Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://github.com/mrdimosthenis/FSharp.Synapses below:

mrdimosthenis/FSharp.Synapses: A plug-and-play library for neural networks written in FSharp

A plug-and-play library for neural networks written in F#!

// run
dotnet add package Synapses --version 7.4.1
// in the directory of your project

Import Synapses, call NeuralNetwork.init and provide the size of each layer.

open Synapses
let layers = [4; 6; 5; 3]
let neuralNetwork = NeuralNetwork.init(layers)

neuralNetwork has 4 layers. The first layer has 4 input nodes and the last layer has 3 output nodes. There are 2 hidden layers with 6 and 5 neurons respectively.

let inputValues = [1.0; 0.5625; 0.511111; 0.47619]
let prediction =
        NeuralNetwork.prediction(neuralNetwork, inputValues)

prediction should be something like [ 0.8296, 0.6996, 0.4541 ].

Note that the lengths of inputValues and prediction equal to the sizes of input and output layers respectively.

let learningRate = 0.5
let expectedOutput = [0.0; 1.0; 0.0]
let fitNetwork =
        NeuralNetwork.fit(
            neuralNetwork,
            learningRate,
            inputValues,
            expectedOutput
        )

fitNetwork is a new neural network trained with a single observation.

To train a neural network, you should fit with multiple datapoints

The activation function of the neurons created with NeuralNetwork.init, is a sigmoid one. If you want to customize the activation functions and the weight distribution, call NeuralNetwork.customizedInit.

let activationF (layerIndex: int)
        : ActivationFunction =
        match layerIndex with
        | 0 -> ActivationFunction.sigmoid
        | 1 -> ActivationFunction.tanh
        | 2 -> ActivationFunction.leakyReLU
        | _ -> ActivationFunction.identity

let weightInitF (_layerIndex: int): float =
        1.0 - 2.0 * System.Random().NextDouble()

let customizedNetwork =
        NeuralNetwork.customizedInit(
            layers,
            activationF,
            weightInitF
        )

Call NeuralNetwork.toSvg to take a brief look at its svg drawing.

The color of each neuron depends on its activation function while the transparency of the synapses depends on their weight.

let svg = NeuralNetwork.toSvg(customizedNetwork)

JSON instances are compatible across platforms! We can generate, train and save a neural network in Python and then load and make predictions in Javascript!

Call NeuralNetwork.toJson on a neural network and get a string representation of it. Use it as you like. Save json in the file system or insert into a database table.

let loadedNetwork = NeuralNetwork.ofJson(json)

As the name suggests, NeuralNetwork.ofJson turns a json string into a neural network.

One hot encoding is a process that turns discrete attributes into a list of 0.0 and 1.0. Minmax normalization scales continuous attributes into values between 0.0 and 1.0. You can use DataPreprocessor for datapoint encoding and decoding.

The first parameter of DataPreprocessor.init is a list of tuples (attributeName, discreteOrNot).

let setosaDatapoint =
        Map.ofList
            [ ("petal_length", "1.5")
              ("petal_width", "0.1")
              ("sepal_length", "4.9")
              ("sepal_width", "3.1")
              ("species", "setosa") ]

let versicolorDatapoint =
        Map.ofList
            [ ("petal_length", "3.8")
              ("petal_width", "1.1")
              ("sepal_length", "5.5")
              ("sepal_width", "2.4")
              ("species", "versicolor") ]

let virginicaDatapoint =
        Map.ofList
            [ ("petal_length", "6.0")
              ("petal_width", "2.2")
              ("sepal_length", "5.0")
              ("sepal_width", "1.5")
              ("species", "virginica") ]

let dataset = Seq.ofList
                [ setosaDatapoint
                  versicolorDatapoint
                  virginicaDatapoint ]
                
let dataPreprocessor =
        DataPreprocessor.init(
             [ ("petal_length", false)
               ("petal_width", false)
               ("sepal_length", false)
               ("sepal_width", false)
               ("species", true) ],
             dataset
        )

let encodedDatapoints =
        Seq.map (fun datapoint ->
                    DataPreprocessor.encodedDatapoint(dataPreprocessor, datapoint)
                )
                dataset

encodedDatapoints equals to:

[ [ 0.0     , 0.0     , 0.0     , 1.0     , 0.0, 0.0, 1.0 ],
  [ 0.511111, 0.476190, 1.0     , 0.562500, 0.0, 1.0, 0.0 ],
  [ 1.0     , 1.0     , 0.166667, 0.0     , 1.0, 0.0, 0.0 ] ]

Save and load the preprocessor by calling DataPreprocessor.toJson and DataPreprocessor.ofJson.

To evaluate a neural network, you can call Statistics.rootMeanSquareError and provide the expected and predicted values.

let expectedWithOutputValues =
        Seq.ofList [ ( [ 0.0; 0.0; 1.0], [ 0.0; 0.0; 1.0] )
                     ( [ 0.0; 0.0; 1.0], [ 0.0; 1.0; 1.0] ) ]

let rmse = Statistics.rootMeanSquareError(
                        expectedWithOutputValues
)

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4