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…ardless eval loss, because of the correctness of the eval loss calculation is questionable.
…hs without improvement.
nshmyrev
reviewed
Apr 16, 2017
README.md
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| # for the model with size 512: | ||
| --max_steps 150000 | ||
| ``` | ||
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Should work by default, not with options.
g2p_seq2seq/g2p.py
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| num_iter_cover_train = int(sum(train_bucket_sizes) / | ||
| self.params.batch_size / | ||
| self.params.steps_per_checkpoint) | ||
| current_step, iter_inx, num_epochs_last_impr, max_num_epochs,\ |
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"inx" is unclear abbreviation.
g2p_seq2seq/g2p.py
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| self.params.batch_size / | ||
| self.params.steps_per_checkpoint) | ||
| current_step, iter_inx, num_epochs_last_impr, max_num_epochs,\ | ||
| num_up_trends, num_down_trends = 0, 0, 0, 2, 0, 0 |
Contributor
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"trend" is not a proper word here.
g2p_seq2seq/g2p.py
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| current_step, iter_inx, num_epochs_last_impr, max_num_epochs,\ | ||
| num_up_trends, num_down_trends = 0, 0, 0, 2, 0, 0 | ||
| prev_train_losses, prev_valid_losses, prev_epoch_valid_losses = [], [], [] | ||
| num_iter_cover_train = max(1, int(sum(train_bucket_sizes) / |
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This is called "epoch", no? "cover" is not a good word in this context.
g2p_seq2seq/g2p.py
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| if len(prev_epoch_valid_losses) > 0: | ||
| print('Previous min epoch eval loss: %f, current epoch eval loss: %f' % | ||
| (min(prev_epoch_valid_losses), epoch_eval_loss)) | ||
| # Check if there was improvement during last epoch |
g2p_seq2seq/g2p.py
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| # Check if there was improvement during last epoch | ||
| if (epoch_eval_loss < min(prev_epoch_valid_losses)): | ||
| if num_epochs_last_impr > max_num_epochs/1.5: | ||
| max_num_epochs = int(1.5 * num_epochs_last_impr) |
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1.5 must be separated into something standalone and properly named, not used in multiple places in the code without name.
g2p_seq2seq/g2p.py
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| def __calc_epoch_loss(self, epoch_losses): | ||
| """Calculate average loss during the epoch. |
Contributor
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Comment is wrong, this is not really an average.
nshmyrev
requested changes
Apr 18, 2017
g2p_seq2seq/g2p.py
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| prev_train_losses.append(train_loss) | ||
| prev_valid_losses.append(eval_loss) | ||
| step_time, train_loss = 0.0, 0.0 | ||
| iter_idx += 1 |
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Use current step instead of iter_idx
g2p_seq2seq/g2p.py
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| num_epochs_last_impr = 0 | ||
| else: | ||
| print('No improvement during last epoch.') | ||
| num_epochs_last_impr += 1 |
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epochs_without_improvement
g2p_seq2seq/g2p.py
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| num_iter_total += num_iter_cover_valid | ||
| for batch_id in xrange(num_iter_cover_valid): | ||
| iter_total += iter_per_valid | ||
| for batch_id in xrange(iter_per_valid): |
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Use xrange with batch_size step.
g2p_seq2seq/g2p.py
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| self.params.batch_size)) | ||
| num_iter_total += num_iter_cover_valid | ||
| for batch_id in xrange(num_iter_cover_valid): | ||
| iter_total += iter_per_valid |
Contributor
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Count iter_total in inner loop
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Add new stop criteria. Previously number of the allowable number of epochs without improvement was fixed. Training stopped if no improvement was seen during this window. New stop criteria include possibility of increasing of that window depending on the number of epochs needed for previous improvements during training.