Santara

   model_G = nn.Sequential()
   model_G:add(nn.JoinTable(2, 2))
   model_G:add(cudnn.SpatialConvolutionUpsample(3+1, 64, 7, 7, 1, 1)):add(cudnn.ReLU(true))
   model_G:add(nn.SpatialBatchNormalization(64, nil, nil, false))
   model_G:add(cudnn.SpatialConvolutionUpsample(64, 368, 7, 7, 1, 4)):add(cudnn.ReLU(true))
   model_G:add(nn.SpatialBatchNormalization(368, nil, nil, false))
   model_G:add(nn.SpatialDropout(0.5))
   model_G:add(cudnn.SpatialConvolutionUpsample(368, 128, 7, 7, 1, 4)):add(cudnn.ReLU(true))
   model_G:add(nn.SpatialBatchNormalization(128, nil, nil, false))
   model_G:add(nn.FeatureLPPooling(2,2,2,true))

Santara

----------------------------------------------------------------------
-- CIFAR 8x8
opt.scale = 8 
opt.geometry = {3, opt.scale, opt.scale}
local input_sz = opt.geometry[1] * opt.geometry[2] * opt.geometry[3]
local numhid = 600 
model_D = nn.Sequential()
model_D:add(nn.Reshape(input_sz))
model_D:add(nn.Linear(input_sz, numhid))
model_D:add(nn.ReLU())