batch-brad

require 'torch'
require 'nn'
require 'sys'

torch.setdefaulttensortype('torch.FloatTensor')
numInputNodes=30
numBatches=64
numHidden1=64
numHidden2=128
numOutputNodes=1
-------------- Model -------------------------
mlp = nn.Sequential()
mlp:add(nn.Linear(numInputNodes,numHidden1))
mlp:add(nn.Linear(numHidden1,numHidden2))
mlp:add(nn.Linear(numHidden2,numOutputNodes))
mlp:add(nn.Sum(1)) -- sum over the batch dimension which is the first dimension
-------------- End of Model -------------------------
input = torch.rand(numBatches,numInputNodes) -- Send in a batch of 8 frames of 4 numbers each

local count = 0
local numRuns = 10
for i=1,numRuns do-- do 10 runs for benchmarking
   sys.tic()
   out=mlp:forward(input)
   count  = count + sys.toc()
end
print('Time (in seconds) for forward() 1 sample using mlp-batch: ' .. count / numRuns)