我有一个tfrecord格式的数据集，我试图回读并显示图像以调试数据集问题。但是我在阅读时遇到了麻烦，因为事情看起来与tf.train.shuffle_batch（）有关。
The error returned is:
[[Node: DecodeRaw = DecodeRaw[little_endian=true, out_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](ParseSingleExample/Squeeze_data/raw_image)]]
Traceback (most recent call last):
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 1323, in _do_call
return fn(*args)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 1302, in _run_fn
status, run_metadata)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 473, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.OutOfRangeError: RandomShuffleQueue '_2_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 2, current size 0)
[[Node: shuffle_batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](shuffle_batch/random_shuffle_queue, shuffle_batch/n)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "D:\Workspace\eclipse\satnav_turn_classifier\datasetutils\play_tfrecord.py", line 53, in <module>
img, lbl = sess.run([images, labels])
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 889, in run
run_metadata_ptr)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 1120, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 1317, in _do_run
options, run_metadata)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 1336, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.OutOfRangeError: RandomShuffleQueue '_2_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 2, current size 0)
[[Node: shuffle_batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](shuffle_batch/random_shuffle_queue, shuffle_batch/n)]]
Caused by op 'shuffle_batch', defined at:
File "D:\Workspace\eclipse\satnav_turn_classifier\datasetutils\play_tfrecord.py", line 43, in <module>
images, labels = tf.train.shuffle_batch([image, label], batch_size=2, capacity=20, num_threads=1, min_after_dequeue=10)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\training\input.py", line 1225, in shuffle_batch
name=name)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\training\input.py", line 796, in _shuffle_batch
dequeued = queue.dequeue_many(batch_size, name=name)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\ops\data_flow_ops.py", line 464, in dequeue_many
self._queue_ref, n=n, component_types=self._dtypes, name=name)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\ops\gen_data_flow_ops.py", line 2417, in _queue_dequeue_many_v2
component_types=component_types, timeout_ms=timeout_ms, name=name)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\framework\ops.py", line 2956, in create_op
op_def=op_def)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\framework\ops.py", line 1470, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
OutOfRangeError (see above for traceback): RandomShuffleQueue '_2_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 2, current size 0)
[[Node: shuffle_batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](shuffle_batch/random_shuffle_queue, shuffle_batch/n)]]
创建文件的代码包括：
writer = tf.python_io.TFRecordWriter(tfrecord_file)
for i in range(len(addresses)):
image_file = addresses[i]
fp = open(image_file, 'rb')
raw_image = fp.read()
fp.close()
image = coder.decode(raw_image)
height, width, _ = image.shape
label = labels[i]
if 2.0 > float(label) > -2.0:
# Create a feature
feature = {'data/label': _float_feature(label),
'data/raw_image': _bytes_feature(raw_image),
'data/height': _int64_feature(height),
'data/width': _int64_feature(width)}
# Create an example protocol buffer
example = tf.train.Example(features=tf.train.Features(feature=feature))
# Serialize to string and write on the file
try:
writer.write(example.SerializeToString())
except:
print('Error with csv line %d, Steering Angle float value %f' % (i, label))
# print how many images are saved every 128 images
if not i % 128:
print('Converted data: {}/{}'.format(i, len(addresses)))
sys.stdout.flush()
writer.close()
sys.stdout.flush()
读者代码是：
tfrecords_path = 'd:/datasets/gtav/_tfrecord_files/cs003.trn.tfrecords'
with tf.Session() as sess:
feature = {'data/label': tf.FixedLenFeature([], tf.float32),
'data/raw_image': tf.FixedLenFeature([], tf.string),
'data/height': tf.FixedLenFeature([], tf.int64),
'data/width': tf.FixedLenFeature([], tf.int64)}
# Create a list of filenames and pass it to a queue
filename_queue = tf.train.string_input_producer([tfrecords_path], num_epochs=1)
# Define a reader and read the next record
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
# Decode the record read by the reader
features = tf.parse_single_example(serialized_example, features=feature)
# Convert the image data from string back to the numbers
image = tf.decode_raw(features['data/raw_image'], tf.float32)
# Cast label data into float32
label = tf.cast(features['data/label'], tf.float32)
# Reshape image data into the original shape
image = tf.reshape(image, [160, 320, 3])
#image_shape = tf.shape(image);
# Any preprocessing here ...
# Creates batches by randomly shuffling tensors
images, labels = tf.train.shuffle_batch([image, label], batch_size=2, capacity=20, num_threads=1, min_after_dequeue=10)
# Initialize all global and local variables
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
#sess.run(image_shape)
sess.run(init_op)
# Create a coordinator and run all QueueRunner objects
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for batch_index in range(5):
img, lbl = sess.run([images, labels])
img = img.astype(np.uint8)
for j in range(6):
plt.subplot(2, 3, j+1)
plt.imshow(img[j, ...])
plt.title('cat' if lbl[j]==0 else 'dog')
plt.show()
# Stop the threads
coord.request_stop()
# Wait for threads to stop
coord.join(threads)
sess.close()
关于随机shuffle队列错误的任何想法？ 我不确定我做错了什么。