Add extra debugging
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@ -169,6 +169,7 @@ class TextCategoriesDataset(Dataset):
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return (
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self.textTransform()(text),
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cats.fillna(0).values.tolist(),
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text,
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)
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def textTransform(self):
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@ -214,7 +215,7 @@ class CollateBatch:
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batch: a list of tuples with (text, cats), each of which
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is a list of tokens
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'''
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batch_text, batch_cats = zip(*batch)
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batch_text, batch_cats, batch_orig = zip(*batch)
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# Pad text to the longest
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text_tensor = nn.utils.rnn.pad_sequence(
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@ -236,6 +237,7 @@ class CollateBatch:
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return (
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text_tensor,
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cats_tensor,
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batch_orig,
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)
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def tensor2cat(dataset, tensor):
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@ -267,28 +269,26 @@ def train(dataloader, dataset, model, optimizer, criterion, epoch=0):
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total_acc, total_count = 0, 1 # XXX
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log_interval = 500
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torch.set_printoptions(precision=2)
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model.train()
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batch = tqdm.tqdm(dataloader, unit="batch")
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for idx, data in enumerate(batch):
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batch.set_description(f"Train {epoch}.{idx}")
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text, cats = data
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text, cats, orig_text = data
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optimizer.zero_grad()
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output = model(text)
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#print("output", output)
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#print("output shape", output.shape)
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optimizer.zero_grad()
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loss = criterion(input=output, target=cats)
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optimizer.zero_grad()
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loss.backward()
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nn.utils.clip_grad_norm_(model.parameters(), 0.1)
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#nn.utils.clip_grad_norm_(model.parameters(), 0.1)
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optimizer.step()
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print("train loss", loss)
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#print("train loss", loss)
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##predicted = np.round(output)
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##total_acc += (predicted == cats).sum().item()
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@ -299,10 +299,12 @@ def train(dataloader, dataset, model, optimizer, criterion, epoch=0):
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predictions[output < 0.5] = False ## assign 0 label to those with less than 0.5
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batch.clear()
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for target, out, pred in list(zip(cats, output, predictions)):
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for target, out, pred, orig in list(zip(cats, output, predictions, orig_text)):
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expect = tensor2cat(dataset, target)
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raw = tensor2cat(dataset, out)
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predict = tensor2cat(dataset, pred)
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print("Text:", orig)
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print("Loss:", loss.item())
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print("Expected: ", expect)
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print("Predicted: ", predict)
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print("Raw output:", raw)
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@ -333,7 +335,7 @@ def evaluate(dataloader, dataset, model, criterion, epoch=0):
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batch = tqdm.tqdm(dataloader, unit="batch")
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for idx, data in enumerate(batch):
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batch.set_description(f"Evaluate {epoch}.{idx}")
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text, cats = data
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text, cats, orig_text = data
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output = model(text)
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#print("eval predicted", output)
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@ -346,10 +348,12 @@ def evaluate(dataloader, dataset, model, criterion, epoch=0):
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predictions[output < 0.5] = False ## assign 0 label to those with less than 0.5
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batch.clear()
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for target, out, pred in list(zip(cats, output, predictions)):
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for target, out, pred, orig in list(zip(cats, output, predictions, orig_text)):
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expect = tensor2cat(dataset, target)
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raw = tensor2cat(dataset, out)
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predict = tensor2cat(dataset, pred)
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print("Evaluate Text:", orig)
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print("Evaluate Loss:", loss.item())
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print("Evaluate expected: ", expect)
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print("Evaluate predicted: ", predict)
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print("Evaluate raw output:", raw)
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@ -465,17 +469,20 @@ def main():
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)
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print(f"Using {device} device")
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torch.set_printoptions(precision=2)
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# Hyperparameters
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#epochs = 10 # epoch
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epochs = 6 # epoch
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epochs = 10 # epoch
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#epochs = 6 # epoch
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#epochs = 4 # epoch
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#lr = 5 # learning rate
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#lr = 0.5
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#lr = 0.05
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#lr = 0.005 # initial learning rate; too small may result in a long training process that could get stuck, whereas a value too large may result in learning a sub-optimal set of weights too fast or an unstable training process -- perhaps the most important hyperparameter. If you have time to tune only one hyperparameter, tune the learning rate
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lr = 0.0001
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lr = 0.00005
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#batch_size = 64 # batch size for training
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batch_size = 16 # batch size for training
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batch_size = 32 # batch size for training
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#batch_size = 16 # batch size for training
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#batch_size = 8 # batch size for training
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#batch_size = 4 # batch size for training
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@ -485,8 +492,9 @@ def main():
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#hidden_size = 8 # hidden size of rnn module, should be tweaked manually
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mean_seq = True # use mean of rnn output
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#mean_seq = False # use mean of rnn output
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weight_decay = 1e-4 # helps the neural networks to learn smoother / simpler functions which most of the time generalizes better compared to spiky, noisy ones ; try 1e-3, 1e-4
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#weight_decay = 1e-3 # helps the neural networks to learn smoother / simpler functions which most of the time generalizes better compared to spiky, noisy ones ; try 1e-3, 1e-4
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#weight_decay = 1e-4 # helps the neural networks to learn smoother / simpler functions which most of the time generalizes better compared to spiky, noisy ones ; try 1e-3, 1e-4
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weight_decay = 1e-5 # helps the neural networks to learn smoother / simpler functions which most of the time generalizes better compared to spiky, noisy ones ; try 1e-3, 1e-4
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'''
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dataloader = DataLoader(dataset,
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@ -545,7 +553,9 @@ def main():
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# optimizer and loss
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criterion = nn.BCEWithLogitsLoss()
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#optimizer = torch.optim.SGD(model.parameters(), lr=lr)
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optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=lr, weight_decay=weight_decay)
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#optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=lr, weight_decay=weight_decay)
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optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
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if args.verbose:
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print(criterion)
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print(optimizer)
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@ -560,17 +570,17 @@ def main():
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accu_val = evaluate(valid_dataloader, valid_dataset, model, criterion, epoch)
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if total_accu is not None and total_accu > accu_val:
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optimizer.step()
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else:
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total_accu = accu_val
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#if total_accu is not None and total_accu > accu_val:
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# optimizer.step()
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#else:
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# total_accu = accu_val
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e.set_postfix({
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"accuracy": accu_val,
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})
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# print("Checking the results of test dataset.")
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# accu_test = evaluate(test_dataloader, test_dataset)
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# print("test accuracy {:8.3f}".format(accu_test))
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print("Checking the results of test dataset.")
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accu_test = evaluate(test_dataloader, test_dataset)
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print("test accuracy {:8.3f}".format(accu_test))
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if model_out is not None:
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torch.save(model.state_dict(), model_out)
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