Stuff

rgbAI/lib/func.py

@@ -98,9 +98,8 @@ class AIlib:
         # 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
+            inp = np.asarray(np.random.rand( 1, inputNum ))[0] # create a random learning sample
             maxLen = len(obj.bias)
             grads, res, curCost = AIlib.gradient( inp, obj, theta, maxLen - 1 )
             obj = AIlib.mutateProps( obj, maxLen, grads ) # mutate the props for next round

rgbAI/main.py

@@ -29,7 +29,7 @@ class rgb(object):
         return cost
 
     def learn( self ):
-        ai.learn( 3, 0.01, self, 0.001 )
+        ai.learn( 3, 0.0001, self, 0.001 )
 
     def think( self, inp:np.array ):
         print("\n-Input-")