Python Tfrecords错误："；无效参数：重塑的输入是一个值为71680的张量，但请求的形状有8960“；

在那些日子里，当我运行代码读取“tfrecords”数据时，就会出现这个错误。问题是如何找到“值为71680的张量”或“形状为8960”的张量

错误是：

我认为关键在于

images=tf.decode_raw（功能['image_raw'，tf.uint8）

，但我不知道原因和方法。这里我给出了生成“tfrecords”数据的代码

---

当我使用

PIL.Image

替换

cv2

时，它工作正常！这里的图像高=32，宽=280。有人知道为什么它发生在cv2中吗？或者我如何从

tfrecords

数据中正确读取图像。

问题已经解决。问题在这里：

image=image/255.0-0.5

。有人知道为什么吗？

问题已经解决。问题在这里：

image=image/255.0-0.5

。有人知道为什么吗？

请编辑这篇文章，包括

crnn

看起来像。对不起，我不明白我的错误。在这里我重新写了我的错误地点的详细信息请编辑这篇文章，包括

crnn

的样子。对不起，我没有理解我的错误。在这里，我重新写下我的错误详细的地方

I0807 09:54:11.147000 353860 coordinator.py:224] Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.InvalidArgumentError'>, 2 root error(s) found.
 (0) Invalid argument: Input to reshape is a tensor with 71680 values, but the requested shape has 8960
 [[{{node Reshape}}]]
 (1) Invalid argument: Input to reshape is a tensor with 71680 values, but the requested shape has 8960
 [[{{node Reshape}}]]
 [[sub/_21]]
 0 successful operations.
 0 derived errors ignored.
 Traceback (most recent call last):
 File "D:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1356, in _do_call
return fn(*args)
 File "D:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1341, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
 File "D:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1429, in _call_tf_sessionrun
run_metadata)
 tensorflow.python.framework.errors_impl.OutOfRangeError: 2 root error(s) found.
 (0) Out of range: RandomShuffleQueue '_2_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 4, current size 0)
 [[{{node shuffle_batch}}]]
 (1) Out of range: RandomShuffleQueue '_2_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 4, current size 0)
 [[{{node shuffle_batch}}]]
 [[CTCLoss/_121]]
 0 successful operations.
 0 derived errors ignored.

def read_tfrecords():
  ......
  ......
  filename_queue = tf.train.string_input_producer([tfrecords_path])
  reader = tf.TFRecordReader()
  _,serizalized_example = reader.read(filename_queue)
  features = tf.parse_single_example(serizalized_example,
                                   features = {
                                       'image_raw':tf.FixedLenFeature([], tf.string),
                                       'label': tf.VarLenFeature(tf.int64),
                                       })
  images = tf.decode_raw(features['image_raw'],tf.uint8) # Maybe error is here
  images = tf.cast(images,dtype = tf.float32)
  images = tf.reshape(images, [32, 280, 1])
  labels = tf.cast(features['label'], tf.int32)
  images_batch, label_batch = tf.train.shuffle_batch([images,labels],batch_size=batch_size,num_threads=1,
                       capacity=5000, min_after_dequeue=1000)
  return images_batch, label_batch

def write2tfrecords():
....
....
image = cv2.imread(image_path,0)
image = cv2.resize(image, (280, 32))
image = image/255.0-0.5
image_raw = image.tobytes() ## I think the error is here
#image_raw = image.tostring() ## It also don't work
value_label = lable
example = tf.train.Example(features = tf.train.Features(
        feature={
            'image_raw':tf.train.Feature(bytes_list=tf.train.BytesList(value=[image_raw])),
            'label':tf.train.Feature(int64_list=tf.train.Int64List(value=[value_label]))
        }))
    writer.write(example.SerializeToString())
    log.info('Finished image {:s}'.format(key))
writer.close()