credit.models.base_model

Attributes

logger

Classes

BaseModel

Base class for all neural network modules.

Module Contents

credit.models.base_model.logger

class credit.models.base_model.BaseModel

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.

concat_and_reshape(x1, x2)

x1: upper-air variables with level dimensions. x2: surface variables.

reshape_only(x1)

As in “concat_and_reshape”, but for upper-air variables only.

split_and_reshape(tensor)

classmethod load_model(conf)

classmethod load_model_name(conf, model_name)

save_model(conf)