conv-brad

require 'torch'
require 'nn'
 
torch.setdefaulttensortype('torch.FloatTensor')

frameSize = 4 -- each input frame has 4 numbers
hiddenSize = 5 -- each hidden layer has 5 units
outputSize = 1 -- each output layer has 1 output
-------------- Model -------------------------
mlp = nn.Sequential()
mlp:add(nn.TemporalConvolution(frameSize,hiddenSize,1,1))  
mlp:add(nn.TemporalConvolution(hiddenSize,outputSize,1,1))
mlp:add(nn.Sum(1)) -- sum up all the outputs
-------------- End of Model -------------------------
 
input = torch.rand(8,frameSize) -- 8 frames of 4 numbers each = 32 numbers in total
print(mlp:forward(input))