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115 lines
3.9 KiB
115 lines
3.9 KiB
import numpy as np
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from copy import deepcopy as copy
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class AIlib:
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def sigmoid(x):
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return 1/(1 + np.exp(-x))
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def correctFunc(inp:np.array): # generates the correct answer for the AI
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return np.asarray( [1.0 - inp[0], 1.0 - inp[1], 1.0 - inp[2]] ) # basically invert the rgb values
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def calcCost( predicted:np.array, correct:np.array ): # cost function, lower -> good, higher -> bad, bad bot, bad
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costSum = 0
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maxLen = len(correct)
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for i in range(maxLen):
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costSum += (predicted[i] - correct[i])**2
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return costSum / maxLen
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def getThinkCost( inp:np.array, predicted:np.array ):
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corr = AIlib.correctFunc(inp)
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return AIlib.calcCost( predicted, corr )
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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
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# apply ranger with * and -
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mat = np.random.rand(x, y) - 0.25
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return mat
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def think( inp:np.array, obj, layerIndex: int=0 ): # recursive thinking, hehe
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maxLayer = len(obj.weights) - 1
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weightedLayer = np.dot( inp, obj.weights[layerIndex] ) # dot multiply the input and the weights
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layer = AIlib.sigmoid( np.add(weightedLayer, obj.bias[layerIndex]) ) # add the biases
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if( layerIndex < maxLayer ):
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return AIlib.think( layer, obj, layerIndex + 1 )
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else:
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out = np.squeeze(np.asarray(layer))
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return out
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def propDer( dCost, dProp ):
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# Calculate the partial derivative for that prop
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return dCost / dProp
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def compareInstance( obj, neuronIndex ):
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# Create new instances of the object
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if( not obj1 or not obj2 ):
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obj1 = copy(obj) # annoying way to create a new instance of the object
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obj2 = copy(obj)
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obj2.weights[layerIndex][neuronIndex] += theta # mutate the second object
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# obj2.bias[layerIndex] += theta
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# Compare the two instances
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res1 = AIlib.think( inp, obj1 )
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cost1 = AIlib.getThinkCost( inp, res1 ) # get the cost
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res2 = AIlib.think( inp, obj2 )
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cost2 = AIlib.getThinkCost( inp, res2 ) # get the second cost
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# Actually calculate stuff
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dCost = cost2 - cost1
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dWeight = obj2.weights[layerIndex] - obj1.weights[layerIndex]
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#dBias = obj2.bias[layerIndex] - obj1.bias[layerIndex]
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return dCost, dWeight
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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
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# Check if grads exists, if not create the buffer
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if( not grads ):
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grads = [None] * (maxLayer+1)
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# Calculate the gradient for the layer
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weightDer = AIlib.propDer( dCost, dWeight )
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biasDer = AIlib.propDer( dCost, dBias )
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# Append the gradients to the list
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grads[layerIndex] = {
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"weight": weightDer,
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"bias": biasDer
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}
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newLayer = layerIndex + 1
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if( newLayer <= maxLayer ):
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return AIlib.gradient( inp, obj, theta, maxLayer, newLayer, grads, obj1, obj2 )
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else:
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return grads, res1, cost1
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def mutateProps( inpObj, maxLen:int, gradient:list ):
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obj = copy(inpObj)
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for i in range(maxLen):
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obj.weights[i] -= obj.learningrate * gradient[i]["weight"] # mutate the weights
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obj.bias[i] -= obj.learningrate * gradient[i]["bias"]
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return obj
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def learn( inputNum:int, targetCost:float, obj, theta:float, curCost: float=None ):
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# Calculate the derivative for:
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# Cost in respect to weights
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# Cost in respect to biases
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# i.e. : W' = W - lr * gradient (respect to W in layer i) = W - lr*[ dC / dW[i] ... ]
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# So if we change all the weights with i.e. 0.01 = theta, then we can derive the gradient with math and stuff
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inp = np.asarray(np.random.rand( 1, inputNum ))[0] # create a random learning sample
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while( not curCost or curCost > targetCost ): # targetCost is the target for the cost function
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maxLen = len(obj.bias)
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grads, res, curCost = AIlib.gradient( inp, obj, theta, maxLen - 1 )
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obj = AIlib.mutateProps( obj, maxLen, grads ) # mutate the props for next round
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print("Cost:", curCost, "|", inp, res)
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print("DONE\n")
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print(obj.weights)
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print(obj.bias)
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