AlexNet/layers/layers-116.cfg
Laurent El Shafey 9fdd561586 Initial commit
2024-12-10 08:56:11 -08:00

616 lines
6.2 KiB
INI

[data]
type=data
dataIdx=0
[labels]
type=data
dataIdx=1
[blur0]
type=blur
inputs=data
stdev=4
filterSize=9
channels=3
gpu=0
[nails0]
type=nailbed
inputs=blur0
stride=4
channels=3
# GPU 0
[conv1a]
type=conv
inputs=data
channels=3
filters=32
padding=0
stride=4
filterSize=11
initW=0.01
partialSum=5
sharedBiases=1
gpu=0
[pool1a]
type=pool
pool=max
inputs=conv1a
sizeX=3
stride=2
channels=32
neuron=relu
[rnorm1a]
type=cmrnorm
inputs=pool1a
channels=32
size=5
[conv2a]
type=conv
inputs=nails0,rnorm1a
filters=128,128
padding=0,2
stride=2,1
filterSize=5,5
channels=3,32
initW=0.01,0.01
initB=1
partialSum=3
sharedBiases=1
neuron=relu
gpu=0
[rnorm2a]
type=cmrnorm
inputs=conv2a
channels=128
size=5
[pool2a]
type=pool
pool=max
inputs=rnorm2a
sizeX=3
stride=2
channels=128
# GPU 1
[conv1b]
type=conv
inputs=data
channels=3
filters=32
padding=0
stride=4
filterSize=11
initW=0.01
partialSum=5
sharedBiases=1
gpu=1
[pool1b]
type=pool
pool=max
inputs=conv1b
sizeX=3
stride=2
channels=32
neuron=relu
[rnorm1b]
type=cmrnorm
inputs=pool1b
channels=32
size=5
[conv2b]
type=conv
inputs=nails0,rnorm1b
filters=128,128
padding=0,2
stride=2,1
filterSize=5,5
channels=3,32
initW=0.01,0.01
initB=1
partialSum=3
sharedBiases=1
neuron=relu
gpu=1
[rnorm2b]
type=cmrnorm
inputs=conv2b
channels=128
size=5
[pool2b]
type=pool
pool=max
inputs=rnorm2b
sizeX=3
stride=2
channels=128
# GPU 2
[conv1c]
type=conv
inputs=data
channels=3
filters=32
padding=0
stride=4
filterSize=11
initW=0.01
partialSum=5
sharedBiases=1
gpu=2
[pool1c]
type=pool
pool=max
inputs=conv1c
sizeX=3
stride=2
channels=32
neuron=relu
[rnorm1c]
type=cmrnorm
inputs=pool1c
channels=32
size=5
[conv2c]
type=conv
inputs=nails0,rnorm1c
filters=128,128
padding=0,2
stride=2,1
filterSize=5,5
channels=3,32
initW=0.01,0.01
initB=1
partialSum=3
sharedBiases=1
neuron=relu
gpu=2
[rnorm2c]
type=cmrnorm
inputs=conv2c
channels=128
size=5
[pool2c]
type=pool
pool=max
inputs=rnorm2c
sizeX=3
stride=2
channels=128
# GPU 3
[conv1d]
type=conv
inputs=data
channels=3
filters=32
padding=0
stride=4
filterSize=11
initW=0.01
partialSum=5
sharedBiases=1
gpu=3
[pool1d]
type=pool
pool=max
inputs=conv1d
sizeX=3
stride=2
channels=32
neuron=relu
[rnorm1d]
type=cmrnorm
inputs=pool1d
channels=32
size=5
[conv2d]
type=conv
inputs=nails0,rnorm1d
filters=128,128
padding=0,2
stride=2,1
filterSize=5,5
channels=3,32
initW=0.01,0.01
initB=1
partialSum=3
sharedBiases=1
neuron=relu
gpu=3
[rnorm2d]
type=cmrnorm
inputs=conv2d
channels=128
size=5
[pool2d]
type=pool
pool=max
inputs=rnorm2d
sizeX=3
stride=2
channels=128
# GPU 0
[conv3a]
type=conv
inputs=pool2a,pool2b
filters=192,192
padding=1,1
stride=1,1
filterSize=3,3
channels=128,128
initW=0.03,0.03
partialSum=13
sharedBiases=1
neuron=relu
gpu=0
[conv4a]
type=conv
inputs=conv3a
filters=192
padding=1
stride=1
filterSize=3
channels=192
neuron=relu
initW=0.03
initB=1
partialSum=13
sharedBiases=1
[conv5a]
type=conv
inputs=conv4a
filters=128
padding=1
stride=1
filterSize=3
channels=192
initW=0.03
initB=1
partialSum=13
groups=1
randSparse=0
[pool3a]
type=pool
pool=max
inputs=conv5a
sizeX=3
stride=2
channels=128
neuron=relu
# GPU 1
[conv3b]
type=conv
inputs=pool2a,pool2b
filters=192,192
padding=1,1
stride=1,1
filterSize=3,3
channels=128,128
initW=0.03,0.03
partialSum=13
sharedBiases=1
neuron=relu
gpu=1
[conv4b]
type=conv
inputs=conv3b
filters=192
padding=1
stride=1
filterSize=3
channels=192
neuron=relu
initW=0.03
initB=1
partialSum=13
sharedBiases=1
[conv5b]
type=conv
inputs=conv4b
filters=128
padding=1
stride=1
filterSize=3
channels=192
initW=0.03
initB=1
partialSum=13
groups=1
randSparse=0
[pool3b]
type=pool
pool=max
inputs=conv5b
sizeX=3
stride=2
channels=128
neuron=relu
# GPU 2
[conv3c]
type=conv
inputs=pool2c,pool2d
filters=192,192
padding=1,1
stride=1,1
filterSize=3,3
channels=128,128
initW=0.03,0.03
partialSum=13
sharedBiases=1
neuron=relu
gpu=2
[conv4c]
type=conv
inputs=conv3c
filters=192
padding=1
stride=1
filterSize=3
channels=192
neuron=relu
initW=0.03
initB=1
partialSum=13
sharedBiases=1
[conv5c]
type=conv
inputs=conv4c
filters=128
padding=1
stride=1
filterSize=3
channels=192
initW=0.03
initB=1
partialSum=13
groups=1
randSparse=0
[pool3c]
type=pool
pool=max
inputs=conv5c
sizeX=3
stride=2
channels=128
neuron=relu
# GPU 3
[conv3d]
type=conv
inputs=pool2c,pool2d
filters=192,192
padding=1,1
stride=1,1
filterSize=3,3
channels=128,128
initW=0.03,0.03
partialSum=13
sharedBiases=1
neuron=relu
gpu=3
[conv4d]
type=conv
inputs=conv3d
filters=192
padding=1
stride=1
filterSize=3
channels=192
neuron=relu
initW=0.03
initB=1
partialSum=13
sharedBiases=1
[conv5d]
type=conv
inputs=conv4d
filters=128
padding=1
stride=1
filterSize=3
channels=192
initW=0.03
initB=1
partialSum=13
groups=1
randSparse=0
[pool3d]
type=pool
pool=max
inputs=conv5d
sizeX=3
stride=2
channels=128
neuron=relu
# GPU 0
[fc1024a]
type=fc
inputs=pool3a,pool3b,pool3c,pool3d
outputs=1024
initW=0.01,0.01,0.01,0.01
initB=1
neuron=relu
gpu=0
[hs1a]
type=hs
keep=0.5
inputs=fc1024a
# GPU 1
[fc1024b]
type=fc
inputs=pool3a,pool3b,pool3c,pool3d
outputs=1024
initW=0.01,0.01,0.01,0.01
initB=1
neuron=relu
gpu=1
[hs1b]
type=hs
keep=0.5
inputs=fc1024b
# GPU 2
[fc1024c]
type=fc
inputs=pool3a,pool3b,pool3c,pool3d
outputs=1024
initW=0.01,0.01,0.01,0.01
initB=1
neuron=relu
gpu=2
[hs1c]
type=hs
keep=0.5
inputs=fc1024c
# GPU 3
[fc1024d]
type=fc
inputs=pool3a,pool3b,pool3c,pool3d
outputs=1024
initW=0.01,0.01,0.01,0.01
initB=1
neuron=relu
gpu=3
[hs1d]
type=hs
keep=0.5
inputs=fc1024d
# GPU 0
[fc1024-2a]
type=fc
inputs=hs1a,hs1b,hs1c,hs1d
outputs=1024
initW=0.01,0.01,0.01,0.01
initB=1
neuron=relu
gpu=0
[hs2a]
type=hs
keep=0.5
inputs=fc1024-2a
# GPU 1
[fc1024-2b]
type=fc
inputs=hs1a,hs1b,hs1c,hs1d
outputs=1024
initW=0.01,0.01,0.01,0.01
initB=1
neuron=relu
gpu=1
[hs2b]
type=hs
keep=0.5
inputs=fc1024-2b
# GPU 2
[fc1024-2c]
type=fc
inputs=hs1a,hs1b,hs1c,hs1d
outputs=1024
initW=0.01,0.01,0.01,0.01
initB=1
neuron=relu
gpu=2
[hs2c]
type=hs
keep=0.5
inputs=fc1024-2c
# GPU 3
[fc1024-2d]
type=fc
inputs=hs1a,hs1b,hs1c,hs1d
outputs=1024
initW=0.01,0.01,0.01,0.01
initB=1
neuron=relu
gpu=3
[hs2d]
type=hs
keep=0.5
inputs=fc1024-2d
# GPU 0
[fc1000]
type=fc
outputs=1000
inputs=hs2a,hs2b,hs2c,hs2d
initW=0.01,0.01,0.01,0.01
gpu=0
[probs]
type=softmax
inputs=fc1000
[logprob]
type=cost.logreg
inputs=labels,probs
gpu=0