def main():
adj, features, all_labels, train, val, test = load_data(
args.dataset, task_type=args.task_type)
whole_batch = args.whole_batch
print('adj:', adj.shape)
print('features:', features.shape)
MAX_NBS = params['max_degree']
# padding adj to N*K, where K is the number of nbs
adj_list = get_adj_list(adj, MAX_NBS)
adj_mask = adj_list + 1
adj_mask[adj_mask > 0] = 1
features = preprocess_features(
features) # [49216, 2], [49216], [2708, 1433]
# add a row
fea = np.asarray(features)
fea = np.insert(fea, -1, 0, axis=0)
fea.reshape((features.shape[0] + 1, features.shape[-1]))
features = fea
dl = data_loader(features, adj_list, train, val, test)
tf.keras.backend.clear_session()
model = GCN(input_dim=features.shape[1], output_dim=all_labels.shape[1], num_features_nonzero=features[1].shape, \
feature=features, label=all_labels, adj_list=adj_list, adj_mask=adj_mask) # [1433]
optimizer = optimizers.Adam(lr=params['learning_rate'])
persist = 0
best_val_acc = 0
for epoch in range(args.epochs):
if whole_batch:
with tf.GradientTape() as tape:
train_loss, train_acc = model(train, training=True)
grads = tape.gradient(train_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
else:
while not dl.train_end():
batch = dl.get_train_batch(
batch_size=params['train_batch_size'])
with tf.GradientTape() as tape:
train_loss, train_acc = model(batch, training=True)
grads = tape.gradient(train_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads,
model.trainable_variables))
val_loss = 0
val_acc = 0
if whole_batch:
val_loss, val_acc = model(val)
else:
while not dl.val_end():
batch = dl.get_val_batch(batch_size=args.val_batch_size)
loss, acc = model(batch, training=False)
val_loss += loss * len(batch)
val_acc += acc * len(batch)
val_acc /= len(val)
val_loss /= len(val)
if val_acc > best_val_acc:
best_val_acc = val_acc
persist = 0
model.save_weights(check_file)
else:
persist += 1
if persist > args.early_stopping:
break
if epoch % 10 == 0:
print(epoch, float(train_loss), float(train_acc), '\tval:',
float(val_acc))
print('train done')
model.load_weights(check_file)
print('read checkpoint done')
test_loss = 0
test_acc = 0
if whole_batch:
test_loss, test_acc = model(test)
else:
while not dl.test_end():
batch = dl.get_test_batch(batch_size=args.test_batch_size)
loss, acc = model(batch, training=False)
test_loss += loss * len(batch)
test_acc += acc * len(batch)
test_acc /= len(test)
test_loss /= len(test)
print('final results', test_acc.numpy())
]
with tf.GradientTape() as tape:
loss, acc = model(input)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
all_loss.append(loss)
all_acc.append(acc)
if ct % samples_per_log == 0:
line = '{} {} loss={:.4f} acc={:.4f} time_sample={:.1f}'.format(
epoch + 1, ct, np.mean(all_loss[-samples_per_log:]),
np.mean(all_acc[-samples_per_log:]),
time.time() - t2)
log(line, log_file)
loss_cur_epoch = np.mean(all_loss)
line = '[{} finished!] loss={:.4f} acc={:.4f} time_epoch={:.1f}'.format(
epoch + 1, loss_cur_epoch, np.mean(all_acc),
time.time() - t1)
log(line, log_file)
if loss_cur_epoch < best_loss:
log(f'best model currently, save to {save_model_to}', log_file)
model.save_weights(f'{save_model_to}/model.ckpt')
best_loss = loss_cur_epoch
sys.stdout.flush()
log_file.flush()
log_file.close()
print("Optimization Finished!")
