How to filter tensor from queue based on some predicate in tensorflow?

Question

How can I filter data stored in a queue using a predicate function? For example, let's say we have a queue that stores tensors of features and labels and we just need those that meet the predicate. I tried the following implementation without success:

feature, label = queue.dequeue()
if (predicate(feature, label)):
    enqueue_op = another_queue.enqueue(feature, label)

Answer 1

The most straightforward way to do this is to dequeue a batch, run them through the predicate test, use tf.where to produce a dense vector of the ones that match the predicate, and use tf.gather to collect the results, and enqueue that batch. If you want that to happen automatically, you can start a queue runner on the second queue - the easiest way to do that is to use tf.train.batch:

Example:

import numpy as np
import tensorflow as tf

a = tf.constant(np.array([5, 1, 9, 4, 7, 0], dtype=np.int32))

q = tf.FIFOQueue(6, dtypes=[tf.int32], shapes=[])
enqueue = q.enqueue_many([a])
dequeue = q.dequeue_many(6)
predmatch = tf.less(dequeue, [5])
selected_items = tf.reshape(tf.where(predmatch), [-1])
found = tf.gather(dequeue, selected_items)

secondqueue = tf.FIFOQueue(6, dtypes=[tf.int32], shapes=[])
enqueue2 = secondqueue.enqueue_many([found])
dequeue2 = secondqueue.dequeue_many(3) # XXX, hardcoded

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  sess.run(enqueue)  # Fill the first queue
  sess.run(enqueue2) # Filter, push into queue 2
  print sess.run(dequeue2) # Pop items off of queue2

The predicate produces a boolean vector; the tf.where produces a dense vector of the indexes of the true values, and the tf.gather collects items from your original tensor based upon those indexes.

A lot of things are hardcoded in this example that you'd need to make not-hardcoded in reality, of course, but hopefully it shows the structure of what you're trying to do (create a filtering pipeline). In practice, you'd want QueueRunners on there to keep things churning automatically. Using tf.train.batch is very useful to handle that automatically -- see Threading and Queues for more detail.