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Christian Clauss 69eae6de49 [Fix] Use ==/!= to compare constant literals (str, bytes, int, float, tuple) (#3415 )

* Use ==/!= to compare constant literals (str, bytes, int, float, tuple)

Avoid Syntax Warnings on Python >= 3.8

$ `python3`
```
>>> "" == ""
True
>>> "" is ""
<stdin>:1: SyntaxWarning: "is" with a literal. Did you mean "=="?
True
```

* Use ==/!= to compare constant literals (str, bytes, int, float, tuple)

2021-11-04 08:16:05 +00:00

.gitignore

[NN] nn modules & examples update (#890 )

2019-11-03 17:29:42 -05:00

coarsening.py

[Fix] Use ==/!= to compare constant literals (str, bytes, int, float, tuple) (#3415 )

2021-11-04 08:16:05 +00:00

coordinate.py

[NN] nn modules & examples update (#890 )

2019-11-03 17:29:42 -05:00

grid_graph.py

[NN] nn modules & examples update (#890 )

2019-11-03 17:29:42 -05:00

mnist.py

[Doc/Feature] Refactor, doc update and behavior fix for graphs (#1983 )

2020-08-18 04:26:29 +08:00

README.md

[NN] nn modules & examples update (#890 )

2019-11-03 17:29:42 -05:00

README.md

Geometric Deep Learning models

This example shows how to use geometric deep learning models defined in dgl.nn.pytorch.conv for graph classification.

Currently we support following models:

Image Classification on MNIST

By transforming images to graphs, graph classifcation algorithms could be applied to image classification problems.

Usage

python mnist.py --model cheb --gpu 0
python mnist.py --model monet --gpu 0

Acknowledgement

We thank Xavier Bresson for providing code for graph coarsening algorithm and grid graph building in
CE7454_2019 Labs.