This project aims at visualize loss and other important metrics for analysis.

This project also implement an instance of neural-style, follows the idea of a keras example keras/examples/neural_style_transfer.py, but with a mostly different design.

Visualization is important and fun. It tells us what's going on.

For example, you may find an bad output

after comparing the loss, you will found negative correlation between style loss and content loss(against the assumption of neural-style):

so a very small picture may not be very suitable for neural-style task.

Here's a better result with nearly independent loss:

A very high learning rate:

You can stop your training at any time and continue at the last epoch.

You are free to adjust hyperparameter