vak.models.definition.ModelDefinition¶

class vak.models.definition.ModelDefinition(network: Module | dict, loss: dict, optimizer: Optimizer, metrics: dict, default_config: dict)[source]¶

A class that represents the definition of a neural network model.

A model definition is any class that has the following class variables:

network: torch.nn.Module or dict: Neural network. If a dict, should map string network names to torch.nn.Module classes.
loss: torch.nn.Module, callable: Either a built-in loss module, or a callable function that computes loss.
optimizer: torch.optim.Optimizer: Optimizer used to optimize neural network parameters during training.
metrics: dict: Metrics used to evaluate network. Should map string names of metric to callable classes that compute metric.
default_configdict: That specifies default keyword arguments to use when instantiating any classes in network, optimizer, loss, or metrics. Used by vak.models.base.Model and its sub-classes that represent model families. E.g., those classes will do: network = self.definition.network(**self.definition.default_config['network']).

Note it is not necessary to sub-class this class; it exists mainly for type-checking purposes.

For more detail, see vak.models.decorator.model() and vak.models.ModelFactory.

__init__(network: Module | dict, loss: dict, optimizer: Optimizer, metrics: dict, default_config: dict) → None¶

Methods

__init__(network, loss, optimizer, metrics, ...)

Attributes