{"id":3386,"date":"2023-08-31T10:59:22","date_gmt":"2023-08-31T10:59:22","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/export-expression-javascrip\/"},"modified":"2025-09-22T18:31:57","modified_gmt":"2025-09-22T16:31:57","slug":"export-expression-javascript","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/export-expression-javascript\/","title":{"rendered":"Export Neural Designer models to JavaScript"},"content":{"rendered":"
\n

Contents<\/h3>\n
    \n
  1. \n
      \n
    1. Introduction<\/a>.<\/li>\n
    2. How to export the mathematical expression?<\/a><\/li>\n
    3. Using a Pre-Trained Neural Network Model in JS<\/a><\/li>\n
    4. Conclusions<\/a><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      <\/p>\n<\/section>\n

      \n

      1. Introduction<\/h2>\n

      Neural Designer is a powerful tool for building and analyzing neural network models.<\/p>\n

      However, when working with these models, it is crucial to have access to the underlying mathematical expressions that govern their behavior.<\/p>\n

      Fortunately, Neural Designer provides several options for working with these expressions.<\/p>\n

      The predictive model takes the form of a function of the outputs concerning the inputs.<\/p>\n

      We can export the mathematical expression that the model represents to different programming languages.<\/p>\n

      In this tutorial, we explain how to export the mathematical expression of our model in the JavaScript programming language.<\/p>\n<\/section>\n

      \n

      2. How to export the mathematical expression?<\/h2>\n

      Once we have trained the model and performed the testing analysis, the neural network can predict outputs for inputs it has never seen.<\/p>\n

      This process is called model deployment.<\/p>\n

      We obtain the mathematical expression of the trained neural network.<\/p>\n

      We can implement this expression in any programming language to obtain the output for our input.<\/p>\n

      Example: The mathematical expression of the iris flower classification model<\/h3>\n

      Classification of iris flowers is the best-known example of machine learning.<\/p>\n

      The aim is to classify iris flowers among three species (Setosa, Versicolor, or Virginica) from the measurements of sepals’ and petals’ length and width.<\/p>\n

      The central goal is to design a model that accurately classifies new flowers. In other words, one that exhibits good generalization.<\/p>\n

      After training and testing the model, we move on to the model deployment process.<\/p>\n

      \"\"<\/p>\n

      One such option is “write expression”, which allows users to view the mathematical expression represented by the model’s neural network.<\/p>\n

      By clicking on this option, Neural Designer displays the model’s function to predict outputs based on inputs.<\/p>\n

      This information can be crucial for understanding how the model works and making modifications to improve its performance.<\/p>\n

      Another option available to users is the “export expression” feature.<\/p>\n

      Users can export the mathematical expression that the model represents in several programming languages by selecting this option.<\/p>\n

      This can be particularly useful for integrating the model into other software programs or larger systems.<\/p>\n

      \"\"<\/p>\n

      To export the mathematical expression in JS code, choose this option from the drop-down menu and save it in the model directory.<\/p>\n<\/section>\n

      \n

      3. Using a Pre-Trained Neural Network Model in JS<\/h2>\n

      Exporting the mathematical expression of a pre-trained neural network in JS allows us to use the trained model to make predictions on new datasets without the need to retrain the network from scratch.<\/p>\n

      Example: Using the Pre-Trained iris flower classification model in JS<\/h3>\n

      The next listing is an iris flower classification model neural network in the JavaScript programming language<\/p>\n


      \n<!--
      \nArtificial Intelligence Techniques SL
      \nartelnics@artelnics.com\t<\/code><\/p>\n

      <\/code><\/p>\n

      Your model has been exported to this JavaScript file. You can manage it with the main method, where you can change the values of your inputs. For example:<\/p>\n

      If we want to add these 3 values (0.3, 2.5, and 1.8) to our 3 inputs (Input_1, Input_2, and Input_3), the main program has to look like this:<\/p>\n

      Inputs Names:\r\n<!--\r\nArtificial Intelligence Techniques SL\r\nartelnics@artelnics.com<\/pre>\n
       <\/p>\n
      int neuralNetwork(){
      \nvector<float> inputs(3);<\/p>\n
       <\/p>\n
      const float asdas = 0.3;
      \ninputs[0] = asdas;
      \nconst float input2 = 2.5;
      \ninputs[1] = input2;
      \nconst float input3 = 1.8;
      \ninputs[2] = input3;
      \n. . .<\/p>\n
       <\/p>\n
      Inputs Names:
      \n0) sepal_length
      \n1) sepal_width
      \n2) petal_length
      \n3) petal_width
      \n–><\/p>\n
       <\/p>\n
      <!DOCTYPE HTML>
      \n<html lang=”en”><\/p>\n
       <\/p>\n
      <head>
      \n<link href=”https:\/\/www.neuraldesigner.com\/assets\/css\/neuraldesigner.css” rel=”stylesheet” \/>
      \n<link href=”https:\/\/www.neuraldesigner.com\/images\/fav.ico” rel=”shortcut icon” type=”image\/x-icon” \/>
      \n<\/head><\/p>\n
       <\/p>\n
      <style>
      \n.button
      \n{
      \nbackground-color: #6F8FAF;
      \nborder: none; color: white;
      \ntext-align: center;
      \nfont-size: 16px;
      \nmargin: 4px 4px;
      \ncursor: pointer;
      \n}
      \n<\/style><\/p>\n
       <\/p>\n
      <body><\/p>\n
       <\/p>\n
      <section>
      \n<br\/><\/p>\n
       <\/p>\n
      <div align=”center” style=”display:block;text-align: center;”>
      \n<!– MENU OPTIONS HERE –>
      \n<form style=”display: inline-block;margin-left: auto; margin-right: auto;”><\/p>\n
       <\/p>\n
      <table border=”1px”><\/p>\n
       <\/p>\n
      INPUTS
      \n<!– 0scaling layer –>
      \n<tr style=”height:3.5em”>
      \n<td> sepal_length <\/td>
      \n<td style=”text-align:center”>
      \n<input type=”range” id=”sepal_length” value=”6.1″ min=”4.3″ max=”7.9″ step=”0.036″ onchange=”updateTextInput1(this.value, ‘sepal_length_text’)” \/>
      \n<input type=”number” id=”sepal_length_text” value=”6.1″ min=”4.3″ max=”7.9″ step=”0.036″ onchange=”updateTextInput1(this.value, ‘sepal_length’)”>
      \n<\/td>
      \n<\/tr><\/p>\n
       <\/p>\n
      <!– 1scaling layer –>
      \n<tr style=”height:3.5em”>
      \n<td> sepal_width <\/td>
      \n<td style=”text-align:center”>
      \n<input type=”range” id=”sepal_width” value=”3.2″ min=”2″ max=”4.4″ step=”0.024″ onchange=”updateTextInput1(this.value, ‘sepal_width_text’)” \/>
      \n<input type=”number” id=”sepal_width_text” value=”3.2″ min=”2″ max=”4.4″ step=”0.024″ onchange=”updateTextInput1(this.value, ‘sepal_width’)”>
      \n<\/td>
      \n<\/tr><\/p>\n
       <\/p>\n
      <!– 2scaling layer –>
      \n<tr style=”height:3.5em”>
      \n<td> petal_length <\/td>
      \n<td style=”text-align:center”>
      \n<input type=”range” id=”petal_length” value=”3.95″ min=”1″ max=”6.9″ step=”0.059″ onchange=”updateTextInput1(this.value, ‘petal_length_text’)” \/>
      \n<input type=”number” id=”petal_length_text” value=”3.95″ min=”1″ max=”6.9″ step=”0.059″ onchange=”updateTextInput1(this.value, ‘petal_length’)”>
      \n<\/td>
      \n<\/tr><\/p>\n
       <\/p>\n
      <!– 3scaling layer –>
      \n<tr style=”height:3.5em”>
      \n<td> petal_width <\/td>
      \n<td style=”text-align:center”>
      \n<input type=”range” id=”petal_width” value=”1.3″ min=”0.1″ max=”2.5″ step=”0.024″ onchange=”updateTextInput1(this.value, ‘petal_width_text’)” \/>
      \n<input type=”number” id=”petal_width_text” value=”1.3″ min=”0.1″ max=”2.5″ step=”0.024″ onchange=”updateTextInput1(this.value, ‘petal_width’)”>
      \n<\/td>
      \n<\/tr><\/p>\n
       <\/p>\n
      <\/table>
      \n<\/form><\/p>\n
       <\/p>\n
      <div align=”center”>
      \n<!– BUTTON HERE –>
      \n<button onclick=”neuralNetwork()”>calculate outputs<\/button>
      \n<\/div><\/p>\n
       <\/p>\n
      <br\/><\/p>\n
       <\/p>\n
      <table border=”1px”>
      \nOUTPUTS
      \n<tr style=”height:3.5em”>
      \n<td> iris_setosa <\/td>
      \n<td>
      \n<input style=”text-align:right; padding-right:20px;” id=”iris_setosa” value=”” type=”text” disabled\/>
      \n<\/td>
      \n<\/tr><\/p>\n
       <\/p>\n
      <tr style=”height:3.5em”>
      \n<td> iris_versicolor <\/td>
      \n<td>
      \n<input style=”text-align:right; padding-right:20px;” id=”iris_versicolor” value=”” type=”text” disabled\/>
      \n<\/td>
      \n<\/tr><\/p>\n
       <\/p>\n
      <tr style=”height:3.5em”>
      \n<td> iris_virginica <\/td>
      \n<td>
      \n<input style=”text-align:right; padding-right:20px;” id=”iris_virginica” value=”” type=”text” disabled\/>
      \n<\/td>
      \n<\/tr><\/p>\n
       <\/p>\n
      <\/table><\/p>\n
       <\/p>\n
      <\/form>
      \n<\/div><\/p>\n
       <\/p>\n
      <\/section><\/p>\n
       <\/p>\n
      <script>
      \nfunction neuralNetwork()
      \n{
      \nvar inputs = [];
      \nvar sepal_length = document.getElementById(“sepal_length”).value;
      \ninputs.push(sepal_length);
      \nvar sepal_width = document.getElementById(“sepal_width”).value;
      \ninputs.push(sepal_width);
      \nvar petal_length = document.getElementById(“petal_length”).value;
      \ninputs.push(petal_length);
      \nvar petal_width = document.getElementById(“petal_width”).value;
      \ninputs.push(petal_width);<\/p>\n
       <\/p>\n
      var outputs = calculate_outputs(inputs);
      \nvar iris_setosa = document.getElementById(“iris_setosa”);
      \niris_setosa.value = outputs[0].toFixed(4);
      \nvar iris_versicolor = document.getElementById(“iris_versicolor”);
      \niris_versicolor.value = outputs[1].toFixed(4);
      \nvar iris_virginica = document.getElementById(“iris_virginica”);
      \niris_virginica.value = outputs[2].toFixed(4);
      \nupdate_LSTM();
      \n}<\/p>\n
       <\/p>\n
      function calculate_outputs(inputs)
      \n{
      \nvar sepal_length = +inputs[0];
      \nvar sepal_width = +inputs[1];
      \nvar petal_length = +inputs[2];
      \nvar petal_width = +inputs[3];<\/p>\n
       <\/p>\n
      var scaled_sepal_length = (sepal_length-5.843329906)\/0.8280659914;
      \nvar scaled_sepal_width = (sepal_width-3.053999901)\/0.4335939884;
      \nvar scaled_petal_length = (petal_length-3.758670092)\/1.764420033;
      \nvar scaled_petal_width = (petal_width-1.19867003)\/0.7631610036;<\/p>\n
       <\/p>\n
      var perceptron_layer_1_output_0 = Math.tanh( -0.454828 + (scaled_sepal_length*-0.702639) + (scaled_sepal_width*1.58777) + (scaled_petal_length*-1.55384) + (scaled_petal_width*-1.00021) );
      \nvar perceptron_layer_1_output_1 = Math.tanh( -2.33981 + (scaled_sepal_length*3.26766) + (scaled_sepal_width*0.645213) + (scaled_petal_length*2.71958) + (scaled_petal_width*3.2668) );
      \nvar perceptron_layer_1_output_2 = Math.tanh( -3.16773 + (scaled_sepal_length*-0.00059909) + (scaled_sepal_width*-0.664832) + (scaled_petal_length*2.84724) + (scaled_petal_width*1.88258) );<\/p>\n
       <\/p>\n
      var probabilistic_layer_combinations_0 = -0.53698 +3.6124*perceptron_layer_1_output_0 -3.02915*perceptron_layer_1_output_1 -0.0966353*perceptron_layer_1_output_2 ;
      \nvar probabilistic_layer_combinations_1 = 1.54913 -1.85394*perceptron_layer_1_output_0 +0.152214*perceptron_layer_1_output_1 -2.55223*perceptron_layer_1_output_2 ;
      \nvar probabilistic_layer_combinations_2 = -1.03593 -1.78098*perceptron_layer_1_output_0 +2.85544*perceptron_layer_1_output_1 +2.74533*perceptron_layer_1_output_2 ;<\/p>\n
       <\/p>\n
      var sum = Math.exp(probabilistic_layer_combinations_0) + Math.exp(probabilistic_layer_combinations_1) + Math.exp(probabilistic_layer_combinations_2);<\/p>\n
       <\/p>\n
      var iris_setosa = Math.exp(probabilistic_layer_combinations_0)\/sum;
      \nvar iris_versicolor = Math.exp(probabilistic_layer_combinations_1)\/sum;
      \nvar iris_virginica = Math.exp(probabilistic_layer_combinations_2)\/sum;<\/p>\n
       <\/p>\n
      var out = [];
      \nout.push(iris_setosa);
      \nout.push(iris_versicolor);
      \nout.push(iris_virginica);<\/p>\n
       <\/p>\n
      return out;
      \n}<\/p>\n
       <\/p>\n
      function updateTextInput1(val, id)
      \n{
      \ndocument.getElementById(id).value = val;
      \n}<\/p>\n
       <\/p>\n
      window.onresize = showDiv;<\/p>\n
       <\/p>\n
      <\/script><\/p>\n
       <\/p>\n
      <!–script src=”https:\/\/www.neuraldesigner.com\/app\/htmlparts\/footer.js”><\/script–><\/p>\n
       <\/p>\n
      <\/body><\/p>\n
       <\/p>\n
          <\/html>
      \n<\/code><\/p>\n
      The code you have shared is an HTML page with embedded JavaScript code.<\/p>\n
      This webpage enables interaction with a neural network model exported from the Neural Designer software.<\/p>\n
      The JavaScript code is responsible for receiving input values from the sliders and text fields in the “INPUTS” section.<\/p>\n
      Then, it uses those values to calculate the outputs of the neural network model using the calculate_outputs function.<\/p>\n
      These outputs are displayed in the text fields in the “OUTPUTS” section.<\/p>\n
      To export this neural network model to your JavaScript project, you need to follow these steps:<\/p>\n
        \n
      1. Save the HTML and JavaScript code into a file with the “.html” extension, for example, “neural_network.html”.<\/li>\n
      2. Create an HTML page in your JavaScript project where you want to use the neural network model.<\/li>\n
      3. Inside your project’s HTML page, add an <iframe> tag to load the “neural_network.html” page that contains the exported model.<\/li>\n<\/ol>\n
        For example:<\/p>\n

        \n<iframe src=\"neural_network.html\"><\/iframe>
        \n<\/code><\/p>\n
        Ensure the “neural_network.html” file is located in the same directory as your project’s HTML page.<\/p>\n
        By loading the “neural_network.html” page in your JavaScript project, you will be able to interact with the neural network model and use it in your application.<\/p>\n
        The input values can be modified using the sliders and text fields, and the outputs will be displayed in the corresponding text fields.<\/p>\n
        Remember that for the neural network model to work correctly, it’s crucial to load all the necessary dependencies and CSS styles, as done in the provided code.<\/p>\n<\/section>\n
        \n
        4. Conclusions<\/h2>\n
        The mathematical expression of a pre-trained neural network, exported in different programming languages, enables us to use the trained model to make predictions on new datasets without retraining the network from scratch.<\/p>\n
        Exporting the pre-trained mathematical expression of a neural network enables us to leverage the model’s knowledge and generalization capabilities in various applications and projects.<\/p>\n
        This is a fundamental step to fully leverage the potential of artificial intelligence and machine learning in JavaScript.<\/p>\n
        Related posts<\/h2>\n<\/section>\n
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