如何解决TensorFlow聊天bot的RuntimeError: Attempted to use a closed Session问题

我使用Python和TensorFlow开发一款聊天Bot，该Bot读取JSON格式的意图列表文件，通过TFLearn训练神经网络模型，以此预测用户输入的意图并给出合适响应。运行时出现RuntimeError: Attempted to use a closed Session错误，相关代码及报错信息如下：

import nltk
nltk.download('punkt')
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()
from tensorflow.python.framework import ops

import numpy
import tensorflow as tf
import tflearn
import random
import json
import pickle

from time import sleep

with open("intents.json") as file:
    data = json.load(file)

try:
    with open("data.pickle", "rb") as f:
        words, labels, training, output = pickle.load(f)
except:
    words = []
    labels = []
    docs_x = []
    docs_y = []
    for intent in data ["intents"]:
        for pattern in intent["patterns"]:
            wrds = nltk.word_tokenize(pattern)
            words.extend(wrds)
            docs_x.append(wrds)
            docs_y.append(intent["tag"])

        if intent["tag"] not in labels:
            labels.append(intent["tag"])

    words = [stemmer.stem(w.lower()) for w in words if w != "?"]
    words = sorted(list(set(words)))

    labels = sorted(labels)

    training = []
    output = []

    out_empty = [0 for _ in range(len(labels))]

    for x, doc in enumerate(docs_x):
        bag = []

        wrds = [stemmer.stem(w) for w in doc]

        for w in words:
            if w in wrds:
                bag.append(1)
            else:
                bag.append(0)


        output_row = out_empty[:]
        output_row[labels.index(docs_y[x])] = 1

        training.append(bag)
        output.append(output_row)


    training = numpy.array(training)
    output = numpy.array(output)

    with open("data.pickle", "wb") as f:
        pickle.dump((words, labels, training, output), f)

ops.reset_default_graph()

net = tflearn.input_data(shape=[None, len(training[0])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, len(output[0]), activation = "softmax")
net = tflearn.regression(net)

model = tflearn.DNN(net)

try:
    model.load("model.tflearn")
except:
    model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
    model.save("model.tflearn")

def bag_of_words(s, words):
    bag = [0 for _ in range(len(words))]

    s_words = nltk.word_tokenize(s)
    s_words = [stemmer.stem(word.lower()) for word in s_words]

    for se in s_words:
        for i, w in enumerate(words):
            if w == se:
                bag[i] = 1

    return numpy.array(bag)

def chat():
    print("Hi, How can i help you ?")
    while True:
        inp = input("You: ")
        if inp.lower() == "quit":
            break

        results = model.predict([bag_of_words(inp, words)])[0]
        results_index = numpy.argmax(results)
        tag = labels[results_index]
        if results[results_index] > 0.8:
            for tg in data["intents"]:
                if tg['tag'] == tag:
                    responses = tg['responses']
            sleep(3)
            Bot = random.choice(responses)
            print(Bot)
        else:
            print("I don't understand!")
chat()

报错信息：

raise RuntimeError('Attempted to use a closed Session.')
RuntimeError: Attempted to use a closed Session.

错误原因 #

该错误是因为TFLearn的DNN模型依赖的TensorFlow会话被意外关闭，调用model.predict()时无法访问活跃的会话上下文。通常发生在模型加载后，会话未被正确保留或初始化。

解决方法 #

修改模型加载代码块，在加载完成后显式初始化并保留会话：

try:
    model.load("model.tflearn")
    # 重新初始化会话，确保会话处于活跃状态
    model.session = tf.Session()
    model.session.run(tf.global_variables_initializer())
except:
    model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
    model.save("model.tflearn")

如果上述方法无效，可在每次预测前检查会话状态，确保会话可用：
在chat()函数的预测代码前添加以下内容：

# 检查会话是否关闭，若关闭则重新初始化
if model.session is None or model.session._closed:
    model.session = tf.Session()
    model.session.run(tf.global_variables_initializer())
results = model.predict([bag_of_words(inp, words)])[0]

原理说明 #

TFLearn的DNN模型保存时仅存储模型参数，会话状态不会被持久化。加载模型后需要重新创建并初始化会话，确保模型预测时能访问到活跃的TensorFlow会话上下文。

内容的提问来源于stack exchange，提问作者Mohamed Ashour