Comments (3)
Hi,
- Usually the larger the
batch_size
, the better. - You can decide it based on the performance on the validation set.
- Two aspects to improve training speed: (1) mixed precision training (use
--fp16
) (2) the learned absorbing state makes the training converge faster.
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Hi,
- Usually the larger the
batch_size
, the better.- You can decide it based on the performance on the validation set.
- Two aspects to improve training speed: (1) mixed precision training (use
--fp16
) (2) the learned absorbing state makes the training converge faster.
Thank you!
from diffuseq.
Hi,
- Usually the larger the
batch_size
, the better.- You can decide it based on the performance on the validation set.
- Two aspects to improve training speed: (1) mixed precision training (use
--fp16
) (2) the learned absorbing state makes the training converge faster.
Hi author, read your article and code. I have some new doubts which I hope you can answer:
-
You said bigger batch_size is better, why did you just set 425 during your training, A100 80G can allow bigger batch_size
-
Is the loss convergence of the validation set equivalent to the metric convergence of the validation set, e.g., BELU?
-
Add the soft absorption state ,My understanding is that you parameterize the clamp() operation with the trainable parameter mean_embed, and it in your code as follows:
x_t = ( _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * (x_start - mean_embed[None, None]) + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise + mean_embed[None, None] * mask )
I don't understand how this formula is derived, can you explain?
-
In your paper on the table of BELUs obtained based on training time, I would like to ask if the BELUs are calculated directly using the X0 predicted by the model -> x0 = model(xt,t) , or based on the X0 obtained from the T-step REVERSE sampling ?
Thanks!
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Related Issues (20)
- Padding during training results in a "Killed"
- BERT parameter
- Try to train the model with another dataset, but get so many [UNK] token.
- a few questions about the 'MBR' decoding strategy. HOT 2
- Version of many packages
- Incorrect self-BLEU Computation
- a question about --local_rank
- Could not find a version that satisfies the requirement torch==1.9.0+cu111
- i face some promble Dataset(2) in "text_datasets.py" HOT 1
- If there is any rule to modify the parameters HOT 1
- Machine Translation Task with DiffuSeq HOT 6
- A question about the loss in V2
- Implementation of using soft absorbing state in the forward process in training. HOT 1
- ddim sampling HOT 2
- DDPM HOT 3
- train
- Where is CommonsenseConversation/test.jsonl ? When I run train. sh and then run run_decode_solver. sh or run_decode. sh, I always can't find test.jsonl HOT 2
- 'grad_norm' is NaN HOT 2
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- questions on source data
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