deepmd.pt.model.model.model

Module Contents

Classes

BaseModel

Base class for all neural network modules.

class deepmd.pt.model.model.model.BaseModel(*args, **kwargs)

Bases: torch.nn.Module, make_base_model()

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.

abstract compute_or_load_stat(sampled_func, stat_file_path: deepmd.utils.path.DPPath | None = None)

Compute or load the statistics parameters of the model, such as mean and standard deviation of descriptors or the energy bias of the fitting net. When sampled is provided, all the statistics parameters will be calculated (or re-calculated for update), and saved in the stat_file_path`(s). When `sampled is not provided, it will check the existence of `stat_file_path`(s) and load the calculated statistics parameters.

Parameters:

sampled_func

The sampled data frames from different data systems.

stat_file_path

The path to the statistics files.

get_model_def_script() → str

Get the model definition script.

get_ntypes()

Returns the number of element types.