Core Module Documentation#
- neuralprophet.utils_torch.create_optimizer_from_config(optimizer_name, optimizer_args)#
Translate the optimizer name and arguments into a torch optimizer. If an optimizer object is provided, it is returned as is. The optimizer is not initialized yet since this is done by the trainer.
- Parameters
- optimizer_nameint
Object provided to NeuralProphet as optimizer.
- optimizer_argsdict
Arguments for the optimizer.
- optimizertorch.optim.Optimizer
The optimizer object.
- optimizer_argsdict
The optimizer arguments.
- neuralprophet.utils_torch.init_parameter(dims)#
Create and initialize a new torch Parameter.
- Parameters
dims (list or tuple) – Desired dimensions of parameter
- Returns
initialized Parameter
- Return type
nn.Parameter
- neuralprophet.utils_torch.interprete_model(target_model: pytorch_lightning.core.module.LightningModule, net: str, forward_func: str, _num_in_features: Optional[int] = None, _num_out_features: Optional[int] = None, _input: Optional[torch.Tensor] = None, additional_forward_args: Optional[Any] = None)#
Returns model input attributions for a given network and forward function.
- Parameters
target_model (pl.LightningModule) – The model for which input attributions are to be computed.
net (str) – Name of the network for which input attributions are to be computed.
forward_func (str) – Name of the forward function for which input attributions are to be computed.
_input (torch.Tensor) – Input for which the attributions are to be computed.
- Returns
Input attributions for the given network and forward function.
- Return type
torch.Tensor