else:
ctx = mx.gpu(args.num_gpu) # pylint: disable=invalid-name
# Set seed for random number generators in numpy and mxnet
np.random.seed(args.seed)
mx.random.seed(args.seed)
adj, features, labels, idx_train, idx_val, idx_test = load_data(ctx=ctx) # pylint: disable=invalid-name
model = GCN(
nfeat=features.shape[1], # pylint: disable=invalid-name
nhid=args.hidden,
nclass=int(labels.max().asnumpy().item()) + 1,
dropout=args.dropout)
model.collect_params().initialize(ctx=ctx)
trainer = gluon.Trainer(
model.collect_params(), # pylint: disable=invalid-name
'adam',
{
'learning_rate': args.lr,
})
# Note: Original implementation uses
# Negative Log Likelihood and not
# SoftmaxCrossEntropyLoss
loss = gluon.loss.SoftmaxCrossEntropyLoss() # pylint: disable=invalid-name
accs = [] # pylint: disable=invalid-name
for epoch in range(args.epochs):
for i in np.argsort(probability).tolist()[0][::-1][:t]:
if i in train_dict:
continue
train_dict[i] = k
print('new dataset size: %s' % (len(train_dict)))
new_train_index = sorted(train_dict.keys())
new_train_label = [train_dict[i] for i in new_train_index]
net = GCN()
net.initialize(ctx=ctx)
net.hybridize()
loss_function = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'adam',
{'learning_rate': 1e-3})
for epoch in range(100):
with autograd.record():
output = net(features, A_)
l = loss_function(output[new_train_index],
nd.array(new_train_label, ctx=ctx))
l.backward()
trainer.step(1)
print('training loss: %.2f' % (l.mean().asnumpy()[0]))
output = net(features, A_)
l = loss_function(output[idx[val_mask]],
nd.argmax(y_val[idx[val_mask]], axis=1))
print('validation loss %.2f' % (l.mean().asnumpy()[0]))
