Comments (2)
zhongkaifu
commented on May 27, 2024
Template feature is sparse feature. If current token hits some features, only these features values become non-zero, however, other features values are zero.
Embedding feature is dense feature. each token can point to a vector and the value of this vector will be used as embedding feature.
Runtime feature uses the output of previous tokens as features for current token.
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My-Khan
commented on May 27, 2024
Many thanks for details.
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Related Issues (20)
- Impact of run-time feature HOT 4
- LSTM RNN HOT 2
- Plan to Use LSTM for Time Series HOT 1
- Need Help With PRETRAIN_TYPE=Autoencoder HOT 2
- Null reference exception with ForwardRNN HOT 3
- Incremental training HOT 3
- imncremental training NullReferenceException after last update HOT 2
- Training takes too long!! HOT 20
- Using the library from .Net Core HOT 3
- maybe it's time to support CUDA ! HOT 2
- AdvUtils.dll Issue HOT 3
- puzzled about function : logsumexp HOT 1
- A question regarding usage or RNNSharp for a particular problem
- NullReferenceException in DropoutLayer
- System Total Fail due to double-Number conversion problems for non "es-US" Cultures
- A few questions regarding how to exploit RNNSharp
- Is there a way to further reduce file size of the model without affecting network size?
- Time Complexity
- RNNSharp Pseudo code
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