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Deep Convolutional Generative Adverserial Network

This project makes an attempt to implement a DCGAN network consisting of a generator n/w and discriminator n/w. The dataset used to train the generator is CIFAR-10 because of the simplicity.

Implementation Details

  • python 3+
  • Tensorflow 2.0
  • CIFAR - 10

Architecture

Generator

  1. Dense 8x8x256 (Reshaped) + LeakyReLU
  2. Upconvolution Conv2DTranspose (5x5x128-s-1) + BatchNorm + LeakyReLU
  3. Upconvolution Conv2DTranspose (5x5x64-s-2) + BatchNorm + LeakyReLU
  4. Upconvolution Conv2DTranspose (5x5x3-s-2) + BatchNorm + LeakyReLU

Discriminator

  1. Conv2D (5x5x64-s-2) + BatchNorm + LeakyReLU + Dropout
  2. Conv2D (5x5x128-s-2) + BatchNorm + LeakyReLU
  3. Conv2D (5x5x128-s-2) + BatchNorm + LeakyReLU
  4. Dense (1) + Sigmoid