Tensorflow v1.10: store images as byte strings or per channel?

Question

Context

It is known that, at the moment, TF\'s Record documentation leaves something to be desired.

My question is in regards to what is optimal for storing:

Answer 1

This is an extension to my first answer which some may find useful.

Rather than considering the encoding, I here consider the opposite, e.g. how one retrieves the data from a TFRecord.

The colab can be found here.

In essence I survey 10 ways of encoding an array / array of arrays.

1. Example: Int64 feature (int array)
2. Example: Float feature (float array)
3. Example: Bytes feature (int array dumped to byte string)
4. SequenceExample: Int64 feature list (array of int arrays)
5. SequenceExample: Float feature list (array of float arrays)
6. SequenceExample: Bytes feature list (array of int arrays dumped to byte strings)
7. Example: Bytes feature (array of int arrays all of which is dumped to byte string)
8. SequenceExample: Bytes feature list (array of int arrays dumped to byte strings)
9. SequenceExample: Bytes feature list (array of int arrays all of which is dumped to byte string)
10. SequenceExample: Bytes feature list (array of int arrays, where each int is dumped to byte string)

There are more ways to do this.

In short, with the exception of 8, I was able to 'recover' (write to tf.record and read back the data).

However, it should be noted that for methods 7 and 10, the retrieved array is flattened.