Learning deep learning (project 5, generate new celebrity faces)

37 sec read

In this class project, I used generative adversarial network (GAN) to generate new images of faces, similar to celebrity faces in the database.

The model we use is a deep convolutional network, which has been used widely in image classification.

First, we use the MNIST database (collection of 60,000 handwriting digits). After the training, the model can generate digits similar to what we have in the training set. We only trained it for two epochs.  I believe we can generate more realistic images if we train it longer.

Generate new handwriting digits
Generate new handwriting digits

Then we use ~200,000 images of celebrity faces to train our model. The training takes much longer time, but with my Nvidia 1080 Ti it’s fast. In the beginning, just after learning from 20,000 images, the model was able to generate face-like patterns. Then after the complete 10 epoch training, it generate very clear faces.

Generate new faces
Generate new faces

The project can be found at https://www.alivelearn.net/deeplearning/dlnd_face_generation.html

Deep learning training speed with 1080 Ti and M1200

I compared the speed of Nvidia’s 1080 Ti on a desktop (Intel i5-3470 CPU, 3.2G Hz, 32G memory) and NVIDIA Quadro M1200 w/4GB GDDR5,...
Xu Cui
1 min read

Learning deep learning (project 4, language translation)

In this project, I built a neural network for machine translation (English -> French).  I built and trained a sequence to sequence model on...
Xu Cui
27 sec read

Deep learning speed test, my laptop vs AWS g2.2xlarge…

It requires a lot of resources, especially GPU and GPU memory, to train a deep-learning model efficiently. Here I test the time it took...
Xu Cui
2 min read

Leave a Reply

Your email address will not be published. Required fields are marked *