session_val_ids = session_val_ids[:args.n_base]
model = MLP(input_dim=len(input_attrs),
hidden_dims=args.hidden_dims,
output_dim=len(output_attrs),
n_heads=args.n_base + n_inc)
model.to(device)
# restore base model
if (args.save_models):
# load model stored in statistics of the base training
model.load_state_dict(base_stats[-1])
else:
if (args.offload_aux_models):
# load model from file
model.load(model_path_base)
else:
# load model from internal copy
model.load_state_dict(model_base_state_dict)
for inc_id in range(n_inc):
# update session
new_task = sequence.pop(0)
session.append(new_task)
# update training, testing and valudation sets
train_ids, test_ids, val_ids = parts[new_task]
# scale training and validation sets to the subset fraction (if specified, defaults to 1.0)
train_ids = train_ids[:max(
def run():
# Config
config = {
'model_folder': 'tmp',
'embedding_size': 50,
'hidden_size': 25,
'batch_size': 50,
'epochs': 100
}
# Data
tokens_ted, labels = load_ted_data('ted_en-20160408.xml')
tokens_train, tokens_dev, tokens_test = split_dataset(tokens_ted)
labels_train, labels_dev, labels_test = split_dataset(labels)
train_dataset = TedDataset(tokens_train, labels_train, min_frequency=10)
dev_dataset = TedDataset(tokens_dev,
labels_dev,
vocabulary=train_dataset.vocabulary,
raw_output=True)
test_dataset = TedDataset(tokens_test,
labels_test,
vocabulary=train_dataset.vocabulary,
raw_output=True)
train_dataloader = DataLoader(train_dataset,
collate_fn=train_dataset.collate_fn,
batch_size=config['batch_size'],
num_workers=4)
dev_dataloader = DataLoader(dev_dataset,
collate_fn=train_dataset.collate_fn,
batch_size=config['batch_size'],
num_workers=4)
test_dataloader = DataLoader(test_dataset,
collate_fn=train_dataset.collate_fn,
batch_size=config['batch_size'],
num_workers=4)
# Model
model = MLP(config)
model.initialize_features(data=train_dataset)
model.build_model()
# Logger
logger = BasicLogger(metric=accuracy_score, score_optimization='max')
# Trainer
trainer = Trainer(model=model, logger=logger)
trainer.fit(train_dataloader, dev_dataloader, epochs=config['epochs'])
model.load('{}/{}.torch'.format(model.config['model_folder'],
type(model).__name__.lower()))
target = []
for batch in test_dataloader:
target.extend(batch['output'].tolist())
predictions = trainer.test(test_dataloader)
print("Test Accuracy:", accuracy_score(target, predictions))
