Added DistNLLCriterion, to support Neg Likelihood for distributions.

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

1 files changed, 61 insertions, 0 deletions

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