timesead.models.common.mlp

Classes

MLP	Base class for all neural network modules.

Module Contents

class timesead.models.common.mlp.MLP(input_features: int, hidden_layers: int | Sequence[int], output_features: int, activation: Callable = torch.nn.Identity(), activation_after_last_layer: bool = False)

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested 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) -> None:
        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 also have their parameters converted 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.

Parameters:

• input_features (int)

• hidden_layers (Union[int, Sequence[int]])

• output_features (int)

• activation (Callable)

• activation_after_last_layer (bool)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

activation

activation_after_last_layer = False

layers

forward(x: torch.Tensor) → torch.Tensor

Parameters:

x (torch.Tensor)

Return type:

torch.Tensor