Flethuseo - 1 year ago 120

Ruby Question

I am trying to train a feedforward network to work to perform an XOR operations with the Ruby Library AI4R. However,

when I evaluate for the XOR after training it. I am not getting the correct output. Has anyone used this library before

and gotten it to learn the XOR operation.

I am using two input neurons, three neurons in a hidden layer, and one layer for the output, as I saw a precomputed

XOR feed forward neural network like this before.

`require "rubygems"`

require "ai4r"

# Create the network with:

# 2 inputs

# 1 hidden layer with 3 neurons

# 1 outputs

net = Ai4r::NeuralNetwork::Backpropagation.new([2, 3, 1])

example = [[0,0],[0,1],[1,0],[1,1]]

result = [[0],[1],[1],[0]]

# Train the network

400.times do |i|

j = i % result.length

puts net.train(example[j], result[j])

end

# Use it: Evaluate data with the trained network

puts "evaluate 0,0: #{net.eval([0,0])}" # => evaluate 0,0: 0.507531383375123

puts "evaluate 0,1: #{net.eval([0,1])}" # => evaluate 0,1: 0.491957823618629

puts "evaluate 1,0: #{net.eval([1,0])}" # => evaluate 1,0: 0.516413912471401

puts "evaluate 1,1: #{net.eval([1,1])}" # => evaluate 1,1: 0.500197884691668

Ted

Answer

You haven't trained it for enough iterations. If you change `400.times`

to `8000.times`

you'll come much closer (and closer still at `20000.times`

).

At `20000.times`

, I get

```
puts "evaluate 0,0: #{net.eval([0,0])}" # => evaluate 0,0: 0.030879848321403
puts "evaluate 0,1: #{net.eval([0,1])}" # => evaluate 0,1: 0.97105714994505
puts "evaluate 1,0: #{net.eval([1,0])}" # => evaluate 1,0: 0.965055940880282
puts "evaluate 1,1: #{net.eval([1,1])}" # => evaluate 1,1: 0.0268317078331645
```

You can also increase `net.learning_rate`

(but not too much).

Source (Stackoverflow)