import common.perceptron as nn import numpy as np import random import tensorflow as tf import sys model_file1 = sys.argv[1] layer1 = int(sys.argv[2]) model_file2 = sys.argv[3] layer2 = int(sys.argv[4]) b = bm.BoardManager() n1 = nn.Perceptron(layer1, 81, 81) n1.load(model_file1) n2 = nn.Perceptron(layer2, 81, 81) n2.load(model_file2) ns = [n1, n2] n_battle = int(sys.argv[5]) count = 0 #b.clear_data(data_file_d) #b.clear_data(data_file_l) wins = [0, 0]
import common.perceptron as nn
import numpy as np
import random
import tensorflow as tf
import sys
model_file = sys.argv[2]
data_file_d = sys.argv[3]
data_file_l = sys.argv[4]
layer = int(sys.argv[1])
b = bm.BoardManager()
n = nn.Perceptron(layer, 81, 81)
n.load(model_file)
train_n = 10000 # number of train battle
count = 0
#b.clear_data(data_file_d)
#b.clear_data(data_file_l)
while True:
if count >= train_n:
count = 0
n.train(data_file_d, data_file_l, 1)
n.save(model_file)
b.clear_data(data_file_d)
import common.board_manager as bm
import common.perceptron as nn
import sys
import numpy as np
import random
model_file = sys.argv[2]
data_file_d = sys.argv[3]
data_file_l = sys.argv[4]
layer = int(sys.argv[1])
b = bm.BoardManager()
n = nn.Perceptron(layer, 81, 81)
n.train(data_file_d, data_file_l, 1)
n.save(model_file)
n.load(model_file)
n2 = nn.Perceptron(0, 81, 81)
ns = [n, n2]
count = 0
n_battle = int(sys.argv[5])
wins = [0, 0]
while True:
count += 1