Tensorflow: training on JSON data to generate similar output - tensorflow

Question

Assume one has JSON data containining instructions for generating the following 10x5 cell patterns, and that each cell can contain one of the following characters: _ 0 x y z
Also assume that each character can be displayed in various colors.
pattern 1:
_yx_0zzyxx
_0__yz_0y_
x0_0x000yx
_y__x000zx
zyyzx_z_0y
pattern 2:
xx0z00yy_z
zzx_0000_x
_yxy0y__yx
_xz0z__0_y
y__x0_0_y_
pattern 3:
yx0x_xz0_z
xz_x0_xxxz
_yy0x_0z00
zyy0__0zyx
z_xy0_0xz0
These were randomly generated, and are all black, but assume they were devised according to some set of rules, and in color.
The JSON for the first pattern would look something like:
{
width: 10,
height: 5,
cells: [
{
value: '_',
color: 'red'
},
{
value: 'y',
color: 'blue'
}, ...
]
}
If one wanted to train on this data in order to generate new yet similar patterns (again, assuming these were not randomly generated), what is the recommended approach for:
reading the data in (I'd imagine putting the JSON into an Example protobuf, serializing the buffer to string with tf.parse_example, and then writing that to TFRecord files)
training on that data
generating new patterns based on the trained model
supplying seed data for the generated patterns, e.g. first cell is the character "x' with the color blue.
I want to achieve something similar to what I've seen in style transfer with art/photos, and with music/MIDI data (see: Google Magenta). In those cases, here the model is trained an a distinctive set of artwork or melodic style, and a seed in the form of a photograph or primer melody is supplied in order to generate content similar to the data used in training.
Thanks!

Answer 1

I dislike preprocessing the dataset into new forms, it makes it difficult to change later on and slows future development, it's like technical debt in my opinion.
My approach would be to keep your JSON as-is and write some simple python code (a generator specifically which mean you use yield instead of return statements) to read the JSON file and spit out samples in sequence.
Then use the tensorflow Dataset input pipeline with Dataset.from_generator(...) to take data from your input function.
https://www.tensorflow.org/programmers_guide/datasets
The Dataset pipeline provides everything you need to manage the various transformations you'll want to apply, you can buffer, shuffle, batch, prefetch, and map functions onto your data trivially and in a nice modular, testable, framework that feeds naturally into your tensorflow model.