Add class weights to core hyperparameters and classifier library.
PiperOrigin-RevId: 550962843
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@ -15,7 +15,7 @@
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import dataclasses
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import tempfile
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from typing import Optional
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from typing import Mapping, Optional
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import tensorflow as tf
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@ -36,6 +36,8 @@ class BaseHParams:
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steps_per_epoch: An optional integer indicate the number of training steps
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per epoch. If not set, the training pipeline calculates the default steps
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per epoch as the training dataset size divided by batch size.
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class_weights: An optional mapping of indices to weights for weighting the
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loss function during training.
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shuffle: True if the dataset is shuffled before training.
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export_dir: The location of the model checkpoint files.
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distribution_strategy: A string specifying which Distribution Strategy to
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@ -57,6 +59,7 @@ class BaseHParams:
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batch_size: int
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epochs: int
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steps_per_epoch: Optional[int] = None
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class_weights: Optional[Mapping[int, float]] = None
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# Dataset-related parameters
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shuffle: bool = False
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@ -110,7 +110,9 @@ class Classifier(custom_model.CustomModel):
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# dataset is exhausted even if there are epochs remaining.
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steps_per_epoch=None,
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validation_data=validation_dataset,
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callbacks=self._callbacks)
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callbacks=self._callbacks,
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class_weight=self._hparams.class_weights,
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)
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def evaluate(self, data: dataset.Dataset, batch_size: int = 32) -> Any:
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"""Evaluates the classifier with the provided evaluation dataset.
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