xor-mlp.lua

require "nn"
mlp = nn.Sequential();  -- make a multi-layer perceptron
inputs = 2; outputs = 1; HUs = 20; -- parameters
mlp:add(nn.Linear(inputs, HUs))
mlp:add(nn.Tanh())
mlp:add(nn.Linear(HUs, outputs))

criterion = nn.MSECriterion()  

for i = 1,2500 do
  -- random sample
  local input= torch.randn(2);     -- normally distributed example in 2d
  local output= torch.Tensor(1);
  if input[1]*input[2] > 0 then  -- calculate label for XOR function
    output[1] = -1
  else
    output[1] = 1
  end

  -- feed it to the neural network and the criterion
  criterion:forward(mlp:forward(input), output)

  -- train over this example in 3 steps
  -- (1) zero the accumulation of the gradients
  mlp:zeroGradParameters()
  -- (2) accumulate gradients
  mlp:backward(input, criterion:backward(mlp.output, output))
  -- (3) update parameters with a 0.01 learning rate
  mlp:updateParameters(0.01)
end

x = torch.Tensor(2)
x[1] =  0.5; x[2] =  0.5; print("input: \n", x, "\noutput:", mlp:forward(x))
x[1] =  0.5; x[2] = -0.5; print("input: \n", x, "\noutput:", mlp:forward(x))
x[1] = -0.5; x[2] =  0.5; print("input: \n", x, "\noutput:", mlp:forward(x))
x[1] = -0.5; x[2] = -0.5; print("input: \n", x, "\noutput:", mlp:forward(x))