Tensorflow Runtime-Wörterbuch-Lookup, hendling Ausgabe von tf.parse_single_sequence_example

Question

Ich verwende Tensorflow parse_single_sequence_example für die Decodierung der record_string von TFRecordReader. Es gibt zwei dicts zurück, eines für context_features und eines für sequence_features.

filename_queue = tf.train.string_input_producer('temp.text', num_epochs=1, shuffle=True) 
reader = tf.TFRecordReader() 
key, record_string = reader.read(filename_queue) 
context_features={ 
    "output":tf.FixedLenFeature([],tf.int64) 
    } 
sequence_features={ 
    "input_sequence":tf.FixedLenSequenceFeature([5,],tf.float32) 
    } 
context_parsed, sequence_parsed = tf.parse_single_sequence_example(serialized=record_string,context_features=context_features,sequence_features=sequence_features) 

context_parsed und sequence_parsed sind beide Wörterbücher. Wie bekomme ich das Tensor-Objekt, das den Schlüsseln zugeordnet ist? Wenn ich die folgende holen tun Betrieb

with tf.Session() as sess: 
    a=sess.run([context_parsed],feed_dict=None) 

Es schlägt fehl, und das ist verständlich.

Fetch argument {'output': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:1' shape=() dtype=int64>} of {'output': <tf.Tensor 'ParseSingleSequenceExample/ParseSingleSequenceExample:1' shape=() dtype=int64>} has invalid type <class 'dict'>, must be a string or Tensor. (Can not convert a dict into a Tensor or Operation.) 

Wie hole ich den context_parsed ['output'] Tensor? Wie füttere ich diesen Tensor zu einem Platzhalter in meinem Diagramm?

out=context_parsed['output'] 

Ich füge die Zeile oben und versuchen, sie zu holen, aber es funktioniert nicht, und das Terminal wird gerade in ipython stecken.

with tf.Session() as sess: 
    a=sess.run(out,feed_dict=None) 

Ich füge auch die Ausgabe von tf.contrib.learn.run_n

In [13]: context = tf.contrib.learn.run_n(context_parsed, n=1, feed_dict=None) 
In [14]: context[0] 
Out[14]: {'length': 6, 'output': 4} 
In [15]: context = tf.contrib.learn.run_n(out, n=1, feed_dict=None) 
--------------------------------------------------------------------------- 
TypeError         Traceback (most recent call last) 
<ipython-input-15-e5d7d977676f> in <module>() 

----> 1 context = tf.contrib.learn.run_n (out, n = 1, feed_dict = None)

/home/ankgoyal/anaconda3/lib/python3.5/site- packages/tensorflow/contrib/learn/python/learn/graph_actions.py in run_n(output_dict, feed_dict, restore_checkpoint_path, n) 
    553  output_dict=output_dict, 
    554  feed_dicts=itertools.repeat(feed_dict, n), 
--> 555  restore_checkpoint_path=restore_checkpoint_path) 
    556 
    557 

/home/ankgoyal/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/graph_actions.py in run_feeds(output_dict, feed_dicts, restore_checkpoint_path) 
    579  ValueError: if `output_dict` or `feed_dicts` is None or empty. 
    580 """ 
    --> 581 if not output_dict: 
    582  raise ValueError('output_dict is invalid: %s.' % output_dict) 
    583 if not feed_dicts: 

/home/ankgoyal/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in __bool__(self) 
    513  `TypeError`. 
    514  """ 
--> 515  raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. " 
    516      "Use `if t is not None:` instead of `if t:` to test if a " 
    517      "tensor is defined, and use the logical TensorFlow ops " 

TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use the logical TensorFlow ops to test the value of a tensor. 

Wie hole ich das context_parsed [ 'Ausgang'] Tensor? Wie füttere ich diesen Tensor zu einem Platzhalter in meinem Diagramm?

Answer 1

Ich fand den Fehler, den ich tat. Eigentlich habe ich den neuen Thread für TFReader() nicht gestartet und deswegen wird das Terminal gehängt.

with tf.Session() as sess: 
    sess.run(tf.initialize_all_variables()) 
    coord = tf.train.Coordinator() 
    threads = tf.train.start_queue_runners(coord=coord) 
    print(sess.run(context_parsed['length'])) 
    coord.join(threads) 

Der Ausgang wird als 6 gedruckt