config = get_config()
dataset = DataGenerator()
real_tour_concorde = {}
pool = Pool(processes=8)
last = 10000
if not os.path.exists("supervised_data"):
os.mkdir("supervised_data")
for i in tqdm(range(10000, 30000)): # test instance
seed_ = 1 + i
reward, tour = [], []
input_batch, dist_batch = dataset.create_batch(config.batch_size,
config.graph_dimension,
config.dimension,
seed=seed_)
sys.stdout = open(os.devnull, "w")
result_tours = [
pool.apply(pool_operation, args=(input_batch, x))
for x in range(config.batch_size)
]
sys.stdout = sys.__stdout__
real_tour_concorde[seed_] = result_tours
if seed_ % 100 == 0:
f = open(
"supervised_data/batch_seed_" + str(last) + "_" + str(seed_) +
".pkl", "wb")
os.makedirs(save_path)
restore_path = "saveSL/" + dir_actor + "/actor.ckpt"
with tf.Session() as sess: # start session+ "next"
count_no_weights()
sess.run(tf.global_variables_initializer()) # run initialize op
# saver_all.restore(sess, restore_path)
writer = tf.summary.FileWriter('summarySL/' + dir_actor,
sess.graph) # summary writer
for i in tqdm(range(10000,
10000 + config.nb_steps)): # Forward pass & train step
seed_ = i + 1
input_batch, dist_batch = dataset.create_batch(actor.batch_size,
actor.max_length,
actor.dimension,
seed=seed_)
if i % 100 == 0:
with open(
"supervised_data/batch_seed_" + str(i) + "_" +
str(i + 100) + ".pkl", 'rb') as handle:
optimal_tour_dic = pickle.load(handle)
optimal_tours = np.array(optimal_tour_dic[seed_])
feed = {
actor.input_: input_batch,
actor.optimal_tour: optimal_tours,
actor.temperature: np.array([temperature])
} # get feed dict actor.predictions: real_lengths
if actor.version == 'graph':
import pickle
from dataGenerator import DataGenerator
from utils import visualize_2D_trip
last = 19000
i = 19100
seed_ = 19053
with open("supervised_data/batch_seed_" + str(last) + "_" + str(i) + ".pkl",
'rb') as handle:
b = pickle.load(handle)
dataset = DataGenerator()
input_batch, dist_batch = dataset.create_batch(256, 50, 2, seed=seed_)
visualize_2D_trip(input_batch[100], b[seed_][100], 'niente', 1)