The generator is the fun part of this structure. The generator is going to take inputs from the latent space (a sample from a normal distribution in this recipe) and produce realistic looking data. The generator will also be added to the adversarial part of the training. The GAN will take in latent examples with labels and train on that until the generator itself is able to produce realistic looking images. We'll see some examples of the generated images in the near future.
Explaining your second GAN component – generator
by Josh Kalin
Generative Adversarial Networks Cookbook
- Title Page
- Copyright and Credits
- About Packt
- Dedication
- Contributors
- Preface
- Dedication2
- What Is a Generative Adversarial Network?
- Introduction
- Generative and discriminative models
- A neural network love story
- Deep neural networks
- Architecture structure basics
- Basic building block – generator
- Basic building block – discriminator
- Basic building block – loss functions
- Training
- GAN pieces come together in different ways
- What does a GAN output?
- Understanding the benefits of a GAN structure
- Exercise
- Data First, Easy Environment, and Data Prep
- My First GAN in Under 100 Lines
- Introduction
- From theory to code – a simple example
- Building a neural network in Keras and TensorFlow
- Explaining your first GAN component – discriminator
- Explaining your second GAN component – generator
- Putting all the GAN pieces together
- Training your first GAN
- Training the model and understanding the GAN output
- Exercise
- Dreaming of New Outdoor Structures Using DCGAN
- Introduction
- What is DCGAN? A simple pseudocode example
- Tools – do I need any unique tools?
- Parsing the data – is our data unique?
- Code implementation – generator
- Code implementation – discriminator
- Training
- Evaluation – how do we know it worked?
- Adjusting parameters for better performance
- Exercise
- Pix2Pix Image-to-Image Translation
- Style Transfering Your Image Using CycleGAN
- Using Simulated Images To Create Photo-Realistic Eyeballs with SimGAN
- Introduction
- How SimGAN architecture works
- Pseudocode – how does it work?
- How to work with training data
- Code implementation – loss functions
- Code implementation – generator
- Code implementation – discriminator
- Code implementation – GAN
- Training the simGAN network
- Exercise
- From Image to 3D Models Using GANs
- Introduction
- Introduction to using GANs in order to produce 3D models
- Environment preparation
- Encoding 2D data and matching to 3D objects
- Code implementation – generator
- Code implementation – discriminator
- Code implementation – GAN
- Training this model
- Exercise
- Other Books You May Enjoy
Explaining your second GAN component – generator
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