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July 17, 2014 20:05
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Lua Neural Network
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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( _numInputs, _numOutputs, _numHiddenLayers, _neuronsPerLayer, _learningRate) | |
_numInputs = _numInputs or 1 | |
_numOutputs = _numOutputs or 1 | |
_numHiddenLayers = _numHiddenLayers or math.ceil(_numInputs/2) | |
_neuronsPerLayer = _neuronsPerLayer or math.ceil(_numInputs*.66666+_numOutputs) | |
_learningRate = _learningRate or .5 | |
--order goes network[layer][neuron][wieght] | |
local network = setmetatable({ | |
learningRate = _learningRate | |
},{ __index = NeuralNetwork}); | |
network[1] = {} --Input Layer | |
for i = 1,_numInputs do | |
network[1][i] = {} | |
end | |
for i = 2,_numHiddenLayers+2 do --plus 2 represents the output layer (also need to skip input layer) | |
network[i] = {} | |
local neuronsInLayer = _neuronsPerLayer | |
if i == _numHiddenLayers+2 then | |
neuronsInLayer = _numOutputs | |
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:forewardPropagate(...) | |
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:forewardPropagate(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, _neuronsPerLayer, _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 | |
_neuronsPerLayer = 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 = _neuronsPerLayer | |
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 = _neuronsPerLayer | |
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 |
Well first things off, There's an error already in the code that you must fix.
The error is at line 257.
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I don't know if this is still looked at, but there's a game that can run Lua and with some modification I would like to use this for an Ai? I can't find where to input though.....
To elaborate, how would you train this? Online Lua runners just return nothing- I have no experience whatsoever, could I have some help?