tensorflow_privacy/privacy/estimators/binary_class_head.py [100:143]:
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                base_head.PREDICT_SERVING_KEY:
                    export_output.PredictOutput(predictions)
            })
      regularized_training_loss = self.loss(
          logits=logits,
          labels=labels,
          features=features,
          mode=mode,
          regularization_losses=regularization_losses)
      scalar_loss = tf.reduce_mean(regularized_training_loss)
      # Eval.
      if mode == ModeKeys.EVAL:
        eval_metrics = self.metrics(regularization_losses=regularization_losses)
        return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
            mode=ModeKeys.EVAL,
            predictions=predictions,
            loss=scalar_loss,
            eval_metrics=base_head.create_eval_metrics_tuple(
                self.update_metrics, {
                    'eval_metrics': eval_metrics,
                    'features': features,
                    'logits': logits,
                    'labels': labels,
                    'regularization_losses': regularization_losses
                }))
      # Train.
      train_op = base_head.create_estimator_spec_train_op(
          head_name=self._name,
          optimizer=optimizer,
          train_op_fn=train_op_fn,
          update_ops=update_ops,
          trainable_variables=trainable_variables,
          regularized_training_loss=regularized_training_loss,
          loss_reduction=self._loss_reduction)
    # Create summary.
    base_head.create_estimator_spec_summary(
        regularized_training_loss=scalar_loss,
        regularization_losses=regularization_losses,
        summary_key_fn=self._summary_key)
    return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
        mode=ModeKeys.TRAIN,
        predictions=predictions,
        loss=scalar_loss,
        train_op=train_op)
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tensorflow_privacy/privacy/estimators/multi_label_head.py [105:148]:
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                base_head.PREDICT_SERVING_KEY:
                    export_output.PredictOutput(predictions)
            })
      regularized_training_loss = self.loss(
          logits=logits,
          labels=labels,
          features=features,
          mode=mode,
          regularization_losses=regularization_losses)
      scalar_loss = tf.reduce_mean(regularized_training_loss)
      # Eval.
      if mode == ModeKeys.EVAL:
        eval_metrics = self.metrics(regularization_losses=regularization_losses)
        return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
            mode=ModeKeys.EVAL,
            predictions=predictions,
            loss=scalar_loss,
            eval_metrics=base_head.create_eval_metrics_tuple(
                self.update_metrics, {
                    'eval_metrics': eval_metrics,
                    'features': features,
                    'logits': logits,
                    'labels': labels,
                    'regularization_losses': regularization_losses
                }))
      # Train.
      train_op = base_head.create_estimator_spec_train_op(
          head_name=self._name,
          optimizer=optimizer,
          train_op_fn=train_op_fn,
          update_ops=update_ops,
          trainable_variables=trainable_variables,
          regularized_training_loss=regularized_training_loss,
          loss_reduction=self._loss_reduction)
    # Create summary.
    base_head.create_estimator_spec_summary(
        regularized_training_loss=scalar_loss,
        regularization_losses=regularization_losses,
        summary_key_fn=self._summary_key)
    return model_fn._TPUEstimatorSpec(  # pylint: disable=protected-access
        mode=ModeKeys.TRAIN,
        predictions=predictions,
        loss=scalar_loss,
        train_op=train_op)
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