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> To "produce a tensor with shape [bs, sl]" from `logits` and `sample_id`, you may use [`sequence_sparse_softmax_cross_entropy`](https://texar.readthedocs.io/en/latest/code/losses.html#texar.tf.losses.sequence_sparse_softmax_cross_entropy) and set > > ``` > average_across_batch=False, > average_across_timesteps=False, > sum_over_batch=False, > sum_over_timesteps=False...

@ZhitingHu Actually I am getting a OOM error when I add this RL loss the way I showed earlier to mle loss. MLE loss works fine, part of the code...

Actually, the code will work fine if I have only loss_mle, it is working even when I have multiple loss_mle (which means the ```sequence_sparse_softmax_cross_entropy``` is called several time for each...

@ZhitingHu Here is the error when I remove train_op from fetches. But I am not pretty sure why we want to do that. Since when ```loss = loss_mle``` and I...

> Removing `train_op` or using `tf.stop_gradient` is for debugging purpose -- to locate which portion of the code causes OOM. Once it's located and fixed, you do need to add...

@ZhitingHu I really appreciate your help. Yeah, that is a good test and actually I tried with just ```loss==rl_loss_fine``` and it threw the same error. Note that, I used a...

@ZhitingHu I changed the ```max_seq_len``` from 200 to 128 and still get the same error for ```rl_loss_fine```. Technically since both ```loss_mle``` and ```rl_loss_fine``` are using CE loss with respect to...

I just wanted to check if negative loss (may happen when reward of greedy output (r_base) is greater than reward of sampled output (r_sample) ) or very small loss (most...

> > @ZhitingHu I really appreciate your help. > > Yeah, that is a good test and actually I tried with just `loss==rl_loss_fine` and it threw the same error. Note...

> Glad to hear that! :) Could you briefly explain the cause of OOM, for future reference? > Glad to hear that! :) Could you briefly explain the cause of...