创建一个ai聊天机器人，但得到一个跟踪错误

Question

我正试图在python中创建一个ai chatbox。我试着遵循本教程：https://techwithtim.net/tutorials/ai-chatbot/part-1/，但是我得到了很多不推荐的错误，并得到了一些跟踪错误。下面是代码：

import json
import random
import tensorflow
import tflearn
import numpy
import sys
import pickle
import nltk
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()
nltk.download('punkt')


with open("trainingData.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.lower()) 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)

tensorflow.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("Start talking with the bot (type quit to stop)!")
    while True:
        inp = input("You: ")
        if inp.lower() == "quit":
            break

        results = model.predict([bag_of_words(inp, words)])
        results_index = numpy.argmax(results)
        tag = labels[results_index]

        for tg in data["intents"]:
            if tg['tag'] == tag:
                responses = tg['responses']

        print(random.choice(responses))

chat()

这是我要犯的错误。如何修正弃用错误、追溯错误？

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以下是错误的文本：

Run id: VOB3W4
Log directory: /tmp/tflearn_logs/
---------------------------------
Training samples: 20
Validation samples: 0
--
--
Traceback (most recent call last):
  File "script.py", line 91, in <module>
    model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
  File "/usr/local/lib/python2.7/site-packages/tflearn/models/dnn.py", line 216, in fit
    callbacks=callbacks)
  File "/usr/local/lib/python2.7/site-packages/tflearn/helpers/trainer.py", line 339, in fit
    show_metric)
  File "/usr/local/lib/python2.7/site-packages/tflearn/helpers/trainer.py", line 816, in _train
    tflearn.is_training(True, session=self.session)
  File "/usr/local/lib/python2.7/site-packages/tflearn/config.py", line 95, in is_training
    tf.get_collection('is_training_ops')[0].eval(session=session)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 731, in eval
    return _eval_using_default_session(self, feed_dict, self.graph, session)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 5579, in _eval_using_default_session
    return session.run(tensors, feed_dict)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 950, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1096, in _run
    raise RuntimeError('Attempted to use a closed Session.')
RuntimeError: Attempted to use a closed Session.

Answer 1

在启动文件时，"model.tflearn"不存在，当代码试图加载该文件并运行fit()和save()时，try/except会捕获错误

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

但是这个错误似乎关闭了tf.session()，所以它不能正确地运行fit()。

如果使用try/except删除load()，只保留fit()和save()，那么创建模型并将其保存在文件中没有问题。

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

在创建文件"model.ftlearn"之后，您可以在load()中再次使用try/except，如果您不使用模型删除文件，它应该可以工作。

更好的解决方案应该检查文件是否存在，但它将数据保存在少数文件( "model.tflearn.index"、"model.tflearn.meta"和"model.tflearn.data-00000-of-00001" )中，因此应该检查其中一个文件而不是"model.tflearn"。

使用

import os

if os.path.exists("model.tflearn.meta"):
    model.load("model.tflearn")
else:
    model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
    model.save("model.tflearn")

而不是

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

编辑：看来这个问题至少存在两年：RuntimeError: Attempted to use a closed Session in tflearn

Answer 2

试着在下面这样做：

try:
    model.load("model3.tflearn")
except:
    model = tflearn.DNN(net)
    model.fit(training,output, n_epoch = 1000, batch_size = 8, show_metric = True)
    model.save("model3.tflearn")