deepmd.pt.loss.ener_spin

Module Contents

Classes

EnergySpinLoss

Base class for all neural network modules.

class deepmd.pt.loss.ener_spin.EnergySpinLoss(starter_learning_rate=1.0, 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: float = 0.0, limit_pref_ae: float = 0.0, start_pref_pf: float = 0.0, limit_pref_pf: float = 0.0, use_l1_all: bool = False, inference=False, **kwargs)

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 to nest them 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):
        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 have their parameters converted too 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.

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

Return data label requirements needed for this loss calculation.

forward(input_dict, model, label, natoms, learning_rate, mae=False)

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.