Keras Graph Convolutional Network
Graph convolutional layers.
Install
pip install keras-gcn
Usage
GraphConv
from tensorflow import keras
from keras_gcn import GraphConv
DATA_DIM = 3
data_layer = keras.layers.Input(shape=(None, DATA_DIM))
edge_layer = keras.layers.Input(shape=(None, None))
conv_layer = GraphConv(
units=32,
step_num=1,
)([data_layer, edge_layer])
step_num is the maximum distance of two nodes that could be considered as neighbors. If step_num is greater than 1, then the inputs of edges must be 0-1 matrices.
GraphMaxPool & GraphAveragePool
Pooling layers with the step_num argument.
1 maintainer
Metadata
1 maintainer
Release files for keras-gcn 0.15.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| keras-gcn-0.15.0.tar.gz | 4.6 kB | Details |
Release files / keras-gcn-0.15.0.tar.gz
| Download URL | keras-gcn-0.15.0.tar.gz |
|---|---|
| Size | 4.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
2ff8911bbc34c1d69b96a17893dcf8214e3c36055dcbdc0c4c65479aa4ef5147
|
|
BLAKE2b-256 checksum How to use checksums |
118caabdf68946d86f3c71de1fa870ec1583d649a99f7d5b6c331a68d6dfdd64
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.7.4
|
