Welcome to the whimsical world of deep learning where we explore how to generate pictures of our feline friends using Generative Adversarial Networks (GANs) and transform them into pieces of art using deep convolutional neural networks. Whether you’re a coding novice or a seasoned programmer, this guide will take you through the steps you need to embark on this fun project!

Objectives of the Project

• Generate images of cats using various types of Generative Adversarial Networks (GAN):
• DCGAN (Done)
• WGAN (Done)
• WGAN-GP (Done)
• LSGAN (Done)
• BEGAN
• Transform real cats into art pieces:
• CycleGAN
• Fast neural style (Done)
• Various others:
• Try adding Frechet Inception Distance (FID)
• Try soft and noisy labels
• Try adding decaying noise to input

Required Tools

Before we dive into the code, let’s ensure you have everything you need:

• Python 3.6
• PyTorch
• Tensorflow (for TensorBoard)
• Cat Dataset (available here or here)
• TensorBoard logger (available here)

Set Up Your Environment

Let’s set up your environment and directory structure:

bash
# Download dataset and preprocess cat pictures
# Create two folders, one for cats bigger than 64x64 and one for cats bigger than 128x128
sh setting_up_script.sh
# Move to your favorite place
mv cats_bigger_than_64x64 your_input_folder_64x64
mv cats_bigger_than_128x128 your_input_folder_128x128

Generating Cat Images

Now, let’s generate those adorable cat images using different GAN architectures. Remember, each command is like asking a cat to do a trick—each one requires specific inputs to perform correctly!

# Generate 64x64 cats using DCGAN
python DCGAN.py --input_folder your_input_folder_64x64 --output_folder your_output_folder

# Generate 128x128 cats using DCGAN
python DCGAN.py --input_folder=your_input_folder_128x128 --image_size 128 --G_h_size 64 --D_h_size 64 --SELU True

# Generate 64x64 cats using WGAN
python WGAN.py --input_folder your_input_folder_64x64 --output_folder your_output_folder

# Generate 64x64 cats using WGAN-GP
python WGAN-GP.py --input_folder your_input_folder_64x64 --output_folder your_output_folder --SELU True

# Generate 64x64 cats using LSGAN (Least Squares GAN)
python LSGAN.py --input_folder your_input_folder_64x64 --output_folder your_output_folder

Visualizing Results with TensorBoard

Once you’ve generated your cat art, you can visualize the training process using TensorBoard:

bash
tensorboard --logdir your_input_folder

Troubleshooting Tips

If you run into any hiccups during the installation or execution, don’t fret! Here are some troubleshooting tips:

• **Environment Issues**: Make sure that all the required packages are installed correctly. Sometimes, a simple reinstallation can fix version conflicts.
• **Dataset Problems**: If your dataset doesn’t load properly, double-check the paths to the folders. Ensure that the images are in the correct format and size.
• **TensorBoard Not Launching**: If TensorBoard doesn’t start, ensure you have the correct log directory specified and that you have TensorBoard installed in your Python environment.
• **Insufficient Memory**: During image generation, if you run into memory errors, try reducing the image size or batch size.

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Conclusion

And there you have it—a fun adventure into creating and transforming cat images with deep learning! Remember, the world of AI is vast and full of playful possibilities. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.