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| class | TMVA_SOFIE_GNN_Parser.MLPGraphNetwork |
| |
|
| | GLOBAL_FEATURE_SIZE=1) |
| |
| | GLOBAL_FEATURE_SIZE=1) |
| |
| | TMVA_SOFIE_GNN_Parser.make_mlp_model () |
| |
| | TMVA_SOFIE_GNN_Parser.printMemory (s="") |
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| |
| | LATENT_SIZE) |
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| list | TMVA_SOFIE_GNN_Parser.dataset = [] |
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| | LATENT_SIZE) |
| |
| | filename = "decoder") |
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| | edge_size) |
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| TMVA_SOFIE_GNN_Parser.edge_size = 4 |
| |
| | filename = "encoder") |
| |
| | TMVA_SOFIE_GNN_Parser.end = time.time() |
| |
| | EncodeProcessDecode() |
| |
| | TMVA_SOFIE_GNN_Parser.fileOut = ROOT.TFile.Open("graph_data.root","RECREATE") |
| |
| TMVA_SOFIE_GNN_Parser.firstEvent = True |
| |
| | global_size) |
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| TMVA_SOFIE_GNN_Parser.global_size = 1 |
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| | global_size) |
| |
| | global_size) |
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| | output",40,1,0) |
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| | output",40,1,0) |
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| | output",40,1,0) |
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| | CoreGraphData]) |
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| | GraphData]) |
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| TMVA_SOFIE_GNN_Parser.LATENT_SIZE = 100 |
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| | node_size) |
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| TMVA_SOFIE_GNN_Parser.node_size = 4 |
| |
| | graphData['edges'].shape[0] |
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| TMVA_SOFIE_GNN_Parser.NUM_LAYERS = 4 |
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| TMVA_SOFIE_GNN_Parser.num_max_edges = 300 |
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| TMVA_SOFIE_GNN_Parser.num_max_nodes = 100 |
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| TMVA_SOFIE_GNN_Parser.numevts = 100 |
| |
| | TMVA_SOFIE_GNN_Parser.outgnn = ROOT.std.vector['float'](3) |
| |
| | output_gnn[-1].edges.numpy() |
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| | output_gnn[-1].globals.numpy() |
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| | processing_steps) |
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| | processing_steps) |
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| | output_gnn[-1].nodes.numpy() |
| |
| | filename = "output_transform") |
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| TMVA_SOFIE_GNN_Parser.processing_steps = 5 |
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| | num_max_edges) |
| |
| | size |
| |
| | size |
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| | num_max_edges) |
| |
| | TMVA_SOFIE_GNN_Parser.start = time.time() |
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| | graphData]) |
| |
| | size))) |
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| | data") |
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| TMVA_SOFIE_GNN_Parser.verbose = False |
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Tutorial showing how to parse a GNN from GraphNet and make a SOFIE model The tutorial also generate some data which can serve as input for the tutorial TMVA_SOFIE_GNN_Application.C
Definition in file TMVA_SOFIE_GNN_Parser.py.
