-
-
Save EdsonAvelar/7f5e0f0c733d3987413566a8d81cc364 to your computer and use it in GitHub Desktop.
Lua Neural Network
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| 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 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment