Introduction to GANs with TensorFlow | step-by-step guide

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In this tutorial, you will learn how to create Generative Adversarial Networks (GANs) in TensorFlow to generate new images using the MNIST digits dataset. Instead of using simple GAN, you will use DCGAN as it provides faster results. You will first load and preprocess the MNIST dataset, define the generator and discriminator models, and then define the loss functions for the generator and discriminator. You will then write a custom TensorFlow model implementation for GAN and train it using a batch of real and generated images. Finally, you will generate new MNIST digits using the trained model. You can use callbacks to track the training process and results.

#machinelearning #python #tensorflow #gans
  1. Python Lessons
  2. generative adversarial networks
  3. deep learning
  4. machine learning
  5. GANs
  6. TensorFlow
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Hi, thanks for the explanation. Why are you doing conv2d and then upsampling2d? I think in most other codes/tutorials they used a conv2d transposed to upscale the image.
Would be grateful for an explanation

DanielSchafhäutle