def read_my_data(filename_queue):


    class ImageRecord(object):
        pass
result = ImageRecord()
label_bytes = 1
result.height = IMAGE_HEIGHT
result.width = IMAGE_WIDTH
result.depth = IMAGE_DEPTH
image_bytes = result.height * result.width * result.depth
record_bytes = label_bytes + image_bytes
assert record_bytes == 22501
reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
result.key, value = reader.read(filename_queue)
record_bytes = tf.decode_raw(value, tf.uint8)
result.label = tf.cast(tf.slice(record_bytes, [0], [label_bytes]), tf.int32)
depth_major = tf.reshape(tf.slice(record_bytes, [label_bytes], [image_bytes
    ]), [result.depth, result.height, result.width])
result.uint8image = tf.transpose(depth_major, [1, 2, 0])
return result
Attach a queue to a numpy array in tensorflow for data fetch instead of files?