diff --git a/rgbAI/lib/ailib.py b/rgbAI/lib/ailib.py new file mode 100644 index 0000000..56ac6e7 --- /dev/null +++ b/rgbAI/lib/ailib.py @@ -0,0 +1,147 @@ +import numpy as np +from copy import deepcopy as copy + +def sigmoid(x): + return 1/(1 + np.exp(-x)) + +def correctFunc(inp:np.array): # generates the correct answer for the AI + return np.asarray( [1.0 - inp[0], 1.0 - inp[1], 1.0 - inp[2]] ) # basically invert the rgb values + +def calcCost( predicted:np.array, correct:np.array ): # cost function, lower -> good, higher -> bad, bad bot, bad + costSum = 0 + maxLen = len(correct) + + for i in range(maxLen): + costSum += abs((predicted[i] - correct[i])) + + return costSum / maxLen + +def getThinkCost( inp:np.array, predicted:np.array ): + corr = correctFunc(inp) + return calcCost( predicted, corr ) + +def genRandomMatrix( x:int, y:int, min: float=0.0, max: float=1.0 ): # generate a matrix with x, y dimensions with random values from min-max in it + # apply ranger with * and - + mat = np.random.rand(x, y) - 0.25 + return mat + +def think( inp:np.array, obj, layerIndex: int=0 ): # recursive thinking, hehe + maxLayer = len(obj.weights) - 1 + weightedLayer = np.dot( inp, obj.weights[layerIndex] ) # dot multiply the input and the weights + layer = sigmoid( np.add(weightedLayer, obj.bias[layerIndex]) ) # add the biases + + if( layerIndex < maxLayer ): + return think( layer, obj, layerIndex + 1 ) + else: + out = np.squeeze(np.asarray(layer)) + return out + +def propDer( dCost, dProp ): + # Calculate the partial derivative for that prop + return dCost / dProp + +def compareAIobjects( inp, obj1, obj2 ): + # Compare the two instances + res1 = think( inp, obj1 ) + cost1 = getThinkCost( inp, res1 ) # get the cost + + res2 = think( inp, obj2 ) + cost2 = getThinkCost( inp, res2 ) # get the second cost + + # Actually calculate stuff + dCost = cost2 - cost1 + return dCost, cost1 + +def compareInstanceWeight( obj, inp, theta:float, layerIndex:int, neuronIndex_X:int, neuronIndex_Y:int ): + # Create new a instance of the object + obj2 = copy(obj) # annoying way to create a new instance of the object + + obj2.weights[layerIndex][neuronIndex_X][neuronIndex_Y] += theta # mutate the second objects neuron + dCost, curCost = compareAIobjects( inp, obj, obj2 ) # compare the two and get the dCost with respect to the weights + + return dCost, curCost + +def compareInstanceBias( obj, inp, theta:float, layerIndex:int, biasIndex:int ): + obj2 = copy(obj) + + obj2.bias[layerIndex][0][biasIndex] += theta # do the same thing for the bias + dCost, curCost = compareAIobjects( inp, obj, obj2 ) + + return dCost, curCost + +def getChangeInCost( obj, inp, theta, layerIndex ): + mirrorObj = copy(obj) + + # Fill the buffer with None so that the dCost can replace it later + dCost_W = np.zeros( shape = mirrorObj.weights[layerIndex].shape ) # fill it with a placeholder + dCost_B = np.zeros( shape = mirrorObj.bias[layerIndex].shape ) + + # Get the cost change for the weights + weightLenX = len(dCost_W) + weightLenY = len(dCost_W[0]) + + for x in range(weightLenX): # get the dCost for each x,y + for y in range(weightLenY): + dCost_W[x][y], curCostWeight = compareInstanceWeight( obj, inp, theta, layerIndex, x, y ) + + # Get the cost change for the biases + biasLenY = len(dCost_B[0]) + for index in range(biasLenY): + dCost_B[0][index], curCostBias = compareInstanceBias( obj, inp, theta, layerIndex, index ) + + return dCost_W, dCost_B, (curCostBias + curCostWeight)/2 + + + +def gradient( inp:np.array, obj, theta:float, maxLayer:int, layerIndex: int=0, grads=None, obj1=None, obj2=None ): # Calculate the gradient for that prop + # Check if grads exists, if not create the buffer + if( not grads ): + grads = [None] * (maxLayer+1) + + dCost_W, dCost_B, meanCurCost = getChangeInCost( obj, inp, theta, layerIndex ) + + # Calculate the gradient for the layer + weightDer = propDer( dCost_W, theta ) + biasDer = propDer( dCost_B, theta ) + + # Append the gradients to the list + grads[layerIndex] = { + "weight": weightDer, + "bias": biasDer + } + + newLayer = layerIndex + 1 + if( newLayer <= maxLayer ): + return gradient( inp, obj, theta, maxLayer, newLayer, grads, obj1, obj2 ) + else: + return grads, meanCurCost + +def mutateProps( inpObj, maxLen:int, gradient:list ): + obj = copy(inpObj) + for i in range(maxLen): + obj.weights[i] -= obj.learningrate * gradient[i]["weight"] # mutate the weights + obj.bias[i] -= obj.learningrate * gradient[i]["bias"] + + return obj + +def learn( inputNum:int, targetCost:float, obj, theta:float, curCost: float=None ): + # Calculate the derivative for: + # Cost in respect to weights + # Cost in respect to biases + + # i.e. : W' = W - lr * gradient (respect to W in layer i) = W - lr*[ dC / dW[i] ... ] + # So if we change all the weights with i.e. 0.01 = theta, then we can derive the gradient with math and stuff + + inp = np.asarray(np.random.rand( 1, inputNum ))[0] # create a random learning sample + + while( not curCost or curCost > targetCost ): # targetCost is the target for the cost function + maxLen = len(obj.bias) + grads, curCost = gradient( inp, obj, theta, maxLen - 1 ) + + obj = mutateProps( obj, maxLen, grads ) # mutate the props for next round + print(f"Cost: {curCost}") + + + print("DONE\n") + print(obj.weights) + print(obj.bias) diff --git a/rgbAI/lib/func.py b/rgbAI/lib/func.py deleted file mode 100644 index b5f240c..0000000 --- a/rgbAI/lib/func.py +++ /dev/null @@ -1,148 +0,0 @@ -import numpy as np -from copy import deepcopy as copy - -class AIlib: - def sigmoid(x): - return 1/(1 + np.exp(-x)) - - def correctFunc(inp:np.array): # generates the correct answer for the AI - return np.asarray( [1.0 - inp[0], 1.0 - inp[1], 1.0 - inp[2]] ) # basically invert the rgb values - - def calcCost( predicted:np.array, correct:np.array ): # cost function, lower -> good, higher -> bad, bad bot, bad - costSum = 0 - maxLen = len(correct) - - for i in range(maxLen): - costSum += abs((predicted[i] - correct[i])) - - return costSum / maxLen - - def getThinkCost( inp:np.array, predicted:np.array ): - corr = AIlib.correctFunc(inp) - return AIlib.calcCost( predicted, corr ) - - def genRandomMatrix( x:int, y:int, min: float=0.0, max: float=1.0 ): # generate a matrix with x, y dimensions with random values from min-max in it - # apply ranger with * and - - mat = np.random.rand(x, y) - 0.25 - return mat - - def think( inp:np.array, obj, layerIndex: int=0 ): # recursive thinking, hehe - maxLayer = len(obj.weights) - 1 - weightedLayer = np.dot( inp, obj.weights[layerIndex] ) # dot multiply the input and the weights - layer = AIlib.sigmoid( np.add(weightedLayer, obj.bias[layerIndex]) ) # add the biases - - if( layerIndex < maxLayer ): - return AIlib.think( layer, obj, layerIndex + 1 ) - else: - out = np.squeeze(np.asarray(layer)) - return out - - def propDer( dCost, dProp ): - # Calculate the partial derivative for that prop - return dCost / dProp - - def compareAIobjects( inp, obj1, obj2 ): - # Compare the two instances - res1 = AIlib.think( inp, obj1 ) - cost1 = AIlib.getThinkCost( inp, res1 ) # get the cost - - res2 = AIlib.think( inp, obj2 ) - cost2 = AIlib.getThinkCost( inp, res2 ) # get the second cost - - # Actually calculate stuff - dCost = cost2 - cost1 - return dCost, cost1 - - def compareInstanceWeight( obj, inp, theta:float, layerIndex:int, neuronIndex_X:int, neuronIndex_Y:int ): - # Create new a instance of the object - obj2 = copy(obj) # annoying way to create a new instance of the object - - obj2.weights[layerIndex][neuronIndex_X][neuronIndex_Y] += theta # mutate the second objects neuron - dCost, curCost = AIlib.compareAIobjects( inp, obj, obj2 ) # compare the two and get the dCost with respect to the weights - - return dCost, curCost - - def compareInstanceBias( obj, inp, theta:float, layerIndex:int, biasIndex:int ): - obj2 = copy(obj) - - obj2.bias[layerIndex][0][biasIndex] += theta # do the same thing for the bias - dCost, curCost = AIlib.compareAIobjects( inp, obj, obj2 ) - - return dCost, curCost - - def getChangeInCost( obj, inp, theta, layerIndex ): - mirrorObj = copy(obj) - - # Fill the buffer with None so that the dCost can replace it later - dCost_W = np.zeros( shape = mirrorObj.weights[layerIndex].shape ) # fill it with a placeholder - dCost_B = np.zeros( shape = mirrorObj.bias[layerIndex].shape ) - - # Get the cost change for the weights - weightLenX = len(dCost_W) - weightLenY = len(dCost_W[0]) - - for x in range(weightLenX): # get the dCost for each x,y - for y in range(weightLenY): - dCost_W[x][y], curCostWeight = AIlib.compareInstanceWeight( obj, inp, theta, layerIndex, x, y ) - - # Get the cost change for the biases - biasLenY = len(dCost_B[0]) - for index in range(biasLenY): - dCost_B[0][index], curCostBias = AIlib.compareInstanceBias( obj, inp, theta, layerIndex, index ) - - return dCost_W, dCost_B, (curCostBias + curCostWeight)/2 - - - - def gradient( inp:np.array, obj, theta:float, maxLayer:int, layerIndex: int=0, grads=None, obj1=None, obj2=None ): # Calculate the gradient for that prop - # Check if grads exists, if not create the buffer - if( not grads ): - grads = [None] * (maxLayer+1) - - dCost_W, dCost_B, meanCurCost = AIlib.getChangeInCost( obj, inp, theta, layerIndex ) - - # Calculate the gradient for the layer - weightDer = AIlib.propDer( dCost_W, theta ) - biasDer = AIlib.propDer( dCost_B, theta ) - - # Append the gradients to the list - grads[layerIndex] = { - "weight": weightDer, - "bias": biasDer - } - - newLayer = layerIndex + 1 - if( newLayer <= maxLayer ): - return AIlib.gradient( inp, obj, theta, maxLayer, newLayer, grads, obj1, obj2 ) - else: - return grads, meanCurCost - - def mutateProps( inpObj, maxLen:int, gradient:list ): - obj = copy(inpObj) - for i in range(maxLen): - obj.weights[i] -= obj.learningrate * gradient[i]["weight"] # mutate the weights - obj.bias[i] -= obj.learningrate * gradient[i]["bias"] - - return obj - - def learn( inputNum:int, targetCost:float, obj, theta:float, curCost: float=None ): - # Calculate the derivative for: - # Cost in respect to weights - # Cost in respect to biases - - # i.e. : W' = W - lr * gradient (respect to W in layer i) = W - lr*[ dC / dW[i] ... ] - # So if we change all the weights with i.e. 0.01 = theta, then we can derive the gradient with math and stuff - - inp = np.asarray(np.random.rand( 1, inputNum ))[0] # create a random learning sample - - while( not curCost or curCost > targetCost ): # targetCost is the target for the cost function - maxLen = len(obj.bias) - grads, curCost = AIlib.gradient( inp, obj, theta, maxLen - 1 ) - - obj = AIlib.mutateProps( obj, maxLen, grads ) # mutate the props for next round - print(f"Cost: {curCost}") - - - print("DONE\n") - print(obj.weights) - print(obj.bias) diff --git a/rgbAI/main.py b/rgbAI/main.py index 3042392..06ec8b5 100755 --- a/rgbAI/main.py +++ b/rgbAI/main.py @@ -1,6 +1,6 @@ #!/usr/bin/env python import numpy as np -from lib.func import AIlib as ai +import lib.ailib as ai class rgb(object): def __init__(self, loadedWeights: np.matrix=None, loadedBias: np.matrix=None):