TensorFlow random_shuffle_queue已关闭且元素不足

Question

我通过获取想法阅读一批图像这里从tfrecords(通过转换此)

我的图像是cifar图像,[32,32,3],你可以看到,在阅读和传递图像时,形状是正常的(batch_size=100)

据我所知,日志中陈述的两个最值得注意的问题是

1. 形状12228,我不知道从哪里得到这个.我的所有张量都是形状[32,32,3]或[无,3072]
2. 用完了样品

Compute status: Out of range: RandomSuffleQueue '_2_input/shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 100, current size 0)

我怎么解决这个问题？

日志:

1- image shape is  TensorShape([Dimension(3072)])
1.1- images batch shape is  TensorShape([Dimension(100), Dimension(3072)])
2- images shape is  TensorShape([Dimension(100), Dimension(3072)])

W tensorflow/core/kernels/queue_ops.cc:79] Invalid argument: Shape mismatch in tuple component 0. Expected [3072], got [12288]
W tensorflow/core/common_runtime/executor.cc:1027] 0x7fa72abc89a0 Compute status: Invalid argument: Shape mismatch in tuple component 0. Expected [3072], got [12288]
     [[Node: input/shuffle_batch/random_shuffle_queue_enqueue = QueueEnqueue[Tcomponents=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](input/shuffle_batch/random_shuffle_queue, input/sub, input/Cast_1)]]
W tensorflow/core/kernels/queue_ops.cc:79] Invalid argument: Shape mismatch in tuple component 0. Expected [3072], got [12288]
W tensorflow/core/common_runtime/executor.cc:1027] 0x7fa72ab9d080 Compute status: Invalid argument: Shape mismatch in tuple component 0. Expected [3072], got [12288]
     [[Node: input/shuffle_batch/random_shuffle_queue_enqueue = QueueEnqueue[Tcomponents=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](input/shuffle_batch/random_shuffle_queue, input/sub, input/Cast_1)]]
W tensorflow/core/kernels/queue_ops.cc:79] Invalid argument: Shape mismatch in tuple component 0. Expected [3072], got [12288]
W tensorflow/core/common_runtime/executor.cc:1027] 0x7fa7285e55a0 Compute status: Invalid argument: Shape mismatch in tuple component 0. Expected [3072], got [12288]
     [[Node: input/shuffle_batch/random_shuffle_queue_enqueue = QueueEnqueue[Tcomponents=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](input/shuffle_batch/random_shuffle_queue, input/sub, input/Cast_1)]]
W tensorflow/core/kernels/queue_ops.cc:79] Invalid argument: Shape mismatch in tuple component 0. Expected [3072], got [12288]
W tensorflow/core/common_runtime/executor.cc:1027] 0x7fa72aadb080 Compute status: Invalid argument: Shape mismatch in tuple component 0. Expected [3072], got [12288]
     [[Node: input/shuffle_batch/random_shuffle_queue_enqueue = QueueEnqueue[Tcomponents=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](input/shuffle_batch/random_shuffle_queue, input/sub, input/Cast_1)]]
W tensorflow/core/common_runtime/executor.cc:1027] 0x7fa72ad499a0 Compute status: Out of range: RandomSuffleQueue '_2_input/shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 100, current size 0)
     [[Node: input/shuffle_batch = QueueDequeueMany[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](input/shuffle_batch/random_shuffle_queue, input/shuffle_batch/n)]]
Traceback (most recent call last):
  File "/Users/HANEL/Documents/my_cifar_train.py", line 110, in <module>
    tf.app.run()
  File "/Users/HANEL/tensorflow/lib/python2.7/site-packages/tensorflow/python/platform/default/_app.py", line 11, in run
    sys.exit(main(sys.argv))
  File "/Users/HANEL/my_cifar_train.py", line 107, in main
    train()
  File "/Users/HANEL/my_cifar_train.py", line 76, in train
    _, loss_value = sess.run([train_op, loss])
  File "/Users/HANEL/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 345, in run
    results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
  File "/Users/HANEL/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 419, in _do_run
    e.code)
tensorflow.python.framework.errors.OutOfRangeError: RandomSuffleQueue '_2_input/shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 100, current size 0)
     [[Node: input/shuffle_batch = QueueDequeueMany[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](input/shuffle_batch/random_shuffle_queue, input/shuffle_batch/n)]]
Caused by op u'input/shuffle_batch', defined at:
  File "/Users/HANEL/my_cifar_train.py", line 110, in <module>
    tf.app.run()
  File "/Users/HANEL/tensorflow/lib/python2.7/site-packages/tensorflow/python/platform/default/_app.py", line 11, in run
    sys.exit(main(sys.argv))
  File "/Users/HANEL/my_cifar_train.py", line 107, in main
    train()
  File "/Users/HANEL/my_cifar_train.py", line 39, in train
    images, labels = my_input.inputs()
  File "/Users/HANEL/my_input.py", line 157, in inputs
    min_after_dequeue=200)
  File "/Users/HANEL/tensorflow/lib/python2.7/site-packages/tensorflow/python/training/input.py", line 453, in shuffle_batch
    return queue.dequeue_many(batch_size, name=name)
  File "/Users/HANEL/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/data_flow_ops.py", line 245, in dequeue_many
    self._queue_ref, n, self._dtypes, name=name)
  File "/Users/HANEL/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 319, in _queue_dequeue_many
    timeout_ms=timeout_ms, name=name)
  File "/Users/HANEL/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 633, in apply_op
    op_def=op_def)
  File "/Users
/HANEL/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1710, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/Users/HANEL/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 988, in __init__
    self._traceback =

_extract_stack()

Answer 1

我遇到了类似的问题.在网上挖掘,结果发现如果你使用一些num_epochs参数,你必须初始化所有local变量,所以你的代码应该看起来像:

with tf.Session() as sess:
    sess.run(tf.local_variables_initializer())
    sess.run(tf.global_variables_initializer())
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    # do your stuff here

    coord.request_stop()
    coord.join(threads)

如果您发布更多代码,也许我可以深入了解它.在此期间,HTH.

Answer 2

您可能正在处理解析的TFRecord示例错误.例如,尝试将张量重塑为不兼容的大小.您可以使用tf_record_iterator进行调试,以确认您正在阅读的数据以您认为的方式存储:

import tensorflow as tf
import numpy as np

tfrecords_filename = '/path/to/some.tfrecord'
record_iterator = tf.python_io.tf_record_iterator(path=tfrecords_filename)

for string_record in record_iterator:
    # Parse the next example
    example = tf.train.Example()
    example.ParseFromString(string_record)

    # Get the features you stored (change to match your tfrecord writing code)
    height = int(example.features.feature['height']
                                 .int64_list
                                 .value[0])

    width = int(example.features.feature['width']
                                .int64_list
                                .value[0])

    img_string = (example.features.feature['image_raw']
                                  .bytes_list
                                  .value[0])
    # Convert to a numpy array (change dtype to the datatype you stored)
    img_1d = np.fromstring(img_string, dtype=np.float32)
    # Print the image shape; does it match your expectations?
    print(img_1d.shape)

Answer 3

总结一下评论，

Compute status: Out of range: RandomSuffleQueue '_2_input/shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 100, current size 0)

是由于队列数据耗尽造成的。这通常是由于认为您有足够的数据进行 N 次迭代，而实际上您只有足够的数据进行 M 次迭代（其中 M < N）。

要确定实际拥有多少数据，一项建议是计算在队列抛出 OutOfRangeError 异常之前可以读取数据的次数。