Fixed thining again

lib/func.py

@@ -32,10 +32,11 @@ class AIlib:
             exit()
 
         try:
-            print("Think " + str(layerIndex))
+            weightedInput = np.dot( inp, weights[layerIndex] ) # dot multiply the input and the weights
-            weightedInput = np.dot( weights[layerIndex], inp ) # dot multiply the input and the weights
             layer = AIlib.sigmoid( np.add(weightedInput, bias[layerIndex]) ) # add the biases
 
+            if( layerIndex < maxLayer ):
+                print("Layer " + str(layerIndex))
                 print(layer)
                 print("\n")
 

main.py

@@ -7,11 +7,11 @@ class rgb(object):
 
         if( not loadedWeights or not loadedBias ): # if one is null (None) then just generate new ones
             print("Generating weights and biases...")
-            self.weights = [ ai.genRandomMatrix(4, 3), ai.genRandomMatrix(4, 4), ai.genRandomMatrix(3, 4) ] # array of matrices of weights
+            self.weights = [ ai.genRandomMatrix(3, 4), ai.genRandomMatrix(4, 4), ai.genRandomMatrix(4, 3) ] # array of matrices of weights
             # 3 input neurons -> 4 hidden neurons -> 4 hidden neurons -> 3 output neurons
 
             # Generate the biases
-            self.bias = [ ai.genRandomMatrix(4, 1), ai.genRandomMatrix(4, 1), ai.genRandomMatrix(3, 1) ]
+            self.bias = [ ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 3) ]
             # This doesn't look very good, but it works so...
 
         else: # if we want to load our progress from before then this would do it
@@ -22,8 +22,13 @@ class rgb(object):
         print("learn")
 
     def think(self, inp:np.array):
+        print("\n-Input-")
+        print(inp)
+        print("\n")
+
         res = ai.think( inp, self.weights, self.bias )
-        print("\n-Result-")
+
+        print("\n-Output-")
         print(res)
 
 def init(): # init func