[Pytorch] Autoencoder Base code

import torch
import torchvision
import torch.nn.functional as F
from torch import nn, optim
from torchvision import transforms, datasets

class Autoencoder(nn.Module):
    def __init__(self):
        super(Autoencoder, self).__init__()
        
        self.encoder = nn.Sequential(
            nn.Linear(28*28, 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, 12),
            nn.ReLU(),
            nn.Linear(12, 3),
                    )
        
        self.decoder = nn.Sequential(
            nn.Linear(3, 12),
            nn.ReLU(),
            nn.Linear(12, 64),
            nn.ReLU(),
            nn.Linear(64, 128),
            nn.ReLU(),
            nn.Linear(128, 28*28),
            nn.Sigmoid(),
        )
        
    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return encoded, decoded

autoencoder = Autoencoder()
optimizer = torch.optim.Adam(autoencoder.parameters(), lr = 0.005)
criterion = nn.MSELoss()

학습진행시 input과 output을 동일한 대상으로 넣어줘서 학습을 진행