【err】tensorflow.python.framework.errors_impl.OutOfRangeError: RandomShuffleQueue

problem

Traceback (most recent call last):
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _do_call
    return fn(*args)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.OutOfRangeError: RandomShuffleQueue '_0_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested , current size )
     [[{{node shuffle_batch}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "test_TFRecord.py", line , in <module>
    print(sess.run(label_batch))
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in run
    run_metadata_ptr)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _do_run
    run_metadata)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.OutOfRangeError: RandomShuffleQueue '_0_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested , current size )
     [[node shuffle_batch (defined at test_TFRecord.py:) ]]

Original stack trace for 'shuffle_batch':
  File "test_TFRecord.py", line , in <module>
    num_threads=)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line , in new_func
    return func(*args, **kwargs)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/training/input.py", line , in shuffle_batch
    name=name)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/training/input.py", line , in _shuffle_batch
    dequeued = queue.dequeue_many(batch_size, name=name)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/ops/data_flow_ops.py", line , in dequeue_many
    self._queue_ref, n=n, component_types=self._dtypes, name=name)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line , in queue_dequeue_many_v2
    timeout_ms=timeout_ms, name=name)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py", line , in _apply_op_helper
    op_def=op_def)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line , in new_func
    return func(*args, **kwargs)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line , in create_op
    op_def=op_def)
  File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line , in __init__
    self._traceback = tf_stack.extract_stack()

code

# -*- coding: utf-8 -*-
"""
@author: friedhelm

"""
import tensorflow as tf
img_size = 12
filename_queue = tf.train.string_input_producer(["/home/xxx/workspace/test_code/github_test/MTCNN-tensorflow/version_1_0/DATA/12/neg_12_train.tfrecords"],shuffle=True,num_epochs=10)

reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue) #返回文件名和文件

features = tf.parse_single_example(serialized_example,
                               features={
                               'img':tf.FixedLenFeature([],tf.string),
                               'label':tf.FixedLenFeature([],tf.int64),
                               'roi':tf.FixedLenFeature([4],tf.float32),
                               'landmark':tf.FixedLenFeature([10],tf.float32),
                               })
img=tf.decode_raw(features['img'],tf.uint8)
label=tf.cast(features['label'],tf.int64)
roi=tf.cast(features['roi'],tf.float32)
landmark=tf.cast(features['landmark'],tf.float32)
# img = tf.reshape(img, [48,48,3])
img = tf.reshape(img, [img_size,img_size,3])
#     img=img_preprocess(img)
min_after_dequeue = 1000  #
batch_size = 10  #
capacity = min_after_dequeue + 10 * batch_size
image_batch, label_batch, roi_batch, landmark_batch = tf.train.shuffle_batch([img,label,roi,landmark],
                                                    batch_size=batch_size,
                                                    capacity=capacity,
                                                    min_after_dequeue=min_after_dequeue,
                                                    num_threads=7)

i=0
with tf.Session() as sess:
    sess.run((tf.global_variables_initializer(),
              tf.local_variables_initializer()))
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess,coord=coord)
    while(1):
        i=i+1
        if(i%9==1):
            print(sess.run(label_batch))

why?

解决方法：详见here;

完