deepmd.pt.model.model.dp_zbl_model

Module Contents

Classes

DPZBLModel

Base class for all neural network modules.

Attributes

DPZBLModel_

deepmd.pt.model.model.dp_zbl_model.DPZBLModel_

class deepmd.pt.model.model.dp_zbl_model.DPZBLModel(*args, **kwargs)

Bases: DPZBLModel_

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.

model_type = 'ener'

forward(coord, atype, box: torch.Tensor | None = None, fparam: torch.Tensor | None = None, aparam: torch.Tensor | None = None, do_atomic_virial: bool = False) → Dict[str, torch.Tensor]

forward_lower(extended_coord, extended_atype, nlist, mapping: torch.Tensor | None = None, fparam: torch.Tensor | None = None, aparam: torch.Tensor | None = None, do_atomic_virial: bool = False)

classmethod update_sel(global_jdata: dict, local_jdata: dict)

Update the selection and perform neighbor statistics.

Parameters:

global_jdatadict

The global data, containing the training section

local_jdatadict

The local data refer to the current class