You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
53 lines
1.5 KiB
53 lines
1.5 KiB
#!/usr/bin/env python
|
|
import numpy as np
|
|
from lib.ailib import ai
|
|
|
|
class rgb(object):
|
|
def __init__(self, loadedWeights: np.matrix=None, loadedBias: np.matrix=None):
|
|
|
|
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
|
|
|
|
# Generate the biases
|
|
self.bias = [ ai.genRandomMatrix(1, 8), 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
|
|
|
|
print( self.weights )
|
|
print( self.bias )
|
|
|
|
else: # if we want to load our progress from before then this would do it
|
|
self.weights = loadedWeights
|
|
self.bias = loadedBias
|
|
|
|
def calcError( self, inp:np.array, out:np.array ):
|
|
cost = ai.calcCost( inp, out )
|
|
# Cost needs to get to 0, we can figure out this with backpropagation
|
|
return cost
|
|
|
|
def learn( self ):
|
|
ai.learn( 3, 0.0001, self, 0.000001 )
|
|
|
|
def think( self, inp:np.array ):
|
|
print("\n-Input-")
|
|
print(inp)
|
|
|
|
res = ai.think( inp, self )
|
|
|
|
print("\n-Output-")
|
|
print(res)
|
|
return res
|
|
|
|
def init():
|
|
bot = rgb()
|
|
bot.learn()
|
|
|
|
inpArr = np.asarray([1.0, 1.0, 1.0])
|
|
res = bot.think( inpArr )
|
|
err = bot.calcError( inpArr, res )
|
|
print(err)
|
|
|
|
init()
|
|
|