On this project I implemented, with tensorflow 2.0 and keras, Sketch-rnn, a Variational Autoencoder for generating sketches. I followed the original paper.
The architecture is the following.
I have created also a colab notebook where everyone can train the model and check the results.
I have trained the model with two different sketches:
- Carrot
- Cat
Carrot where the hidden variable is sampled from the IID gaussian:
Cat where the hidden variable is sampled from the IID gaussian:
There is also a Jupyter notebook for testing the sampling. We can sample from a hidden variable produced by the encoder or form a sample of a IID gaussian. The weights of the trained model are in the model directory.