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  4. skorch.callbacks.BatchScoring
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How to use the skorch.callbacks.BatchScoring function in skorch

To help you get started, we’ve selected a few skorch examples, based on popular ways it is used in public projects.

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github skorch-dev / skorch / skorch / classifier.py View on Github external
def _default_callbacks(self):
        return [
            ('epoch_timer', EpochTimer()),
            ('train_loss', BatchScoring(
                train_loss_score,
                name='train_loss',
                on_train=True,
                target_extractor=noop,
            )),
            ('valid_loss', BatchScoring(
                valid_loss_score,
                name='valid_loss',
                target_extractor=noop,
            )),
            ('valid_acc', EpochScoring(
                'accuracy',
                name='valid_acc',
                lower_is_better=False,
            )),
            ('print_log', PrintLog()),
        ]
github skorch-dev / skorch / skorch / net.py View on Github external
def _default_callbacks(self):
        return [
            ('epoch_timer', EpochTimer()),
            ('train_loss', BatchScoring(
                train_loss_score,
                name='train_loss',
                on_train=True,
                target_extractor=noop,
            )),
            ('valid_loss', BatchScoring(
                valid_loss_score,
                name='valid_loss',
                target_extractor=noop,
            )),
            ('print_log', PrintLog()),
        ]
github skorch-dev / skorch / skorch / net.py View on Github external
def _default_callbacks(self):
        return [
            ('epoch_timer', EpochTimer()),
            ('train_loss', BatchScoring(
                train_loss_score,
                name='train_loss',
                on_train=True,
                target_extractor=noop,
            )),
            ('valid_loss', BatchScoring(
                valid_loss_score,
                name='valid_loss',
                target_extractor=noop,
            )),
            ('print_log', PrintLog()),
        ]
github skorch-dev / skorch / skorch / net.py View on Github external
def _default_callbacks(self):
        return [
            ('epoch_timer', EpochTimer()),
            ('train_loss', BatchScoring(
                train_loss_score,
                name='train_loss',
                on_train=True,
                target_extractor=noop,
            )),
            ('valid_loss', BatchScoring(
                valid_loss_score,
                name='valid_loss',
                target_extractor=noop,
            )),
            ('print_log', PrintLog()),
        ]
github skorch-dev / skorch / skorch / net.py View on Github external
def _default_callbacks(self):
        return [
            ('epoch_timer', EpochTimer()),
            ('train_loss', BatchScoring(
                train_loss_score,
                name='train_loss',
                on_train=True,
                target_extractor=noop,
            )),
            ('valid_loss', BatchScoring(
                valid_loss_score,
                name='valid_loss',
                target_extractor=noop,
            )),
            ('print_log', PrintLog()),
        ]
github skorch-dev / skorch / skorch / classifier.py View on Github external
def _default_callbacks(self):
        return [
            ('epoch_timer', EpochTimer()),
            ('train_loss', BatchScoring(
                train_loss_score,
                name='train_loss',
                on_train=True,
                target_extractor=noop,
            )),
            ('valid_loss', BatchScoring(
                valid_loss_score,
                name='valid_loss',
                target_extractor=noop,
            )),
            ('valid_acc', EpochScoring(
                'accuracy',
                name='valid_acc',
                lower_is_better=False,
            )),
            ('print_log', PrintLog()),

skorch

scikit-learn compatible neural network library for pytorch

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BSD-3-Clause
Latest version published 5 months ago

Package Health Score

82 / 100
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