Tensorflow批量训练超出范围错误

这是基于20w图像训练图像分类模型的输出。我在批处理中设置了allow_minger_final_batch=True。经过一段时间后，发生了OutOfRange错误

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我不知道原因，谢谢你的帮助。

因为你得到了一个

OutOfRangeError

，可能是因为你训练的时间比

max\u num\u epochs

多，这将导致

slice\u input\u producer

抛出此异常

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一种可能的解决方法是从

切片输入\u生产者

中删除

num\u epochs=max\u num\u epochs

，因为这将允许它即使在达到最大历元数之后也能生成。

我已经与这个特定错误斗争了好几天。我终于找到了原因。您收到此错误是因为您的文件在某处损坏。尝试在其他列车上运行此代码并测试数据

谢谢您的帮助。我根据您的建议运行代码，但出现了相同的错误。您是否有一段代码，我们可以用来重现该问题？我在问题中添加了一段代码，但我不知道如何粘贴整个代码。您能否粘贴运行ops的部分？很抱歉，我在尝试访问此页面时遇到了问题，代码很长，所以我参考了链接

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Traceback (most recent call last):
Caused by op u'batch', defined at:
  File "ava_train.py", line 155, in <module>
    image_batch, label_batch = tf.train.batch([image, label], batch_size=batch_size, allow_smaller_final_batch=True)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py", line 872, in batch
name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py", line 665, in _batch
dequeued = queue.dequeue_up_to(batch_size, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/data_flow_ops.py", line 510, in dequeue_up_to
self._queue_ref, n=n, component_types=self._dtypes, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 1402, in _queue_dequeue_up_to_v2
timeout_ms=timeout_ms, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2395, in create_op
original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1264, in __init__
self._traceback = _extract_stack()

OutOfRangeError (see above for traceback): FIFOQueue '_1_batch/fifo_queue' is closed and has insufficient elements (requested 100, current size 0)
     [[Node: batch = QueueDequeueUpToV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](batch/fifo_queue, batch/n)]]

with tf.Graph().as_default():
     global_step = tf.Variable(0, trainable=False)
     # process same as cifar10.distorted_inputs
     log_dir =  '../log'
     model_dir = '../model'
     max_num_epoch = 80
     if not os.path.exists(log_dir):
         os.makedirs(log_dir)
     if not os.path.exists(model_dir):
         os.makedirs(model_dir)
     num_train_example = len(os.listdir('../images/'))
     # Reads pfathes of images together with their labels
     image_list, label_list = read_labeled_image_list('../raw.txt')
     images = ops.convert_to_tensor(image_list, dtype=dtypes.string)
     labels = ops.convert_to_tensor(label_list, dtype=dtypes.int32)
     # Makes an input queue
     # input_queue = tf.train.slice_input_producer([images, labels], num_epochs=max_num_epoch, shuffle=True)
     input_queue = tf.train.slice_input_producer([images, labels], shuffle=True)
     image, label = read_images_from_disk(input_queue)
     image_size = 240
     keep_probability = 0.8
     weight_decay = 5e-5
     image = preprocess(image, image_size, image_size, None)
     batch_size = 100
     epoch_size = 1000
     embedding_size = 128
     # Optional Image and Label Batching
     image_batch, label_batch = tf.train.batch([image, label], batch_size=batch_size, allow_smaller_final_batch=True)