Mastering Image Generation with GANs using Python and Keras

سرفصل های دوره

Hands-On Image Generation with Generative Adversarial Networks (GANs) using Python, TensorFlow, & Keras in Google Colab

1. Fundamentals

1. Introduction

2. About this Project

3. Why should we learn

4. Applications

5. Why Python and Keras

6. Why Google Colab

2. Model Building and Training

1.1 cats.zip

1. Download Dataset

2.1 code.zip

2. Python Code

3. Activate GPU

4. Current Status of GPU

5. Mounts Google Drive to a Google Colab notebook

6. Importing libraries and Modules

7. Enabling NumPy-like Behavior in TensorFlow

8. Setting the Random Seed

9. Setting up a Directory

10. Visualizing Large Datasets of Images

11. Settings for the Training Process

12. Sets the Number of Training Samples

13. Training Images are Loaded and Preprocessed

14. List of Preprocessed Images Converted into a Numpy Array

15. Normalizing the pixel values

16. Creating a Shuffled and Batched TensorFlow Dataset

17. Define Discriminator Neural Network Model

18. Define Generator Neural Network Model

19. Generators loss

20. Loss of the Discriminator

21. Define and Summarize the Generator and Discriminator Networks

22. Define Optimizers

23. Define a Loss Function

24. Visualize the Generated Images

25. Create and Save Checkpoints During Training

26. Value of the latent dim Hyperparameter

27. Generating a Tensor of Random Noise

28. Define a TensorFlow training step

29. Plot Training Metrics During the GAN Training

30. Define Training Function

31. Function Generates Images Using the Generator Model

32. Training

33. Creates an Animated GIF

34. Generate and Visualize Images from the Generator

35. Showing Examples of Images Generated by the GAN Model

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تولید کننده: Udemy-Training