jczic / Micromlp

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

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 :

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!" )

Graphe	Activation function name	Const	Detail
	"Heaviside"	MicroMLP.ACTFUNC_HEAVISIDE	Heaviside binary step
	"Sigmoid"	MicroMLP.ACTFUNC_SIGMOID	Logistic (sigmoid or soft step)
	"TanH"	MicroMLP.ACTFUNC_TANH	Hyperbolic tangent
	"SoftPlus"	MicroMLP.ACTFUNC_SOFTPLUS	SoftPlus rectifier
	"ReLU"	MicroMLP.ACTFUNC_RELU	Rectified linear unit
	"Gaussian"	MicroMLP.ACTFUNC_GAUSSIAN	Gaussian function

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 :

By JC`zic for HC² ;')

Keep it simple, stupid 👍