deeppavlov.core.models¶
Abstract model classes and interfaces.
- class deeppavlov.core.models.component.Component[source]¶
Abstract class for all callables that could be used in Chainer’s pipe.
- class deeppavlov.core.models.serializable.Serializable(save_path: Optional[Union[Path, str]], load_path: Optional[Union[Path, str]] = None, mode: str = 'infer', *args, **kwargs)[source]¶
Abstract base class that expresses the interface for all models that can serialize data to a path.
- class deeppavlov.core.models.estimator.Estimator(save_path: Optional[Union[Path, str]], load_path: Optional[Union[Path, str]] = None, mode: str = 'infer', *args, **kwargs)[source]¶
Abstract class for components that could be fitted on the data as a whole.
- class deeppavlov.core.models.nn_model.NNModel(save_path: Optional[Union[Path, str]], load_path: Optional[Union[Path, str]] = None, mode: str = 'infer', *args, **kwargs)[source]¶
Abstract class for deep learning components.
- class deeppavlov.core.models.torch_model.TorchModel(model: torch.nn.Module, device: Union[torch.device, str] = 'cuda', optimizer: str = 'AdamW', optimizer_parameters: Optional[dict] = None, learning_rate_drop_patience: Optional[int] = None, learning_rate_drop_div: Optional[float] = None, load_before_drop: bool = True, min_learning_rate: float = 1e-07, clip_norm: Optional[float] = None, *args, **kwargs)[source]¶
Class implements torch model’s main methods.
- Parameters
model – torch.nn.Model-based neural network model
device – device to use
optimizer – name of torch.optim optimizer
optimizer_parameters – dictionary with optimizer parameters
learning_rate_drop_patience – how many validations with no improvements to wait
learning_rate_drop_div – the divider of the learning rate after learning_rate_drop_patience unsuccessful validations
load_before_drop – whether to load best model before dropping learning rate or not
min_learning_rate – min value of learning rate if learning rate decay is used
args –
kwargs – dictionary with other model parameters
- device¶
cpu or cuda device to use
- opt¶
dictionary with all model parameters
- model¶
torch model
- epochs_done¶
number of epochs that were done
- optimizer¶
torch.optim instance
- learning_rate_drop_patience¶
how many validations with no improvements to wait
- learning_rate_drop_div¶
the divider of the learning rate after learning_rate_drop_patience unsuccessful validations
- load_before_drop¶
whether to load best model before dropping learning rate or not
- min_learning_rate¶
min value of learning rate if learning rate decay is used
- clip_norm¶
clip gradients by norm coefficient