def evaluate(model: TextCNN, data_path):
print('evaluate ...')
train_loader, test_loader, vocab = get_dataloader(data_path=data_path, bs=32, seq_len=50)
train_y_true, train_y_pred = [], []
test_y_true, test_y_pred = [], []
model.eval()
with torch.no_grad():
for batch in tqdm(test_loader):
inputs, targets = batch.text, batch.label
output = model(inputs)
pred = torch.max(output.data, dim=1)[1].cpu().numpy().tolist()
test_y_pred.extend(pred)
test_y_true.extend(targets.data)
for batch in tqdm(train_loader):
inputs, targets = batch.text, batch.label
output = model(inputs)
pred = torch.max(output.data, dim=1)[1].cpu().numpy().tolist()
train_y_pred.extend(pred)
train_y_true.extend(targets.data)
model.train()
test_acc = metrics.accuracy_score(test_y_true, test_y_pred)
test_f1 = metrics.f1_score(test_y_true, test_y_pred, average='macro')
train_acc = metrics.accuracy_score(train_y_true, train_y_pred)
train_f1 = metrics.f1_score(train_y_true, train_y_pred, average='macro')
print(f'Train Accuracy: {train_acc}, F1-Score: {train_f1}')
print(f'Test Accuracy: {test_acc}, F1-Score: {test_f1}')
return train_acc, train_f1, test_acc, test_f1
def train(data_path):
train_loader, test_loader, vocab = get_dataloader(data_path=data_path, bs=32, seq_len=50)
model = TextCNN(ModelConfig())
print(model)
config = TrainConfig()
optimizer = optim.Adam(model.parameters(), lr=config.lr)
criterion = nn.CrossEntropyLoss(ignore_index=1) # Ignoring <PAD> Token
model.train()
gs = 0
for epoch in tqdm(range(config.num_epochs)):
for idx, batch in tqdm(enumerate(train_loader)):
gs += 1
inputs, targets = batch.text, batch.label
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
if gs % 500 == 0:
writer.add_scalar('train/loss', loss.item(), gs)
print(f'{gs} loss : {loss.item()}')
train_acc, train_f1, test_acc, test_f1 = evaluate(model, './rsc/data/')
writer.add_scalar('train/acc', train_acc, epoch)
writer.add_scalar('train/f1', train_f1, epoch)
writer.add_scalar('test/acc', test_acc, epoch)
writer.add_scalar('test/f1', test_f1, epoch)
