Mimetik

Mimetik is a machine learning software.
Main features:

• Creation of a neural network (multiLayer perceptron)
• Load Training data set from a file
• Learning
• Save neural network state
• Compute new inputs
• Execute script

You can use it as a command line program:

network 2 5 1
loadTrainingSet xor.txt
learning 5000
saveState save_xor
compute 0 0
compute 0 1
compute 1 0
compute 1 1

Or a c++ library (multilayerPerceptron.h):

    vector<int> layers;
    layers.resize(3);
    layers[0] = 2;
    layers[1] = 5;
    layers[2] = 1;
    
    multilayerPerceptron network(layers);
    network.learning("./xor.txt", 5000);
    network.saveState("save_xor");
    network.computeFile("./xor.txt");

Installation

make 
make install

Execution

Start mimetik :

mimetik

Run a mimetik script:

mimetik script.mimetik

Command line

usage:
	network nbLayer1 nbLayer2 ... - Create neural network layers
	loadTrainingSet trainingset.txt - Load training set from file
	setEta eta - Set learning rate factor [0,1] (default = 0.5)
	setAlpha alpha - Set momentum factor [0,1] (default = 0.9)
	learning limit verbose(booleen) randomOrder(booleen) - Start learning
	compute input1 input2 ... - Compute outputs
	computeFile fileIn fileOut - Compute a file
	saveState filename - Save neural network state in binary file
	saveStateText filename.txt - Load neural network state in text file
	loadState filename - Load neural network state from binary file
	loadStateText filename.txt - Load neural network state from text file
	execute script.mimetik - Execute mimetik script
	exit - Quit the software

examples:
	network 2 10 5 1
	loadTrainingSet trainingset.txt
	setEta 0.5
	setAlpha 0.9
	learning 5000 true false (or: learning 5000)
	compute 0.5 0.1
	computeFile fileIn.txt
	saveState weights.bin
	saveStateText weights.txt
	loadState weights.bin
	loadStateText weights.txt
	execute script.mimetik

Files

Training set (loadTrainingSet)

Training set files contain inputs and outputs to allow the neural network to learn by example
xor:

[mlp]
2 1 4

[inputs]
0 0
0 1
1 0
1 1

[outputs]
0
1
1
0

The header [mlp] defines: The number of inputs, the number of outputs, the number of examples
[inputs] are Float values (scaled in [0;1] for better results)
[outputs] are Float values

SaveStateText Files

[mlp_layers]
3 2 5 1

[mlp_weights]
-2.39106 -2.11835 
-5.64354 -5.69497 
0.626959 0.60677 
-7.0111 3.14889 
3.16021 -7.02828 

-2.49848 -14.4462 -4.83591 7.35598 7.31272 

The header [mlp_layers] defines:
The number of layers, the number of neurons in layer1, the number of neurons in layer2 ...
[mlp_weights] defines weights of the neural network layer by layer

SaveState Files

Same information as SaveStateText but in binary format (less disk space)

Use cases

The folder "examples" contains some use cases.

• xor: Learning xor

• sin: Learning sinus

• cross_circle: Image recognition (cross or circle)

• vehicle: Prediction of a transport means (see vehicle_readme.txt)

  cd examples
  mimetik xor_script.mimetik
  

Simple perceptron

In addition, mimetik provides a simple implementation of a perceptron: perceptron.h
See the perceptron folder