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@ -1,4 +1,5 @@ |
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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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@ -38,8 +39,6 @@ class AIlib: |
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def propDer( dCost, dProp ): |
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# Calculate the partial derivative for that prop |
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#print("################") |
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#print(dCost, dProp) |
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return dCost / dProp |
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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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@ -49,8 +48,8 @@ class AIlib: |
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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 = obj |
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obj2 = obj |
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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] += theta # mutate the second object |
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obj2.bias[layerIndex] += theta |
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