TensorFlow random_shuffle_queue 关闭且元素不足 [英] TensorFlow random_shuffle_queue is closed and has insufficient elements
问题描述
我正在通过此处 来自 tfrecords(由 this 转换)
I'm reading batch of images by getting idea here from tfrecords(converted by this)
我的图像是 cifar 图像,[32, 32, 3],正如您在读取和传递图像时所看到的,形状是正常的 (batch_size=100
)
My images are cifar images, [32, 32, 3] and as you can see while reading and passing images the shapes are normal (batch_size=100
)
据我所知,日志中提到的 2 个最显着的问题是
the 2 most notable problems stated in the log, as far as I know is
- 12228 的形状,我不知道从哪里得到的.我所有的张量要么是 [32, 32, 3] 的形状,要么是 [None, 3072]
- 样品用完
计算状态:超出范围:RandomSuffleQueue '_2_input/shuffle_batch/random_shuffle_queue' 已关闭且元素不足(请求 100,当前大小为 0)
我该如何解决这个问题?
How can I solve this?
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()
推荐答案
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,你必须初始化所有的 local
变量,所以你的代码最终应该是这样的:>
I had a similar problem. Digging around the web, it turned out that if you use some num_epochs
argument, you have to initialize all the local
variables, so your code should end up looking like:
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,也许我可以更深入地研究它.与此同时,HTH.
If you post some more code, maybe I could take a deeper look into it. In the meantime, HTH.
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