You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
113 lines
4.3 KiB
113 lines
4.3 KiB
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 += (predicted[i] - correct[i])**2
|
|
|
|
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 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)
|
|
|
|
# Create new instances of the object
|
|
if( not obj1 or not obj2 ):
|
|
obj1 = copy(obj) # annoying way to create a new instance of the object
|
|
obj2 = copy(obj)
|
|
|
|
obj2.weights[layerIndex] += theta # mutate the second object
|
|
obj2.bias[layerIndex] += theta
|
|
|
|
# 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
|
|
dWeight = obj2.weights[layerIndex] - obj1.weights[layerIndex]
|
|
dBias = obj2.bias[layerIndex] - obj1.bias[layerIndex]
|
|
|
|
# Calculate the gradient for the layer
|
|
weightDer = AIlib.propDer( dCost, dWeight )
|
|
biasDer = AIlib.propDer( dCost, dBias )
|
|
|
|
# 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, res1, cost1
|
|
|
|
def mutateProps( obj, maxLen:int, gradient:list ):
|
|
for i in range(maxLen):
|
|
obj.weights[i] -= obj.learningrate * gradient[i]["weight"] # mutate the weights
|
|
obj.bias[i] -= obj.learningrate * gradient[i]["bias"]
|
|
|
|
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
|
|
|
|
if( not curCost or curCost > targetCost ): # targetCost is the target for the cost function
|
|
inp = np.asarray(np.random.rand( 1, inputNum ))[0]
|
|
|
|
maxLen = len(obj.bias)
|
|
grads, res, curCost = AIlib.gradient( inp, obj, theta, maxLen - 1 )
|
|
AIlib.mutateProps( obj, maxLen, grads ) # mutate the props for next round
|
|
print("Cost:", curCost, "|", inp, res)
|
|
|
|
return AIlib.learn( inputNum, targetCost, obj, theta, curCost )
|
|
|
|
else:
|
|
print("DONE\n")
|
|
print(obj.weights)
|
|
print(obj.bias)
|
|
return
|
|
|
|
|