python 2.7 - How to mix queue-based and feed-based input in TensorFlow -


i've migrated fully_connected style model reads inputs queue generated tfrecords file. has proven more efficient, still pass parameters interactively placeholder/feed_dict.

is there way use same computation graph (say have model class builds graph in init method) both feed_dict , full_connected functionality? can placeholder receive values dequeue?

you feed output of dequeue operation. tensorflow not dequeue item, use value provided. example:

q = tf.fifoqueue(capacity=10, dtypes=[tf.float32], shapes=[()]) v = tf.placeholder(tf.float32) enqueue = q.enqueue([v]) dequeue = q.dequeue() output = dequeue + 10.0  tf.session() sess:     sess.run(enqueue, feed_dict={v: 1.0})     sess.run(enqueue, feed_dict={v: 2.0})     sess.run(enqueue, feed_dict={v: 3.0})     print(sess.run(output)) # 11.0     print(sess.run(output, feed_dict={dequeue: 5.0})) # 15.0     print(sess.run(output)) # 12.0     print(sess.run(output)) # 13.0

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