Comments (2)
Not that I remember, but feel free to ask the original authors of the model or just experiment and submit a PR if you get better results with bias terms.
from attention-ocr.
Hi,
I looked into this question and I found the answer. When applying batch norm you do not need the bias terms. Batch norm will recenter the feature map such that bias terms are not needed.
Cheers
from attention-ocr.
Related Issues (20)
- Works with phrases/sentences? HOT 1
- Input Shape HOT 1
- Prediction on negative class
- Attribute Error: 'int' object has no attribute 'value' for attn_length
- Calculating training steps with dataset size and batchsize? HOT 1
- 关于注意力可视化,我有点读不懂
- Error on train phase
- Significance of hyperparameters in defaults.py HOT 1
- TF 2 Implementation error HOT 3
- Unable to export model after training HOT 1
- can it detect numbers
- How to stop the log message appear on console
- Someone please share their trained model on Synth 90k or equivalent
- attention masks from saved model
- how to Upgrade to Tensorflow 2 ? HOT 1
- given num-epoch 200 but the training is continuing Step 328+, for how many steps it will continue? HOT 1
- Exporting the model to tensorflowjs HOT 1
- README.md
- OSS License compatibility question
- Training dataset
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from attention-ocr.