在Redis中查询任务时，我尝试加载一个预训练的tensorflow模型检查点。但它只显示“恢复检查点”

Question

我试图在Redis中对任务排队时加载一个预训练的tensorflow模型检查点。但它只显示“恢复检查点”，之后没有响应。尽管在没有排队的情况下调用检查点时已成功加载雷迪斯。什么可能是这个原因问题是什么有什么方法可以检查redis中到底发生了什么？你知道吗

import os
import tensorflow as tf
import redis
from rq import Worker, Queue, Connection

import pandas as pd
numDimensions = 300
maxSeqLength = 250
batchSize = 24
lstmUnits = 64
numClasses = 2
iterations = 100000

import numpy as np
wordsList = np.load('wordsList.npy').tolist()
wordsList = [word.decode('UTF-8') for word in wordsList] #Encode words as UTF-8
wordVectors = np.load('wordVectors.npy')

#tf.Graph().as_default()


# Removes punctuation, parentheses, question marks, etc., and leaves only alphanumeric characters
import re
strip_special_chars = re.compile("[^A-Za-z0-9 ]+")

def cleanSentences(string):
    string = string.lower().replace("<br />", " ")
    return re.sub(strip_special_chars, "", string.lower())

def getSentenceMatrix(sentence):
    arr = np.zeros([batchSize, maxSeqLength])
    sentenceMatrix = np.zeros([batchSize,maxSeqLength], dtype='int32')
    cleanedSentence = cleanSentences(sentence)
    split = cleanedSentence.split()
    for indexCounter,word in enumerate(split):
        try:
            sentenceMatrix[0,indexCounter] = wordsList.index(word)
        except ValueError:
            sentenceMatrix[0,indexCounter] = 399999 #Vector for unkown words
    return sentenceMatrix



def small(post):
    import tensorflow as tf
    tf.reset_default_graph()

    labels = tf.placeholder(tf.float32, [batchSize, numClasses])
    input_data = tf.placeholder(tf.int32, [batchSize, maxSeqLength])

    data = tf.Variable(tf.zeros([batchSize, maxSeqLength, numDimensions]),dtype=tf.float32)
    data = tf.nn.embedding_lookup(wordVectors,input_data)

    lstmCell = tf.contrib.rnn.BasicLSTMCell(lstmUnits)
    lstmCell = tf.contrib.rnn.DropoutWrapper(cell=lstmCell, output_keep_prob=0.25)
    value, _ = tf.nn.dynamic_rnn(lstmCell, data, dtype=tf.float32)

    weight = tf.Variable(tf.truncated_normal([lstmUnits, numClasses]))
    bias = tf.Variable(tf.constant(0.1, shape=[numClasses]))
    value = tf.transpose(value, [1, 0, 2])
    last = tf.gather(value, int(value.get_shape()[0]) - 1)
    prediction = (tf.matmul(last, weight) + bias)

    correctPred = tf.equal(tf.argmax(prediction,1), tf.argmax(labels,1))
    accuracy = tf.reduce_mean(tf.cast(correctPred, tf.float32))

    sess = tf.InteractiveSession()
    saver = tf.train.Saver()
    saver.restore(sess, tf.train.latest_checkpoint('pos_negative'))
    print('sentiment called')
    inputText=post
    a = 'Positive'
    b = 'Negative'
    inputMatrix = getSentenceMatrix(inputText)
    print('matrix formed')

    predictedSentiment = sess.run(prediction, {input_data: inputMatrix})[0]

    #if (predictedSentiment[0] > predictedSentiment[1]):
    #  return a
    #else:
    #    return b
    print('done')
    return 'Positive'


#response = small('the car was good')

listen = ['default']

redis_url = os.getenv('REDISTOGO_URL', 'redis://localhost:6379')

conn = redis.from_url(redis_url)

if __name__ == '__main__':
    with Connection(conn):
        worker = Worker(list(map(Queue, listen)))
        worker.work()