Coopss / Emnist
Licence: mit
A project designed to explore CNN and the effectiveness of RCNN on classifying the EMNIST dataset.
Stars: ✭ 81
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EMNIST
Developed by @coopss
Description
This project was intended to explore the properties of convolution neural networks (CNN) and see how they compare to recurrent convolution neural networks (RCNN). This was inspired by a paper I read that details the effectiveness of RCNNs in object recognition as they perform or even out perform their CNN counterparts with fewer parameters. Aside from exploring CNN/RCNN effectiveness, I built a simple interface to test the more challenging EMNIST dataset dataset (as opposed to the MNIST dataset)
Current Implementation
- Multistack CNN
- Web-applet testing environment
- Touch screen compatible
- Works best when letter takes up a good portion of the canvas
- Read in .mat file
- Currently training on the byclass dataset (direct download link)
- See paper for more info
Todo
- Update gif with new webapp
- Train more models
- RCNN
- Optimize hyperparameters
- Add a noise (gaussian or likewise) layer to input in an attempt to boost accuracy
- Move webapp to a host service like PythonAnywhere
Environment
Anaconda: Python 3.5.3
- Tensorflow or tensorflow-gpu (See here for more info)
- Keras
- Flask
- Numpy
- Scipy
Note: All dependencies for current build can be found in dependencies.txt
Usage
training.py
A training program for classifying the EMNIST dataset
usage: training.py [-h] --file [--width WIDTH] [--height HEIGHT] [--max MAX] [--epochs EPOCHS] [--verbose]
Required Arguments:
-f FILE, --file FILE Path .mat file data
Optional Arguments
-h, --help show this help message and exit
--width WIDTH Width of the images
--height HEIGHT Height of the images
--max MAX Max amount of data to use
--epochs EPOCHS Number of epochs to train on
--verbose Enables verbose printing
server.py
A webapp for testing models generated from training.py on the EMNIST dataset
usage: server.py [-h] [--bin BIN] [--host HOST] [--port PORT]
Optional Arguments:
-h, --help show this help message and exit
--bin BIN Directory to the bin containing the model yaml and model h5 files
--host HOST The host to run the flask server on
--port PORT The port to run the flask server on
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