deepmd.pt.loss.denoise

Module Contents

Classes

DenoiseLoss

Base class for all neural network modules.

class deepmd.pt.loss.denoise.DenoiseLoss(ntypes, masked_token_loss=1.0, masked_coord_loss=1.0, norm_loss=0.01, use_l1=True, beta=1.0, mask_loss_coord=True, mask_loss_token=True, **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.

forward(model_pred, label, natoms, learning_rate, mae=False)

Return loss on coord and type denoise.

Returns:

• loss: Loss to minimize.