Module ml4opf.models.penalty_nn.socopf_penalty_nn

Classes

class SOCPenaltyNN (opfmodel: OPFModel,
slices: list[slice],
optimizer: str = 'adam',
loss: str = 'mse',
hidden_sizes: list[int] = [100, 100],
activation: str = 'relu',
boundrepair: str = 'none',
learning_rate: float = 0.001,
exclude_keys: str | list[str] | None = None,
multipliers: float | dict[str, float] | None = None,
weight_init_seed: int = 42)

Browse git

Hooks to be used in LightningModule.

Ancestors

• PenaltyNN
• SOCBasicNN
• BasicNN
• pytorch_lightning.core.module.LightningModule
• lightning_fabric.utilities.device_dtype_mixin._DeviceDtypeModuleMixin
• pytorch_lightning.core.mixins.hparams_mixin.HyperparametersMixin
• pytorch_lightning.core.hooks.ModelHooks
• pytorch_lightning.core.hooks.DataHooks
• pytorch_lightning.core.hooks.CheckpointHooks
• torch.nn.modules.module.Module

Class variables

var opfmodel : SOCModel

var violation : SOCViolation

Inherited members

• PenaltyNN:
  • configure_optimizers
  • forward
  • test_step
  • training_step
  • validation_step

class SOCPenaltyNeuralNet (config: dict,
problem: OPFProblem)

Browse git

A basic feed-forward neural network.

Args

config : dict

Dictionary containing the model configuration.

optimizer (str): Optimizer. Supported: "adam", "adamw", "sgd".

loss (str): Loss function. Supported: "mse", "l1".

hidden_sizes (list[int]): List of hidden layer sizes.

activation (str): Activation function. Supported: "relu", "tanh", "sigmoid".

boundrepair (str): Bound clipping method. Supported: "none", "relu", "clamp", "sigmoid".

learning_rate (float): Learning rate.

problem : OPFProblem

The OPFProblem object.

Ancestors

• PenaltyNeuralNet
• SOCBasicNeuralNet
• BasicNeuralNet
• SOCModel
• OPFModel
• abc.ABC

Class variables

var model : SOCPenaltyNN

Inherited members

• PenaltyNeuralNet:
  • evaluate_model
  • load_from_checkpoint
  • model_cls
  • predict
  • save_checkpoint