-- All inputs have to be 3D or 4D(batch-mode), except ReLU, Tanh and Sigmoid
cudnn.SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, [dW = 1], [dH = 1], [padW = 0], [padH = 0], [groups = 1])
cudnn.SpatialMaxPooling(kW, kH, dW, dH, padW, padH)
cudnn.SpatialAveragePooling(kW, kH, dW, dH, padW, padH)

-- the pointwise functions take an additional optional argument. if inplace=true then they do operations in-place without using any extra memory for themselves
cudnn.ReLU(inplace[=false])
cudnn.Tanh(inplace[=false])
cudnn.Sigmoid(inplace[=false])

-- SoftMax can be run in fast mode or accurate mode. Default is accurate mode.
cudnn.SoftMax(fastMode [= false])          -- SoftMax across each image (just like nn.SoftMax)
cudnn.LogSoftMax()                         -- LogSoftMax across each image (just like nn.LogSoftMax)
cudnn.SpatialSoftMax(fastMode [= false])   -- SoftMax across feature-maps (per spatial location)
cudnn.SpatialLogSoftMax()                  -- LogSoftMax across feature-maps (per spatial location)

cudnn.SpatialCrossEntropyCriterion()       -- A spatial version of LogSoftMax + ClassNLLCriterion in one shot

-- Volumetric inputs (4D or 5D batched mode)
cudnn.VolumetricConvolution(nInputPlane, nOutputPlane, kT, kW, kH, dT, dW, dH, padT, padW, padH)
cudnn.VolumetricMaxPooling(kT, kW, kH, dT, dW, dH, padT, padW, padH)
cudnn.VolumetricAveragePooling(kT, kW, kH, dT, dW, dH, padT, padW, padH)

require 'cudnn'
cudnn.benchmark = true -- uses the inbuilt cudnn auto-tuner to find the fastest convolution algorithms.
                       -- If this is set to false, uses some in-built heuristics that might not always be fastest.

cudnn.fastest = true -- this is like the :fastest() mode for the Convolution modules,
                     -- simply picks the fastest convolution algorithm, rather than tuning for workspace size

cudnn.verbose = true -- this prints out some more verbose information useful for debugging