pymazes-vae

Maze generation with Variational Autoencoders

How to run the script

1. Create a virtual environment (Optional)

2. Install the requirements: pip install -r requirements.txt

3. Generate input images to train the variational autoencoder on: python augment_maze.py

4. Train the variational autoencoder: python maze_vae.py

5. By default, the script plots samples from the latent space and prints one random sample to output.gif

Loading weights without retraining

Comment the line:

history = vae.fit(x_train, x_train, epochs=5000, batch_size=128, callbacks=[model_checkpoint_callback, tf.keras.callbacks.EarlyStopping(patience=10, monitor='loss')], validation_data=(x_test, x_test))

and the line:

plot_history(history)

Notes

Maze size should match variational autoencoder layers architecture. In augment_maze.py: size=36 In vae_maze.py: size = 36 * 3