158.png
0.04
0.2
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5
[0.0, 0.0, 0.0, 0.0, 0.0, 0.9, 0.1, 0.0, 0.0, 0.0, 0.0]
[0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.2]
train
mnist
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47,141
[ 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 ]
1159.png
0
0
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
1
[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, 0.0, 0.0]
train
mnist
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47,149
[ 0, 0, 0, 0, 0, 0, 0, 1, 0, 0 ]
1160.png
0.016667
0.166667
[0.0, 0.05555555555555555, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9444444444444443, 0.0, 0.0, 0.0]
7
[0.0, 0.08333333333333333, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9166666666666666, 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]
train
mnist