Comments (3)
Great idea. I think I'll do something like freezing the embedding weights until the validation loss stops decreasing and then unfreeze the embedding weights. Sound good?
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Sounds reasonable, yeah try it. Although most papers i've seen did different things. But this also looks good, at least seeing basic functionality of freeze-unfreeze under certain condition will be already good for people using tutorial.
from pytorch-sentiment-analysis.
OK, I've implemented a notebook appendix C which covers how to freeze/unfreeze embeddings (and a few other things).
In the implementation it freezes the embeddings for the first 5 epoches and then unfreezes them for the next/last 5. I also added a code snippet on how to unfreeze when the validation loss stops decreasing.
Let me know what you think.
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Related Issues (20)
- The train_data built from my own dataset after following the Appendix A looks wrong HOT 1
- migrating to the new API HOT 4
- .squeeze(1) HOT 5
- for word embedding in RNN model HOT 2
- Representation of similar words HOT 1
- Using a target size (torch.Size([64, 1])) that is different to the input size (torch.Size([304800, 1])) is deprecated. Please ensure they have the same size HOT 7
- train_test_split in LSTM HOT 2
- pad sequence in my dataset
- 6 - Transformers for Sentiment Analysis HOT 1
- Question in fasttext HOT 1
- ModuleNotFoundError
- How can I predict on one example?
- User Interface
- Got error" 'lengths' argument should be a 1D CPU int64 tensor, but got 1D cuda:0 Long tensor " HOT 10
- where is the trained model parameters ? HOT 1
- TypeError HOT 2
- Multi-class Sentiment Analysis: How to use custom dataset?
- how did you build torchtext from source HOT 4
- how does pytorch pad sentences ? HOT 3
- Pad Sequence error HOT 2
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