[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