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Change suggested by Natalia Gimelshein
To align behaviour on other modules (linear, spatial convolution, etc.).
This allows model definitions such as:
local lenet = nn.Sequential()
lenet:add(nn.MulConstant(0.00390625))
lenet:add(nn.SpatialConvolution(1,20,5,5,1,1,0)) -- 1*28*28 -> 20*24*24
lenet:add(nn.SpatialMaxPooling(2, 2, 2, 2)) -- 20*24*24 -> 20*12*12
lenet:add(nn.SpatialConvolution(20,50,5,5,1,1,0)) -- 20*12*12 -> 50*8*8
lenet:add(nn.SpatialMaxPooling(2,2,2,2)) -- 50*8*8 -> 50*4*4
lenet:add(nn.View(-1):setNumInputDims(3)) -- 50*4*4 -> 800
lenet:add(nn.Linear(800,500)) -- 800 -> 500
lenet:add(nn.ReLU())
lenet:add(nn.Linear(500, 10)) -- 500 -> 10
lenet:add(nn.LogSoftMax())
lenet:get(1).gradInput = nil
lenet:get(2).gradInput = nil
Setting gradInput to nil on the first two layers removes unnecessary
dgrad computations and saves about 5% of compute utilization.
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