python - Issues with implementing TensorFlow's MNIST example without feed_dict using a queue -


i came across tensorflow's deep mnist experts , wanted adapt more efficient use on gpus. since feed_dict seems incredibly slow, implemented input pipeline using tf.train.shuffle_batch , fifoqueue feed data model.

here's gist stock implementation of tensorflow guide , here's gist attempt @ optimized implementation.

now in example on tensorflow page, accuracy pretty approaches 1 after few thousand iterations. in code, aside queue implementation same model, accuracy seems oscillate between ~0.05 , ~0.15. further, loss reaches 2.3 after couple hundred iterations , doesn't decrease farther that.

another noteworthy point: when make comparison original batch created , batch used in subsequent iterations, appear equivalent. perhaps issue lies in queuing/dequeuing i'm not sure how fix it. if sees issues implementation pointers appreciated!

found solution. turns out tf.train.shuffle_batch implicitly implements randomshufflequeue. loading results of tf.train.shuffle_batch fifoqueue presumably caused fifoqueue not update input batch, while labels being updated because weren't being passed fifoqueue. removing fifoqueue entirely solved issue.


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