Train neural network with sine function - ruby

Question

I want to train a neural network with the sine() function.
Currently I use this code and the (cerebrum gem):
require 'cerebrum'
input = Array.new
300.times do |i|
inputH = Hash.new
inputH[:input]=[i]
sinus = Math::sin(i)
inputH[:output] = [sinus]
input.push(inputH)
end
network = Cerebrum.new
network.train(input, {
error_threshold: 0.00005,
iterations: 40000,
log: true,
log_period: 1000,
learning_rate: 0.3
})
res = Array.new
300.times do |i|
result = network.run([i])
res.push(result[0])
end
puts "#{res}"
But it does not work, if I run the trained network I get some weird output values (instead of getting a part of the sine curve).
So, what I am doing wrong?

Answer 1

Cerebrum is a very basic and slow NN implementation. There are better options in Ruby, such as ruby-fann gem.
Most likely your problem is the network is too simple. You have not specified any hidden layers - it looks like the code assigns a default hidden layer with 3 neurons in it for your case.
Try something like:
network = Cerebrum.new({
learning_rate: 0.01,
momentum: 0.9,
hidden_layers: [100]
})
and expect it to take forever to train, plus still not be very good.
Also, your choice of 300 outputs is too broad - to the network it will look mostly like noise and it won't interpolate well between points. A neural network does not somehow figure out "oh, that must be a sine wave" and match to it. Instead it interpolates between the points - the clever bit happens when it does so in multiple dimensions at once, perhaps finding structure that you could not spot so easily with a manual inspection. To give it a reasonable chance of learning something, I suggest you give it much denser points e.g. where you currently have sinus = Math::sin(i) instead use:
sinus = Math::sin(i.to_f/10)
That's still almost 5 iterations through the sine wave. Which should hopefully be enough to prove that the network can learn an arbitrary function.