def activate_networks():
p = [1, 0, 1, 6]
networks = []
new_networks = []
for i in range(1, 7):
network = Network.read_networks(i)[0]
networks.append(network)
for i in range(0, 6):
temp_i_network = networks[i]
for j in range(i, 6):
temp_j_network = networks[j]
if (temp_i_network.error.data[0] > temp_j_network.error.data[0]):
temp_network = networks[i]
networks[i] = networks[j]
networks[j] = temp_network
for i in range(0, 3):
network = Network(p)
network.create_weights()
network_1 = Network(p)
network_1.create_weights()
parent1_index = randint(0, 4)
parent2_index = randint(0, 4)
w1 = networks[parent1_index].dict_layers[0]
w2 = networks[parent2_index].dict_layers[0]
w_new, w_new_1 = create_new_weight(w1, w2)
network.dict_layers[0] = w_new
network_1.dict_layers[0] = w_new_1
w1 = networks[parent1_index].dict_layers[1]
w2 = networks[parent2_index].dict_layers[1]
w_new, w_new_1 = create_new_weight(w1, w2)
network.dict_layers[1] = w_new
network_1.dict_layers[1] = w_new_1
new_networks.append(network)
new_networks.append(network_1)
print(len(new_networks))
with Pool(6) as p:
p.map(
worker,
[[new_networks[0], 1], [new_networks[1], 2], [new_networks[2], 3],
[new_networks[3], 4], [new_networks[4], 5], [new_networks[5], 6]])
import torch
from torch.autograd import Variable
from multiprocessing import Pool
from random import randint
from mutate import Mutate
from nueral import Network
from cardata import createCarDataList
import logging
logging.basicConfig(filename='data.log',level=logging.INFO)
car_data_list=createCarDataList()
print("Complete")
networks=Network.read_networks()
def sort(networks):
for i in range(0,len(networks)):
temp_i=networks[i]
for j in range(i+1,len(networks)):
temp_j=networks[j]
if(temp_i.error.data[0]>temp_j.error.data[0]):
temp=temp_i
networks[i]=networks[j]
networks[j]=temp
networks2=[]
for i in range(0,10):
networks2.append(networks[i])
return networks2