DCGAN 만들기

A DCGAN is a GAN built from convolutional layers. It swaps dense layers for convolutions, which makes it great at generating realistic images. 🖼️

The generator uses transposed convolutions to upsample a tiny latent vector step by step into a full-size image.

The discriminator does the reverse: strided convolutions shrink the image down to a single real-or-fake score.

DCGAN puts batch normalization between layers in both networks. It keeps activations healthy and makes training far more stable.

The generator uses ReLU activations on its hidden layers to keep gradients flowing as the image is built up.

The discriminator prefers LeakyReLU, which lets a small signal through for negative values and avoids dead neurons.

The generator ends with Tanh, squeezing pixels into the range minus one to one to match normalized training images.

The discriminator finishes with a sigmoid, turning its final feature into a probability between zero and one.

DCGAN starts weights from a small normal distribution centered near zero. Good init helps both networks train smoothly from the first step.

The latent dimension, often 100, sets how many random numbers seed each image. Larger latents give the generator more room to vary.

Save a grid of generated images every few epochs. Watching them get sharper is how you confirm a DCGAN is actually learning.

Which activation belongs at the very end of the generator?

You assembled a DCGAN: transposed convolutions grow images, strided convolutions judge them, and batch norm keeps it all stable. 🛠️