Merge pull request #117 from torch/mulfix

removing the requirement for providing size in nn.Mul

3 files changed, 6 insertions, 10 deletions

diff --git a/Mul.lua b/Mul.lua
index 7841470..289d83a 100644
--- a/Mul.lua
+++ b/Mul.lua
@@ -1,15 +1,11 @@
 local Mul, parent = torch.class('nn.Mul', 'nn.Module')
 
-function Mul:__init(inputSize)
+function Mul:__init()
    parent.__init(self)
   
    self.weight = torch.Tensor(1)
    self.gradWeight = torch.Tensor(1)
    
-   -- state
-   self.gradInput:resize(inputSize)
-   self.output:resize(inputSize) 
-
    self:reset()
 end
 
@@ -25,13 +21,13 @@ function Mul:reset(stdv)
 end
 
 function Mul:updateOutput(input)
-   self.output:copy(input);
+   self.output:resizeAs(input):copy(input);
    self.output:mul(self.weight[1]);
    return self.output 
 end
 
 function Mul:updateGradInput(input, gradOutput) 
-   self.gradInput:zero()
+   self.gradInput:resizeAs(input):zero()
    self.gradInput:add(self.weight[1], gradOutput)
    return self.gradInput
 end
diff --git a/doc/simple.md b/doc/simple.md
index aa1a94d..2cc3ccf 100644
--- a/doc/simple.md
+++ b/doc/simple.md
@@ -262,7 +262,7 @@ to produce the output _y_.
 <a name="nn.Mul"/>
 ## Mul ##
 
-`module` = `Mul(inputDimension)`
+`module` = `Mul()`
 
 Applies a _single_ scaling factor to the incoming data, i.e.
 _y= w x_, where _w_ is a scalar. 
@@ -271,7 +271,7 @@ Example:
 ```lua
 y=torch.Tensor(5);  
 mlp=nn.Sequential()
-mlp:add(nn.Mul(5))
+mlp:add(nn.Mul())
 
 function gradUpdate(mlp, x, y, criterion, learningRate) 
   local pred = mlp:forward(x)
diff --git a/test.lua b/test.lua
index 4615b23..89ff7d5 100644
--- a/test.lua
+++ b/test.lua
@@ -671,7 +671,7 @@ function nntest.Mul()
    local inj = math.random(3,5)
    local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj,ink):zero()
-   local module = nn.Mul(ini*inj*ink)
+   local module = nn.Mul()
 
    local err = jac.testJacobian(module,input)
    mytester:assertlt(err,precision, 'error on state ')