ann.lua

require 'nn'

dataset = {}
function dataset:size() return 1000 end
for i = 1, dataset:size() do
        local input = torch.randn(2)
        print(input)
        local output = torch.Tensor(1)
        if input[1] * input[2] > 0 then
                output[1] = 1
        else
                output[1] = -1
        end
        dataset[i] = {input, output}
end

mlp = nn.Sequential()
inputs = 2
outputs = 1
hiddens = 20
mlp:add( nn.Linear(inputs, hiddens) )
mlp:add( nn.Tanh() )
mlp:add( nn.Linear(hiddens, outputs) )

criterion = nn.MSECriterion()
trainer = nn.StochasticGradient(mlp, criterion)
trainer.learningRate = 0.01
trainer:train(dataset)

x = torch.Tensor(2)
x[1] =  0.5; x[2] =  0.5; print( mlp:forward(x) )
x[1] =  0.5; x[2] = -0.5; print( mlp:forward(x) )
x[1] = -0.5; x[2] =  0.5; print( mlp:forward(x) )
x[1] = -0.5; x[2] = -0.5; print( mlp:forward(x) )