Add *_on_batch functions to torch trainer #328
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fchollet
reviewed
Jun 12, 2023
keras_core/backend/torch/trainer.py
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| @@ -58,6 +58,64 @@ def train_step(self, data): | |||
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| return self.compute_metrics(x, y, y_pred, sample_weight=sample_weight) | |||
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| def test_step(self, data): | |||
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We should avoid adding new public Trainer APIs. Could these be floating functions in make_*_function?
keras_core/backend/torch/trainer.py
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| if self.train_function is not None and not force: | ||
| return self.train_function | ||
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| def one_step_on_data(data): |
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E.g. just inline test_step here instead of define a wrapper function (which is not needed until we have step fusing or compilation)
| class_weight=None, | ||
| return_dict=False, | ||
| ): | ||
| """Runs a single gradient update on a single batch of data. |
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Something we should do soon is make sure the base Trainer class has docstrings everywhere, that the Trainer subclasses don't have docstrings, then we programatically set the docstrings on the subclasses (using the ones from the base class)
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Cool. Issue created: #329
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