import graphgallery
import tensorflow as tf
from graphgallery import functional as gf
graphgallery.set_memory_growth()
print("GraphGallery version: ", graphgallery.__version__)
print("TensorFlow version: ", tf.__version__)
'''
Load Datasets
- cora/citeseer/pubmed/dblp/polblogs/cora_ml, etc...
'''
from graphgallery.datasets import Planetoid, NPZDataset
data = NPZDataset('cora',
root="~/GraphData/datasets/",
transform=gf.Standardize(),
verbose=False)
graph = data.graph
splits = data.split_nodes(random_state=15)
from graphgallery.gallery import GCN
model = GCN(graph, graph_transform="SVD", device="gpu", seed=123)
model.build()
history = model.train(splits.train_nodes,
splits.val_nodes,
verbose=1,
epochs=100)
results = model.test(splits.test_nodes)
print(f'Test loss {results.loss:.5}, Test accuracy {results.accuracy:.2%}')
#!/usr/bin/env python
# coding: utf-8
import torch
import graphgallery
from graphgallery import functional as gf
print("GraphGallery version: ", graphgallery.__version__)
print("Torch version: ", torch.__version__)
'''
Load Datasets
- cora/citeseer/pubmed/dblp/polblogs/cora_ml, etc...
'''
from graphgallery.datasets import Planetoid, NPZDataset
data = NPZDataset('cora', root="~/GraphData/datasets/", transform=gf.Standardize(), verbose=False)
graph = data.graph
splits = data.split_nodes(random_state=15)
graphgallery.set_backend("pytorch")
from graphgallery.gallery.nodeclas import GCN
trainer = GCN(device="gpu", seed=123).make_data(graph, graph_transform="SVD").build()
history = trainer.fit(splits.train_nodes, splits.val_nodes, verbose=1, epochs=100)
results = trainer.evaluate(splits.test_nodes)
print(f'Test loss {results.loss:.5}, Test accuracy {results.accuracy:.2%}')
def standardize(self):
"""Select the largest connected components (LCC) of
the unweighted/undirected/no-self-loop graph."""
return gf.Standardize()(self)