renato145 / fastexplorer

Utility to visualize fastai models via javascript

Fast Explorer

Work in progress

A utility to visualize models.

Main idea:

• Use this library to attach a learner and initialize a proxy server.
• Connect a javascript client via fastexplorer-js.
• Send back and forth relevant information to be visualized.

This will allow to use tools like d3, react and threejs to visualize NN information.

Install

pip install git+https://github.com/renato145/fastexplorer.git

How to use

# all_slow
%reload_ext autoreload
%autoreload 2

Load you Learner as usual and import fastexplorer:

from fastai.vision.all import *
from fastexplorer.all import *

path = untar_data(URLs.PETS)
files = get_image_files(path/"images")
def label_func(f): return f[0].isupper()
dls = ImageDataLoaders.from_name_func(path, files, label_func, item_tfms=Resize(224))
learn = cnn_learner(dls, resnet34, metrics=accuracy)

When ready, start serving the server:

#srv
learn.fastexplorer(True)

INFO:root:To visualize the model information, go to:
INFO:root:https://renato145.github.io/fastexplorer-js
INFO:     Started server process [4507]
INFO:uvicorn.error:Started server process [4507]
INFO:     Waiting for application startup.
INFO:uvicorn.error:Waiting for application startup.
INFO:     Application startup complete.
INFO:uvicorn.error:Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
INFO:uvicorn.error:Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
INFO:     Shutting down
INFO:uvicorn.error:Shutting down
INFO:     Waiting for application shutdown.
INFO:uvicorn.error:Waiting for application shutdown.
INFO:     Application shutdown complete.
INFO:uvicorn.error:Application shutdown complete.
INFO:     Finished server process [4507]
INFO:uvicorn.error:Finished server process [4507]

Finally, go to https://renato145.github.io/fastexplorer-js/ to visualize the model:

Also, you can visualize the Loss Landscape:

Note that the original work calculates the landscape using the whole dataset which can take a lot of time. In this library, I am using just one batch of data to calculate the landscape (this will probably change once I find a faster way to calculate it).