Tweaked the network etc

4 years ago · bfa7c451c7
parent 21f531669d
commit bfa7c451c7
3 changed files with 187 additions and 8 deletions
--- a/rgbAI/lib/ailib/ai.py
+++ b/rgbAI/lib/ailib/ai.py
@ -1,13 +1,15 @@
 import numpy as np
 from copy import deepcopy as copy
-import os

 DEBUG_BUFFER = {
 	"cost": None,
 	"lr": {
 		"weight": None,
 		"bias": None
-	}
+	},
+	"inp": None,
+	"predicted": None,
+	"correct": None
 }

 def sigmoid(x):
@ -27,6 +29,10 @@ def calcCost( predicted:np.array, correct:np.array ): # cost function, lower ->

 def getThinkCost( inp:np.array, predicted:np.array ):
 	corr = correctFunc(inp)
+
+	global DEBUG_BUFFER
+	DEBUG_BUFFER["correct"] = corr
+
 	return calcCost( predicted, corr )

 def genRandomMatrix( x:int, y:int, min: float=0.0, max: float=1.0 ): # generate a matrix with x, y dimensions with random values from min-max in it
@ -56,6 +62,7 @@ def compareAIobjects( inp, obj1, obj2 ):
 	
 	global DEBUG_BUFFER
 	DEBUG_BUFFER["cost"] = cost1
+	DEBUG_BUFFER["predicted"] = res1

 	res2 = think( inp, obj2 )
 	cost2 = getThinkCost( inp, res2 ) # get the second cost
@ -163,11 +170,14 @@ def mutateProps( inpObj, curCost:float, maxLayer:int, gradient:list ):
 	return obj

 def printProgress():
-	global DEBUG_BUFFER
+	import os

+	global DEBUG_BUFFER
 	os.system("clear")
 	print(f"LR: {DEBUG_BUFFER['lr']}")
 	print(f"Cost: {DEBUG_BUFFER['cost']}")
+	print("")
+	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 ):
 	# Calculate the derivative for:
@ -177,7 +187,11 @@ 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
+	#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
 	
 	while( trainForever or not curCost or curCost > targetCost ): # targetCost is the target for the cost function
 		maxLen = len(obj.bias)
@ -190,3 +204,5 @@ def learn( inputNum:int, targetCost:float, obj, theta:float, curCost: float=None
 	print("DONE\n")
 	print(obj.weights)
 	print(obj.bias)
+
+	return obj
--- a/rgbAI/main.py
+++ b/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, 8), ai.genRandomMatrix(8, 8), ai.genRandomMatrix(8, 8), ai.genRandomMatrix(8, 3) ] # array of matrices of weights
-			# 3 input neurons -> 8 hidden neurons -> 8 hidden neurons -> 3 output neurons
+			self.weights = [ ai.genRandomMatrix(3, 16), ai.genRandomMatrix(16, 16), ai.genRandomMatrix(16, 16), ai.genRandomMatrix(16, 3) ] # array of matrices of weights
+			# 3 input neurons -> 16 hidden neurons -> 16 hidden neurons -> 3 output neurons

 			# Generate the biases
-			self.bias = [ ai.genRandomMatrix(1, 8), ai.genRandomMatrix(1, 8), ai.genRandomMatrix(1, 8), ai.genRandomMatrix(1, 3) ]
+			self.bias = [ ai.genRandomMatrix(1, 16), ai.genRandomMatrix(1, 16), ai.genRandomMatrix(1, 16), ai.genRandomMatrix(1, 3) ]
 			# This doesn't look very good, but it works so...

 			print( self.weights )
@ -41,7 +41,7 @@ class rgb(object):

 def init():
 	bot = rgb()
-	bot.learn()
+	bot = bot.learn()

 	inpArr = np.asarray([1.0, 1.0, 1.0])
 	res = bot.think( inpArr )
--- a/rgbAI/trained_data/2020-10-20.txt
+++ b/rgbAI/trained_data/2020-10-20.txt
@ -0,0 +1,163 @@
+[array([[ 0.13657383, -0.23042832, -0.15878268,  0.01625717,  0.09307448,
+         0.18129695,  0.2116157 ,  0.03005379, -0.23906321,  0.33900869,
+         0.53861629, -0.24311345,  0.56263432,  0.16710256, -0.20531924,
+        -0.04025091],
+       [-0.14493201, -0.03999969, -0.04536574,  0.57153015,  0.02922364,
+        -0.0858576 ,  0.51631858,  0.24529872, -0.20769185,  0.43454781,
+        -0.23097147, -0.16451023,  0.44440651,  0.60449483, -0.14961027,
+         0.00530867],
+       [ 0.47570477,  0.6321929 ,  0.18826831, -0.09720299, -0.04231244,
+        -0.054177  ,  0.31102066,  0.33274832,  0.09374735,  0.22414503,
+         0.2214549 ,  0.18879144,  0.08037877, -0.11656515, -0.03204839,
+         0.43241358]]), array([[ 0.16805438,  0.31040123,  0.57585574,  0.41922052,  0.55521636,
+        -0.06293517, -0.24256846,  0.18656932, -0.13466644,  0.13617174,
+        -0.15533991,  0.72374925,  0.30054544,  0.06856631,  0.23241547,
+         0.7275567 ],
+       [ 0.70020351,  0.21788736,  0.73347487,  0.28366899,  0.55231818,
+         0.51453774,  0.66627062, -0.15582512, -0.04486987,  0.08910557,
+        -0.22164193,  0.61888889,  0.72705845,  0.50076326,  0.17261128,
+         0.60235239],
+       [ 0.1759519 , -0.03520717, -0.1548805 ,  0.50866399, -0.09330594,
+        -0.10404663,  0.57911986, -0.18048319,  0.62594238, -0.23703238,
+         0.57377906, -0.17134602,  0.49190176,  0.28946516,  0.35465312,
+         0.64585328],
+       [ 0.14643067,  0.18990906,  0.12110283,  0.30103004,  0.71224839,
+         0.49863677,  0.21176263,  0.30926655,  0.35554206,  0.67969733,
+         0.54055667,  0.1821767 ,  0.13505438,  0.01354167,  0.71708359,
+         0.38325529],
+       [ 0.21117687, -0.14987266,  0.70728899,  0.28207962, -0.12873177,
+        -0.2409365 ,  0.0372645 ,  0.69199145,  0.45591382, -0.216259  ,
+         0.04768903,  0.61919638,  0.04028485,  0.43677636,  0.34351561,
+        -0.17574492],
+       [-0.08483528,  0.54262422,  0.26430655, -0.09718621, -0.04815816,
+         0.36051139,  0.57963613,  0.18824451,  0.37752941, -0.15812105,
+         0.11207639, -0.10671547,  0.15248495,  0.54355872,  0.02105179,
+        -0.18640931],
+       [ 0.60007982,  0.42082565,  0.50058767, -0.08092875, -0.15311035,
+         0.07481274, -0.06533989,  0.23647472,  0.17528246,  0.55421468,
+         0.60617809,  0.59650491,  0.50975201,  0.30861656,  0.36631228,
+         0.06977136],
+       [ 0.57098305,  0.23880751,  0.40955559,  0.02095128, -0.03336961,
+         0.66587073, -0.13214473, -0.14161289, -0.09664933,  0.67537527,
+        -0.0167142 ,  0.49583207,  0.18313851,  0.15646813,  0.07427188,
+        -0.0718608 ],
+       [-0.00987391,  0.55932519, -0.16337579, -0.05121916,  0.05253206,
+         0.6599166 ,  0.04628956,  0.34949889, -0.1062319 , -0.10879115,
+         0.56021827,  0.66759027,  0.23128271,  0.63949833,  0.03886562,
+         0.4834711 ],
+       [ 0.32905225,  0.11594583,  0.65178434, -0.04223889,  0.41896363,
+         0.57333568, -0.00198302,  0.65220265,  0.68543686,  0.3683571 ,
+        -0.02603741,  0.20018236,  0.16239414,  0.41868448,  0.18097101,
+         0.74529124],
+       [ 0.59622387,  0.10582384,  0.01224179,  0.62591807, -0.09860738,
+         0.58708991, -0.15917445, -0.23433273,  0.38125883,  0.62119401,
+         0.53009452,  0.36465919,  0.58722475,  0.50328685,  0.11084216,
+         0.70020325],
+       [-0.08007414,  0.70406113,  0.28146817, -0.23281128,  0.72570987,
+         0.39208543,  0.54568921,  0.64135421,  0.13809933,  0.17369943,
+         0.37720265,  0.65876394,  0.72315255,  0.2288451 ,  0.07655611,
+         0.27356539],
+       [ 0.5132845 ,  0.06317595,  0.18229536, -0.14240562, -0.19588846,
+         0.41032122,  0.12737565,  0.39377548,  0.31637434,  0.25621596,
+         0.57986848,  0.23893487,  0.72227966,  0.00080269,  0.17514379,
+        -0.11195404],
+       [ 0.39934212,  0.49710534,  0.2748569 ,  0.043366  ,  0.1890571 ,
+         0.18852156, -0.04550403,  0.37443474,  0.05917768,  0.18225269,
+         0.35863941,  0.35493   ,  0.02507568,  0.60231666,  0.12705062,
+        -0.18656577],
+       [-0.21762182,  0.28420968,  0.0664954 ,  0.61769083, -0.22446421,
+        -0.15199846,  0.06882507,  0.41997501, -0.19671519,  0.53733707,
+         0.13745353, -0.0277646 ,  0.31886772, -0.09922969,  0.27121758,
+         0.16454755],
+       [-0.10369499,  0.57374993,  0.67271829,  0.49812178,  0.43472414,
+        -0.19226256,  0.59947474,  0.73502608,  0.13070667,  0.23848043,
+         0.04348717, -0.14117485,  0.29202391,  0.31610878,  0.5516485 ,
+         0.31596777]]), array([[ 0.34477668, -0.06425723,  0.58359001, -0.14815158, -0.12798902,
+         0.01122578, -0.09406077, -0.08156269,  0.47678927, -0.16832157,
+         0.2989899 , -0.03149517,  0.54102489,  0.27687279,  0.07762957,
+         0.30630602],
+       [-0.18628032,  0.48140631,  0.45127588,  0.04217173, -0.04761508,
+         0.53454407,  0.07562896,  0.02482407,  0.34120148,  0.34104659,
+         0.00800182,  0.30055017,  0.10554095,  0.34496294, -0.12992065,
+         0.34394313],
+       [-0.13523876, -0.0971336 ,  0.63950755,  0.36780703,  0.56103293,
+        -0.14544073,  0.50998027,  0.69755403,  0.17758161,  0.71673517,
+        -0.19777061,  0.34413252,  0.44859899,  0.19193068,  0.49557605,
+         0.35980889],
+       [ 0.31448824,  0.55498673,  0.45547964,  0.39560346,  0.43964609,
+         0.65156501,  0.60092278,  0.16079557,  0.29967172,  0.45635459,
+        -0.15559738,  0.09677894, -0.1473755 ,  0.48921584,  0.03380254,
+         0.53496187],
+       [ 0.20548827,  0.34162208,  0.24291551,  0.09517771,  0.42799721,
+        -0.21587983,  0.6276331 ,  0.17878733, -0.09394476,  0.49486964,
+         0.05069814, -0.03731232,  0.61765291,  0.39744186,  0.62045988,
+         0.20484799],
+       [ 0.73550131,  0.58870611, -0.2149463 ,  0.12072379,  0.33219201,
+         0.26109787,  0.0982951 ,  0.1284664 ,  0.64710056,  0.0152397 ,
+         0.18381652,  0.73768473,  0.00092139,  0.03235858,  0.15103459,
+         0.51981606],
+       [ 0.08840694,  0.26297443,  0.30844606, -0.1824745 ,  0.18769289,
+         0.63229512,  0.42350295,  0.31092172,  0.25027055,  0.74948354,
+         0.22888569, -0.01773346, -0.11401654, -0.03108823, -0.13451111,
+         0.63129517],
+       [ 0.54493297,  0.63825299,  0.24219368,  0.22564053,  0.06205102,
+         0.30013008, -0.20026111,  0.36154652,  0.012798  ,  0.35960868,
+         0.14043231,  0.25682745,  0.61403935,  0.36427773, -0.15509794,
+         0.18238906],
+       [ 0.6308805 , -0.12446523,  0.16979726,  0.10546963,  0.19526405,
+        -0.10779107,  0.69112703,  0.11795955,  0.59537578,  0.10703378,
+        -0.17694736,  0.17672196,  0.68900486, -0.1828981 ,  0.01611003,
+         0.03623836],
+       [ 0.38947013,  0.27422655,  0.10986504,  0.20315569, -0.14706311,
+         0.0376341 , -0.05265269,  0.397534  ,  0.52942913,  0.65569717,
+         0.68041829, -0.07898684,  0.29142613, -0.05156493,  0.18032057,
+         0.53108115],
+       [ 0.68532707,  0.34462347,  0.31177709,  0.47765148,  0.05164668,
+         0.44140461,  0.19140832,  0.73120288,  0.49197314,  0.22130038,
+         0.65074739, -0.05677663, -0.05086617,  0.08561628,  0.1741059 ,
+         0.04711781],
+       [ 0.30792548,  0.47748182, -0.13492255, -0.17833334,  0.43625885,
+         0.3399424 ,  0.72112565,  0.72721836,  0.18942578,  0.37540945,
+         0.44928576,  0.19833672, -0.06612378,  0.44243888,  0.42291882,
+         0.47173218],
+       [ 0.32018247,  0.49908758,  0.05845455,  0.6764048 ,  0.61555869,
+         0.14059561,  0.15291535,  0.58760176, -0.18642172,  0.68411407,
+         0.72957624,  0.17706223,  0.50910493,  0.18660864,  0.73956186,
+         0.07935623],
+       [ 0.7506918 ,  0.24560067, -0.02707633,  0.53720368, -0.05693541,
+         0.70035174, -0.07927034,  0.43913472,  0.39959292, -0.1352873 ,
+         0.27732047,  0.06998323,  0.06058959,  0.58006482, -0.24189536,
+         0.08312415],
+       [-0.00128582,  0.3933684 ,  0.51069906,  0.43526889,  0.09348662,
+         0.03881429,  0.22353862, -0.02266519, -0.23932934, -0.22200674,
+         0.33799059,  0.0798281 ,  0.61122009,  0.00619084,  0.56015456,
+         0.4992037 ],
+       [ 0.31080398,  0.74357545,  0.34472705,  0.03124108,  0.32283712,
+        -0.11436023, -0.18757332, -0.0401612 ,  0.56430347,  0.74013264,
+        -0.21379409,  0.09312194,  0.65398465,  0.47030407, -0.24225389,
+         0.34722671]]), array([[-0.72712555, -0.2209617 , -0.9213511 ],
+       [-0.23583579, -0.5001959 , -1.03502363],
+       [-0.54812013, -0.55230555, -0.10796313],
+       [-0.40919237, -0.27320155, -0.23281955],
+       [-0.60576907, -0.72301966, -0.19292567],
+       [-0.25151921, -0.68429653, -0.62658037],
+       [-0.92354466, -0.72998648, -0.48274747],
+       [-0.30593891, -0.68002346, -0.55250976],
+       [-0.37302378, -0.1585595 , -0.2190985 ],
+       [-0.86951793, -0.47558322, -0.150991  ],
+       [-0.55356092, -0.38416017, -0.4341505 ],
+       [-0.77230558, -0.01271597, -0.6261722 ],
+       [-0.15072927, -0.66071288, -1.08968044],
+       [-0.76838745, -0.51264552, -0.10541016],
+       [-0.21458776, -0.18523628, -0.39002985],
+       [-0.37013168, -1.05653186, -0.70163522]])]
+[array([[ 0.66669102,  0.35633831, -0.07490808,  0.55737519,  0.37080628,
+         0.4170183 ,  0.38348049,  0.04615565,  0.71602324, -0.1080244 ,
+         0.48753738, -0.00088796,  0.72443262,  0.43165147,  0.41511408,
+         0.73129322]]), array([[ 0.64329873, -0.19463148,  0.50162217,  0.43143105,  0.10745485,
+         0.26871549,  0.29615103, -0.11371631,  0.67287694,  0.2765771 ,
+         0.29021985,  0.37520536,  0.37632065,  0.26134689,  0.42186981,
+        -0.11257989]]), array([[ 0.55071741,  0.54357322,  0.49354933,  0.02243901, -0.02043161,
+         0.18415808,  0.58299901, -0.1365678 ,  0.39731518,  0.51806039,
+        -0.00358498,  0.23929732,  0.65433423,  0.10841841,  0.70569666,
+         0.25425315]]), array([[-1.36108333, -1.52971319, -1.49134245]])]