Comments (1)
philipperemy
commented on August 30, 2024
First, I replaced weight normalization with layer normalization. They won’t be the same, but should have similar effects in stabilizing training.
You cannot place weight normalization after ReLU because it normalizes the cell weights, as the name suggested, not the cell output.
As the author of the paper stated, this TCN architecture really aims to be a simple baseline that you can improve upon. So personally I think replicating the exact same results is not that important.
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Related Issues (20)
- ValueError: Unknown layer: TCN when trying to load saved model HOT 3
- Dilations and nb_stack relationship HOT 4
- The parameter of the TCN HOT 7
- Masking time steps in order to use TCN for variable length sequences HOT 2
- data shape of tcn layer HOT 1
- help one regression per sequence HOT 1
- Ensuring unique weights when a model uses multiple TCNs HOT 2
- Question: TCN Dilated Convolutions? HOT 3
- How can i use TCN to build seq2seq model? HOT 1
- keras-tcn for R HOT 3
- Visualization of internal structure of TCN block HOT 1
- Low accuracy in keras-tuner based TCN model for audio classification HOT 6
- Saving a loaded model gets warning and then fails to open HOT 1
- exchange_rate.txt HOT 5
- Question: Skip connections HOT 2
- Why is the data form (batch_size, timesteps, input_dim) instead less accurate than (batch_size, input_dim, timesteps)?
- Keras 3 support HOT 7
- Confusion about regression or classification head HOT 1
- AttributeError: 'tuple' object has no attribute 'as_list' HOT 3
- Question regarding return_sequences=True HOT 3
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