Read the examples in tfrecords files in batches using Tensorflow queue runners

read_tfrecords_batches

import tensorflow as tf

def read_and_decode_example(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
    serialized_example,
    # Defaults are not specified since both keys are required.
    features={        
      'label/image/encoded': tf.FixedLenFeature([], tf.string),
      'orig/image/encoded': tf.FixedLenFeature([], tf.string),
    })

    image = tf.reshape(tf.image.decode_jpeg(features['orig/image/encoded'], channels=3),[100,100,3])
    label = tf.reshape(tf.image.decode_jpeg(features['label/image/encoded'], channels=3),[100,100,3])
    return image, label
    
    def read_in_batches(files):
    sess = tf.Session()
    filename_queue = tf.train.string_input_producer(files)
    image, label = read_and_decode_example(filename_queue)

    images_batch, labels_batch = tf.train.shuffle_batch(
                [image, label], batch_size=128,
                capacity=2000,
                min_after_dequeue=1000)

    init_op = tf.global_variables_initializer()
    sess.run(init_op)

    tf.train.start_queue_runners(sess=sess)
    labels, images= sess.run([labels_batch, images_batch])
    
    
read_in_batches(['../Data/tfrecords/00000-of-00004','../Data/tfrecords/00001-of-00004'])