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| Args: | ||
| data_ops: dict with keys 'sequence,' 'label,' and 'na.' | ||
| augment_rc: Boolean | ||
| augment_shifts: Int |
| augment_rc: Boolean | ||
| augment_shifts: Int | ||
| Returns | ||
| data_ops: augmented data |
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can you document what fields get added to the dict?
| return data_ops_list | ||
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| def augment_deterministic(data_ops, augment_rc=False, augment_shift=0): |
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when would you use this function? It seems odd to call with augment_shift != 0, but only a single value.
| if augment_rc: | ||
| data_ops_aug = augment_deterministic_rc(data_ops_aug) | ||
| else: | ||
| data_ops_aug['reverse_preds'] = tf.zeros((), dtype=tf.bool) |
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what are the semantics of these as targets?
| target_subset=None): | ||
| augment_rc=False, augment_shifts=[0], | ||
| ensemble_rc=False, ensemble_shifts=[0], | ||
| penultimate=False, target_subset=None): |
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what does penultimate mean here?
| self.loss_train, self.loss_train_targets, self.targets_train = loss_returns | ||
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| # optimizer | ||
| self.update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) |
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shouldn't this be done after you make the optimizer?
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