Merge pull request #313 from mys007/batchmode_hingeemb

HingeEmbeddingCriterion: batch mode

3 files changed, 47 insertions, 16 deletions

diff --git a/HingeEmbeddingCriterion.lua b/HingeEmbeddingCriterion.lua
index b075847..fe8f1a6 100644
--- a/HingeEmbeddingCriterion.lua
+++ b/HingeEmbeddingCriterion.lua
@@ -2,24 +2,42 @@ local HingeEmbeddingCriterion, parent = torch.class('nn.HingeEmbeddingCriterion'
 
 function HingeEmbeddingCriterion:__init(margin)
    parent.__init(self)
-   margin = margin or 1 
-   self.margin = margin 
-   self.gradInput = torch.Tensor(1)
+   self.margin = margin or 1
+   self.sizeAverage = true
 end 
  
 function HingeEmbeddingCriterion:updateOutput(input,y)
-   self.output = input[1]
-   if y == -1 then
-	   self.output = math.max(0,self.margin - self.output);
+   self.buffer = self.buffer or input.new()
+   if not torch.isTensor(y) then 
+      self.ty = self.ty or input.new():resize(1)
+      self.ty[1]=y
+      y=self.ty
    end
+
+   self.buffer:resizeAs(input):copy(input)
+   self.buffer[torch.eq(y, -1)] = 0
+   self.output = self.buffer:sum()
+   
+   self.buffer:fill(self.margin):add(-1, input)
+   self.buffer:cmax(0)
+   self.buffer[torch.eq(y, 1)] = 0
+   self.output = self.output + self.buffer:sum()
+   
+   if (self.sizeAverage == nil or self.sizeAverage == true) then 
+      self.output = self.output / input:nElement()
+   end
+
    return self.output
 end
 
 function HingeEmbeddingCriterion:updateGradInput(input, y)
-  self.gradInput[1] = y
-  local dist = input[1]
-  if y == -1 and  dist > self.margin then
-     self.gradInput[1] = 0;
-  end
-  return self.gradInput 
+   if not torch.isTensor(y) then self.ty[1]=y; y=self.ty end
+   self.gradInput:resizeAs(input):copy(y)
+   self.gradInput[torch.cmul(torch.eq(y, -1), torch.gt(input, self.margin))] = 0
+   
+   if (self.sizeAverage == nil or self.sizeAverage == true) then
+      self.gradInput:mul(1 / input:nElement())
+   end
+      
+   return self.gradInput 
 end
diff --git a/doc/criterion.md b/doc/criterion.md
index d0c9366..64e6d63 100755
--- a/doc/criterion.md
+++ b/doc/criterion.md
@@ -401,12 +401,12 @@ Creates a criterion that measures the loss given  an input `x` which is a 1-dime
 This is usually used for measuring whether two inputs are similar or dissimilar, e.g. using the L1 pairwise distance, and is typically used for learning nonlinear embeddings or semi-supervised learning.
 
 ```lua
-             ⎧ x,                  if y ==  1
-loss(x, y) = ⎨
-             ⎩ max(0, margin - x), if y == -1
+                 ⎧ x_i,                  if y_i ==  1
+loss(x, y) = 1/n ⎨
+                 ⎩ max(0, margin - x_i), if y_i == -1
 ```
 
-The `margin` has a default value of `1`, or can be set in the constructor.
+If `x` and `y` are `n`-dimensional `Tensor`s, the sum operation still operates over all the elements, and divides by `n` (this can be avoided if one sets the internal variable `sizeAverage` to `false`). The `margin` has a default value of `1`, or can be set in the constructor.
 
 ### Example
 
diff --git a/test.lua b/test.lua
index f5e5295..f00c7c0 100644
--- a/test.lua
+++ b/test.lua
@@ -3830,6 +3830,19 @@ function nntest.CosineEmbeddingCriterion()
   equal(grads[2], zero, 'gradient should be zero')
 end
 
+function nntest.HingeEmbeddingCriterion()
+  local x = torch.Tensor{0.3,2.1,1.8,0}
+  local y = torch.Tensor{1,-1,-1,1}
+  local expgrads = torch.Tensor{1,0,-1,1} / 4
+
+  local crit = nn.HingeEmbeddingCriterion(2)
+  local output = crit:forward(x, y) -- must be called before backward
+  local grads = crit:backward(x, y)
+
+  mytester:assert(math.abs(output - (0.3 + 0.2) / 4) < 1e-10)
+  equal(grads, expgrads)
+end
+
 function nntest.Replicate()
    local vector = torch.rand(3)