Machine-Learning-Tensorflow.js/linearregression.html at master · CodeBalch25/Machine-Learning-Tensorflow.js · GitHub

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<!doctype html>
<html lang="en">

<head>
    <!-- Required meta tags -->
    <meta charset="utf-8">
    <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
    <!-- Tensorflow js  -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis"></script>
    <!-- Compiled and minified CSS -->
    <link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet">
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/1.0.0/css/materialize.min.css">

    <!-- Compiled and minified JavaScript -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/1.0.0/js/materialize.min.js"></script>
    <!-- Bootstrap CSS -->
    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css" integrity="sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh" crossorigin="anonymous">

    <title>tensorflow.js</title>
</head>

<body>
    <script>
        console.log("Connected");
        //---------------------------------------------------------------------------------
        async function plot(pointsArray, featureName, predictedPointsArray = null) {
            const values = [pointsArray.slice(0, 1000)];
            const series = ["original"];
            if (Array.isArray(predictedPointsArray)) {
                values.push(predictedPointsArray);
                series.push("predicted");
            }

            tfvis.render.scatterplot({
                name: `${featureName} vs House Price`
            }, {
                values,
                series
            }, {
                xLabel: featureName,
                yLabel: "Price",
                height: 300,
            })
        }


        //--------------------------------------------------------
        async function plotPredictionLine() {
            const [xs, ys] = tf.tidy(() => {

                const normalisedXs = tf.linspace(0, 1, 100);
                const normalisedYs = model.predict(normalisedXs.reshape([100, 1]));

                const xs = denormalise(normalisedXs, normalisedFeature.min, normalisedFeature.max)
                const ys = denormalise(normalisedYs, normalisedLabel.min, normalisedLabel.max);
                return [xs.dataSync(), ys.dataSync()];
            });
            const predictedPoints = Array.from(xs).map((val, index) => {
                return {
                    x: val,
                    y: ys[index]
                };

            });


            await plot(points, "Square feet", predictedPoints);


        }

        //-------------------------------------------------------------------------

        function normalise(tensor, previousMin = null, previousMax = null) {
            const min = previousMin || tensor.min();
            const max = previousMax || tensor.max();
            const normalisedTensor = tensor.sub(min).div(max.sub(min));
            return {
                tensor: normalisedTensor,
                min,
                max
            };
        }


        function denormalise(tensor, min, max) {
            const denormalisedTensor = tensor.mul(max.sub(min)).add(min);
            return denormalisedTensor;
        }

        //---------------------------------------------------------------------


        //creating model area
        function createModel() {
            model = tf.sequential();

            model.add(tf.layers.dense({
                units: 10,
                useBias: true,
                activation: 'sigmoid',
                inputDim: 1,
            }));
            model.add(tf.layers.dense({
                units: 10,
                useBias: true,
                activation: 'sigmoid',

            }));
            model.add(tf.layers.dense({
                units: 1,
                useBias: true,
                activation: 'sigmoid',

            }));

            const optimizer = tf.train.sgd(0.1);
            model.compile({
                loss: 'meanSquaredError',
                optimizer,
            })

            return model;
        }
        //---------------------------------------------------------------------

        async function trainModel(model, trainingFeatureTensor, trainingLabelTensor) {

            const {
                onBatchEnd,
                onEpochEnd


            } = tfvis.show.fitCallbacks({
                name: "Training Performance"
            }, ['loss'])

            return model.fit(trainingFeatureTensor, trainingLabelTensor, {
                batchSize: 32,
                epochs: 20,
                validationSplit: 0.2,
                callbacks: {
                    onEpochEnd,
                    onEpochBegin: async function() {
                        await plotPredictionLine();
                        const layer = model.getLayer(undefined, 0);
                        tfvis.show.layer({
                            name: "Layer 1"
                        }, layer);
                    }
                }
            });
        }

        //----------------------------------------------------------------------------

        async function predict() {
            const predictionInput = parseInt(document.getElementById("prediction-input").value);
            if (isNaN(predictionInput)) {
                alert("Please enter a valid number!");

            } else if (predictionInput < 200) {
                alert("please enter a number greater then 200 or equal");
            } else {


                tf.tidy(() => {
                    const inputTensor = tf.tensor1d([predictionInput]);
                    const normalisedInput = normalise(inputTensor, normalisedFeature.min, normalisedFeature.max);
                    const normalisedOutputTensor = model.predict(normalisedInput.tensor);
                    const outputTensor = denormalise(normalisedOutputTensor, normalisedLabel.min, normalisedLabel.max);
                    const outputValue = outputTensor.dataSync()[0];
                    const outputValueRounded = (outputValue / 1000).toFixed(0) * 1000;
                    document.getElementById("prediction-output").innerHTML = `The Predicted house price is <br>` +
                        `<span style = "font-size: 2em">\$ ${outputValueRounded}</span>`;

                });
            }
        }


        //---------------------------------------------------------------------------------------------
        async function load() {
            const storageKey = `localstorage://${storageID}`;
            const models = await tf.io.listModels();
            const modelInfo = models[storageKey];
            if (modelInfo) {
                model = await tf.loadLayersModel(storageKey);
                tfvis.show.modelSummary({
                    name: "Model summary"
                }, model);
                await plotPredictionLine();
                const layer = model.getLayer(undefined, 0);
                tfvis.show.layer({
                    name: "Layer 1"
                }, layer);
                document.getElementById("model-status").innerHTML = `Trained(saved ${modelInfo.dateSaved})`;
                document.getElementById("predict-button").removeAttribute("disabled");
                document.getElementById("test-button").removeAttribute("disabled");

            } else {
                alert("Could not load:no saved model found");
            }
        }

        //------------------------------------------------------------------------------------
        const storageID = "king-county-price-regression"
        async function save() {
            const saveResults = await model.save(`localstorage://${storageID}`);
            document.getElementById("model-status").innerHTML = `Trained(saved ${saveResults.modelArtifactsInfo.dateSaved})`;

        }

        //-----------------------------------------------------------------------------

        async function test() {
            const lossTensor = model.evaluate(testingFeatureTensor, testingLabelTensor);
            const loss = (await lossTensor.dataSync())[0];
            console.log(`Testing set loss:${loss}`);
            document.getElementById("testing-status").innerHTML = `Testing set loss:${loss.toPrecision(5)}`;

        }


        //-----------------------------------------------------------------------------
        // Train  model
        async function train() {

            // Disable all buttons and update status
            ["train", "test", "load", "predict", "save"].forEach(id => {
                document.getElementById(`${id}-button`).setAttribute("disabled", "disabled");
            });
            document.getElementById("model-status").innerHTML = "Training in progress....";


            const model = createModel();
            tfvis.show.modelSummary({
                name: "Model summary"
            }, model);
            const layer = model.getLayer(undefined, 0);
            tfvis.show.layer({
                name: "Layer 1"
            }, layer);
            await plotPredictionLine();

            const result = await trainModel(model, trainingFeatureTensor, trainingLabelTensor);
            console.log(result);
            const trainingLoss = result.history.loss.pop();
            console.log(`Training set loss: ${trainingLoss}`);
            const validationLoss = result.history.val_loss.pop();
            console.log(`Validation set loss: ${validationLoss}`);

            document.getElementById("model-status").innerHTML = "Trained(unsaved)\n" +
                `Loss: ${trainingLoss.toPrecision(5)}\n` + `Validation loss: ${validationLoss.toPrecision(5)}`;
            document.getElementById("test-button").removeAttribute("disabled");
            document.getElementById("save-button").removeAttribute("disabled");

        }

        async function toggleVisor() {
            tfvis.visor().toggle();
        }

        //---------------------------------------------------------------------------------------

        // we need to make variables global so we can call them from anywhere.
        let normalisedFeature, normalisedLabel;
        let trainingFeatureTensor, trainingLabelTensor;
        let model;
        let points;


        //---------------------------------------------------------------

        async function plotParams(weight, bias) {
            model.getLayer(null, 0).setWeights([
                tf.tensor2d([
                    [weight]
                ]), //kernel or weight (input multiplier)
                tf.tensor1d([bias]), //bias
            ])
            await plotPredictionLine();
            const layer = model.getLayer(undefined, 0);
            tfvis.show.layer({
                name: "Layer 1"
            }, layer);
        }
        //-----------------------------------------------------------------

        async function run() {
            // Ensure backend has initialized
            await tf.ready();
            // Import from CSV
            const houseSalesDataset = tf.data.csv("http://127.0.0.1:8080/kc_house_data.csv");

            // Extract x and y values to plot
            const pointsDataset = houseSalesDataset.map(record => ({
                x: record.sqft_living,
                y: record.price,
            }));
            points = await pointsDataset.toArray();
            if (points.length % 2 !== 0) { // If odd number of elements
                points.pop(); // remove one element
            }
            tf.util.shuffle(points);
            plot(points, "Square feet");

            // Extract Features (inputs)
            const featureValues = points.map(p => p.x);
            const featureTensor = tf.tensor2d(featureValues, [featureValues.length, 1]);

            // Extract Labels (outputs)
            const labelValues = points.map(p => p.y);
            const labelTensor = tf.tensor2d(labelValues, [labelValues.length, 1]);

            // Normalise features and labels
            normalisedFeature = normalise(featureTensor);
            normalisedLabel = normalise(labelTensor);

            // memory management
            featureTensor.dispose();
            labelTensor.dispose();
            ///*********************************

            [trainingFeatureTensor, testingFeatureTensor] = tf.split(normalisedFeature.tensor, 2);
            [trainingLabelTensor, testingLabelTensor] = tf.split(normalisedLabel.tensor, 2);


            //update status and enable train button section
            document.getElementById("model-status").innerHTML = "Model not Trained";
            document.getElementById("train-button").removeAttribute("disabled");
            document.getElementById("load-button").removeAttribute("disabled");


        }

        run();
    </script>


    <!-- start of UI -->
    <!-- Header  -->
    <div class="section no-pad-bot" id="index-banner">
        <div class="container">
            <h5 class="header center blue-text small">Linear regression with TensorFlow.js</h5>
            <div class="row center">
                <h6 class="header col s12 light">Train a model to predict house price from living space.</h6>
            </div>
        </div>
    </div>

    <!--   Misc buttons   -->
    <div class="section no-pad-bot light-blue lighten-4">
        <div class="container">
            <div class="row center">
                <button id="toggle-button" class="waves-effect waves-light light-blue btn-small" onclick="toggleVisor()">Toggle Visor</button>
                <br/><br/>
            </div>
        </div>
    </div>

    <!--   Main functionality   -->
    <div class="container">
        <div class="section">

            <div class="row">
                <!--   Training   -->
                <div class="col s12 m6">
                    <div class="icon-block">
                        <h3 class="center light-blue-text"><i class="material-icons" style="font-size: 2em">build</i></h3>
                        <h5 class="center">Train & Test</h5>

                        <p class="light"></p>

                        <div>
                            <p><label>Training status:</label></p>
                            <pre class="grey lighten-4" style="overflow-x: auto"><em id="model-status">Loading data...</em></pre>

                            <p><label>Testing status:</label></p>
                            <pre class="grey lighten-4" style="overflow-x: auto"><em id="testing-status">Not yet tested</em></pre>

                            <button autocomplete="off" id="train-button" class="waves-effect light-blue waves-light btn" disabled onclick="train()">Train New Model</button>
                            <button autocomplete="off" id="test-button" class="waves-effect light-blue waves-light btn" disabled onclick="test()">Test Model</button>
                        </div>
                        <br/>

                        <div>
                            <button autocomplete="off" id="load-button" class="waves-effect light-blue waves-light btn-small" disabled onclick="load()">Load Model</button>
                            <button autocomplete="off" id="save-button" class="waves-effect light-blue waves-light btn-small" disabled onclick="save()">Save Model</button>
                        </div>

                    </div>
                </div>

                <!--   Prediction   -->
                <div class="col s12 m6">
                    <div class="icon-block">
                        <h3 class="center light-blue-text"><i class="material-icons" style="font-size: 2em">timeline</i></h3>
                        <h5 class="center">Predict</h5>

                        <label>Square feet of living space: <input type="number" id="prediction-input" placeholder="2000"/></label>
                        <button autocomplete="off" id="predict-button" class="waves-effect light-blue waves-light btn" disabled onclick="predict()">Predict house price</button>
                        <p><strong id="prediction-output"></strong></p>
                    </div>
                </div>
            </div>

        </div>
    </div>


</body>

</html>