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https://github.com/jczic/micromlp

A micro neural network multilayer perceptron for MicroPython (used on ESP32 and Pycom modules)
https://github.com/jczic/micromlp

ai ann artificial-intelligence deep-learning deeplearning esp32 hc2 lopy machine-learning micropython mlp multilayer-perceptron neural-network neurons predictive-modeling pycom q-learning qlearning wipy

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

Host: GitHub
URL: https://github.com/jczic/micromlp
Owner: jczic
License: mit
Created: 2018-08-17T02:00:19.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2020-12-23T10:15:13.000Z (almost 4 years ago)
Last Synced: 2024-04-22T12:32:49.386Z (8 months ago)
Topics: ai, ann, artificial-intelligence, deep-learning, deeplearning, esp32, hc2, lopy, machine-learning, micropython, mlp, multilayer-perceptron, neural-network, neurons, predictive-modeling, pycom, q-learning, qlearning, wipy
Language: Python
Homepage: https://micromlp.hc2.fr
Size: 209 KB
Stars: 172
Watchers: 12
Forks: 26
Open Issues: 4
Metadata Files:
- Readme: README.md
- License: LICENSE.md

Awesome Lists containing this project

README

        ## MicroMLP is a micro artificial neural network multilayer perceptron (principally used on ESP32 and [Pycom](http://www.pycom.io) modules)

![HC²](hc2.png "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 :

| Name | Function |

| - | - |

| Create | `mlp = MicroMLP.Create(neuronsByLayers, activationFuncName, layersAutoConnectFunction=None, useBiasValue=1.0)` |

| LoadFromFile | `mlp = MicroMLP.LoadFromFile(filename)` |

### Using *MicroMLP* speedly creation of a neural network :

```python

from microMLP import MicroMLP

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

```

### Using *MicroMLP* main class :

| Name | Function |

| - | - |

| Constructor | `mlp = MicroMLP()` |

| GetLayer | `layer = mlp.GetLayer(layerIndex)` |

| GetLayerIndex | `idx = mlp.GetLayerIndex(layer)` |

| RemoveLayer | `mlp.RemoveLayer(layer)` |

| GetInputLayer | `inputLayer = mlp.GetInputLayer()` |

| GetOutputLayer | `outputLayer = mlp.GetOutputLayer()` |

| Learn | `ok = mlp.Learn(inputVectorNNValues, targetVectorNNValues)` |

| Test | `ok = mlp.Test(inputVectorNNValues, targetVectorNNValues)` |

| Predict | `outputVectorNNValues = mlp.Predict(inputVectorNNValues)` |

| QLearningLearnForChosenAction | `ok = mlp.QLearningLearnForChosenAction(stateVectorNNValues, rewardNNValue, pastStateVectorNNValues, chosenActionIndex, terminalState=True, discountFactorNNValue=None)` |

| QLearningPredictBestActionIndex | `bestActionIndex = mlp.QLearningPredictBestActionIndex(stateVectorNNValues)` |

| SaveToFile | `ok = mlp.SaveToFile(filename)` |

| AddExample | `ok = mlp.AddExample(inputVectorNNValues, targetVectorNNValues)` |

| ClearExamples | `mlp.ClearExamples()` |

| LearnExamples | `learnCount = mlp.LearnExamples(maxSeconds=30, maxCount=None, stopWhenLearned=True, printMAEAverage=True)` |

| 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) :

```python

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!" )

```

| Variable | Description | Default |

| - | - | - |

| `mlp.Eta` | Weighting of the error correction | 0.30 |

| `mlp.Alpha` | Strength of connections plasticity | 0.75 |

| `mlp.Gain` | Network learning gain | 0.99 |

| `mlp.CorrectLearnedMAE` | Threshold of self-learning error | 0.02 |

| Graphe | Activation function name | Const | Detail |

| - | - | - | - |

| ![HC²](graphe_heaviside.png "Heaviside binary step") | `"Heaviside"` | MicroMLP.ACTFUNC_HEAVISIDE | Heaviside binary step |

| ![HC²](graphe_sigmoid.png "Logistic (sigmoid or soft step)") | `"Sigmoid"` | MicroMLP.ACTFUNC_SIGMOID | Logistic (sigmoid or soft step) |

| ![HC²](graphe_tanh.png "Hyperbolic tangent") | `"TanH"` | MicroMLP.ACTFUNC_TANH | Hyperbolic tangent |

| ![HC²](graphe_softplus.png "SoftPlus rectifier") | `"SoftPlus"` | MicroMLP.ACTFUNC_SOFTPLUS | SoftPlus rectifier |

| ![HC²](graphe_relu.png "Rectified linear unit") | `"ReLU"` | MicroMLP.ACTFUNC_RELU | Rectified linear unit |

| ![HC²](graphe_gaussian.png "Gaussian function") | `"Gaussian"` | MicroMLP.ACTFUNC_GAUSSIAN | Gaussian function |

| Layers auto-connect function | Detail |

| - | - |

| `MicroMLP.LayersFullConnect` | Network fully connected |

### Using *MicroMLP.Layer* class :

| Name | Function |

| - | - |

| Constructor | `layer = MicroMLP.Layer(parentMicroMLP, activationFuncName=None, neuronsCount=0)` |

| GetLayerIndex | `idx = layer.GetLayerIndex()` |

| GetNeuron | `neuron = layer.GetNeuron(neuronIndex)` |

| GetNeuronIndex | `idx = layer.GetNeuronIndex(neuron)` |

| AddNeuron | `layer.AddNeuron(neuron)` |

| RemoveNeuron | `layer.RemoveNeuron(neuron)` |

| GetMeanSquareError | `mse = layer.GetMeanSquareError()` |

| GetMeanAbsoluteError | `mae = layer.GetMeanAbsoluteError()` |

| GetMeanSquareErrorAsPercent | `mseP = layer.GetMeanSquareErrorAsPercent()` |

| GetMeanAbsoluteErrorAsPercent | `maeP = layer.GetMeanAbsoluteErrorAsPercent()` |

| Remove | `layer.Remove()` |

| Property | Example | Read/Write |

| - | - | - |

| ParentMicroMLP | `layer.ParentMicroMLP` | get |

| ActivationFuncName | `layer.ActivationFuncName` | get |

| Neurons | `layer.Neurons` | get |

| NeuronsCount | `layer.NeuronsCount` | get |

### Using *MicroMLP.InputLayer(Layer)* class :

| Name | Function |

| - | - |

| Constructor | `inputLayer = MicroMLP.InputLayer(parentMicroMLP, neuronsCount=0)` |

| SetInputVectorNNValues | `ok = inputLayer.SetInputVectorNNValues(inputVectorNNValues)` |

### Using *MicroMLP.OutputLayer(Layer)* class :

| Name | Function |

| - | - |

| Constructor | `outputLayer = MicroMLP.OutputLayer(parentMicroMLP, activationFuncName, neuronsCount=0)` |

| GetOutputVectorNNValues | `outputVectorNNValues = outputLayer.GetOutputVectorNNValues()` |

| ComputeTargetLayerError | `ok = outputLayer.ComputeTargetLayerError(targetVectorNNValues)` |

### Using *MicroMLP.Neuron* class :

| Name | Function |

| - | - |

| Constructor | `neuron = MicroMLP.Neuron(parentLayer)` |

| GetNeuronIndex | `idx = neuron.GetNeuronIndex()` |

| GetInputConnections | `connections = neuron.GetInputConnections()` |

| GetOutputConnections | `connections = neuron.GetOutputConnections()` |

| AddInputConnection | `neuron.AddInputConnection(connection)` |

| AddOutputConnection | `neuron.AddOutputConnection(connection)` |

| RemoveInputConnection | `neuron.RemoveInputConnection(connection)` |

| RemoveOutputConnection | `neuron.RemoveOutputConnection(connection)` |

| SetBias | `neuron.SetBias(bias)` |

| GetBias | `neuron.GetBias()` |

| SetOutputNNValue | `neuron.SetOutputNNValue(nnvalue)` |

| ComputeValue | `neuron.ComputeValue()` |

| ComputeError | `neuron.ComputeError(targetNNValue=None)` |

| Remove | `neuron.Remove()` |

| Property | Example | Read/Write |

| - | - | - |

| ParentLayer | `neuron.ParentLayer` | get |

| ComputedOutput | `neuron.ComputedOutput` | get |

| ComputedDeltaError | `neuron.ComputedDeltaError` | get |

| ComputedSignalError | `neuron.ComputedSignalError` | get |

### Using *MicroMLP.Connection* class :

| Name | Function |

| - | - |

| Constructor | `connection = MicroMLP.Connection(neuronSrc, neuronDst, weight=None)` |

| UpdateWeight | `connection.UpdateWeight(eta, alpha)` |

| Remove | `connection.Remove()` |

| Property | Example | Read/Write |

| - | - | - |

| NeuronSrc | `connection.NeuronSrc` | get |

| NeuronDst | `connection.NeuronDst` | get |

| Weight | `connection.Weight` | get |

### Using *MicroMLP.Bias* class :

| Name | Function |

| - | - |

| Constructor | `bias = MicroMLP.Bias(neuronDst, value=1.0, weight=None)` |

| UpdateWeight | `bias.UpdateWeight(eta, alpha)` |

| Remove | `bias.Remove()` |

| Property | Example | Read/Write |

| - | - | - |

| NeuronDst | `bias.NeuronDst` | get |

| Value | `bias.Value` | get |

| Weight | `bias.Weight` | get |

### Using *MicroMLP.NNValue* static functions :

| Name | Function |

| - | - |

| FromPercent | `nnvalue = MicroMLP.NNValue.FromPercent(value)` |

| NewPercent | `nnvalue = MicroMLP.NNValue.NewPercent()` |

| FromByte | `nnvalue = MicroMLP.NNValue.FromByte(value)` |

| NewByte | `nnvalue = MicroMLP.NNValue.NewByte()` |

| FromBool | `nnvalue = MicroMLP.NNValue.FromBool(value)` |

| NewBool | `nnvalue = MicroMLP.NNValue.NewBool()` |

| FromAnalogSignal | `nnvalue = MicroMLP.NNValue.FromAnalogSignal(value)` |

| NewAnalogSignal | `nnvalue = MicroMLP.NNValue.NewAnalogSignal()` |

### Using *MicroMLP.NNValue* class :

| Name | Function |

| - | - |

| Constructor | `nnvalue = MicroMLP.NNValue(minValue, maxValue, value)` |

| Property | Example | Read/Write |

| - | - | - |

| AsFloat | `nnvalue.AsFloat = 639.513` | get / set |

| AsInt | `nnvalue.AsInt = 12345` | get / set |

| AsPercent | `nnvalue.AsPercent = 65` | get / set |

| AsByte | `nnvalue.AsByte = b'\x75'` | get / set |

| AsBool | `nnvalue.AsBool = True` | get / set |

| AsAnalogSignal | `nnvalue.AsAnalogSignal = 0.39472` | get / set |

### By JC`zic for [HC²](https://www.hc2.fr) ;')

*Keep it simple, stupid* :+1: