deepmd.pt.model.network.mlp
Module Contents
Classes
Base class for all neural network modules. | |
Native representation of a neural network. | |
PyTorch implementation of NetworkCollection. |
Functions
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Attributes
- class deepmd.pt.model.network.mlp.MLPLayer(num_in, num_out, bias: bool = True, use_timestep: bool = False, activation_function: str | None = None, resnet: bool = False, bavg: float = 0.0, stddev: float = 1.0, precision: str = DEFAULT_PRECISION)[source]
Bases:
torch.nn.ModuleBase 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.