power_cogs.config package¶
Subpackages¶
Submodules¶
power_cogs.config.config module¶
Import your main config files here:
power_cogs.config.config_utils module¶
power_cogs.config.load_config module¶
power_cogs.config.mnist_config module¶
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class
power_cogs.config.mnist_config.
MNISTConfig
(defaults: List[Any] = <factory>, trainer: Any = '???')[source]¶ Bases:
object
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trainer
= '???'¶
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class
power_cogs.config.mnist_config.
MNISTDatasetConfig
(_target_: str = 'power_cogs.dataset.mnist_dataset.MNISTDataset')[source]¶
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class
power_cogs.config.mnist_config.
MNISTModelConfig
(_target_: str = 'power_cogs.model.mnist_model.MNISTModel', input_dims: Union[int, NoneType] = None, hidden_dims: List[int] = <factory>, output_dims: Union[int, NoneType] = None, output_activation: str = 'torch.nn.functional.relu', use_normal_init: bool = True, normal_std: float = 0.01, zero_bias: bool = False)[source]¶ Bases:
power_cogs.config.base.base_config.BaseModelConfig
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input_dims
= None¶
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normal_std
= 0.01¶
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output_activation
= 'torch.nn.functional.relu'¶
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output_dims
= None¶
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use_normal_init
= True¶
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zero_bias
= False¶
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class
power_cogs.config.mnist_config.
MNISTTrainerConfig
(defaults: List[Any] = <factory>, _target_: str = 'power_cogs.trainer.mnist_trainer.MNISTTrainer', name: 'Optional[str]' = None, pretrained_path: 'Optional[str]' = None, visualize_output: 'bool' = True, use_cuda: 'bool' = False, device_id: 'int' = 0, early_stoppage: 'bool' = False, loss_threshold: 'float' = -inf, batch_size: 'int' = 32, epochs: 'int' = 100, checkpoint_interval: 'int' = 100, num_samples: 'Optional[int]' = None, model_config: 'Any' = '???', dataset_config: 'Any' = '???', optimizer_config: 'Any' = '???', scheduler_config: 'Any' = '???', logging_config: 'Any' = '???', dataloader_config: 'Any' = '???', tune_config: 'Any' = '???', config: 'Any' = <factory>)[source]¶