Tweaked AI a lot, made tests in the end of training

rgbAI/lib/ailib/ai.py

@@ -9,7 +9,8 @@ DEBUG_BUFFER = {
 	},
 	"inp": None,
 	"predicted": None,
-	"correct": None
+	"correct": None,
+	"gen": None
 }
 
 def sigmoid(x):
@@ -155,7 +156,7 @@ def getLearningRate( cost:float, gradient:dict, maxLen:int ):
 
 
 def mutateProps( inpObj, curCost:float, maxLayer:int, gradient:list ):
-	obj = copy(inpObj)
+	obj = inpObj
 
 	for layer in range(maxLayer):
 		lr = getLearningRate( curCost, gradient[layer], maxLayer )
@@ -163,11 +164,8 @@ def mutateProps( inpObj, curCost:float, maxLayer:int, gradient:list ):
 
 		obj.weights[layer] -= lr["weight"] * gradient[layer]["weight"] # mutate the weights
 		obj.bias[layer] -= lr["bias"] * gradient[layer]["bias"]
-		# obj.weights[i] -= obj.learningrate * gradient[i]["weight"] # mutate the weights
+		# obj.weights[layer] -= 0.0001 * gradient[layer]["weight"] # mutate the weights
-		# obj.bias[i] -= obj.learningrate * gradient[i]["bias"]
+		# obj.bias[layer] -= 0.0001 * gradient[layer]["bias"]
-
-
-	return obj
 
 def printProgress():
 	import os
@@ -176,10 +174,10 @@ def printProgress():
 	os.system("clear")
 	print(f"LR: {DEBUG_BUFFER['lr']}")
 	print(f"Cost: {DEBUG_BUFFER['cost']}")
-	print("")
+	print(f"Gen: {DEBUG_BUFFER['gen']}")
 	print(f"inp: {DEBUG_BUFFER['inp']} | pre: {DEBUG_BUFFER['predicted']} cor: {DEBUG_BUFFER['correct']}")
 
-def learn( inputNum:int, targetCost:float, obj, theta:float, curCost: float=None, trainForever: bool=False ):
+def learn( inputNum:int, targetCost:float, obj, theta:float, curCost: float=None ):
 	# Calculate the derivative for:
 	# Cost in respect to weights
 	# Cost in respect to biases
@@ -187,17 +185,20 @@ def learn( inputNum:int, targetCost:float, obj, theta:float, curCost: float=None
 	# 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
+	count = 0
-	inp = np.asarray([1.0, 1.0, 1.0])
+	while( count <= 1000 ): # 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])
+
+		global DEBUG_BUFFER
+		DEBUG_BUFFER["inp"] = inp
+		DEBUG_BUFFER["gen"] = count
 
-	global DEBUG_BUFFER
-	DEBUG_BUFFER["inp"] = inp
-	
-	while( trainForever or not curCost or curCost > targetCost ): # targetCost is the target for the cost function
 		maxLen = len(obj.bias)
 		grads, costW, costB, curCost = gradient( inp, obj, theta, maxLen - 1 )
 
-		obj = mutateProps( obj, curCost, maxLen, grads ) # mutate the props for next round
+		mutateProps( obj, curCost, maxLen, grads ) # mutate the props for next round
 
 		printProgress()
 
@@ -205,4 +206,8 @@ def learn( inputNum:int, targetCost:float, obj, theta:float, curCost: float=None
 	print(obj.weights)
 	print(obj.bias)
 
-	return obj
+	test = think( np.asarray([1.0, 1.0, 1.0]), obj )
+	print(f"Test 1: {test}")
+
+	test2 = think( np.asarray([0.0, 0.0, 0.0]), obj )
+	print(f"Test 2: {test2}")

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, 16), ai.genRandomMatrix(16, 16), ai.genRandomMatrix(16, 16), ai.genRandomMatrix(16, 3) ] # array of matrices of weights
+			self.weights = [ ai.genRandomMatrix(3, 3), ai.genRandomMatrix(3, 3), ai.genRandomMatrix(3, 3) ] # array of matrices of weights
-			# 3 input neurons -> 16 hidden neurons -> 16 hidden neurons -> 3 output neurons
+			# 3 input neurons -> 3 hidden neurons -> 3 hidden neurons -> 3 output neurons
 
 			# Generate the biases
-			self.bias = [ ai.genRandomMatrix(1, 16), ai.genRandomMatrix(1, 16), ai.genRandomMatrix(1, 16), ai.genRandomMatrix(1, 3) ]
+			self.bias = [ ai.genRandomMatrix(1, 3), ai.genRandomMatrix(1, 3), ai.genRandomMatrix(1, 3) ]
 			# This doesn't look very good, but it works so...
 
 			print( self.weights )
@@ -27,7 +27,7 @@ class rgb(object):
 		return cost
 
 	def learn( self ):
-		ai.learn( 3, 0.0001, self, 3e-7 )
+		ai.learn( 3, 0.001, self, 0.001 )
 
 	def think( self, inp:np.array ):
 		print("\n-Input-")
@@ -41,11 +41,11 @@ class rgb(object):
 
 def init():
 	bot = rgb()
-	bot = bot.learn()
+	bot.learn()
 
-	inpArr = np.asarray([1.0, 1.0, 1.0])
+	#inpArr = np.asarray([1.0, 1.0, 1.0])
-	res = bot.think( inpArr )
+	#res = bot.think( inpArr )
-	err = bot.calcError( inpArr, res )
+	#err = bot.calcError( inpArr, res )
-	print(err)
+	#print(err)
 
 init()