Mastering GANs: Image Generation with Python and GauGAN

Set up the working directory in Google Drive by downloading 'Gorgon image generation' folder and uploading it, including 'Facts data' folder and 'code dot ipynb' file, before proceeding.

Explore the facts dataset for computer vision and image-to-image translation, featuring real-world photographs of facades (jpg), segmentation maps (bmp), and labeled images (png) used to train GauGAN.

Explore the code notebook ipynb that contains the code and documentation for the Gauguin project, outlining the steps for building, training, and evaluating the Goguen model across upcoming lectures.

Launch the code notebook in Google Colab to run and modify the project interactively, following each notebook section as guided.

Enable the GPU in Colab by selecting Change runtime type, choosing GPU from the hardware accelerator dropdown, and saving to accelerate computations for efficient training of our models.

Mount Google Drive in Google Colab to access Drive files from the interactive cloud-based Python notebook. Authenticate, then read and write data for datasets and collaboration.

Install and upgrade pip to access TensorFlow and Keras libraries. Learn how TensorFlow provides the backend and Keras offers an easier API for building and training models.

Configure the environment by setting the TensorFlow backend for Keras, explain the OS module and os.environ for environment variables, and ensure consistent tensor operations across systems.

Import numpy, matplotlib.pyplot, tensorflow, and keras to build, train, and visualize neural networks. Use ops and layers for network components, and globe to load images from a directory.

Set the dataset path on Google Drive and load the data for training and testing, and set the split to 0.2 for 20% testing and 80% training.

Use the globe function to gather all jpg files in the facts data directory, store them in the files list, and shuffle them to improve generalization during training.

Split the dataset into training and validation sets using a proportion, train on the training data, and use the validation set to evaluate and tune hyperparameters to prevent overfitting.

Display dataset sizes and split proportions to verify loading and the 80/20 training-validation split. Confirm that the dataset contains 378 images, with 302 for training and 76 for validation.

Set batch size to four and fix image dimensions to 256 by 256, training with 12 classes and autotune to optimize the data pipeline for faster, efficient GAN image generation.

Load and preprocess image data for training and validation, including random cropping, normalization to -1 to 1, and one-hot encoding of segmentation labels, then batch the data for efficient training.

Load and prepare the training and validation datasets, applying augmentation to training data for robust learning. Use batch size for both; validation has no augmentation.

Visualize segmentation maps and real images in paired batches to assess alignment, verify data loading, and understand preprocessing when rescaling from -1 to 1 for display.

Master the spade layer for spatially adaptive normalization conditioned by a semantic segmentation mask. Learn to normalize inputs and modulate with gamma and beta via convolutional paths for image generation.

Explore a residual block with spade layers that adapt normalization to a segmentation mask, enabling image generation and image-to-image translation in gan-based architectures.

Implement and use the gaussian sampler class to draw latent vectors from a learned gaussian distribution in a variational encoder, employing the reparameterization trick for differentiable sampling.

Create a downsampling block by stacking a convolutional layer with channels, kernels, strides, and same padding, optionally adding group normalization, leaky relu, and dropout for encoders in autoencoders and GANs.

Build an encoder model for a variational autoencoder by downsampling images through convolutional layers, flattening to a latent mean and variance, enabling reparameterization and sampling.

Generate a realistic image from a latent vector and segmentation mask using a generator with dense reshaping, upsampling blocks, adaptive normalization, leaky ReLU, and a tanh final layer.

Defines the KL divergence loss function to regularize the latent space of a variational encoder, enforcing a standard normal distribution and promoting a smooth latent representation.

implement feature matching loss in GAN training with Keras to compare intermediate feature maps of real and generated images, stabilizing training with mean absolute error.

Explore a custom VGG feature matching loss that uses a pre-trained VGG19 and mean absolute error to compare real and generated image feature maps, weighting layer contributions for realism.

Apply hinge loss to train the discriminator in GANs, using a custom Keras loss class to distinguish real from fake samples and encourage a margin between correct and incorrect classifications.

Explore a custom Keras callback GAN monitor that tracks GAN training, generating and visualizing mask, ground truth, and generated images from the validation set at regular epoch intervals using matplotlib.

Defines the Gorgon model class, a gan for image synthesis from semantic segmentation maps, detailing the generator, discriminator, encoder, and sampler components and losses.

Instantiate the GauGAN model with configured height, classes, batch size, and latent space, compile optimizers and losses, and train for 15 epochs with a Gan monitor visualizing the generator's progress.

Visualize training progress by plotting training and validation metrics across epochs with a history object, labeling axes, and adding a legend, grid, and metric-specific title.

Visualize GAN training progress by plotting discriminator, generator, feature matching, VGG feature matching, and KL divergence losses across epochs, comparing training and validation curves to diagnose training dynamics.

Generate fake images from latent vectors and segmentation maps using the trained Gorgon model, then compare them with real validation images and masks in a grid.