Question

我正在通过从tfrecords获取想法here来阅读一批图片（由this转换）

我的图像是cifar图像，[32,32,3]，你在阅读和传递图像时可以看到形状正常（batch_size=100）

据我所知，日志中陈述的两个最值得注意的问题是

12228的形状，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]
用完样本

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

我该如何解决这个问题？

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

我有类似的问题。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。与此同时，HTH。

Answer 2

您可能处理解析的TFRecord示例错误。例如。试图将张量重塑为不相容的大小。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)

是由队列耗尽数据引起的。这通常是因为认为你有足够的数据用于N次迭代，而实际上你只有足够的M次迭代，其中M <1。 N.

确定实际拥有多少数据的一个建议是计算在队列抛出OutOfRangeError异常之前可以读取数据的次数。

Answer 4

今天我遇到了完全相同的问题，后来我发现这是我从＃34;着名数据集＆＃34;下载的输入数据文件。（例如https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data）导致错误：文件末尾有一些空行。删除空行，错误消失了！

Answer 5

这也可能是由根本不存在的错误的tf记录文件名引起的。在进行其他检查之前，请确保指定了正确的文件路径。

Answer 6

我遇到了同样的问题，之前的答案似乎都没有解决，所以我也会插话。

对我来说，问题最终成为我传递给parse_single_example的功能列表。无论出于何种原因（因为我在使用float_list？），我需要在我的功能列表中指定数组的长度或使用tf.VarLenFeature，即：

feature_structure = {'features': tf.FixedLenFeature([FEATURE_SIZE], tf.float32),
           'outputs': tf.FixedLenFeature([OUTPUT_SIZE], tf.float32)}
d_features = tf.parse_single_example(serialized_example, features=feature_structure)

如果没有这个我继续得到＆＃34; random_shuffle_queue已经关闭且元素不足＆＃34;我猜的错误是因为我的解析示例中没有数据。

TensorFlow random_shuffle_queue已关闭且元素不足

6 个答案: