time.strftime('%Y-%m-%d %H-%M-%S', time.localtime(time.time())))
else:
pass
print("\n测试模式...")
# timestamp = input("请输入要restore的模型生成时间戳,例如2018-12-12 19-58-25:")
timestamp = "2018-12-19 17-39-12"
# timestamp = "2018-12-19 17-03-59"
print("\n当前加载的模型是: ", timestamp, '\n')
args.is_training = False
args.model_path = os.path.join('.', "model", timestamp + "/")
# args.model_path = os.path.join('.', "model", 'test' + "/")
if not os.path.exists(args.model_path):
os.makedirs(args.model_path)
if args.is_training:
model = Graph(args, cn_words_embedding, en_words_embedding,
cn_word2id_dict, en_word2id_dict, en_id2word_dict)
model.build_graph()
model.train(cn_train_data, en_train_data)
else:
args.model_path = tf.train.latest_checkpoint(args.model_path)
model = Graph(args, cn_words_embedding, en_words_embedding,
cn_word2id_dict, en_word2id_dict, en_id2word_dict)
model.build_graph()
print("=============================")
model.test(cn_dev_data, en_dev_data)
# Config default value
cfg = config.cfg
# Training files name
cfg.queue.filename = [
os.path.join(os.path.dirname(os.path.basename(__file__)), "examples", "train{}.tfrecords").format(index)
for index in range(args.train_first_file,
args.train_first_file +
args.train_set_size //
cfg.queue.nb_examples_per_file)]
print(cfg.queue.filename)
# Whether we create a validation set
cfg.queue.is_val_set = args.val_set
# Whether to train with adversarial cost
cfg.gan.train_adversarial = args.train_adversarial
# Size of a batch
cfg.train.batch_size = args.batch_size
# Build model and train or fill images
b = Graph(cfg)
b.build()
if args.train:
b.train()
else:
# TODO: add a queue for validation set (change args parameter in consequences)
b.fill_image(20)