Merge pull request #1225 from nicholas-leonard/ZeroGrad

nn.ZeroGrad

ZeroGrad.lua

@@ -0,0 +1,14 @@
+local ZeroGrad, parent = torch.class('nn.ZeroGrad', 'nn.Module')
+
+function ZeroGrad:updateOutput(input)
+   self.output:set(input)
+   return self.output
+end
+
+-- the gradient is simply zeroed.
+-- useful when you don't want to backpropgate through certain paths.
+function ZeroGrad:updateGradInput(input, gradOutput)
+   self.gradInput = nn.utils.recursiveResizeAs(self.gradInput, input)
+   self.gradInput = nn.utils.recursiveFill(self.gradInput, 0)
+   return self.gradInput
+end

doc/simple.md

@@ -61,6 +61,7 @@ Simple Modules are used for various tasks like adapting Tensor methods and provi
     * [WhiteNoise](#nn.WhiteNoise) : adds isotropic Gaussian noise to the signal when in training mode;
     * [OneHot](#nn.OneHot) : transforms a tensor of indices into [one-hot](https://en.wikipedia.org/wiki/One-hot) encoding;
     * [PrintSize](#nn.PrintSize) : prints the size of `input` and `gradOutput` (useful for debugging);
+    * [ZeroGrad](#nn.ZeroGrad) : forwards the `input` as-is, yet zeros the `gradInput`.
 
 <a name="nn.Linear"></a>
 ## Linear ##
@@ -1762,4 +1763,24 @@ module = nn.PrintSize(name)
 This module is useful for debugging complicated module composites.
 It prints the size of the `input` and `gradOutput` during `forward`
 and `backward` propagation respectively.
-The `name` is a string used to identify the module along side the printed size.
+The `name` is a string used to identify the module along side the printed size.
+
+<a name='nn.ZeroGrad'></a>
+## ZeroGrad ##
+
+```lua
+module = nn.ZeroGrad()
+input = torch.Tensor{1,2}
+gradOutput = torch.Tensor{3,4}
+print(module:forward(input))
+ 1
+ 2
+[torch.DoubleTensor of size 2]
+
+print(module:backward(input, gradOutput))
+ 0
+ 0
+[torch.DoubleTensor of size 2]
+```
+
+The module zeros the `gradInput` but forwards the `input` as-is.

init.lua

@@ -63,6 +63,7 @@ require('nn.VolumetricDropout')
 require('nn.WhiteNoise')
 require('nn.OneHot')
 require('nn.PrintSize')
+require('nn.ZeroGrad')
 
 require('nn.CAddTable')
 require('nn.CDivTable')

test.lua

@@ -8538,6 +8538,16 @@ function nntest.OneHot()
    end
 end
 
+function nntest.ZeroGrad()
+   local input = torch.randn(3,4)
+   local zg = nn.ZeroGrad()
+   local output = zg:forward(input)
+   mytester:assertTensorEq(input, output, 0.00000001)
+   local gradInput = zg:backward(input, input)
+   local gradInput2 = gradInput:clone():zero()
+   mytester:assertTensorEq(gradInput, gradInput2, 0.0000001)
+end
+
 
 mytester:add(nntest)