diff --git a/rgbAI/lib/func.py b/rgbAI/lib/func.py
index f59fb3f..56b86d6 100644
--- a/rgbAI/lib/func.py
+++ b/rgbAI/lib/func.py
@@ -21,28 +21,23 @@ class AIlib:
         mat = np.random.rand(x, y) - 0.25
         return mat
 
-    def think( inp:np.array, weights:list, bias:list, layerIndex: int=0 ): # recursive thinking, hehe
+    def think( inp:np.array, weights:list, bias:list, layerIndex: int=0, layers: list=[] ): # recursive thinking, hehe
         maxLayer = len(weights) - 1
-        weightedInput = np.dot( inp, weights[layerIndex] ) # dot multiply the input and the weights
-        layer = AIlib.sigmoid( np.add(weightedInput, bias[layerIndex]) ) # add the biases
+        weightedLayer = np.dot( inp, weights[layerIndex] ) # dot multiply the input and the weights
+        layer = AIlib.sigmoid( np.add(weightedLayer, bias[layerIndex]) ) # add the biases
+        layers[layerIndex] = layer # save it to the layer buffer
 
         if( layerIndex < maxLayer ):
-            return AIlib.think( layer, weights, bias, layerIndex + 1 )
+            return AIlib.think( layer, weights, bias, layerIndex + 1, layers )
         else:
             out = np.squeeze(np.asarray(layer))
             print("-Result-")
             print(out)
             print("\n")
-            return out
+            return out, layers
 
     def gradient( prop, cost:float, inp:np.array, predicted:np.array, correct:np.array ):
         # Calculate the gradient
-        derv1 = AIlib.calcCost_derv( predicted, correct )
-        derv2 = AIlib.sigmoid_der( predicted )
-
-        gradient = np.transpose( np.asmatrix(derv1 * derv2 * inp) )
-        print("Inp:", inp)
-        print("Grad:", gradient)
         return gradient
 
 
@@ -63,14 +58,8 @@ class AIlib:
         correct = AIlib.correctFunc( inp )
         cost = AIlib.calcCost( predicted, correct ) # Calculate the cost of the thought result
 
-        #inp2 = np.asarray( inp + theta ) # make the new input with `theta` as diff
-        #res2 = AIlib.think( inp2, obj.weights, obj.bias ) # Think the second result
-        #cost2 = AIlib.calcCost( inp2, res2 ) # Calculate the cost
-
-        gradientWeight = AIlib.gradient( obj.weights, cost, inp, predicted, correct )
-        gradientBias = AIlib.gradient( obj.bias, cost, inp, predicted, correct )
-
-        obj.weights = AIlib.mutateProp( obj.weights, obj.learningrate, gradientWeight )
-        obj.bias = AIlib.mutateProp( obj.bias, obj.learningrate, gradientBias )
+        inp2 = np.asarray( inp + theta ) # make the new input with `theta` as diff
+        res2 = AIlib.think( inp2, obj.weights, obj.bias ) # Think the second result
+        cost2 = AIlib.calcCost( inp2, res2 ) # Calculate the cost
 
         print("Cost: ", cost1)