deepmd.pt.loss.ener_spin

Classes

EnergySpinLoss

Base class for all neural network modules.

Module Contents

class deepmd.pt.loss.ener_spin.EnergySpinLoss(starter_learning_rate: float = 1.0, start_pref_e: float = 0.0, limit_pref_e: float = 0.0, start_pref_fr: float = 0.0, limit_pref_fr: float = 0.0, start_pref_fm: float = 0.0, limit_pref_fm: float = 0.0, start_pref_v: float = 0.0, limit_pref_v: float = 0.0, start_pref_ae: float = 0.0, limit_pref_ae: float = 0.0, enable_atom_ener_coeff: bool = False, loss_func: str = 'mse', inference: bool = False, intensive_ener_virial: bool = False, **kwargs: Any)

Bases: deepmd.pt.loss.loss.TaskLoss

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

loss_func = 'mse'

starter_learning_rate = 1.0

has_e

has_fr

has_fm

has_v

has_ae

start_pref_e = 0.0

limit_pref_e = 0.0

start_pref_fr = 0.0

limit_pref_fr = 0.0

start_pref_fm = 0.0

limit_pref_fm = 0.0

start_pref_v = 0.0

limit_pref_v = 0.0

start_pref_ae = 0.0

limit_pref_ae = 0.0

enable_atom_ener_coeff = False

inference = False

intensive_ener_virial = False

forward(input_dict: dict[str, torch.Tensor], model: torch.nn.Module, label: dict[str, torch.Tensor], natoms: int, learning_rate: float, mae: bool = False) → tuple[dict[str, torch.Tensor], torch.Tensor, dict[str, torch.Tensor]]

Return energy loss with magnetic labels.

Parameters:

input_dictdict[str, torch.Tensor]

Model inputs.

modeltorch.nn.Module

Model to be used to output the predictions.

labeldict[str, torch.Tensor]

Labels.

natomsint

The local atom number.

Returns:

model_pred: dict[str, torch.Tensor]

Model predictions.

loss: torch.Tensor

Loss for model to minimize.

more_loss: dict[str, torch.Tensor]

Other losses for display.

property label_requirement: list[deepmd.utils.data.DataRequirementItem]

Return data label requirements needed for this loss calculation.

serialize() → dict

Serialize the loss module.

classmethod deserialize(data: dict) → EnergySpinLoss

Deserialize the loss module.