Tweaked the network

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, 4), ai.genRandomMatrix(4, 4), ai.genRandomMatrix(4, 3) ] # array of matrices of weights
+			self.weights = [ ai.genRandomMatrix(3, 8), ai.genRandomMatrix(8, 8), ai.genRandomMatrix(8, 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(1, 4), ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 3) ]
+			self.bias = [ ai.genRandomMatrix(1, 8), ai.genRandomMatrix(1, 8), ai.genRandomMatrix(1, 3) ]
 			# This doesn't look very good, but it works so...
 
 			self.learningrate = 0.01 # the learning rate of this ai

rgbAI/trained_data/2020-10-17_x8.txt

@@ -0,0 +1,31 @@
+[array([[ 0.663472  ,  0.41281858,  0.35432133,  0.51654697,  0.30128169,
+         0.61125425,  0.12824416,  0.57022693],
+       [-0.1193642 ,  0.50021089,  0.68033888,  0.26198098,  0.31728985,
+         0.45811986,  0.09201576, -0.09319653],
+       [-0.01187079,  0.52099344,  0.45390005, -0.03015151,  0.46498277,
+         0.51502126,  0.63804829,  0.53824189]]), array([[ 0.18016866,  0.60791937, -0.20404823,  0.08990399, -0.05033332,
+        -0.08225963,  0.29963505, -0.27815606],
+       [ 0.18449808,  0.43927444, -0.04845178,  0.53328341,  0.64420625,
+         0.04104459, -0.1278976 ,  0.03197009],
+       [-0.15315493, -0.10153356,  0.6552515 ,  0.19490518, -0.05890817,
+        -0.11421504,  0.06989618, -0.08307894],
+       [-0.09135586,  0.30840721,  0.48576483,  0.67942472,  0.2884017 ,
+         0.12948688,  0.74335114,  0.66117525],
+       [ 0.04180339,  0.09855898, -0.08983036,  0.68314281,  0.08859078,
+        -0.06552509,  0.69546397,  0.68314475],
+       [-0.29351704,  0.10465032, -0.16743309,  0.04157213,  0.56913974,
+         0.43671001,  0.48578339,  0.37119113],
+       [ 0.22142061,  0.25642174,  0.24134435,  0.17945595, -0.20885076,
+         0.34099268,  0.34027114,  0.35100756],
+       [ 0.67421573, -0.08107495,  0.4159875 ,  0.00387256,  0.62741103,
+        -0.06146188, -0.08875579,  0.53884883]]), array([[ 0.02608   , -0.22600872, -0.11118185],
+       [-0.42425855, -0.03368622, -0.37062865],
+       [-0.60155648,  0.19081541, -0.87045635],
+       [ 0.30039832, -0.19428933, -0.13106259],
+       [-0.12102695,  0.11154671, -1.0141122 ],
+       [-0.33021794,  0.03743937, -0.51887829],
+       [-0.65143524,  0.0893009 , -0.92525977],
+       [-0.05812958,  0.57310427, -0.3258051 ]])]
+[array([[ 0.09468388,  0.64740844,  0.20607014, -0.03533906, -0.1799001 ,
+         0.32695011,  0.17449872,  0.56778777]]), array([[ 0.62824449,  0.22028708,  0.27071181, -0.03390911,  0.39744529,
+         0.25055866,  0.11580678, -0.19667358]]), array([[-0.43758743, -0.02608798, -0.63211097]])]