New answers tagged

0

This seems like it would be best interpreted as a graph partition problem instead of looking at it like a 'machine learning' problem. data = { {1, 2, -36.0319, {1, 2} -> "same"}, {1, 3, -33.4696, {1, 3} -> "same"}, {1, 4, -26.8633, {1, 4} -> "different"}, {1, 5, -18.9969, {1, 5} -> "different"}, ...


0

Code autogeneration ^_^ dims = {10, 5}; n = 8; (*LinearLayer*) NetGraph[ Join[Table[{PartLayer[{All, i}], LinearLayer[n]}, {i, dims[[2]]}], {CatenateLayer[]}], Join[Table[NetPort["Input"] -> i, {i, dims[[2]]}], {Range[dims[[2]]] -> dims[[2]] + 1}], "Input" -> dims ]


0

This might not be the most compact or elegant to represent the NetGraph, but at least the size of the diagram is constant no matter how large the parameters n,m and p are. trynet = With[{p = 5, n = 200, m = 100}, With[{}, NetGraph[<| "WeightsLayer" -> NetArrayLayer["Output" -> {Automatic, p, Automatic}], "...


Top 50 recent answers are included