Deep Learning Academy · 강의

nn.Module 상속하기: init과 forward

표준 PyTorch 모델 뼈대를 알아봅니다

레슨 1/413개 단계

nn.Module 상속하기: __init__과 forward은(는) CoddyKit의 무료 Deep Learning Academy 강의입니다. 이것은 4개 중 1번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 Deep Learning Academy 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. Deep Learning Academy 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

Models Are Just Classes

In PyTorch, every model is a Python class. You build yours by subclassing nn.Module, the base that powers all networks.

Two Methods Run the Show

A model needs only two methods to work: __init__ sets up the layers, and forward describes how data flows through them.

Always Call super().init

The very first line inside __init__ must call super().__init__(). This wires your model into PyTorch and lets it track everything. 🔌

class Net(nn.Module):
    def __init__(self):
        super().__init__()

Define Layers in init

Inside __init__ you create your layers and store them as attributes. Saving them on self lets PyTorch register them automatically.

self.fc1 = nn.Linear(4, 8)
self.fc2 = nn.Linear(8, 2)

forward Is the Recipe

The forward method takes an input tensor and returns an output. It spells out the exact order your layers process the data.

def forward(self, x):
    x = self.fc1(x)
    return self.fc2(x)

Never Call forward Directly

You call the model like a function, not model.forward(x). Using model(x) runs hooks and bookkeeping that forward alone skips.

out = model(x)   # preferred
# not: out = model.forward(x)

Layers Become Parameters

Because layers live on self, their weights are collected as the model's parameters. The optimizer later updates exactly these.

Instantiate Then Use

Create the model once, then feed it tensors many times. Each call flows through the same learned weights.

model = Net()
prediction = model(sample_input)

Shapes Must Line Up

Each layer's output size must match the next layer's input size. Plan these dimensions as data travels through forward.

Why This Pattern Wins

Subclassing keeps setup and flow cleanly apart. The same simple skeleton scales from a tiny net to a giant one.

Flexible by Design

Inside forward you can branch, reshape, or reuse layers freely. This freedom is why custom nn.Module classes are so powerful. 💪

Quick Check

Think about which method defines how data moves through the network.

인터랙티브 레슨에서 답해보세요

Recap

You build a model by subclassing nn.Module, defining layers in __init__ and the data flow in forward. Then just call model(x). 🎯

무료로 시작

AI 튜터와 함께 Python을(를) 배우세요 — 무료

브라우저에서 실제 코드를 작성하고 실행하며, 24/7 AI 튜터로부터 즉각적인 도움을 받고, 웹이나 앱에서 중단한 부분부터 계속 학습하세요.

코스: 30
레슨: 120

무료로 학습 시작 전체 코스 보기

자주 묻는 질문