pygcn/train.py
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2022-06-12 19:50:19
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from __future__ import division
from __future__ import print_function
import time
import argparse
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from pygcn.utils import load_data, accuracy
from pygcn.models import GCN
# Training settings
parser = argparse.ArgumentParser()
parser.add_argument('--no-cuda', action='store_true', default=False,
help='Disables CUDA training.')
parser.add_argument('--fastmode', action='store_true', default=False,
help='Validate during training pass.')
parser.add_argument('--seed', type=int, default=42, help='Random seed.')
parser.add_argument('--epochs', type=int, default=200,
help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.01,
help='Initial learning rate.')
parser.add_argument('--weight_decay', type=float, default=5e-4,
help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=16,
help='Number of hidden units.')
parser.add_argument('--dropout', type=float, default=0.5,
help='Dropout rate (1 - keep probability).')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# Load data
adj, features, labels, idx_train, idx_val, idx_test = load_data()
# Model and optimizer
model = GCN(nfeat=features.shape[1],
nhid=args.hidden,
nclass=labels.max().item() + 1,
dropout=args.dropout)
optimizer = optim.Adam(model.parameters(),
lr=args.lr, weight_decay=args.weight_decay)
if args.cuda:
model.cuda()
features = features.cuda()
adj = adj.cuda()
labels = labels.cuda()
idx_train = idx_train.cuda()
idx_val = idx_val.cuda()
idx_test = idx_test.cuda()
def train(epoch):
t = time.time()
model.train()
optimizer.zero_grad()
output = model(features, adj)
loss_train = F.nll_loss(output[idx_train], labels[idx_train])
acc_train = accuracy(output[idx_train], labels[idx_train])
loss_train.backward()
optimizer.step()
if not args.fastmode:
# Evaluate validation set performance separately,
# deactivates dropout during validation run.
model.eval()
output = model(features, adj)
loss_val = F.nll_loss(output[idx_val], labels[idx_val])
acc_val = accuracy(output[idx_val], labels[idx_val])
print('Epoch: {:04d}'.format(epoch+1),
'loss_train: {:.4f}'.format(loss_train.item()),
'acc_train: {:.4f}'.format(acc_train.item()),
'loss_val: {:.4f}'.format(loss_val.item()),
'acc_val: {:.4f}'.format(acc_val.item()),
'time: {:.4f}s'.format(time.time() - t))
def test():
model.eval()
output = model(features, adj)
loss_test = F.nll_loss(output[idx_test], labels[idx_test])
acc_test = accuracy(output[idx_test], labels[idx_test])
print("Test set results:",
"loss= {:.4f}".format(loss_test.item()),
"accuracy= {:.4f}".format(acc_test.item()))
# Train model
t_total = time.time()
for epoch in range(args.epochs):
train(epoch)
print("Optimization Finished!")
print("Total time elapsed: {:.4f}s".format(time.time() - t_total))
# Testing
test()