PT21-22-Reseau-Neurones/testLearningNAND.py
2021-12-22 21:35:06 +01:00

74 lines
1.6 KiB
Python

import numpy as np
import random
from sobek.network import network
import time
random.seed()
myNetwork = network(2, 2, 1)
learningRate = 3
test = []
result = []
test.append(np.zeros(2))
test.append(np.zeros(2))
test.append(np.zeros(2))
test.append(np.zeros(2))
test[1][1] = 1.0
test[2][0] = 1.0
test[3][0] = 1.0
test[3][1] = 1.0
result.append(np.ones(1))
result.append(np.ones(1))
result.append(np.ones(1))
result.append(np.zeros(1))
learningTime = 0
nbRep = 1
for i in range(nbRep):
if (i%(nbRep/10) == 0): print(i)
startTime = time.perf_counter()
#for j in range(10000):
# inputs = []
# desiredOutputs = []
#if (j%1000 == 0):
# print(j)
# myNetwork.train(test, result, learningRate)
myNetwork.train(test, result, learningRate, len(test), 10000, visualize=False)
endTime = time.perf_counter()
learningTime += endTime - startTime
learningTime = learningTime / nbRep
test = []
result = []
test.append(np.zeros(2))
test.append(np.zeros(2))
test.append(np.zeros(2))
test.append(np.zeros(2))
test[1][1] = 1.0
test[2][0] = 1.0
test[3][0] = 1.0
test[3][1] = 1.0
result.append(np.ones(1))
result.append(np.ones(1))
result.append(np.ones(1))
result.append(np.zeros(1))
#print(myNetwork.weights)
#print(myNetwork.biases)
print("0 0 : " + str(myNetwork.process(test[0])) + " == 1 ?")
print("0 1 : " + str(myNetwork.process(test[1])) + " == 1 ?")
print("1 0 : " + str(myNetwork.process(test[2])) + " == 1 ?")
print("1 1 : " + str(myNetwork.process(test[3])) + " == 0 ?")
myNetwork.saveToFile("NAND")
print("Learning time : " + str(endTime - startTime))