neural-network-node

    1.4.7 • Public • Published

    Neural-Network-Module

    A simple easy to use Node JS, HTML JS, and Java module which lets you create your own neural network and train it. This was coded from scratch and uses my numpy-matrix-js module for the Matrix math.

    About

    Types of Neural Networks supported

    • Standard (Feed Forward)
    • DFF (Deep Feed Forward) aka multiple hidden layers module

    Set up

    Quick Start Standard

    Creating a neural network

    The function to create a new neural network is just:

    const nn = new NeuralNetwork.Standard(input_nodes, hidden_nodes, output_nodes);
    

    In this case, you will have to change some of these.

    1. replace the input_nodes with however many inputs you give the network
    2. replace the hidden_nodes with however many hidden nodes you want. this number is arbitrary. You can choose whatever you want but might have to tweak it for optimal results.
    3. replace the output_nodes with however many outputs you want.

    Once you're done with this, you have finished creating the neural network. The next step is to train it with data.

    Training the model

    To train the model, use the following function:

    nn.train(input, output)
    

    where input is the value that you input and output is the value the computer should output.

    Testing the model

    To test the model, use this function:

    nn.predict(input)
    

    This will make the computer use it's previous tested data to make a guess for what the output should be.

    You're done creating a basic neural network. For more functionality, take a look at the documentation.

    Quick Start DFF

    Creating a neural network

    The function to create a new neural network is just:

    const nn = new NeuralNetwork.DFF(input_nodes, hidden_nodes, output_nodes);
    

    In this case, you will have to change some of these.

    1. replace the input_nodes with however many inputs you give the network
    2. replace the hidden_nodes with an array. The first element in the array is the number of hidden nodes for the first hidden layer, the second element is for the second layer, and so on.
    3. replace the output_nodes with however many outputs you want.

    Once you're done with this, you have finished creating the neural network. The next step is to train it with data.

    Training the model

    To train the model, use the following function:

    nn.train(input, output)
    

    where input is the value that you input and output is the value the computer should output.

    Testing the model

    To test the model, use this function:

    nn.predict(input)
    

    Finished.

    Examples

    Documentation

    nn.getWeights()

    Returns all of the current weights of the neural network

    nn.getBias()

    Returns all of the current biases of the neural network

    nn.getLearningRate()

    Returns the current learning rate of the neural network

    nn.setWeights()

    Parameters What it is Required
    weights_array Array of weights Yes

    nn.setBias()

    Parameters What it is Required
    bias_array Array of biases Yes

    nn.setLearningRate()

    Parameters What it is Required
    learning_rate New learning rate Yes

    nn.predict()

    Parameters What it is Required
    input_array Array of input data that matches the number of input_nodes Yes

    nn.train()

    Parameters What it is Required
    input_array the input data Yes
    output_array what the computer should output Yes

    Versions

    Current Version: 1.3.9

    Stable Versions:

    1.3.9 - Changed Matrix library to my numpy-matrix-js module for NPM versions only.

    1.3.1 - Added DFF for browser support

    1.2.9 - Added browser support

    1.2.1 - Succesfully added Deep Feed Forward Network. Removed error message for mismatched input data client-side.

    1.1.7 - Failed to add Perceptron support. Forced to remove it temporarily

    1.1.0 - First stable version

    Install

    npm i neural-network-node

    DownloadsWeekly Downloads

    4

    Version

    1.4.7

    License

    ISC

    Unpacked Size

    42.2 kB

    Total Files

    20

    Last publish

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