Update README.md

README.md

@@ -81,4 +81,13 @@ train_x = np.reshape(load_x, [tss, inputsize])
 train_y = np.reshape(load_y, [tss, outputsize])
 np.save("train_x.npy", train_x)
 np.save("train_y.npy", train_y)
-```
+```
+
+The basic premise of how this network is trained and thus how the dataset is generated in the C program is:
+1. All models are scaled to a normal cubic scale and then scaled again by 0.55 so that they all fit within a perfect unit sphere.
+2. All model vertices are reverse traced from the vertex position to the perimeter of the unit sphere using the vectex normal.
+3. The nearest position on a 10,242 vertex icosphere is found and the network is trained to output the model vertex position and vertex color (6 components) at the index of the icosphere vertex.
+4. The icosphere vertex index is scaled to a 0-1 range before being input to the network.
+5. The network only has two input parameters, the other parameter is a 0-1 model ID which is randomly selected and all vertices for a specific model are trained into the network using the randomly selected ID.
+6. The ID allows one to use this parameter as a random seed, to generate a random Head using this network you would input a random 0-1 seed and then iterate the icosphere index parameter to some sample range between 0-1 so if you wanted a 20,000 vertex head you would iterate between 0-1 at 20,000 increments of 0.00005 as the network outputs one vertex position and vertex color for each forward-pass.
+More about this network topology can be read here: https://gist.github.com/mrbid/1eacdd9d9239b2d324a3fa88591ff852