Added DistNLLCriterion, to support Neg Likelihood for distributions.

ClassNLLCriterion only supports simple distributions, e.g. one-of-N.
DistNLLCriterion supports arbitrary distributions.

4 files changed, 74 insertions, 2 deletions

diff --git a/DataList.lua b/DataList.lua
index 9677022..4922e8b 100644
--- a/DataList.lua
+++ b/DataList.lua
@@ -13,6 +13,7 @@ function DataList:__init()
    self.nbClass = 0
    self.ClassName = {}
    self.nbSamples = 0
+   self.targetIsProbability = false
    self.spatialTarget = false
 end
 
@@ -32,10 +33,18 @@ function DataList:__index__(key)
 
       -- create target vector on the fly
       if self.spatialTarget then
-         self.datasets[class][elmt][2] = torch.Tensor(self.nbClass,1,1):fill(-1)
+         if self.targetIsProbability then
+            self.datasets[class][elmt][2] = torch.Tensor(self.nbClass,1,1):zero()
+         else
+            self.datasets[class][elmt][2] = torch.Tensor(self.nbClass,1,1):fill(-1)
+         end
          self.datasets[class][elmt][2][class][1][1] = 1
       else
-         self.datasets[class][elmt][2] = torch.Tensor(self.nbClass):fill(-1)
+         if self.targetIsProbability then
+            self.datasets[class][elmt][2] = torch.Tensor(self.nbClass):zero()
+         else
+            self.datasets[class][elmt][2] = torch.Tensor(self.nbClass):fill(-1)
+         end
          self.datasets[class][elmt][2][class] = 1
       end
 
diff --git a/DistNLLCriterion.lua b/DistNLLCriterion.lua
new file mode 100644
index 0000000..c0b69e3
--- /dev/null
+++ b/DistNLLCriterion.lua
@@ -0,0 +1,61 @@
+local DistNLLCriterion, parent = torch.class('nn.DistNLLCriterion', 'nn.Criterion')
+
+function DistNLLCriterion:__init()
+   parent.__init(self)
+   -- user options
+   self.inputIsProbability = false
+   self.inputIsLogProbability = false
+   self.targetIsProbability = false
+   -- internal
+   self.targetSoftMax = nn.SoftMax()
+   self.inputLogSoftMax = nn.LogSoftMax()
+   self.gradLogInput = torch.Tensor()
+end
+
+function DistNLLCriterion:normalize(input, target)
+   -- normalize target
+   if not self.targetIsProbability then
+      self.probTarget = self.targetSoftMax:forward(target)
+   else
+      self.probTarget = target
+   end
+
+   -- normalize input
+   if not self.inputIsLogProbability and not self.inputIsProbability then
+      self.logProbInput = self.inputLogSoftMax:forward(input)
+   elseif not self.inputIsLogProbability then
+      print('TODO: implement nn.Log()')
+   else
+      self.logProbInput = input
+   end
+end
+
+function DistNLLCriterion:denormalize(input)
+   -- denormalize gradients
+   if not self.inputIsLogProbability and not self.inputIsProbability then
+      self.gradInput = self.inputLogSoftMax:backward(input, self.gradLogInput)
+   elseif not self.inputIsLogProbability then
+      print('TODO: implement nn.Log()')
+   else
+      self.gradInput = self.gradLogInput
+   end
+end
+
+function DistNLLCriterion:forward(input, target)
+   self:normalize(input, target)
+   self.output = 0
+   for i = 1,input:size(1) do
+      self.output = self.output - self.logProbInput[i] * self.probTarget[i]
+   end
+   return self.output
+end
+
+function DistNLLCriterion:backward(input, target)
+   self:normalize(input, target)
+   self.gradLogInput:resizeAs(input)
+   for i = 1,input:size(1) do
+      self.gradLogInput[i] = -self.probTarget[i]
+   end
+   self:denormalize(input)
+   return self.gradInput
+end
diff --git a/init.lua b/init.lua
index 20246bc..ea44de0 100644
--- a/init.lua
+++ b/init.lua
@@ -93,6 +93,7 @@ torch.include('nnx', 'SpatialColorTransform.lua')
 -- criterions:
 torch.include('nnx', 'SuperCriterion.lua')
 torch.include('nnx', 'SparseCriterion.lua')
+torch.include('nnx', 'DistNLLCriterion.lua')
 torch.include('nnx', 'SpatialMSECriterion.lua')
 torch.include('nnx', 'SpatialClassNLLCriterion.lua')
 torch.include('nnx', 'SpatialSparseCriterion.lua')
diff --git a/nnx-1.0-1.rockspec b/nnx-1.0-1.rockspec
index cfbc571..3af08d0 100644
--- a/nnx-1.0-1.rockspec
+++ b/nnx-1.0-1.rockspec
@@ -62,6 +62,7 @@ build = {
          install_files(/lua/nnx init.lua)
          install_files(/lua/nnx Abs.lua)
          install_files(/lua/nnx ConfusionMatrix.lua)
+         install_files(/lua/nnx DistNLLCriterion.lua)
          install_files(/lua/nnx Logger.lua)
          install_files(/lua/nnx Probe.lua)
          install_files(/lua/nnx HardShrink.lua)