665 lines
7.1 KiB
INI
665 lines
7.1 KiB
INI
[data]
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type=data
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dataIdx=0
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[labels]
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type=data
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dataIdx=1
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[conv1a]
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type=conv
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inputs=data
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channels=3
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filters=48
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padding=0
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stride=4
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filterSize=11
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initW=0.01
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partialSum=11
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sharedBiases=1
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gpu=0
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[conv1b]
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type=conv
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inputs=data
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channels=3
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filters=48
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padding=0
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stride=4
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filterSize=11
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initW=0.01
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partialSum=11
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sharedBiases=1
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gpu=1
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[conv1c]
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type=conv
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inputs=data
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channels=3
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filters=48
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padding=0
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stride=4
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filterSize=11
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initW=0.01
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partialSum=11
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sharedBiases=1
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gpu=2
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[conv1d]
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type=conv
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inputs=data
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channels=3
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filters=48
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padding=0
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stride=4
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filterSize=11
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initW=0.01
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partialSum=11
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sharedBiases=1
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gpu=3
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[rnorm1a]
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type=cmrnorm
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inputs=conv1a
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channels=48
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size=5
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[rnorm1b]
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type=cmrnorm
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inputs=conv1b
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channels=48
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size=5
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[rnorm1c]
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type=cmrnorm
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inputs=conv1c
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channels=48
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size=5
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[rnorm1d]
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type=cmrnorm
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inputs=conv1d
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channels=48
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size=5
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[pool1a]
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type=pool
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pool=max
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inputs=rnorm1a
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sizeX=3
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stride=2
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channels=48
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neuron=relu
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[pool1b]
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type=pool
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pool=max
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inputs=rnorm1b
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sizeX=3
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stride=2
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channels=48
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neuron=relu
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[pool1c]
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type=pool
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pool=max
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inputs=rnorm1c
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sizeX=3
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stride=2
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channels=48
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neuron=relu
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[pool1d]
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type=pool
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pool=max
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inputs=rnorm1d
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sizeX=3
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stride=2
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channels=48
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neuron=relu
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[conv2a]
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type=conv
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inputs=pool1a
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filters=128
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padding=2
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stride=1
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filterSize=5
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channels=48
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initW=0.01
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initB=1
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partialSum=9
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sharedBiases=1
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neuron=relu
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[conv2b]
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type=conv
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inputs=pool1b
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filters=128
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padding=2
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stride=1
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filterSize=5
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channels=48
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initW=0.01
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initB=1
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partialSum=9
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sharedBiases=1
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neuron=relu
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[conv2c]
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type=conv
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inputs=pool1c
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filters=128
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padding=2
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stride=1
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filterSize=5
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channels=48
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initW=0.01
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initB=1
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partialSum=9
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sharedBiases=1
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neuron=relu
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[conv2d]
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type=conv
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inputs=pool1d
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filters=128
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padding=2
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stride=1
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filterSize=5
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channels=48
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initW=0.01
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initB=1
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partialSum=9
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sharedBiases=1
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neuron=relu
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[rnorm2a]
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type=cmrnorm
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inputs=conv2a
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channels=128
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size=5
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[rnorm2b]
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type=cmrnorm
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inputs=conv2b
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channels=128
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size=5
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[rnorm2c]
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type=cmrnorm
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inputs=conv2c
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channels=128
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size=5
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[rnorm2d]
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type=cmrnorm
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inputs=conv2d
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channels=128
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size=5
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[cnorm2a]
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type=rnorm
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inputs=rnorm2a
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channels=128
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size=5
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[cnorm2b]
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type=rnorm
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inputs=rnorm2b
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channels=128
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size=5
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[cnorm2c]
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type=rnorm
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inputs=rnorm2c
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channels=128
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size=5
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[cnorm2d]
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type=rnorm
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inputs=rnorm2d
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channels=128
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size=5
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[pool2a]
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type=pool
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pool=max
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inputs=cnorm2a
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sizeX=3
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stride=2
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channels=128
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[pool2b]
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type=pool
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pool=max
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inputs=cnorm2b
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sizeX=3
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stride=2
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channels=128
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[pool2c]
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type=pool
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pool=max
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inputs=cnorm2c
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sizeX=3
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stride=2
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channels=128
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[pool2d]
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type=pool
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pool=max
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inputs=cnorm2d
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sizeX=3
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stride=2
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channels=128
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[conv3a]
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type=conv
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inputs=pool2a,pool2b,pool2c
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filters=192,192,192
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padding=1,1,1
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stride=1,1,1
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filterSize=3,3,3
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channels=128,128,128
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initW=0.03,0.03,0.03
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partialSum=13
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sharedBiases=1
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neuron=relu
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gpu=0
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[conv3b]
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type=conv
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inputs=pool2a,pool2b,pool2d
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filters=192,192,192
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padding=1,1,1
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stride=1,1,1
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filterSize=3,3,3
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channels=128,128,128
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initW=0.03,0.03,0.03
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partialSum=13
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sharedBiases=1
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neuron=relu
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gpu=1
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[conv3c]
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type=conv
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inputs=pool2c,pool2d,pool2a
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filters=192,192,192
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padding=1,1,1
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stride=1,1,1
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filterSize=3,3,3
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channels=128,128,128
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initW=0.03,0.03,0.03
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partialSum=13
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sharedBiases=1
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neuron=relu
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gpu=2
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[conv3d]
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type=conv
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inputs=pool2c,pool2d,pool2b
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filters=192,192,192
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padding=1,1,1
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stride=1,1,1
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filterSize=3,3,3
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channels=128,128,128
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initW=0.03,0.03,0.03
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partialSum=13
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sharedBiases=1
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neuron=relu
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gpu=3
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[conv4a]
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type=conv
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inputs=conv3a
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filters=192
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padding=1
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stride=1
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filterSize=3
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channels=192
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neuron=relu
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initW=0.03
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initB=1
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partialSum=13
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sharedBiases=1
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[conv4b]
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type=conv
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inputs=conv3b
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filters=192
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padding=1
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stride=1
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filterSize=3
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channels=192
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neuron=relu
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initW=0.03
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initB=1
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partialSum=13
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sharedBiases=1
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[conv4c]
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type=conv
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inputs=conv3c
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filters=192
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padding=1
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stride=1
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filterSize=3
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channels=192
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neuron=relu
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initW=0.03
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initB=1
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partialSum=13
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sharedBiases=1
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[conv4d]
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type=conv
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inputs=conv3d
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filters=192
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padding=1
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stride=1
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filterSize=3
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channels=192
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neuron=relu
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initW=0.03
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initB=1
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partialSum=13
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sharedBiases=1
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[conv5a]
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type=conv
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inputs=conv4a
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filters=128
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padding=1
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stride=1
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filterSize=3
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channels=192
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initW=0.03
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initB=1
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partialSum=13
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groups=1
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[conv5b]
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type=conv
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inputs=conv4b
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filters=128
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padding=1
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stride=1
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filterSize=3
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channels=192
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initW=0.03
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initB=1
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partialSum=13
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groups=1
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[conv5c]
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type=conv
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inputs=conv4c
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filters=128
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padding=1
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stride=1
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filterSize=3
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channels=192
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initW=0.03
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initB=1
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partialSum=13
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groups=1
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[conv5d]
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type=conv
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inputs=conv4d
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filters=128
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padding=1
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stride=1
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filterSize=3
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channels=192
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initW=0.03
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initB=1
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partialSum=13
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groups=1
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[pool3a]
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type=pool
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pool=max
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inputs=conv5a
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sizeX=3
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stride=2
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channels=128
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neuron=relu
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[pool3b]
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type=pool
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pool=max
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inputs=conv5b
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sizeX=3
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stride=2
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channels=128
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neuron=relu
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[pool3c]
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type=pool
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pool=max
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inputs=conv5c
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sizeX=3
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stride=2
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channels=128
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neuron=relu
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[pool3d]
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type=pool
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pool=max
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inputs=conv5d
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sizeX=3
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stride=2
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channels=128
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neuron=relu
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[conv6a]
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type=conv
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inputs=pool3a,pool3b,pool3c
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filters=64,64,64
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padding=1,1,1
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stride=1,1,1
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filterSize=3,3,3
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channels=128,128,128
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initW=0.03,0.03,0.03
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initB=1
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partialSum=4
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neuron=relu
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gpu=0
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[conv6b]
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type=conv
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inputs=pool3a,pool3b,pool3d
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filters=64,64,64
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padding=1,1,1
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stride=1,1,1
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filterSize=3,3,3
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channels=128,128,128
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initW=0.03,0.03,0.03
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initB=1
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partialSum=4
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neuron=relu
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gpu=1
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[conv6c]
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type=conv
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inputs=pool3c,pool3d,pool3a
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filters=64,64,64
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padding=1,1,1
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stride=1,1,1
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filterSize=3,3,3
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channels=128,128,128
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initW=0.03,0.03,0.03
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initB=1
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partialSum=4
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neuron=relu
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gpu=2
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[conv6d]
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type=conv
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inputs=pool3c,pool3d,pool3b
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filters=64,64,64
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padding=1,1,1
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stride=1,1,1
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filterSize=3,3,3
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channels=128,128,128
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initW=0.03,0.03,0.03
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initB=1
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partialSum=4
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neuron=relu
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gpu=3
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[fc1024a]
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type=fc
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inputs=conv6a,conv6b,conv6c,conv6d
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outputs=1024
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initW=0.01,0.01,0.01,0.01
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initB=1
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neuron=relu
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gpu=0
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[fc1024b]
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type=fc
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inputs=conv6a,conv6b,conv6c,conv6d
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outputs=1024
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initW=0.01,0.01,0.01,0.01
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initB=1
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neuron=relu
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gpu=1
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[fc1024c]
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type=fc
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inputs=conv6a,conv6b,conv6c,conv6d
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outputs=1024
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initW=0.01,0.01,0.01,0.01
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initB=1
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neuron=relu
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gpu=2
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[fc1024d]
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type=fc
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inputs=conv6a,conv6b,conv6c,conv6d
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outputs=1024
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initW=0.01,0.01,0.01,0.01
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initB=1
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neuron=relu
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gpu=3
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[hs1a]
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type=hs
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keep=0.5
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inputs=fc1024a
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[hs1b]
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type=hs
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keep=0.5
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inputs=fc1024b
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[hs1c]
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type=hs
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keep=0.5
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inputs=fc1024c
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[hs1d]
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type=hs
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keep=0.5
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inputs=fc1024d
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[fc1024ba]
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type=fc
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inputs=hs1a,hs1b,hs1c,hs1d
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outputs=1024
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initW=0.01,0.01,0.01,0.01
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initB=1
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neuron=relu
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gpu=0
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[fc1024bb]
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type=fc
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inputs=hs1a,hs1b,hs1c,hs1d
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outputs=1024
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initW=0.01,0.01,0.01,0.01
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initB=1
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neuron=relu
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gpu=1
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[fc1024bc]
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type=fc
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inputs=hs1a,hs1b,hs1c,hs1d
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outputs=1024
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initW=0.01,0.01,0.01,0.01
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initB=1
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neuron=relu
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gpu=2
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[fc1024bd]
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type=fc
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inputs=hs1a,hs1b,hs1c,hs1d
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outputs=1024
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initW=0.01,0.01,0.01,0.01
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initB=1
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neuron=relu
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gpu=3
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[hs2a]
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type=hs
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keep=0.5
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inputs=fc1024ba
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[hs2b]
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type=hs
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keep=0.5
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inputs=fc1024bb
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[hs2c]
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type=hs
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keep=0.5
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inputs=fc1024bc
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[hs2d]
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type=hs
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keep=0.5
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inputs=fc1024bd
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[fc1000a]
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type=fc
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outputs=2546
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inputs=hs2a,hs2b,hs2c,hs2d
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initW=0.01,0.01,0.01,0.01
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gpu=0
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[fc1000b]
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type=fc
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outputs=2546
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inputs=hs2a,hs2b,hs2c,hs2d
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initW=0.01,0.01,0.01,0.01
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gpu=1
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[fc1000c]
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type=fc
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outputs=2546
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inputs=hs2a,hs2b,hs2c,hs2d
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initW=0.01,0.01,0.01,0.01
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gpu=2
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[fc1000d]
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type=fc
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outputs=2546
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inputs=hs2a,hs2b,hs2c,hs2d
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initW=0.01,0.01,0.01,0.01
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gpu=3
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[concat]
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type=concat
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inputs=fc1000a,fc1000b,fc1000c,fc1000d
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[probs]
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type=softmax
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inputs=concat
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gpu=0
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[logprob]
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type=cost.logreg
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inputs=labels,probs
|
|
gpu=0
|