[Major] Support Re-Training - #1635
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Model Benchmark
Model training plotsModel TrainingPeytonManningYosemiteTempsAirPassengersEnergyPriceDaily |
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Aug 27, 2024
| n_data: int = field(init=False) | ||
| loss_func_name: str = field(init=False) | ||
| lr_finder_args: dict = field(default_factory=dict) | ||
| optimizer_state: dict = field(default_factory=dict) |
| "three_phase": True, | ||
| } | ||
| ) | ||
| if self.continue_training: |
| delay_weight = 1 | ||
| return delay_weight | ||
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| def set_optimizer_state(self, optimizer_state: dict): |
| -------- | ||
| >>> from neuralprophet import NeuralProphet | ||
| >>> # Step Learning Rate scheduler | ||
| >>> m = NeuralProphet(scheduler="StepLR") |
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add scheduler args example
| newer_samples_start=newer_samples_start, | ||
| trend_reg_threshold=self.config_trend.trend_reg_threshold, | ||
| ) | ||
| self.learning_rate = learning_rate |
| self.config_normalization = config_normalization | ||
| self.compute_components_flag = compute_components_flag | ||
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| # Continued training |
| # Optimizer | ||
| optimizer = self._optimizer(self.parameters(), lr=self.learning_rate, **self.config_train.optimizer_args) | ||
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| if self.continue_training: |
| return config_train_params | ||
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| def test_continue_training(): |
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modify to do without checkpoints
| assert metrics["Loss"].min() >= metrics2["Loss"].min() | ||
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| def test_continue_training_with_scheduler_selection(): |
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modify to work without checkpoints
| assert metrics["Loss"].min() >= metrics2["Loss"].min() | ||
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| def test_save_load_continue_training(): |
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modify to work without checkpoints
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Continuation of #1605 on repository branch to enable metrics CI to run.