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Why does my LSTM keep predicting the same word for every input?
This happens because the model learned a shortcut by always predicting the most frequent word in the dataset. If padding tokens or common words dominate the loss, the LSTM can minimize error by always outputting the same token. This usually means your loss function is not ignoring padding or your daRead more
This happens because the model learned a shortcut by always predicting the most frequent word in the dataset.
If padding tokens or common words dominate the loss, the LSTM can minimize error by always outputting the same token. This usually means your loss function is not ignoring padding or your data is heavily imbalanced.
Make sure your loss ignores padding tokens:
nn.CrossEntropyLoss(ignore_index=pad_token_id)
Also check that during inference you feed the model its own predictions instead of ground-truth tokens.
Using temperature sampling during decoding also helps avoid collapse:
probs = torch.softmax(logits / 1.2, dim=-1)
Common mistakes:
Including
<PAD>in lossUsing greedy decoding
Training on repetitive text
The practical takeaway is that repetition is a training signal problem, not an LSTM architecture problem.
See lessWhy does my deep learning model perform well locally but poorly in production?
This happens when training and production environments are not identical. Differences in preprocessing, floating-point precision, library versions, or hardware can change numerical behavior in neural networks. Make sure the same versions of Python, CUDA, PyTorch, and preprocessing code are used. AlwRead more
This happens when training and production environments are not identical.
Differences in preprocessing, floating-point precision, library versions, or hardware can change numerical behavior in neural networks.
Make sure the same versions of Python, CUDA, PyTorch, and preprocessing code are used. Always export the full inference pipeline, not just the model weights.
Common mistakes:
Rebuilding tokenizers in production
Different image resize algorithms
Mixing CPU and GPU behavior
The practical takeaway is that models do not generalize across environments unless the full pipeline is preserved.
See lessWhy does my GAN produce blurry and repetitive images?
In this situation, the generator stops exploring new variations and keeps reusing similar patterns. This is known as mode collapse, and it is one of the most common failure modes in GAN training. Blurriness also appears when the model is averaging over many possible outputs instead of committing toRead more
In this situation, the generator stops exploring new variations and keeps reusing similar patterns. This is known as mode collapse, and it is one of the most common failure modes in GAN training. Blurriness also appears when the model is averaging over many possible outputs instead of committing to sharp details.
To fix this, the balance between the generator and discriminator needs to be improved. Making the discriminator stronger, using techniques like Wasserstein loss (WGAN), gradient penalty, or spectral normalization gives more stable gradients. Adding diversity-promoting methods such as minibatch discrimination or noise injection helps prevent the generator from reusing the same outputs. In many setups, simply adjusting learning rates so the discriminator learns slightly faster than the generator already makes a big difference.
See lessWhy does my neural network stop improving even though the loss is still high?
This happens when gradients vanish or the learning rate is too small to make progress. Deep networks can get stuck in flat regions where weight updates become tiny. This is common when using sigmoid or tanh activations in deep layers. Switch to ReLU-based activations and use a modern optimizer likeRead more
This happens when gradients vanish or the learning rate is too small to make progress.
Deep networks can get stuck in flat regions where weight updates become tiny. This is common when using sigmoid or tanh activations in deep layers.
Switch to ReLU-based activations and use a modern optimizer like Adam:
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
Also verify that your inputs are normalized.
Common mistakes:
Using sigmoid everywhere
Learning rate too low
Unscaled inputs
The practical takeaway is that stagnation usually means gradients cannot move the weights anymore.
See lessWhy does my Transformer output nonsense when I fine-tune it on a small dataset?
This happens because the model is overfitting and catastrophically forgetting pretrained knowledge. When fine-tuning on small datasets, the Transformer’s weights drift away from what they originally learned. Use a lower learning rate and freeze early layers: for param in model.base_model.parameters(Read more
This happens because the model is overfitting and catastrophically forgetting pretrained knowledge.
When fine-tuning on small datasets, the Transformer’s weights drift away from what they originally learned. Use a lower learning rate and freeze early layers:
for param in model.base_model.parameters():
param.requires_grad = False
Also use weight decay and early stopping.
Common mistakes:
Learning rate too high
Training all layers on tiny datasets
No regularization
The practical takeaway is that pretrained models need gentle fine-tuning, not aggressive retraining.
See lessWhy does my Transformer’s training loss decrease but translation quality stays poor?
This happens because token-level loss does not capture sentence-level quality. Transformers are trained to predict the next token, not to produce coherent or accurate full sequences. A model can become very good at predicting individual words while still producing poor translations. Loss measures hoRead more
This happens because token-level loss does not capture sentence-level quality. Transformers are trained to predict the next token, not to produce coherent or accurate full sequences. A model can become very good at predicting individual words while still producing poor translations.
Loss measures how well each token matches the reference, but translation quality depends on word order, fluency, and semantic correctness across the entire sequence. These properties are not directly optimized by standard cross-entropy loss.
Using better decoding strategies such as beam search, label smoothing, and sequence-level evaluation helps align training with actual quality. In some setups, reinforcement learning or minimum-risk training is used to optimize sequence metrics directly.
See lessWhy does my RNN produce very unstable predictions for longer sequences?
This happens because standard RNNs suffer from vanishing and exploding gradients on long sequences. As the sequence grows, important signals either fade out or blow up, making learning unstable. That is why LSTM and GRU were created. Switch to LSTM or GRU layers and use gradient clipping: torch.nn.uRead more
This happens because standard RNNs suffer from vanishing and exploding gradients on long sequences.
As the sequence grows, important signals either fade out or blow up, making learning unstable. That is why LSTM and GRU were created.
Switch to LSTM or GRU layers and use gradient clipping:
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
Common mistakes:
Using vanilla RNNs for long text
Not clipping gradients
Too long sequences without truncation
The practical takeaway is that plain RNNs are not designed for long-term memory.
See lessWhy does my CNN predict only one class no matter what image I give it?
This happens when the model has collapsed to predicting the most dominant class in the dataset. If one class appears much more often than others, the CNN can minimize loss simply by always predicting it. This gives decent training accuracy but useless predictions. Check your class distribution. If iRead more
This happens when the model has collapsed to predicting the most dominant class in the dataset.
If one class appears much more often than others, the CNN can minimize loss simply by always predicting it. This gives decent training accuracy but useless predictions.
Check your class distribution. If it is skewed, use class weighting or balanced sampling:
loss = nn.CrossEntropyLoss(weight=class_weights)
Also verify that your labels are correctly aligned with your images.
Common mistakes:
Highly imbalanced datasets
Shuffled images but not labels
Incorrect label encoding
The practical takeaway is that class imbalance silently trains your CNN to cheat.
See lessWhy does my image classifier have very high training accuracy but terrible test accuracy?
This happens because the model is overfitting to the training data. The network is learning specific pixel patterns instead of general features, so it performs well only on images it has already seen. You need to increase generalization by adding data augmentation, dropout, and regularization: transRead more
This happens because the model is overfitting to the training data.
The network is learning specific pixel patterns instead of general features, so it performs well only on images it has already seen.
You need to increase generalization by adding data augmentation, dropout, and regularization:
transforms.RandomHorizontalFlip()
transforms.RandomRotation(10)
Also reduce model complexity or add weight decay in the optimizer.
Common mistakes:
Training on small datasets
Using too many layers
Not shuffling data
The practical takeaway is that high training accuracy without test accuracy means your CNN is memorizing, not understanding.
See lessWhy does my Transformer run out of GPU memory only during text generation?
This happens because Transformer models store attention history during generation, which makes memory usage grow with every generated token. During training, the sequence length is fixed. During generation, the model keeps cached key-value tensors for all previous tokens, so memory usage increases aRead more
This happens because Transformer models store attention history during generation, which makes memory usage grow with every generated token.
During training, the sequence length is fixed. During generation, the model keeps cached key-value tensors for all previous tokens, so memory usage increases at each step. This can easily exceed what training required.
You should disable unnecessary caches and limit generation length:
model.config.use_cache = False
outputs = model.generate(input_ids, max_new_tokens=128)
Also make sure inference runs in evaluation mode with gradients disabled:
model.eval()
with torch.no_grad():
...
Using half-precision (
model.half()) can also significantly reduce memory usage.Common mistakes:
Allowing unlimited generation length
Forgetting
torch.no_grad()Using training batch sizes during inference
The practical takeaway is that Transformers consume more memory while generating than while training.
See less