In [3]:

    

import numpy as np
import tensorflow as tf

import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'

# for auto-reloading extenrnal modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%load_ext autoreload
%autoreload 2


    

In [42]:

    

import read_data
tf.set_random_seed(2016)


    

In [43]:

    

q = read_data.learn_string_input_producer()
q


    

    
Out[43]:





<tf.Tensor 'input_producer_37_Dequeue:0' shape=() dtype=string>

In [52]:

    

def main():
    tf.reset_default_graph()
    q = read_data.learn_string_input_producer()
    q1 = q.dequeue()
    q2 = q.dequeue()
    q3 = q.dequeue()
    with tf.Session() as sess:
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        r = sess.run([q1,q2,q3])
        print(r)
        coord.request_stop()
        coord.join(threads)
for i in range(10):
    main()


    

    




['test2.txt', 'test2.txt', 'test1.txt']
['test2.txt', 'test1.txt', 'test2.txt']
['test1.txt', 'test2.txt', 'test1.txt']
['test2.txt', 'test1.txt', 'test2.txt']
['test1.txt', 'test2.txt', 'test1.txt']
['test2.txt', 'test1.txt', 'test2.txt']
['test2.txt', 'test1.txt', 'test2.txt']
['test2.txt', 'test1.txt', 'test1.txt']
['test2.txt', 'test1.txt', 'test2.txt']
['test1.txt', 'test1.txt', 'test2.txt']

In [47]:

    

tf.reset_default_graph()


    

In [ ]: