def main(args):
# load and preprocess dataset
data = load_data(args)
features = mx.nd.array(data.features)
labels = mx.nd.array(data.labels)
train_mask = mx.nd.array(data.train_mask)
val_mask = mx.nd.array(data.val_mask)
test_mask = mx.nd.array(data.test_mask)
in_feats = features.shape[1]
n_classes = data.num_labels
n_edges = data.graph.number_of_edges()
print("""----Data statistics------'
#Edges %d
#Classes %d
#Train samples %d
#Val samples %d
#Test samples %d""" %
(n_edges, n_classes, train_mask.sum().asscalar(),
val_mask.sum().asscalar(), test_mask.sum().asscalar()))
if args.gpu < 0:
cuda = False
ctx = mx.cpu(0)
else:
cuda = True
ctx = mx.gpu(args.gpu)
features = features.as_in_context(ctx)
labels = labels.as_in_context(ctx)
train_mask = train_mask.as_in_context(ctx)
val_mask = val_mask.as_in_context(ctx)
test_mask = test_mask.as_in_context(ctx)
# create GCN model
g = data.graph
if args.self_loop:
g.remove_edges_from(g.selfloop_edges())
g.add_edges_from(zip(g.nodes(), g.nodes()))
g = DGLGraph(g)
# normalization
degs = g.in_degrees().astype('float32')
norm = mx.nd.power(degs, -0.5)
if cuda:
norm = norm.as_in_context(ctx)
g.ndata['norm'] = mx.nd.expand_dims(norm, 1)
model = GCN(g, in_feats, args.n_hidden, n_classes, args.n_layers,
mx.nd.relu, args.dropout)
model.initialize(ctx=ctx)
n_train_samples = train_mask.sum().asscalar()
loss_fcn = gluon.loss.SoftmaxCELoss()
# use optimizer
print(model.collect_params())
trainer = gluon.Trainer(model.collect_params(), 'adam', {
'learning_rate': args.lr,
'wd': args.weight_decay
})
# initialize graph
dur = []
for epoch in range(args.n_epochs):
if epoch >= 3:
t0 = time.time()
# forward
with mx.autograd.record():
pred = model(features)
loss = loss_fcn(pred, labels, mx.nd.expand_dims(train_mask, 1))
loss = loss.sum() / n_train_samples
loss.backward()
trainer.step(batch_size=1)
if epoch >= 3:
loss.asscalar()
dur.append(time.time() - t0)
acc = evaluate(model, features, labels, val_mask)
print(
"Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
"ETputs(KTEPS) {:.2f}".format(epoch, np.mean(dur),
loss.asscalar(), acc,
n_edges / np.mean(dur) / 1000))
# test set accuracy
acc = evaluate(model, features, labels, test_mask)
print("Test accuracy {:.2%}".format(acc))
def main(args):
# load and preprocess dataset
if args.dataset == 'cora':
data = CoraGraphDataset()
elif args.dataset == 'citeseer':
data = CiteseerGraphDataset()
elif args.dataset == 'pubmed':
data = PubmedGraphDataset()
else:
raise ValueError('Unknown dataset: {}'.format(args.dataset))
g = data[0]
if args.gpu < 0:
cuda = False
ctx = mx.cpu(0)
else:
cuda = True
ctx = mx.gpu(args.gpu)
g = g.int().to(ctx)
features = g.ndata['feat']
labels = mx.nd.array(g.ndata['label'], dtype="float32", ctx=ctx)
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
in_feats = features.shape[1]
n_classes = data.num_labels
n_edges = data.graph.number_of_edges()
print("""----Data statistics------'
#Edges %d
#Classes %d
#Train samples %d
#Val samples %d
#Test samples %d""" %
(n_edges, n_classes,
train_mask.sum().asscalar(),
val_mask.sum().asscalar(),
test_mask.sum().asscalar()))
# add self loop
if args.self_loop:
g = dgl.remove_self_loop(g)
g = dgl.add_self_loop(g)
# normalization
degs = g.in_degrees().astype('float32')
norm = mx.nd.power(degs, -0.5)
if cuda:
norm = norm.as_in_context(ctx)
g.ndata['norm'] = mx.nd.expand_dims(norm, 1)
model = GCN(g,
in_feats,
args.n_hidden,
n_classes,
args.n_layers,
mx.nd.relu,
args.dropout)
model.initialize(ctx=ctx)
n_train_samples = train_mask.sum().asscalar()
loss_fcn = gluon.loss.SoftmaxCELoss()
# use optimizer
print(model.collect_params())
trainer = gluon.Trainer(model.collect_params(), 'adam',
{'learning_rate': args.lr, 'wd': args.weight_decay})
# initialize graph
dur = []
for epoch in range(args.n_epochs):
if epoch >= 3:
t0 = time.time()
# forward
with mx.autograd.record():
pred = model(features)
loss = loss_fcn(pred, labels, mx.nd.expand_dims(train_mask, 1))
loss = loss.sum() / n_train_samples
loss.backward()
trainer.step(batch_size=1)
if epoch >= 3:
loss.asscalar()
dur.append(time.time() - t0)
acc = evaluate(model, features, labels, val_mask)
print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
"ETputs(KTEPS) {:.2f}". format(
epoch, np.mean(dur), loss.asscalar(), acc, n_edges / np.mean(dur) / 1000))
# test set accuracy
acc = evaluate(model, features, labels, test_mask)
print("Test accuracy {:.2%}".format(acc))
