def convert(image_paths, labels, out_path):

num_images = len(image_paths)

with tf.python_io.TFRecordWriter(out_path) as writer:
    for i, (path, label) in enumerate(zip(image_paths, labels)):

        print_progress(count=i, total=num_images-1)
        img = open(path, 'rb').read()

        data ={'image': wrap_bytes(img),
            'label': wrap_int64(label)}

        feature = tf.train.Features(feature=data)
        example = tf.train.Example(features=feature)
        serialized = example.SerializeToString()
        writer.write(serialized)

{convert(image_paths=image_paths_train,
    labels=cls_train,
    out_path=path_tfrecords_train)}

def parse(serialized):

features =
    {
        'image': tf.FixedLenFeature([], tf.string),
        'label': tf.FixedLenFeature([], tf.int64)
    }
parsed_example = tf.parse_single_example(serialized=serialized,
                                         features=features)

# Get the image as raw bytes.
image_raw = parsed_example['image']

# Decode the raw bytes so it becomes a tensor with type.
image = tf.image.decode_image(image_raw,channels=3)

#image = tf.decode_raw(image_raw, tf.uint8)

# The type is now uint8 but we need it to be float.
image = tf.cast(image, tf.float32)

# Get the label associated with the image.
label = parsed_example['label']

# The image and label are now correct TensorFlow types.
return image, label