OpenMind

OpenMind is an open source software based on a Deep Learning algorithm.
This is a fast and simple (adaptable) algorithm for application in Data Science projects. This code was implemented in modular shape.

Note: Dear Windows user, if you want to compile this code you must read how to work with cmake, otherwise we suggest try it on linux SO. We are working on make a multi platform code.

If you'd like to contribute to OpenMind, be sure to review the contribution guidelines.

**We use GitHub issues for tracking requests and bugs.

**Mailing List: https://groups.google.com/forum/#!forum/openmind_dl

Requirements

1. cmake
2. gfortran
3. python 3.5 - You can use 2.7 if you change the run_script.py file.

Installation

./setup

Where you have the follow options:

0. Install (the binary go to /bin folder)
1. Run Test: This option run and example test (see examples).
2. Score data basis: This option just run the last ANN saved and allow to score a basis set.
3. Clean test folder: Clean the environment.
4. Debug: Run test and save some variables in temporal file.

Run arbitrary basis set

After compile the code, you get the binary file OpenMind.exe. You can copy that file in whatever folder with the input file from example (test/example/*.inp)

Then you run in shell

OpenMind.exe

Input file

**INPUT FILE NN_KERNEL
*KINDRUN
1
*READFILE
Basis_Train.csv
*SCORE FILE (IF KINDRUN>1)
Basis_Train.csv
*HIDDEN
20
*LAYER
1
*EPOCH
100
*LEARNING RATE (0-2)
1.70
*PERCENT TRAIN (0-100)
70
*PERCENT TEST (0-100)
30
*HEADER LINES
1
*SCREEN OUTPUT
1
*OUTPUT NAME
Basis_Train_scored.csv
*DEBUG
.TRUE.

Where

KINDRUN

have the follow options:

1. Train the Neural Network
2. Add score to basis set
3. Score one register
4. Debug

READFILE

Name of the file for training the neural network.

SCORE FILE (IF KINDRUN>1)

Name of the file of basis set for add score

HIDDEN

Number of hidden neurons

LAYER

Number of layer (in construction...)

EPOCH

Epoch

LEARNING RATE (0-2)

Learning rate

PERCENT TRAIN (0-100) and PERCENT TEST (0-100)

How we split the basis set

HEADER LINES

Number of header lines in basis set

SCREEN OUTPUT

Screen output actualization

OUTPUT NAME

Name of the basis set scored

DEBUG

.TRUE. For debugging .FALSE. Not debugging

Contact

www.carlosagimenez.com

References

1. [Briandolhasky] (http://briandolhansky.com/blog/2014/10/30/artificial-neural-networks-matrix-form-part-5)
2. [Phil Brierley] (http://www.philbrierley.com)