Neuron

CNN implementation from scratch (learning by doing)

I used weather data (temperature, pression, rain, wind ...) to build and test my neural network.

Thereafter a gif pointing out the training session (each image is separated from the previous by 1000 epochs)

The result (orange) on temperature data (blue)

This neural network can predict temperature within an interval below +- 2°C.

More about sources Here

More about Neural style transfert on tensorflow here

About

When training, CNN display information regarding its work (Please, be kind regarding my conf :'()

----------Hardware config information----------
CPU :
          Physical cores: 2
          Total cores: 4
GPU :
          GeForce 920M

----------Forward prop----------
A1: (64, 28, 28, 6)
A2: (64, 14, 14, 6)
A3: (64, 10, 10, 16)
A4: (64, 5, 5, 16)
A5: (400, 64)
A6: (120, 64)
A7: (84, 64)
A8: (10, 64)
----------Cost computation----------
Cost: 1.622069
----------Bacward prop----------
dA9: (10, 64)
dA8: (84, 64)
dA7: (120, 64)
dA6: (400, 64)
dA5: (64, 5, 5, 16)
dA4: (64, 10, 10, 16)
dA3: (64, 14, 14, 6)
dA2: (64, 28, 28, 6)
----------Time spent----------
forward conv: 65.423750
forward pool: 7.893981
forward dense: 0.007658
backward conv: 101.860744
backward pool: 17.962010
backward dense: 0.007316

CNN architecture can be parse following the schema thereafter

AlexNet = (('input', (224, 224, 3)),
           ('conv', (8, 3, 96, 0, 4)),('pool', (3, 2), 'max'), 
           ('conv', (5, 96, 256, 2, 0)), ('pool', (3, 2), 'max'), 
           ('conv', (3, 256, 384, 1, 0)), ('conv', (3, 384, 384, 1, 0)), 
           ('conv', (3, 384, 256, 1, 0)), ('pool', (3, 2)), 
           ('flatten', 9216), 
           ('dense', 4096, 'relu'), ('dense', 4096, 'relu'),
           ('dense', 1000, 'relu'), 
           ('dense', 10, 'sigmoid'))

Error will occur if you do not parse layers in a way that ouput layers l-1 shape fit input l layer shape

Installation

Windows:

git clone

Then run train.py will train different network architectures until it finds one below your specified accuracy. Or Run NeuralFunctions.py to get launch CNN on GPU. (data is randomly generated or now)

Usage example

Split data into classes

Release History

0.2.2 | Currently reworking conv and pool layer to get rid of loops (4000 times faster and dask friendly architecture)
0.2.1 | Improved CNN and hardaware setup display. Dask can't apply on CONV layers. Maybe delayed ?
0.2.0 | Running CNN -> Try Dask and UCX + remove unnecessary files
0.1.0 | Almost done CNN (some bug fix in bacward)
0.0.0 | Rebuild the github (started the project last year where github was out of my scope)

Contributing

Fork it (https://github.com/MaximeAeva/Neuron/fork)
Create your feature branch (git checkout -b feature/fooBar)
Commit your changes (git commit -am 'Add some fooBar')
Push to the branch (git push origin feature/fooBar)
Create a new Pull Request