script.js

async function getData() {
    const carsDataReq = await fetch('https://storage.googleapis.com/tfjs-tutorials/carsData.json');  
    const carsData = await carsDataReq.json();  
    const cleaned = carsData.map(car => ({
      mpg: car.Miles_per_Gallon,
      horsepower: car.Horsepower,
    }))
    .filter(car => (car.mpg != null && car.horsepower != null));
    
    return cleaned;
  }
  async function run() {
    // Load and plot the original input data that we are going to train on.
    const data = await getData();



    const values = data.map(d => ({
      x: d.horsepower,
      y: d.mpg,
    }));
  
    tfvis.render.scatterplot(
      {name: 'Horsepower v MPG'},
      {values}, 
      {
        xLabel: 'Horsepower',
        yLabel: 'MPG',
        height: 300
      }
    );
  
    // More code will be added below

    // Create the model
    const model = createModel();  
tfvis.show.modelSummary({name: 'Model Summary'}, model);
/////run function bottom code train the model for
// Convert the data to a form we can use for training.
const tensorData = convertToTensor(data);
const {inputs, labels} = tensorData;
    
// Train the model  
await trainModel(model, inputs, labels);
console.log('Done Training'); //training finish here

// Make some predictions using the model and compare them to the
// original data
testModel(model, data, tensorData);


  }
  
  document.addEventListener('DOMContentLoaded', run);

  function createModel() {
    // Create a sequential model
    const model = tf.sequential(); 
    
    // Add a single hidden layer
    model.add(tf.layers.dense({inputShape: [1], units: 1, useBias: true}));
    
    // Add an output layer
    model.add(tf.layers.dense({units: 1, useBias: true}));
  
    return model;
  }
  const model = tf.sequential();
  model.add(tf.layers.dense({inputShape: [1], units: 1, useBias: true}));
  model.add(tf.layers.dense({units: 1}));




  /**
 * Convert the input data to tensors that we can use for machine 
 * learning. We will also do the important best practices of _shuffling_
 * the data and _normalizing_ the data
 * MPG on the y-axis.
 */
function convertToTensor(data) {
    // Wrapping these calculations in a tidy will dispose any 
    // intermediate tensors.
    
    return tf.tidy(() => {
      // Step 1. Shuffle the data    
      tf.util.shuffle(data);
  
      // Step 2. Convert data to Tensor
      const inputs = data.map(d => d.horsepower)
      const labels = data.map(d => d.mpg);
  
      const inputTensor = tf.tensor2d(inputs, [inputs.length, 1]);
      const labelTensor = tf.tensor2d(labels, [labels.length, 1]);
  
      //Step 3. Normalize the data to the range 0 - 1 using min-max scaling
      const inputMax = inputTensor.max();
      const inputMin = inputTensor.min();  
      const labelMax = labelTensor.max();
      const labelMin = labelTensor.min();
  
      const normalizedInputs = inputTensor.sub(inputMin).div(inputMax.sub(inputMin));
      const normalizedLabels = labelTensor.sub(labelMin).div(labelMax.sub(labelMin));
  
      return {
        inputs: normalizedInputs,
        labels: normalizedLabels,
        // Return the min/max bounds so we can use them later.
        inputMax,
        inputMin,
        labelMax,
        labelMin,
      }
    });  
  }

  async function trainModel(model, inputs, labels) {
    // Prepare the model for training.  
    model.compile({
      optimizer: tf.train.adam(),
      loss: tf.losses.meanSquaredError,
      metrics: ['mse'],
    });
    
    const batchSize = 32;
    const epochs = 50;
    
    return await model.fit(inputs, labels, {
      batchSize,
      epochs,
      shuffle: true,
      callbacks: tfvis.show.fitCallbacks(
        { name: 'Training Performance' },
        ['loss', 'mse'], 
        { height: 200, callbacks: ['onEpochEnd'] }
      )
    });
  }

  

///model make predictions

function testModel(model, inputData, normalizationData) {
    const {inputMax, inputMin, labelMin, labelMax} = normalizationData;  
    
    // Generate predictions for a uniform range of numbers between 0 and 1;
    // We un-normalize the data by doing the inverse of the min-max scaling 
    // that we did earlier.
    const [xs, preds] = tf.tidy(() => {
      
      const xs = tf.linspace(0, 1, 100);      
      const preds = model.predict(xs.reshape([100, 1]));      
      
      const unNormXs = xs
        .mul(inputMax.sub(inputMin))
        .add(inputMin);
      
      const unNormPreds = preds
        .mul(labelMax.sub(labelMin))
        .add(labelMin);
      
      // Un-normalize the data
      return [unNormXs.dataSync(), unNormPreds.dataSync()];
    });
    
   
    const predictedPoints = Array.from(xs).map((val, i) => {
      return {x: val, y: preds[i]}
    });
    
    const originalPoints = inputData.map(d => ({
      x: d.horsepower, y: d.mpg,
    }));
    
    
    tfvis.render.scatterplot(
      {name: 'Model Predictions vs Original Data'}, 
      {values: [originalPoints, predictedPoints], series: ['original', 'predicted']}, 
      {
        xLabel: 'Horsepower',
        yLabel: 'MPG',
        height: 300
      }
    );
  }