MicroMLP

A micro neural network multilayer perceptron for MicroPython (used on ESP32 and Pycom modules)

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Install / Use

/learn @jczic/MicroMLP

About this skill

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README

MicroMLP is a micro artificial neural network multilayer perceptron (principally used on ESP32 and Pycom modules)

HC²

Very easy to integrate and very light with one file only :

"microMLP.py"

MicroMLP features :

Modifiable multilayer and connections structure
Integrated bias on neurons
Plasticity of the connections included
Activation functions by layer
Parameters Alpha, Eta and Gain
Managing set of examples and learning
QLearning functions to use reinforcement learning
Save and load all structure to/from json file
Various activation functions :
- Heaviside binary step
- Logistic (sigmoid or soft step)
- Hyperbolic tangent
- SoftPlus rectifier
- ReLU (rectified linear unit)
- Gaussian function

Use deep learning for :

Signal processing (speech processing, identification, filtering)
Image processing (compression, recognition, patterns)
Control (diagnosis, quality control, robotics)
Optimization (planning, traffic regulation, finance)
Simulation (black box simulation)
Classification (DNA analysis)
Approximation (unknown function, complex function)

Using MicroMLP static functions :

Using MicroMLP speedly creation of a neural network :

from microMLP import MicroMLP
mlp = MicroMLP.Create([3, 10, 2], "Sigmoid", MicroMLP.LayersFullConnect)

Using MicroMLP main class :

| Property | Example | Read/Write | | - | - | - | | Layers | mlp.Layers | get | | LayersCount | mlp.LayersCount | get | | IsNetworkComplete | mlp.IsNetworkComplete | get | | MSE | mlp.MSE | get | | MAE | mlp.MAE | get | | MSEPercent | mlp.MSEPercent | get | | MAEPercent | mlp.MAEPercent | get | | ExamplesCount | mlp.ExamplesCount | get |

Using MicroMLP to learn the XOr problem (with hyperbolic tangent) :

from microMLP import MicroMLP

mlp = MicroMLP.Create( neuronsByLayers           = [2, 2, 1],
                       activationFuncName        = MicroMLP.ACTFUNC_TANH,
                       layersAutoConnectFunction = MicroMLP.LayersFullConnect )

nnFalse  = MicroMLP.NNValue.FromBool(False)
nnTrue   = MicroMLP.NNValue.FromBool(True)

mlp.AddExample( [nnFalse, nnFalse], [nnFalse] )
mlp.AddExample( [nnFalse, nnTrue ], [nnTrue ] )
mlp.AddExample( [nnTrue , nnTrue ], [nnFalse] )
mlp.AddExample( [nnTrue , nnFalse], [nnTrue ] )

learnCount = mlp.LearnExamples()

print( "LEARNED :" )
print( "  - False xor False = %s" % mlp.Predict([nnFalse, nnFalse])[0].AsBool )
print( "  - False xor True  = %s" % mlp.Predict([nnFalse, nnTrue] )[0].AsBool )
print( "  - True  xor True  = %s" % mlp.Predict([nnTrue , nnTrue] )[0].AsBool )
print( "  - True  xor False = %s" % mlp.Predict([nnTrue , nnFalse])[0].AsBool )

if mlp.SaveToFile("mlp.json") :
	print( "MicroMLP structure saved!" )

Using MicroMLP.Layer class :

Using MicroMLP.InputLayer(Layer) class :

Using MicroMLP.OutputLayer(Layer) class :

Using MicroMLP.Neuron class :

Using MicroMLP.Connection class :

Using MicroMLP.Bias class :

Using MicroMLP.NNValue static functions :

Using MicroMLP.NNValue class :

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