Project Created by George Lancaster

Installing Dependencies

To be able to run the files in this project, create a new Python 3.5 environment and install the packages from the requirements.txt using pip. Instructions for creating a new Python 3.5 environment can be found at the bottom of this readme file, although there are many tutorials online.

• requirements-cpu.txt, if you have not configured an Nvidia gpu with CUDA;
• requirements-gpu.txt, if you have configured an Nvidia gpu with CUDA.

Use the following command in your newly created, and activated Python 3.5 environment:

• pip install -r requirements-cpu.txt

Project Layout

The testing and training data can be found in the test_train_data folder. The various splits of the emnist data set can be found in:

• test_train_data/emnist

When using the "-d" modifier in the commands, the string given must be the type of dataset from within this folder.

For example "letters"

Running the Project

The files in this project perform two actions.

• training and testing neural networks;
• running the web application.

Web Application

To run the web application, navigate to the project folder using cd in the command line and enter the following command:

• python app_main.py

The application will start and can be found on port 5000.

There are a variety of images that can be uploaded and tested found in:

• OCR/test_train_data/app_testing_images

Training a Neural Network

To run train_network.py type into command line:

• python train_network.py -d "dataType" -t "type" -s name -e 1

-d: the type of data to test on. String, must be one of:

• balanced
• byclass
• bymerge
• digits
• letters

-r: apply rotate transformations

-s: apply shift transformations

-z: apply zoom transformations

For example:

• python train_network.py

• python train_network.py -d "letters" -t "lenet" -e "200" -r -z

Creating an Ensemble Classifier

To run ensemble.py type into command line:

• python ensemble.py -1 -2 -3 -p

-1: create ensemble_1

-2: create ensemble_2

-3: create ensemble_3

-p: test on the PDS data set

examples:

• python ensemble.py -1

• python ensemble.py -2 -p

Testing a Neural Network

To run test_network.py type into command line:

• python test_network.py -m "model name" -d "data type" or
• python test_network.py -h

-d: the type of data to test on. String, must be one of:

• balanced
• byclass
• bymerge
• digits
• letters

-m: the name of the model. All models can be found in the "model" folder in the project directory. Give this as a string.

-n: normalise the data in the confusion matrix

examples:

• python test_network.py -m "emnist-digits_200epoch_lenet_rotate_zoom" -d "digits"

• python test_network.py -m "emnist-letters_300epoch_lenet_2_4" -d "letters"

Additional Help

The application has been tested on both Windows and Unix systems, so any problems will be due to an incorrectly configured Python 3.5 environment, or errors when installing the requirements-xxx.txt.

Testing and Training Neural Networks

All arguments must be written as is. Any spelling mistakes will prevent the program from running.

Create a Python 3.5 Environment

To create and activate a new Python 3.5 environment:

1. install anaconda
2. use the command: 2.1 conda create -n ktfo python=3.5
3. activate the new environment 3.1 Windows: activate ktfo 3.2 Unix: source activate ktfo

Additional Help

For additional help, please don't hesitate to contact me on:

• Lancaster0180@gmail.com
• gl162@brighton.ac.uk
• 07581781417

A version of the web application is hosted on:

http://georgelancaster.pythonanywhere.com/ This version is slow, as it is being hosted cheaply.