domainbed_measures/measures/held_out_measures.py [391:421]:
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        logging.info("Obtaining heads")
        callbacks = [
            skorch.callbacks.LRScheduler(
                torch.optim.lr_scheduler.StepLR,
                gamma=lr_decay_gamma,
                step_size=self._train_epochs / 2,
            ),
            skorch.callbacks.EpochScoring(
                self.accuracy_fn,
                lower_is_better=False,
                name='val_accuracy',
            ),
            skorch.callbacks.EpochScoring(
                self.accuracy_fn,
                lower_is_better=False,
                name='train_accuracy',
                on_train=True,
            ),
            skorch.callbacks.EarlyStopping(
                monitor='val_accuracy',
                patience=15,
                threshold=0.0001,
                threshold_mode='rel',
                lower_is_better=False,
            )
        ]

        heads = self.get_heads(
            num_head_batches,
            feat_dim=feat_dim,
            criterion=nn.CrossEntropyLoss,
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domainbed_measures/measures/held_out_measures.py [479:509]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        logging.info("Obtaining heads")
        callbacks = [
            skorch.callbacks.LRScheduler(
                torch.optim.lr_scheduler.StepLR,
                gamma=lr_decay_gamma,
                step_size=self._train_epochs / 2,
            ),
            skorch.callbacks.EpochScoring(
                self.accuracy_fn,
                lower_is_better=False,
                name='val_accuracy',
            ),
            skorch.callbacks.EpochScoring(
                self.accuracy_fn,
                lower_is_better=False,
                name='train_accuracy',
                on_train=True,
            ),
            skorch.callbacks.EarlyStopping(
                monitor='val_accuracy',
                patience=15,
                threshold=0.0001,
                threshold_mode='rel',
                lower_is_better=False,
            )
        ]

        heads = self.get_heads(
            num_head_batches,
            feat_dim=feat_dim,
            criterion=nn.CrossEntropyLoss,
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