dacapo.experiments.trainers.trainer_config
Classes
A class to represent the Trainer Configurations. |
Module Contents
- class dacapo.experiments.trainers.trainer_config.TrainerConfig
A class to represent the Trainer Configurations.
It is the base class for trainer configurations. Each subclass of a Trainer should have a specific config class derived from TrainerConfig.
- name
A unique name for this trainer.
- Type:
str
- batch_size
The batch size to be used during training.
- Type:
int
- learning_rate
The learning rate of the optimizer.
- Type:
float
- verify() Tuple[bool, str]
Verify whether this TrainerConfig is valid or not.
Note
The TrainerConfig class is an abstract class that cannot be instantiated directly. It is meant to be subclassed.
- name: str
- batch_size: int
- learning_rate: float
- verify() Tuple[bool, str]
Verify whether this TrainerConfig is valid or not. A TrainerConfig is considered valid if it has a valid batch size and learning rate.
- Returns:
A tuple containing a boolean indicating whether the TrainerConfig is valid and a message explaining why.
- Return type:
tuple
- Raises:
NotImplementedError – If the method is not implemented by the subclass.
Examples
>>> valid, message = trainer_config.verify() >>> valid True >>> message "No validation for this Trainer"
Note
This method must be implemented by the subclass.