Lua Neural Network

NeuralNetwork.lua

ACTIVATION_RESPONSE = 1


NeuralNetwork = {
	transfer = function(x) return 1 / (1 + math.exp(-x / ACTIVATION_RESPONSE)) end --This is the Transfer function (in this case a sigmoid)
}

function NeuralNetwork.create(inputs, outputs, hiddenlayers, neurons, learningrate)
	inputs = inputs or 1
	outputs = outputs or 1
	hiddenlayers = hiddenlayers or math.ceil(inputs/2)
	neurons = neurons or math.ceil(inputs*.66666+outputs)
	learningrate = learningrate or .5
	--order goes network[layer][neuron][wieght]
	local network = setmetatable({
		learningRate = learningrate
	},{__index = NeuralNetwork});
	network[1] = {}	 --Input Layer
	for i = 1,inputs do
		network[1][i] = {}
	end
	for i = 2,hiddenlayers+2 do --plus 2 represents the output layer (also need to skip input layer)
		network[i] = {}
		local neuronsInLayer = neurons
		if i == hiddenlayers+2 then
			neuronsInLayer = outputs
		end
		for j = 1,neuronsInLayer do
			network[i][j] = {bias = math.random()*2-1}
			local numNeuronInputs = table.getn(network[i-1])
			for k = 1,numNeuronInputs do
				network[i][j][k] = math.random()*2-1	--return random number between -1 and 1
			end
		end
	end
	return network
end

function NeuralNetwork:forwardPropagate(...)
	if table.getn(arg) ~= table.getn(self[1]) and type(arg[1]) ~= "table" then
		error("Neural Network received "..table.getn(arg).." input[s] (expected "..table.getn(self[1]).." input[s])",2)
	elseif type(arg[1]) == "table" and table.getn(arg[1]) ~= table.getn(self[1]) then
		error("Neural Network received "..table.getn(arg[1]).." input[s] (expected "..table.getn(self[1]).." input[s])",2)
	end
	local outputs = {}
	for i = 1,table.getn(self) do
		for j = 1,table.getn(self[i]) do
			if i == 1 then
				if type(arg[1]) == "table" then
					self[i][j].result = arg[1][j]
				else
					self[i][j].result = arg[j]
				end
			else
				self[i][j].result = self[i][j].bias
				for k = 1,table.getn(self[i][j]) do
					self[i][j].result = self[i][j].result + (self[i][j][k]*self[i-1][k].result)
				end
				self[i][j].result = NeuralNetwork.transfer(self[i][j].result)
				if i == table.getn(self) then
					table.insert(outputs,self[i][j].result)
				end
			end
		end

	end
	return outputs
end

function NeuralNetwork:backwardPropagate(inputs,desiredOutputs)
	if table.getn(inputs) ~= table.getn(self[1]) then
		error("Neural Network received "..table.getn(inputs).." input[s] (expected "..table.getn(self[1]).." input[s])",2)
	elseif table.getn(desiredOutputs) ~= table.getn(self[table.getn(self)]) then
		error("Neural Network received "..table.getn(desiredOutputs).." desired output[s] (expected "..table.getn(self[table.getn(self)]).." desired output[s])",2)
	end
	self:forwardPropagate(inputs) --update the internal inputs and outputs
	for i = table.getn(self),2,-1 do --iterate backwards (nothing to calculate for input layer)
		local tempResults = {}
		for j = 1,table.getn(self[i]) do
			if i == table.getn(self) then --special calculations for output layer
				self[i][j].delta = (desiredOutputs[j] - self[i][j].result) * self[i][j].result * (1 - self[i][j].result)
			else
				local weightDelta = 0
				for k = 1,table.getn(self[i+1]) do
					weightDelta = weightDelta + self[i+1][k][j]*self[i+1][k].delta
				end
				self[i][j].delta = self[i][j].result * (1 - self[i][j].result) * weightDelta
			end
		end
	end
	for i = 2,table.getn(self) do
		for j = 1,table.getn(self[i]) do
			self[i][j].bias = self[i][j].delta * self.learningRate
			for k = 1,table.getn(self[i][j]) do
				self[i][j][k] = self[i][j][k] + self[i][j].delta * self.learningRate * self[i-1][k].result
			end
		end
	end
end

function NeuralNetwork:save()
	--[[
	File specs:
		|INFO| - should be FF BP NN
		|I| - number of inputs
		|O| - number of outputs
		|HL| - number of hidden layers
		|NHL| - number of neurons per hidden layer
		|LR| - learning rate
		|BW| - bias and weight values
	]]--
	local data = "|INFO|FF BP NN|I|"..tostring(table.getn(self[1])).."|O|"..tostring(table.getn(self[table.getn(self)])).."|HL|"..tostring(table.getn(self)-2).."|NHL|"..tostring(table.getn(self[2])).."|LR|"..tostring(self.learningRate).."|BW|"
	for i = 2,table.getn(self) do -- nothing to save for input layer
		for j = 1,table.getn(self[i]) do
			local neuronData = tostring(self[i][j].bias).."{"
			for k = 1,table.getn(self[i][j]) do
				neuronData = neuronData..tostring(self[i][j][k])
				neuronData = neuronData..","
			end
			data = data..neuronData.."}"
		end
	end
	data = data.."|END|"
	return data
end

function NeuralNetwork.load(data)
	local dataPos = string.find(data,"|")+1
	local currentChunk = string.sub(data, dataPos, string.find(data,"|",dataPos)-1)
	local dataPos = string.find(data,"|",dataPos)+1
	local _inputs, _outputs, _hiddenLayers, neurons, learningrate
	local biasWeights = {}
	local errorExit = false
	while currentChunk ~= "END" and not errorExit do
		if currentChuck == "INFO" then
			currentChunk = string.sub(data, dataPos, string.find(data,"|",dataPos)-1)
			dataPos = string.find(data,"|",dataPos)+1
			if currentChunk ~= "FF BP NN" then
				errorExit = true
			end
		elseif currentChunk == "I" then
			currentChunk = string.sub(data, dataPos, string.find(data,"|",dataPos)-1)
			dataPos = string.find(data,"|",dataPos)+1
			_inputs = tonumber(currentChunk)
		elseif currentChunk == "O" then
			currentChunk = string.sub(data, dataPos, string.find(data,"|",dataPos)-1)
			dataPos = string.find(data,"|",dataPos)+1
			_outputs = tonumber(currentChunk)
		elseif currentChunk == "HL" then
			currentChunk = string.sub(data, dataPos, string.find(data,"|",dataPos)-1)
			dataPos = string.find(data,"|",dataPos)+1
			_hiddenLayers = tonumber(currentChunk)
		elseif currentChunk == "NHL" then
			currentChunk = string.sub(data, dataPos, string.find(data,"|",dataPos)-1)
			dataPos = string.find(data,"|",dataPos)+1
			neurons = tonumber(currentChunk)
		elseif currentChunk == "LR" then
			currentChunk = string.sub(data, dataPos, string.find(data,"|",dataPos)-1)
			dataPos = string.find(data,"|",dataPos)+1
			learningrate = tonumber(currentChunk)
		elseif currentChunk == "BW" then
			currentChunk = string.sub(data, dataPos, string.find(data,"|",dataPos)-1)
			dataPos = string.find(data,"|",dataPos)+1
			local subPos = 1
			local subChunk
			for i = 1,_hiddenLayers+1 do
				biasWeights[i] = {}
				local neuronsInLayer = neurons
				if i == _hiddenLayers+1 then
					neuronsInLayer = _outputs
				end
				for j = 1,neuronsInLayer do
					biasWeights[i][j] = {}
					biasWeights[i][j].bias = tonumber(string.sub(currentChunk,subPos,string.find(currentChunk,"{",subPos)-1))
					subPos = string.find(currentChunk,"{",subPos)+1
					subChunk = string.sub(currentChunk, subPos, string.find(currentChunk,",",subPos)-1)
					local maxPos = string.find(currentChunk,"}",subPos)
					while subPos < maxPos do
						table.insert(biasWeights[i][j],tonumber(subChunk))
						subPos = string.find(currentChunk,",",subPos)+1
						if string.find(currentChunk,",",subPos) ~= nil then
							subChunk = string.sub(currentChunk, subPos, string.find(currentChunk,",",subPos)-1)
						end
					end
					subPos = maxPos+1
				end
			end
		end
		currentChunk = string.sub(data, dataPos, string.find(data,"|",dataPos)-1)
		dataPos = string.find(data,"|",dataPos)+1
	end
	if errorExit then
		error("Failed to load Neural Network:"..currentChunk,2)
	end
	local network = setmetatable({
		learningRate = learningrate
	},{__index = NeuralNetwork});
	network[1] = {}	 --Input Layer
	for i = 1,_inputs do
		network[1][i] = {}
	end
	for i = 2,_hiddenLayers+2 do --plus 2 represents the output layer (also need to skip input layer)
		network[i] = {}
		local neuronsInLayer = neurons
		if i == _hiddenLayers+2 then
			neuronsInLayer = _outputs
		end
		for j = 1,neuronsInLayer do
			network[i][j] = {bias = biasWeights[i-1][j].bias}
			local numNeuronInputs = table.getn(network[i-1])
			for k = 1,numNeuronInputs do
				network[i][j][k] = biasWeights[i-1][j][k]
			end
		end
	end
	return network
end
return NeuralNetwork