def __init__(self):
print("tensorflow version: ", tf.__version__)
self.dict_file = 'data/word_dict.txt'
self.data_map = "data/map.pkl" # pkl是cpickle模块生成的文件，用于长久保存字符串、列表、字典等数据
self.batch_size = 20 # 每次喂进20个
self.max_epoch = 10000 # 最大100000轮
self.show_batch = 1 #
self.model_path = 'model/'
# jieba导入词典
jieba.load_userdict(self.dict_file)
self.location = ["杭州", "重庆", "上海", "北京"]
self.user_info = {"__UserName__": "yw", "__Location__": "重庆"}
self.robot_info = {"__RobotName__": "xw"}
# 获取输入输出
if os.path.isfile(self.data_map):
with open(self.data_map, "rb") as f:
data_map = cPickle.load(
f) # 使用cpickle读取map.pkl文件内容返回，注意写入是什么类型数据，读取是就是什么类型数据
else:
p = Preprocess()
p.main() # 如果不存在data_map则调用Preprocess()方法重新创建向量和map
data_map = p.data_map # data_map是全局变量的dict，在这里可以取到
# 从data_map中查找各个键值对并赋值,其中也存在字典嵌套
self.encoder_vocab = data_map.get("Q_vocab")
self.encoder_vec = data_map.get("Q_vec")
self.encoder_vocab_size = data_map.get("Q_vocab_size")
self.char_to_vec = self.encoder_vocab
self.decoder_vocab = data_map.get("A_vocab")
self.decoder_vec = data_map.get("A_vec")
self.decoder_vocab_size = data_map.get("A_vocab_size")
self.vec_to_char = {v: k for k, v in self.decoder_vocab.items()}
print("encoder_vocab_size {}".format(self.encoder_vocab_size))
print("decoder_vocab_size {}".format(self.decoder_vocab_size))
# 调用DynamicSeq2seq()方法，将编码解码词典长度导入，初始化模型
self.model = DynamicSeq2seq(
encoder_vocab_size=self.encoder_vocab_size + 1,
decoder_vocab_size=self.decoder_vocab_size + 1,
)
#优先给程序分配显存
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
self.restore_model()
def __init__(self):
print("tensorflow version: ", tf.__version__)
self.dict_file = 'data/word_dict.txt'
self.data_map = "data/map.pkl"
self.batch_size = 20
#self.max_epoch = 100000
self.max_epoch = 10
self.show_batch = 1
self.model_path = 'model/'
# jieba导入词典
jieba.load_userdict(self.dict_file)
self.location = ["杭州", "重庆", "上海", "北京"]
self.user_info = {"__UserName__": "yw", "__Location__": "重庆"}
self.robot_info = {"__RobotName__": "Rr"}
# 获取输入输出
if os.path.isfile(self.data_map):
with open(self.data_map, "rb") as f:
data_map = cPickle.load(f)
else:
p = Preprocess()
p.main()
data_map = p.data_map
self.encoder_vocab = data_map.get("Q_vocab")
self.encoder_vec = data_map.get("Q_vec")
self.encoder_vocab_size = data_map.get("Q_vocab_size")
self.char_to_vec = self.encoder_vocab
self.decoder_vocab = data_map.get("A_vocab")
self.decoder_vec = data_map.get("A_vec")
self.decoder_vocab_size = data_map.get("A_vocab_size")
self.vec_to_char = {v: k for k, v in self.decoder_vocab.items()}
print "encoder_vocab_size {}".format(self.encoder_vocab_size)
print "decoder_vocab_size {}".format(self.decoder_vocab_size)
self.model = DynamicSeq2seq(
encoder_vocab_size=self.encoder_vocab_size + 1,
decoder_vocab_size=self.decoder_vocab_size + 1,
)
self.sess = tf.Session()
self.restore_model()