1 files changed, 2 insertions, 47 deletions
diff --git a/Optimization.lua b/Optimization.lua
index f18c635..daf0a8d 100644
--- a/Optimization.lua
+++ b/Optimization.lua
@@ -1,56 +1,11 @@
 local Optimization = torch.class('nn.Optimization')
 
 function Optimization:__init()
+   self.output = 0
 end
 
 function Optimization:forward(inputs, targets)
-   self:flatten(parameters, gradParameters)
    self.output = 0
-   self:unflatten(parameters, gradParameters)
+   print('<Optimization> WARNING: this is a virtual function, please overload !')
    return self.output
 end
-
-function Optimization:flatten(parameters, gradParameters)
-   if type(parameters) == 'table' then
-      -- create flat parameters
-      self.parameters = self.parameters or torch.Tensor()
-      self.gradParameters = self.gradParameters or torch.Tensor()
-      -- assuming that the parameters won't change their size, 
-      -- we compute offsets once
-      if not self.offsets then
-         self.nParameters = 0
-         self.offsets = {}
-         for _,param in ipairs(parameters) do
-            table.insert(self.offsets, self.nParameters+1)
-            self.nParameters = self.nParameters + param:nElement()
-         end
-         self.parameters:resize(self.nParameters)
-         self.gradParameters:resize(self.nParameters)
-      end
-      -- copy all params in flat array
-      for i = 1,#parameters do
-         local nElement = parameters[i]:nElement()
-         self.parameters:narrow(1,self.offsets[i],nElement):copy(parameters[i])
-         self.gradParameters:narrow(1,self.offsets[i],nElement):copy(gradParameters[i])
-      end
-   else
-      self.parameters = parameters
-      self.gradParameters = gradParameters
-   end
-end
-
-function Optimization:unflatten(parameters, gradParameters)
-   if type(parameters) == 'table' then
-      -- copy all params into unflat arrays
-      local offset = 1
-      for i = 1,#parameters do
-         local nElement = parameters[i]:nElement()
-         parameters[i]:copy(self.parameters:narrow(1,offset,nElement))
-         gradParameters[i]:copy(self.gradParameters:narrow(1,offset,nElement))
-         offset = offset + nElement
-      end
-   else
-      parameters = self.parameters
-      gradParameters = self.gradParameters
-   end
-end