deepmd.pt.loss.ener

Classes

EnergyStdLoss	Helper class that provides a standard way to create an ABC using
EnergyHessianStdLoss	Helper class that provides a standard way to create an ABC using

Functions

custom_huber_loss(→ torch.Tensor)

Module Contents

deepmd.pt.loss.ener.custom_huber_loss(predictions: torch.Tensor, targets: torch.Tensor, delta: float = 1.0) → torch.Tensor

class deepmd.pt.loss.ener.EnergyStdLoss(starter_learning_rate: float = 1.0, start_pref_e: float = 0.0, limit_pref_e: float = 0.0, start_pref_f: float = 0.0, limit_pref_f: 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, start_pref_pf: float = 0.0, limit_pref_pf: float = 0.0, relative_f: float | None = None, enable_atom_ener_coeff: bool = False, start_pref_gf: float = 0.0, limit_pref_gf: float = 0.0, numb_generalized_coord: int = 0, loss_func: str = 'mse', inference: bool = False, use_huber: bool = False, use_default_pf: bool = False, f_use_norm: bool = False, huber_delta: float | list[float] = 0.01, intensive_ener_virial: bool = False, **kwargs: Any)

Bases: deepmd.pt.loss.loss.TaskLoss

Helper class that provides a standard way to create an ABC using inheritance.

loss_func = 'mse'

starter_learning_rate = 1.0

has_e

has_f

has_v

has_ae

has_pf

has_gf

start_pref_e = 0.0

limit_pref_e = 0.0

start_pref_f = 0.0

limit_pref_f = 0.0

start_pref_v = 0.0

limit_pref_v = 0.0

start_pref_ae = 0.0

limit_pref_ae = 0.0

start_pref_pf = 0.0

limit_pref_pf = 0.0

start_pref_gf = 0.0

limit_pref_gf = 0.0

use_default_pf = False

relative_f = None

enable_atom_ener_coeff = False

numb_generalized_coord = 0

inference = False

use_huber = False

f_use_norm = False

intensive_ener_virial = False

huber_delta = 0.01

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 loss on energy and force.

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.

Returns:

dict

The serialized loss module

classmethod deserialize(data: dict) → deepmd.pt.loss.loss.TaskLoss

Deserialize the loss module.

Parameters:

datadict

The serialized loss module

Returns:

Loss

The deserialized loss module

class deepmd.pt.loss.ener.EnergyHessianStdLoss(start_pref_h: float = 0.0, limit_pref_h: float = 0.0, **kwargs: Any)

Bases: EnergyStdLoss

Helper class that provides a standard way to create an ABC using inheritance.

has_h

start_pref_h = 0.0

limit_pref_h = 0.0

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 loss on energy and force.

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]

Add hessian label requirement needed for this loss calculation.