CODEWITHSUNDEEP
tensorflow
denru
denru

Reputation: 199

Tensorflow FIFOQueue error: FIFOQueue is closed and has insufficient elements

Now I am using tensorflow to write a program to validate models. I use the FIFOQueue to queue the input data. For example, I have 50,000 images and enqueue 100 images at a time. The program works beautifully except for the final iteration. At the final iteration, it shows the error "E tensorflow/core/client/tensor_c_api.cc:485] FIFOQueue '_0_path_queue' is closed and has insufficient elements (requested 1, current size 0) [[Node: path_queue_Dequeue = QueueDequeue_class=["loc:@path_queue"], component_types=[DT_INT32, DT_BOOL, DT_STRING], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"]]"

I think that is because it tries to enqueue the 50,001~50,100 images but cannot achieve this. However, I don't need to enqueue these images and will not use them. How can I avoid this error?

Another question is that if I would like to use dequeue_many(100), however, the total number of images is not divisible by 100, say 45678. In this case, tensorflow will throw an error. How can I solve this?

Thanks.

Upvotes: 5

Views: 6055

Answers (4)

Megha
Megha

Reputation: 220

I have faced this issue multiple times and from my experience this is usually caused if the input files cannot be found. my input was a list of pngs from a directory and I was using this to get the input images.

input = tensorflow.train.string_input_producer(tensorflow.train.match_filenames_once("/input/*.png"))

which was somehow not getting the files correctly. Changing it to 

filename_im = tensorflow.train.string_input_producer(glob.glob('/input/*.png'))

solved the issue

Upvotes: 1

fenkerbb
fenkerbb

Reputation: 805

I believe that this is only a warning that the queue is empty, but does not cause errors. I see similar warnings but my program does not break. Does yours? See this thread.

Upvotes: 0

John Wakefield
John Wakefield

Reputation: 527

You could catch the specific error which will gracefully end training once all examples have been exhausted:

try:
    while True:
        # Run training Ops here...

except tf.errors.OutOfRangeError:
    print('Done training -- epoch limit reached')

Upvotes: 1

Peter Hawkins
Peter Hawkins

Reputation: 3211

Try dequeue_up_to instead of dequeue_many: https://www.tensorflow.org/versions/r0.10/api_docs/python/io_ops.html

Hope that helps!

Upvotes: 2

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