

In [2]:

    
import tensorflow as tf
from tensorflow.models.rnn.ptb import reader









    



---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-2-23fc9fab46d8> in <module>()
      1 import tensorflow as tf
----> 2 from tensorflow.models.rnn.ptb import reader

ImportError: No module named models.rnn.ptb

1. 读取数据并打印长度及前100位数据。



In [2]:

    
DATA_PATH = "../../datasets/PTB_data"
train_data, valid_data, test_data, _ = reader.ptb_raw_data(DATA_PATH)
print len(train_data)
print train_data[:100]









    



929589
[9970, 9971, 9972, 9974, 9975, 9976, 9980, 9981, 9982, 9983, 9984, 9986, 9987, 9988, 9989, 9991, 9992, 9993, 9994, 9995, 9996, 9997, 9998, 9999, 2, 9256, 1, 3, 72, 393, 33, 2133, 0, 146, 19, 6, 9207, 276, 407, 3, 2, 23, 1, 13, 141, 4, 1, 5465, 0, 3081, 1596, 96, 2, 7682, 1, 3, 72, 393, 8, 337, 141, 4, 2477, 657, 2170, 955, 24, 521, 6, 9207, 276, 4, 39, 303, 438, 3684, 2, 6, 942, 4, 3150, 496, 263, 5, 138, 6092, 4241, 6036, 30, 988, 6, 241, 760, 4, 1015, 2786, 211, 6, 96, 4]

2. 将训练数据组织成batch大小为4、截断长度为5的数据组。并使用队列读取前3个batch。



In [3]:

    
# ptb_producer返回的为一个二维的tuple数据。
result = reader.ptb_producer(train_data, 4, 5)

# 通过队列依次读取batch。
with tf.Session() as sess:
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    for i in range(3):
        x, y = sess.run(result)
        print "X%d: "%i, x
        print "Y%d: "%i, y
    coord.request_stop()
    coord.join(threads)









    



X0:  [[9970 9971 9972 9974 9975]
 [ 332 7147  328 1452 8595]
 [1969    0   98   89 2254]
 [   3    3    2   14   24]]
Y0:  [[9971 9972 9974 9975 9976]
 [7147  328 1452 8595   59]
 [   0   98   89 2254    0]
 [   3    2   14   24  198]]
X1:  [[9976 9980 9981 9982 9983]
 [  59 1569  105 2231    1]
 [   0  312 1641    4 1063]
 [ 198  150 2262   10    0]]
Y1:  [[9980 9981 9982 9983 9984]
 [1569  105 2231    1  895]
 [ 312 1641    4 1063    8]
 [ 150 2262   10    0  507]]
X2:  [[9984 9986 9987 9988 9989]
 [ 895    1 5574    4  618]
 [   8  713    0  264  820]
 [ 507   74 2619    0    1]]
Y2:  [[9986 9987 9988 9989 9991]
 [   1 5574    4  618    2]
 [ 713    0  264  820    2]
 [  74 2619    0    1    8]]