import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
data_path = 'train.tfrecords'  # address to save the hdf5 file
with tf.Session() as sess:
    feature = {'train/image': tf.FixedLenFeature([], tf.string),
               'train/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['train/image'], tf.float32)

    # Cast label data into int32
    label = tf.cast(features['train/label'], tf.int32)
    # Reshape image data into the original shape
    image = tf.reshape(image, [224, 224, 3])

    # 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)