My Code is to read image from tfrecord file
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
data_path='christmas.tfrecords'
with tf.Session() as sess:
feature={'image':tf.FixedLenFeature([],tf.string),'label':tf.FixedLenFeature([],tf.int64)}
# Create a list of filenames and pass it to a queue
filename_queue = tf.train.string_input_producer([data_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['image'], tf.float32)
# Cast label data into int32
label = tf.cast(features['label'], tf.int32)
# Reshape image data into the original shape
image = tf.reshape(image, [224, 224, 1])
# Any preprocessing here ...
# Creates batches by randomly shuffling tensors
images, labels = tf.train.shuffle_batch([image, label], batch_size=10, capacity=30, num_threads=1, min_after_dequeue=10)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
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()
Error Message
Traceback (most recent call last):
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1323, in _do_call
return fn(*args)
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1302, in _run_fn
status, run_metadata)
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/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 '_1_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 10, current size 0)
[[Node: shuffle_batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_INT32], 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 "read_tfrecords.py", line 35, in <module>
img, lbl = sess.run([images, labels])
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 889, in run
run_metadata_ptr)
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1120, in _run
feed_dict_tensor, options, run_metadata)
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1317, in _do_run
options, run_metadata)
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/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 '_1_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 10, current size 0)
[[Node: shuffle_batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_INT32], 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 "read_tfrecords.py", line 27, in <module>
images, labels = tf.train.shuffle_batch([image, label], batch_size=10, capacity=30, num_threads=1, min_after_dequeue=10)
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/site-packages/tensorflow/python/training/input.py", line 1225, in shuffle_batch
name=name)
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/site-packages/tensorflow/python/training/input.py", line 796, in _shuffle_batch
dequeued = queue.dequeue_many(batch_size, name=name)
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/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 "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 2418, in _queue_dequeue_many_v2
component_types=component_types, timeout_ms=timeout_ms, name=name)
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
op_def=op_def)
File "/home/robinreni/Documents/pythonprojects/cnn/lib/python3.6/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 '_1_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 10, current size 0)
[[Node: shuffle_batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](shuffle_batch/random_shuffle_queue, shuffle_batch/n)]]
I converted all the image and labels to tfrecord file and have to convert the file to raw image and label during training. I am trying to read the image from the tfrecords but the outofrange error pops up. Please anyone help how to fix this error
The OutOfRangeError raises because the shapes are not specified:
images, labels = tf.train.shuffle_batch([image, label], batch_size=10, capacity=30, num_threads=1, min_after_dequeue=10, shapes=[[224, 224, 1], []])
should work.
See documentation here