Hi guys. I’m really super beginer for Learning.

I took a basic course in FAST AI for 2 weeks.

Now I major in physics and about to write a paper.

My objective is that Predict a Image with dataset(which is numeric value)

I have dataset which is atomic coordinate(ex. x : 0.5, y : 0.3, z : 0.7…), lattice parameter ( a : 0.2 , b : 0.6 …) and each dataset has a graph(Image) that corresponds to dataset.

My Idea is … ** Assumed the graph image is 20x20

(1) make each column(coordinate x, coordinate y, … lattice parameter a …) 20x1 embedding.

(2) make weights vector(20x1)

(3) matrix multiplication (1) and (2). then I get 20x20 matrix.

(4) Compare that 20x20 matrix with corresponding graph_Image

(5) Use GDS ~~ also neural network.

The idea that make just raw numerical value dataset into matrix by embedding.

Does it make sense? or my method is wrong in someway?

thanks a lot.

Have a good day