add option to nn.Linear to have no bias

Linear.lua

@@ -1,13 +1,14 @@
 local Linear, parent = torch.class('nn.Linear', 'nn.Module')
 
-function Linear:__init(inputSize, outputSize)
+function Linear:__init(inputSize, outputSize, bias)
    parent.__init(self)
-
+   local bias = ((bias == nil) and true) or bias
    self.weight = torch.Tensor(outputSize, inputSize)
-   self.bias = torch.Tensor(outputSize)
    self.gradWeight = torch.Tensor(outputSize, inputSize)
-   self.gradBias = torch.Tensor(outputSize)
-
+   if bias then
+      self.bias = torch.Tensor(outputSize)
+      self.gradBias = torch.Tensor(outputSize)
+   end
    self:reset()
 end
 
@@ -22,25 +23,28 @@ function Linear:reset(stdv)
          self.weight:select(1, i):apply(function()
             return torch.uniform(-stdv, stdv)
          end)
-         self.bias[i] = torch.uniform(-stdv, stdv)
+      end
+      if self.bias then
+         for i=1,self.bias:nElement() do
+            self.bias[i] = torch.uniform(-stdv, stdv)
+         end
       end
    else
       self.weight:uniform(-stdv, stdv)
-      self.bias:uniform(-stdv, stdv)
+      if self.bias then self.bias:uniform(-stdv, stdv) end
    end
-
    return self
 end
 
 function Linear:updateOutput(input)
    if input:dim() == 1 then
-      self.output:resize(self.bias:size(1))
-      self.output:copy(self.bias)
+      self.output:resize(self.weight:size(1))
+      if self.bias then self.output:copy(self.bias) else self.output:zero() end
       self.output:addmv(1, self.weight, input)
    elseif input:dim() == 2 then
       local nframe = input:size(1)
       local nElement = self.output:nElement()
-      self.output:resize(nframe, self.bias:size(1))
+      self.output:resize(nframe, self.weight:size(1))
       if self.output:nElement() ~= nElement then
          self.output:zero()
       end
@@ -49,7 +53,7 @@ function Linear:updateOutput(input)
          self.addBuffer:resize(nframe):fill(1)
       end
       self.output:addmm(0, self.output, 1, input, self.weight:t())
-      self.output:addr(1, self.addBuffer, self.bias)
+      if self.bias then self.output:addr(1, self.addBuffer, self.bias) end
    else
       error('input must be vector or matrix')
    end
@@ -79,10 +83,12 @@ function Linear:accGradParameters(input, gradOutput, scale)
    scale = scale or 1
    if input:dim() == 1 then
       self.gradWeight:addr(scale, gradOutput, input)
-      self.gradBias:add(scale, gradOutput)
+      if self.bias then self.gradBias:add(scale, gradOutput) end
    elseif input:dim() == 2 then
       self.gradWeight:addmm(scale, gradOutput:t(), input)
-      self.gradBias:addmv(scale, gradOutput:t(), self.addBuffer)
+      if self.bias then
+         self.gradBias:addmv(scale, gradOutput:t(), self.addBuffer)
+      end
    end
 end
 
@@ -92,5 +98,6 @@ Linear.sharedAccUpdateGradParameters = Linear.accUpdateGradParameters
 
 function Linear:__tostring__()
   return torch.type(self) ..
-      string.format('(%d -> %d)', self.weight:size(2), self.weight:size(1))
+      string.format('(%d -> %d)', self.weight:size(2), self.weight:size(1)) ..
+      (self.bias == nil and ' without bias' or '')
 end

doc/simple.md

@@ -46,10 +46,10 @@ Simple Modules are used for various tasks like adapting Tensor methods and provi
 ## Linear ##
 
 ```lua
-module = nn.Linear(inputDimension, outputDimension)
+module = nn.Linear(inputDimension, outputDimension, [bias = true])
 ```
 
-Applies a linear transformation to the incoming data, i.e. `y = Ax + b`. The `input` tensor given in `forward(input)` must be either a vector (1D tensor) or matrix (2D tensor). If the input is a matrix, then each row is assumed to be an input sample of given batch.
+Applies a linear transformation to the incoming data, i.e. `y = Ax + b`. The `input` tensor given in `forward(input)` must be either a vector (1D tensor) or matrix (2D tensor). If the input is a matrix, then each row is assumed to be an input sample of given batch. The layer can be used without bias by setting `bias = false`.
 
 You can create a layer in the following way: