core

Fill in a module description here

---

source

foo

 foo ()

Code

def addition(a,b):
    "Adds two numbers together"
    return a+b

Code + Explanation

def addition(a,b):
    "Adds two numbers together"
    return a+b

a+b

We take the sum of a and b, which is written in python with the “+” symbol

from torch import nn

from fastcore.foundation import docs

@docs
class A(nn.Module):
    def __init__(self):
        super().__init__()
      
    _docs = dict(cls_doc="xxx")

---

source

A

 A ()

xxx

class B(nn.Module):
    # A simple comment preceding a simple print statement
    def __init__(self):
        super().__init__()

---

source

B

 B ()

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 :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool