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							- require 'cunn'
 
- require 'cudnn'
 
- require './LeakyReLU'
 
- function cudnn.SpatialConvolution:reset(stdv)
 
-    stdv = math.sqrt(2 / ( self.kW * self.kH * self.nOutputPlane))
 
-    self.weight:normal(0, stdv)
 
-    self.bias:fill(0)
 
- end
 
- local function create_model()
 
-    local model = nn.Sequential() 
 
-    
 
-    model:add(cudnn.SpatialConvolution(1, 32, 3, 3, 1, 1, 0, 0):fastest())
 
-    model:add(nn.LeakyReLU(0.1))   
 
-    model:add(cudnn.SpatialConvolution(32, 32, 3, 3, 1, 1, 0, 0):fastest())
 
-    model:add(nn.LeakyReLU(0.1))
 
-    model:add(cudnn.SpatialConvolution(32, 64, 3, 3, 1, 1, 0, 0):fastest())
 
-    model:add(nn.LeakyReLU(0.1))
 
-    model:add(cudnn.SpatialConvolution(64, 64, 3, 3, 1, 1, 0, 0):fastest())
 
-    model:add(nn.LeakyReLU(0.1))
 
-    model:add(cudnn.SpatialConvolution(64, 128, 3, 3, 1, 1, 0, 0):fastest())
 
-    model:add(nn.LeakyReLU(0.1))
 
-    model:add(cudnn.SpatialConvolution(128, 128, 3, 3, 1, 1, 0, 0):fastest())
 
-    model:add(nn.LeakyReLU(0.1))
 
-    model:add(cudnn.SpatialConvolution(128, 1, 3, 3, 1, 1, 0, 0):fastest())
 
-    model:add(nn.View(-1):setNumInputDims(3))
 
- --model:cuda()
 
- --print(model:forward(torch.Tensor(32, 1, 92, 92):uniform():cuda()):size())
 
-    
 
-    return model, 7
 
- end
 
- return create_model
 
 
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