deepmd.pt.model.task.property

deepmd.pt.model.task.property#

Attributes#

`dtype`
`device`
`log`

Classes#

PropertyFittingNet

Fitting the rotationally invariant properties of task_dim of the system.

Module Contents#

deepmd.pt.model.task.property.dtype[source]#

deepmd.pt.model.task.property.device[source]#

deepmd.pt.model.task.property.log[source]#

class deepmd.pt.model.task.property.PropertyFittingNet(ntypes: int, dim_descrpt: int, property_name: str, task_dim: int = 1, neuron: list[int] = [128, 128, 128], bias_atom_p: torch.Tensor | None = None, intensive: bool = False, resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, dim_case_embd: int = 0, activation_function: str = 'tanh', precision: str = DEFAULT_PRECISION, mixed_types: bool = True, trainable: bool | list[bool] = True, seed: int | None = None, default_fparam: list | None = None, distinguish_types: bool = True, **kwargs: Any)[source]#

Bases: deepmd.pt.model.task.ener.InvarFitting

Fitting the rotationally invariant properties of task_dim of the system.

Parameters:

ntypesint: Element count.
dim_descrptint: Embedding width per atom.
task_dimint: The dimension of outputs of fitting net.
property_name:: The name of fitting property, which should be consistent with the property name in the dataset. If the data file is named humo.npy, this parameter should be “humo”.
neuronlist[int]: Number of neurons in each hidden layers of the fitting net.
bias_atom_ptorch.Tensor, optional: Average property per atom for each element.
intensivebool, optional: Whether the fitting property is intensive.
resnet_dtbool: Using time-step in the ResNet construction.
numb_fparamint: Number of frame parameters.
numb_aparamint: Number of atomic parameters.
dim_case_embdint: Dimension of case specific embedding.
activation_functionstr: Activation function.
precisionstr: Numerical precision.
mixed_typesbool: If true, use a uniform fitting net for all atom types, otherwise use different fitting nets for different atom types.
seedint, optional: Random seed.
distinguish_typesbool: Whether to distinguish atom types when computing output statistics.

task_dim = 1[source]#

intensive = False[source]#

distinguish_types = True[source]#

output_def() → deepmd.dpmodel.FittingOutputDef[source]#: Returns the output def of the fitting net.

get_intensive() → bool[source]#: Whether the fitting property is intensive.

get_distinguish_types() → bool[source]#: Get whether to distinguish atom types when computing output statistics.

classmethod deserialize(data: dict) → PropertyFittingNet[source]#

Deserialize the fitting.

Parameters:

datadict: The serialized data

Returns:

BF: The deserialized fitting

serialize() → dict[source]#: Serialize the fitting to dict.

exclude_types: list[int][source]#