Huge tweaks to the network

rgbAI/lib/ailib/ai.py

@@ -186,7 +186,7 @@ def learn( inputNum:int, targetCost:float, obj, theta:float, curCost: float=None
 	# So if we change all the weights with i.e. 0.01 = theta, then we can derive the gradient with math and stuff
 
 	count = 0
-	while( count <= 1000 ): # targetCost is the target for the cost function
+	while( count <= 10000 ): # targetCost is the target for the cost function
 		count += 1
 		inp = np.asarray(np.random.rand( 1, inputNum ))[0] # create a random learning sample
 		# inp = np.asarray([1.0, 1.0, 1.0])

rgbAI/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(3, 3), ai.genRandomMatrix(3, 3), ai.genRandomMatrix(3, 3) ] # array of matrices of weights
+			self.weights = [ ai.genRandomMatrix(3, 3), ai.genRandomMatrix(3, 3) ] # array of matrices of weights
 			# 3 input neurons -> 3 hidden neurons -> 3 hidden neurons -> 3 output neurons
 
 			# Generate the biases
-			self.bias = [ ai.genRandomMatrix(1, 3), ai.genRandomMatrix(1, 3), ai.genRandomMatrix(1, 3) ]
+			self.bias = [ ai.genRandomMatrix(1, 3), ai.genRandomMatrix(1, 3) ]
 			# This doesn't look very good, but it works so...
 
 			print( self.weights )