DeepSEA/2_model.lua at master · jisraeli/DeepSEA · GitHub

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require 'torch'
require 'nn'
require 'cunn'
require 'math'


nfeats = 4
width = trainData.data:size(3)
height = 1
ninputs = nfeats*width*height
nkernels = {320,480,960}


model = nn.Sequential()

model:add(nn.SpatialConvolutionMM(nfeats, nkernels[1], 1, 8, 1, 1, 0):cuda())
model:add(nn.Threshold(0, 1e-6):cuda())
model:add(nn.SpatialMaxPooling(1,4,1,4):cuda())
model:add(nn.Dropout(0.2):cuda())

model:add(nn.SpatialConvolutionMM(nkernels[1], nkernels[2], 1, 8, 1, 1, 0):cuda())
model:add(nn.Threshold(0, 1e-6):cuda())
model:add(nn.SpatialMaxPooling(1,4,1,4):cuda())
model:add(nn.Dropout(0.2):cuda())

model:add(nn.SpatialConvolutionMM(nkernels[2], nkernels[3], 1, 8, 1, 1, 0):cuda())
model:add(nn.Threshold(0, 1e-6):cuda())
model:add(nn.Dropout(0.5):cuda())

nchannel = math.floor((math.floor((width-7)/4.0)-7)/4.0)-7
model:add(nn.Reshape(nkernels[3]*nchannel))
model:add(nn.Linear(nkernels[3]*nchannel, noutputs))
model:add(nn.Threshold(0, 1e-6):cuda())
model:add(nn.Linear(noutputs , noutputs):cuda())
model:add(nn.Sigmoid():cuda())

print(model)